MLECO-3995: Pylint + Shellcheck compatibility

* All Python scripts updated to abide by Pylint rules
* good-names updated to permit short variable names:
  i, j, k, f, g, ex
* ignore-long-lines regex updated to allow long lines
  for licence headers
* Shell scripts now compliant with Shellcheck

Signed-off-by: Alex Tawse <Alex.Tawse@arm.com>
Change-Id: I8d5af8279bc08bb8acfe8f6ee7df34965552bbe5

25 files changed, 2914 insertions, 1080 deletions

diff --git a/.pylintrc b/.pylintrc
new file mode 100644
index 0000000..b6cb7ee
--- /dev/null
+++ b/.pylintrc
@@ -0,0 +1,653 @@
+#  SPDX-FileCopyrightText:  Copyright 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-License-Identifier: Apache-2.0
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+[MAIN]
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Clear in-memory caches upon conclusion of linting. Useful if running pylint
+# in a server-like mode.
+clear-cache-post-run=no
+
+# Load and enable all available extensions. Use --list-extensions to see a list
+# all available extensions.
+#enable-all-extensions=
+
+# In error mode, messages with a category besides ERROR or FATAL are
+# suppressed, and no reports are done by default. Error mode is compatible with
+# disabling specific errors.
+#errors-only=
+
+# Always return a 0 (non-error) status code, even if lint errors are found.
+# This is primarily useful in continuous integration scripts.
+#exit-zero=
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-allow-list=
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
+# for backward compatibility.)
+extension-pkg-whitelist=
+
+# Return non-zero exit code if any of these messages/categories are detected,
+# even if score is above --fail-under value. Syntax same as enable. Messages
+# specified are enabled, while categories only check already-enabled messages.
+fail-on=
+
+# Specify a score threshold under which the program will exit with error.
+fail-under=10.0
+
+# Interpret the stdin as a python script, whose filename needs to be passed as
+# the module_or_package argument.
+#from-stdin=
+
+# Files or directories to be skipped. They should be base names, not paths.
+ignore=CVS
+
+# Add files or directories matching the regular expressions patterns to the
+# ignore-list. The regex matches against paths and can be in Posix or Windows
+# format. Because '\\' represents the directory delimiter on Windows systems,
+# it can't be used as an escape character.
+ignore-paths=
+
+# Files or directories matching the regular expression patterns are skipped.
+# The regex matches against base names, not paths. The default value ignores
+# Emacs file locks
+ignore-patterns=^\.#
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis). It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use, and will cap the count on Windows to
+# avoid hangs.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python module names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Minimum Python version to use for version dependent checks. Will default to
+# the version used to run pylint.
+py-version=3.9
+
+# Discover python modules and packages in the file system subtree.
+recursive=no
+
+# Add paths to the list of the source roots. Supports globbing patterns. The
+# source root is an absolute path or a path relative to the current working
+# directory used to determine a package namespace for modules located under the
+# source root.
+source-roots=
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# In verbose mode, extra non-checker-related info will be displayed.
+#verbose=
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style. If left empty, argument names will be checked with the set
+# naming style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style. If left empty, attribute names will be checked with the set naming
+# style.
+#attr-rgx=
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Bad variable names regexes, separated by a comma. If names match any regex,
+# they will always be refused
+bad-names-rgxs=
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style. If left empty, class attribute names will be checked
+# with the set naming style.
+#class-attribute-rgx=
+
+# Naming style matching correct class constant names.
+class-const-naming-style=UPPER_CASE
+
+# Regular expression matching correct class constant names. Overrides class-
+# const-naming-style. If left empty, class constant names will be checked with
+# the set naming style.
+#class-const-rgx=
+
+# Naming style matching correct class names.
+class-naming-style=PascalCase
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style. If left empty, class names will be checked with the set naming style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style. If left empty, constant names will be checked with the set naming
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style. If left empty, function names will be checked with the set
+# naming style.
+#function-rgx=
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names=i,
+           j,
+           k,
+           f,
+           g,
+           x,
+           y,
+           ex,
+           Run,
+           _
+
+# Good variable names regexes, separated by a comma. If names match any regex,
+# they will always be accepted
+good-names-rgxs=
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style. If left empty, inline iteration names will be checked
+# with the set naming style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style. If left empty, method names will be checked with the set naming style.
+#method-rgx=
+
+# Naming style matching correct module names.
+module-naming-style=snake_case
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style. If left empty, module names will be checked with the set naming style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Regular expression matching correct type alias names. If left empty, type
+# alias names will be checked with the set naming style.
+#typealias-rgx=
+
+# Regular expression matching correct type variable names. If left empty, type
+# variable names will be checked with the set naming style.
+#typevar-rgx=
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style. If left empty, variable names will be checked with the set
+# naming style.
+#variable-rgx=
+
+
+[CLASSES]
+
+# Warn about protected attribute access inside special methods
+check-protected-access-in-special-methods=no
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp,
+                      asyncSetUp,
+                      __post_init__
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+
+[DESIGN]
+
+# List of regular expressions of class ancestor names to ignore when counting
+# public methods (see R0903)
+exclude-too-few-public-methods=
+
+# List of qualified class names to ignore when counting class parents (see
+# R0901)
+ignored-parents=
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement (see R0916).
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when caught.
+overgeneral-exceptions=builtins.BaseException,builtins.Exception
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$|^#  SPDX-FileCopyrightText.*$
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[IMPORTS]
+
+# List of modules that can be imported at any level, not just the top level
+# one.
+allow-any-import-level=
+
+# Allow explicit reexports by alias from a package __init__.
+allow-reexport-from-package=no
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=
+
+# Output a graph (.gv or any supported image format) of external dependencies
+# to the given file (report RP0402 must not be disabled).
+ext-import-graph=
+
+# Output a graph (.gv or any supported image format) of all (i.e. internal and
+# external) dependencies to the given file (report RP0402 must not be
+# disabled).
+import-graph=
+
+# Output a graph (.gv or any supported image format) of internal dependencies
+# to the given file (report RP0402 must not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+# Couples of modules and preferred modules, separated by a comma.
+preferred-modules=
+
+
+[LOGGING]
+
+# The type of string formatting that logging methods do. `old` means using %
+# formatting, `new` is for `{}` formatting.
+logging-format-style=old
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
+# UNDEFINED.
+confidence=HIGH,
+           CONTROL_FLOW,
+           INFERENCE,
+           INFERENCE_FAILURE,
+           UNDEFINED
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then re-enable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-implicit-booleaness-not-comparison-to-string,
+        use-implicit-booleaness-not-comparison-to-zero,
+        use-symbolic-message-instead
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=
+
+
+[METHOD_ARGS]
+
+# List of qualified names (i.e., library.method) which require a timeout
+# parameter e.g. 'requests.api.get,requests.api.post'
+timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+# Regular expression of note tags to take in consideration.
+notes-rgx=
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit,argparse.parse_error
+
+
+[REPORTS]
+
+# Python expression which should return a score less than or equal to 10. You
+# have access to the variables 'fatal', 'error', 'warning', 'refactor',
+# 'convention', and 'info' which contain the number of messages in each
+# category, as well as 'statement' which is the total number of statements
+# analyzed. This score is used by the global evaluation report (RP0004).
+evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+msg-template=
+
+# Set the output format. Available formats are: text, parseable, colorized,
+# json2 (improved json format), json (old json format) and msvs (visual
+# studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+#output-format=
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[SIMILARITIES]
+
+# Comments are removed from the similarity computation
+ignore-comments=yes
+
+# Docstrings are removed from the similarity computation
+ignore-docstrings=yes
+
+# Imports are removed from the similarity computation
+ignore-imports=yes
+
+# Signatures are removed from the similarity computation
+ignore-signatures=yes
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. No available dictionaries : You need to install
+# both the python package and the system dependency for enchant to work.
+spelling-dict=
+
+# List of comma separated words that should be considered directives if they
+# appear at the beginning of a comment and should not be checked.
+spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains the private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to the private dictionary (see the
+# --spelling-private-dict-file option) instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[STRING]
+
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency=no
+
+# This flag controls whether the implicit-str-concat should generate a warning
+# on implicit string concatenation in sequences defined over several lines.
+check-str-concat-over-line-jumps=no
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of symbolic message names to ignore for Mixin members.
+ignored-checks-for-mixins=no-member,
+                          not-async-context-manager,
+                          not-context-manager,
+                          attribute-defined-outside-init
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices=1
+
+# Regex pattern to define which classes are considered mixins.
+mixin-class-rgx=.*[Mm]ixin
+
+# List of decorators that change the signature of a decorated function.
+signature-mutators=
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid defining new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of names allowed to shadow builtins
+allowed-redefined-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
diff --git a/build_default.py b/build_default.py
index 1d562f9..907bf4d 100755
--- a/build_default.py
+++ b/build_default.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-#  SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,6 +13,9 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+"""
+Script to build the ML Embedded Evaluation kit using default configuration
+"""
 import logging
 import multiprocessing
 import os
@@ -22,6 +25,7 @@ import sys
 import threading
 from argparse import ArgumentDefaultsHelpFormatter
 from argparse import ArgumentParser
+from collections import namedtuple
 from pathlib import Path
 
 from set_up_default_resources import default_npu_config_names
@@ -29,66 +33,178 @@ from set_up_default_resources import get_default_npu_config_from_name
 from set_up_default_resources import set_up_resources
 from set_up_default_resources import valid_npu_config_names
 
+BuildArgs = namedtuple(
+    "BuildArgs",
+    [
+        "toolchain",
+        "download_resources",
+        "run_vela_on_models",
+        "npu_config_name",
+        "make_jobs",
+        "make_verbose",
+    ],
+)
+
 
 class PipeLogging(threading.Thread):
+    """
+    Class used to log stdout from subprocesses
+    """
+
     def __init__(self, log_level):
         threading.Thread.__init__(self)
-        self.logLevel = log_level
-        self.fileRead, self.fileWrite = os.pipe()
-        self.pipeIn = os.fdopen(self.fileRead)
+        self.log_level = log_level
+        self.file_read, self.file_write = os.pipe()
+        self.pipe_in = os.fdopen(self.file_read)
         self.daemon = False
         self.start()
 
     def fileno(self):
-        return self.fileWrite
+        """
+        Get self.file_write
+
+        Returns
+        -------
+        self.file_write
+        """
+        return self.file_write
 
     def run(self):
-        for line in iter(self.pipeIn.readline, ""):
-            logging.log(self.logLevel, line.strip("\n"))
+        """
+        Log the contents of self.pipe_in
+        """
+        for line in iter(self.pipe_in.readline, ""):
+            logging.log(self.log_level, line.strip("\n"))
 
-        self.pipeIn.close()
+        self.pipe_in.close()
 
     def close(self):
-        os.close(self.fileWrite)
+        """
+        Close the pipe
+        """
+        os.close(self.file_write)
+
+
+def get_toolchain_file_name(toolchain: str) -> str:
+    """
+    Get the name of the toolchain file for the toolchain.
+
+    Parameters
+    ----------
+    toolchain   : name of the specified toolchain
+
+    Returns
+    -------
+    name of the toolchain file corresponding to the specified
+    toolchain
+    """
+    if toolchain == "arm":
+        return "bare-metal-armclang.cmake"
+
+    if toolchain == "gnu":
+        return "bare-metal-gcc.cmake"
+
+    raise ValueError("Toolchain must be one of: gnu, arm")
+
+
+def prep_build_dir(
+        current_file_dir: Path,
+        target_platform: str,
+        target_subsystem: str,
+        npu_config_name: str,
+        toolchain: str
+) -> Path:
+    """
+    Create or clean the build directory for this project.
+
+    Parameters
+    ----------
+    current_file_dir    : The current directory of the running script
+    target_platform     : The name of the target platform, e.g. "mps3"
+    target_subsystem:   : The name of the target subsystem, e.g. "sse-300"
+    npu_config_name     : The NPU config name, e.g. "ethos-u55-32"
+    toolchain           : The name of the specified toolchain, e.g."arm"
+
+    Returns
+    -------
+    The path to the build directory
+    """
+    build_dir = (
+            current_file_dir /
+            f"cmake-build-{target_platform}-{target_subsystem}-{npu_config_name}-{toolchain}"
+    )
+
+    try:
+        build_dir.mkdir()
+    except FileExistsError:
+        # Directory already exists, clean it.
+        for filepath in build_dir.iterdir():
+            try:
+                if filepath.is_file() or filepath.is_symlink():
+                    filepath.unlink()
+                elif filepath.is_dir():
+                    shutil.rmtree(filepath)
+            except OSError as err:
+                logging.error("Failed to delete %s. Reason: %s", filepath, err)
+
+    return build_dir
 
 
-def run(
-    toolchain: str,
-    download_resources: bool,
-    run_vela_on_models: bool,
-    npu_config_name: str,
-    make_jobs: int,
-    make_verbose: bool,
+def run_command(
+        command: str,
+        logpipe: PipeLogging,
+        fail_message: str
 ):
     """
+    Run a command and exit upon failure.
+
+    Parameters
+    ----------
+    command         : The command to run
+    logpipe         : The PipeLogging object to capture stdout
+    fail_message    : The message to log upon a non-zero exit code
+    """
+    logging.info("\n\n\n%s\n\n\n", command)
+
+    try:
+        subprocess.run(
+            command, check=True, shell=True, stdout=logpipe, stderr=subprocess.STDOUT
+        )
+    except subprocess.CalledProcessError as err:
+        logging.error(fail_message)
+        logpipe.close()
+        sys.exit(err.returncode)
+
+
+def run(args: BuildArgs):
+    """
     Run the helpers scripts.
 
     Parameters:
     ----------
-    toolchain (str)          :    Specifies if 'gnu' or 'arm' toolchain needs to be used.
-    download_resources (bool):    Specifies if 'Download resources' step is performed.
-    run_vela_on_models (bool):    Only if `download_resources` is True, specifies if run vela on downloaded models.
-    npu_config_name(str)     :    Ethos-U NPU configuration name. See "valid_npu_config_names"
+    args (BuildArgs)    :   Parsed set of build args expecting:
+                            - toolchain
+                            - download_resources
+                            - run_vela_on_models
+                            - np_config_name
+    toolchain (str)             :   Specifies if 'gnu' or 'arm' toolchain needs to be used.
+    download_resources (bool)   :   Specifies if 'Download resources' step is performed.
+    run_vela_on_models (bool)   :   Only if `download_resources` is True, specifies if
+                                    run vela on downloaded models.
+    npu_config_name(str)        :   Ethos-U NPU configuration name. See "valid_npu_config_names"
     """
 
     current_file_dir = Path(__file__).parent.resolve()
 
     # 1. Make sure the toolchain is supported, and set the right one here
-    supported_toolchain_ids = ["gnu", "arm"]
-    assert (
-        toolchain in supported_toolchain_ids
-    ), f"Toolchain must be from {supported_toolchain_ids}"
-    if toolchain == "arm":
-        toolchain_file_name = "bare-metal-armclang.cmake"
-    elif toolchain == "gnu":
-        toolchain_file_name = "bare-metal-gcc.cmake"
+    toolchain_file_name = get_toolchain_file_name(args.toolchain)
 
     # 2. Download models if specified
-    if download_resources is True:
+    if args.download_resources is True:
         logging.info("Downloading resources.")
-        (download_dir, env_path) = set_up_resources(
-            run_vela_on_models=run_vela_on_models,
-            additional_npu_config_names=[npu_config_name],
+        env_path = set_up_resources(
+            run_vela_on_models=args.run_vela_on_models,
+            additional_npu_config_names=(args.npu_config_name,),
             additional_requirements_file=current_file_dir / "scripts" / "py" / "requirements.txt"
         )
 
@@ -96,57 +212,42 @@ def run(
     logging.info("Building default configuration.")
     target_platform = "mps3"
     target_subsystem = "sse-300"
-    ethos_u_cfg = get_default_npu_config_from_name(npu_config_name)
-    build_dir = current_file_dir / f"cmake-build-{target_platform}-{target_subsystem}-{npu_config_name}-{toolchain}"
 
-    try:
-        build_dir.mkdir()
-    except FileExistsError:
-        # Directory already exists, clean it.
-        for filepath in build_dir.iterdir():
-            try:
-                if filepath.is_file() or filepath.is_symlink():
-                    filepath.unlink()
-                elif filepath.is_dir():
-                    shutil.rmtree(filepath)
-            except Exception as e:
-                logging.error(f"Failed to delete {filepath}. Reason: {e}")
+    build_dir = prep_build_dir(
+        current_file_dir,
+        target_platform,
+        target_subsystem,
+        args.npu_config_name,
+        args.toolchain
+    )
 
     logpipe = PipeLogging(logging.INFO)
 
-    cmake_toolchain_file = current_file_dir / "scripts" / "cmake" / "toolchains" / toolchain_file_name
+    cmake_toolchain_file = (
+            current_file_dir /
+            "scripts" /
+            "cmake" /
+            "toolchains" /
+            toolchain_file_name
+    )
+    ethos_u_cfg = get_default_npu_config_from_name(args.npu_config_name)
     cmake_path = env_path / "bin" / "cmake"
     cmake_command = (
         f"{cmake_path} -B {build_dir} -DTARGET_PLATFORM={target_platform}"
-        + f" -DTARGET_SUBSYSTEM={target_subsystem}"
-        + f" -DCMAKE_TOOLCHAIN_FILE={cmake_toolchain_file}"
-        + f" -DETHOS_U_NPU_ID={ethos_u_cfg.ethos_u_npu_id}"
-        + f" -DETHOS_U_NPU_CONFIG_ID={ethos_u_cfg.ethos_u_config_id}"
-        + f" -DTENSORFLOW_LITE_MICRO_CLEAN_DOWNLOADS=ON"
-    )
-
-    logging.info(f"\n\n\n{cmake_command}\n\n\n")
-    state = subprocess.run(
-        cmake_command, shell=True, stdout=logpipe, stderr=subprocess.STDOUT
+        f" -DTARGET_SUBSYSTEM={target_subsystem}"
+        f" -DCMAKE_TOOLCHAIN_FILE={cmake_toolchain_file}"
+        f" -DETHOS_U_NPU_ID={ethos_u_cfg.ethos_u_npu_id}"
+        f" -DETHOS_U_NPU_CONFIG_ID={ethos_u_cfg.ethos_u_config_id}"
+        " -DTENSORFLOW_LITE_MICRO_CLEAN_DOWNLOADS=ON"
     )
 
-    if state.returncode != 0:
-        logging.error("Failed to configure the project.")
-        logpipe.close()
-        sys.exit(state.returncode)
+    run_command(cmake_command, logpipe, fail_message="Failed to configure the project.")
 
-    make_command = f"{cmake_path} --build {build_dir} -j{make_jobs}"
-    if make_verbose:
+    make_command = f"{cmake_path} --build {build_dir} -j{args.make_jobs}"
+    if args.make_verbose:
         make_command += " --verbose"
-    logging.info(f"\n\n\n{make_command}\n\n\n")
-    state = subprocess.run(
-        make_command, shell=True, stdout=logpipe, stderr=subprocess.STDOUT
-    )
 
-    if state.returncode != 0:
-        logging.error("Failed to build project.")
-        logpipe.close()
-        sys.exit(state.returncode)
+    run_command(make_command, logpipe, fail_message="Failed to build project.")
 
     logpipe.close()
 
@@ -185,18 +286,20 @@ if __name__ == "__main__":
     parser.add_argument(
         "--make-verbose", help="Make runs with VERBOSE=1", action="store_true"
     )
-    args = parser.parse_args()
+    parsed_args = parser.parse_args()
 
     logging.basicConfig(
         filename="log_build_default.log", level=logging.DEBUG, filemode="w"
     )
     logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
 
-    run(
-        args.toolchain.lower(),
-        not args.skip_download,
-        not args.skip_vela,
-        args.npu_config_name,
-        args.make_jobs,
-        args.make_verbose,
+    build_args = BuildArgs(
+        toolchain=parsed_args.toolchain.lower(),
+        download_resources=not parsed_args.skip_download,
+        run_vela_on_models=not parsed_args.skip_vela,
+        npu_config_name=parsed_args.npu_config_name,
+        make_jobs=parsed_args.make_jobs,
+        make_verbose=parsed_args.make_verbose
     )
+
+    run(build_args)
diff --git a/download_dependencies.py b/download_dependencies.py
index 3934f94..786fa4c 100755
--- a/download_dependencies.py
+++ b/download_dependencies.py
@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
-
-#  SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,69 +15,92 @@
 #  limitations under the License.
 
 """This script does effectively the same as "git submodule update --init" command."""
+import json
 import logging
 import sys
 import tarfile
 import tempfile
+import typing
+from pathlib import Path
 from urllib.request import urlopen
 from zipfile import ZipFile
-from pathlib import Path
 
-TF = "https://github.com/tensorflow/tflite-micro/archive/568d181ccc1f60e49742fd43b7f97141ee8d45fc.zip"
-CMSIS = "https://github.com/ARM-software/CMSIS_5/archive/a75f01746df18bb5b929dfb8dc6c9407fac3a0f3.zip"
-CMSIS_DSP = "https://github.com/ARM-software/CMSIS-DSP/archive/refs/tags/v1.15.0.zip"
-CMSIS_NN = "https://github.com/ARM-software/CMSIS-NN/archive/refs/85164a811917770d7027a12a57ed3b469dac6537.zip"
-ETHOS_U_CORE_DRIVER = "https://git.mlplatform.org/ml/ethos-u/ethos-u-core-driver.git/snapshot/ethos-u-core-driver-23.08.tar.gz"
-ETHOS_U_CORE_PLATFORM = "https://git.mlplatform.org/ml/ethos-u/ethos-u-core-platform.git/snapshot/ethos-u-core-platform-23.08.tar.gz"
 
+def download(
+        url_file: str,
+        to_path: Path,
+):
+    """
+    Download a file from the specified URL
 
-def download(url_file: str, post_process=None):
+    @param url_file:    The URL of the file to download
+    @param to_path:     The location to download the file to
+    """
     with urlopen(url_file) as response, tempfile.NamedTemporaryFile() as temp:
-        logging.info(f"Downloading {url_file} ...")
+        logging.info("Downloading %s ...", url_file)
         temp.write(response.read())
         temp.seek(0)
-        logging.info(f"Finished downloading {url_file}.")
-        if post_process:
-            post_process(temp)
-
-
-def unzip(file, to_path):
-    with ZipFile(file) as z:
-        for archive_path in z.infolist():
+        logging.info("Finished downloading %s.", url_file)
+        if url_file.endswith(".tar.gz"):
+            untar(temp, to_path)
+        else:
+            unzip(temp, to_path)
+
+
+def unzip(
+        file: typing.IO[bytes],
+        to_path: Path
+):
+    """
+    Unzip the specified file
+
+    @param file:    The file to unzip
+    @param to_path: The location to extract to
+    """
+    with ZipFile(file) as f:
+        for archive_path in f.infolist():
             archive_path.filename = archive_path.filename[archive_path.filename.find("/") + 1:]
             if archive_path.filename:
-                z.extract(archive_path, to_path)
+                f.extract(archive_path, to_path)
                 target_path = to_path / archive_path.filename
                 attr = archive_path.external_attr >> 16
                 if attr != 0:
                     target_path.chmod(attr)
 
 
-def untar(file, to_path):
-    with tarfile.open(file) as z:
-        for archive_path in z.getmembers():
+def untar(
+        file: bytes,
+        to_path: Path
+):
+    """
+    Untar the specified file
+
+    @param file:    The file to untar
+    @param to_path: The location to extract to
+    """
+    with tarfile.open(file) as f:
+        for archive_path in f.getmembers():
             index = archive_path.name.find("/")
             if index < 0:
                 continue
             archive_path.name = archive_path.name[index + 1:]
             if archive_path.name:
-                z.extract(archive_path, to_path)
+                f.extract(archive_path, to_path)
 
 
 def main(dependencies_path: Path):
+    """
+    Download all dependencies
+
+    @param dependencies_path:   The path to which the dependencies will be downloaded
+    """
+    dependency_urls_path = (
+            Path(__file__).parent.resolve() / "scripts" / "py" / "dependency_urls.json")
+    with open(dependency_urls_path, encoding="utf8") as f:
+        dependency_urls = json.load(f)
 
-    download(CMSIS,
-             lambda file: unzip(file.name, to_path=dependencies_path / "cmsis"))
-    download(CMSIS_DSP,
-             lambda file: unzip(file.name, to_path=dependencies_path / "cmsis-dsp"))
-    download(CMSIS_NN,
-             lambda file: unzip(file.name, to_path=dependencies_path / "cmsis-nn"))
-    download(ETHOS_U_CORE_DRIVER,
-             lambda file: untar(file.name, to_path=dependencies_path / "core-driver"))
-    download(ETHOS_U_CORE_PLATFORM,
-             lambda file: untar(file.name, to_path=dependencies_path / "core-platform"))
-    download(TF,
-             lambda file: unzip(file.name, to_path=dependencies_path / "tensorflow"))
+    for name, url in dependency_urls.items():
+        download(url, dependencies_path / name)
 
 
 if __name__ == '__main__':
@@ -88,6 +110,6 @@ if __name__ == '__main__':
     download_dir = Path(__file__).parent.resolve() / "dependencies"
 
     if download_dir.is_dir():
-        logging.info(f'{download_dir} exists. Skipping download.')
+        logging.info('%s exists. Skipping download.', download_dir)
     else:
         main(download_dir)
diff --git a/model_conditioning_examples/post_training_quantization.py b/model_conditioning_examples/post_training_quantization.py
index a39be0e..42069f5 100644
--- a/model_conditioning_examples/post_training_quantization.py
+++ b/model_conditioning_examples/post_training_quantization.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,28 +13,34 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 """
-This script will provide you with an example of how to perform post-training quantization in TensorFlow.
+This script will provide you with an example of how to perform
+post-training quantization in TensorFlow.
 
-The output from this example will be a TensorFlow Lite model file where weights and activations are quantized to 8bit
-integer values.
+The output from this example will be a TensorFlow Lite model file
+where weights and activations are quantized to 8bit integer values.
 
-Quantization helps reduce the size of your models and is necessary for running models on certain hardware such as Arm
-Ethos NPU.
+Quantization helps reduce the size of your models and is necessary
+for running models on certain hardware such as Arm Ethos NPU.
 
-In addition to quantizing weights, post-training quantization uses a calibration dataset to
-capture the minimum and maximum values of all variable tensors in your model.
-By capturing these ranges it is possible to fully quantize not just the weights of the model but also the activations.
+In addition to quantizing weights, post-training quantization uses
+a calibration dataset to capture the minimum and maximum values of
+all variable tensors in your model.  By capturing these ranges it
+is possible to fully quantize not just the weights of the model
+but also the activations.
 
-Depending on the model you are quantizing there may be some accuracy loss, but for a lot of models the loss should
-be minimal.
+Depending on the model you are quantizing there may be some accuracy loss,
+but for a lot of models the loss should be minimal.
 
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targeting an Arm Ethos-U55 NPU then the output
+TensorFlow Lite file will also need to be passed through the Vela
 compiler for further optimizations before it can be used.
 
-For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
-For more information on post-training quantization
-see: https://www.tensorflow.org/lite/performance/post_training_integer_quant
+For more information on using Vela see:
+    https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on post-training quantization see:
+    https://www.tensorflow.org/lite/performance/post_training_integer_quant
 """
+
 import pathlib
 
 import numpy as np
@@ -44,7 +50,8 @@ from training_utils import get_data, create_model
 
 
 def post_training_quantize(keras_model, sample_data):
-    """Quantize Keras model using post-training quantization with some sample data.
+    """
+    Quantize Keras model using post-training quantization with some sample data.
 
     TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing.
 
@@ -76,8 +83,14 @@ def post_training_quantize(keras_model, sample_data):
     return tflite_model
 
 
-def evaluate_tflite_model(tflite_save_path, x_test, y_test):
-    """Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter.
+# pylint: disable=duplicate-code
+def evaluate_tflite_model(
+        tflite_save_path: pathlib.Path,
+        x_test: np.ndarray,
+        y_test: np.ndarray
+):
+    """
+    Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter.
 
     Args:
         tflite_save_path: Path to TensorFlow Lite model to test.
@@ -106,6 +119,9 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test):
 
 
 def main():
+    """
+    Run post-training quantization
+    """
     x_train, y_train, x_test, y_test = get_data()
     model = create_model()
 
@@ -117,7 +133,7 @@ def main():
     model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
 
     # Test the fp32 model accuracy.
-    test_loss, test_acc = model.evaluate(x_test, y_test)
+    test_loss, test_acc = model.evaluate(x_test, y_test)  # pylint: disable=unused-variable
     print(f"Test accuracy float: {test_acc:.3f}")
 
     # Quantize and export the resulting TensorFlow Lite model to file.
@@ -132,7 +148,12 @@ def main():
 
     # Test the quantized model accuracy. Save time by only testing a subset of the whole data.
     num_test_samples = 1000
-    evaluate_tflite_model(quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+    evaluate_tflite_model(
+        quant_model_save_path,
+        x_test[0:num_test_samples],
+        y_test[0:num_test_samples]
+    )
+# pylint: enable=duplicate-code
 
 
 if __name__ == "__main__":
diff --git a/model_conditioning_examples/quantization_aware_training.py b/model_conditioning_examples/quantization_aware_training.py
index 3d492a7..d590763 100644
--- a/model_conditioning_examples/quantization_aware_training.py
+++ b/model_conditioning_examples/quantization_aware_training.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,31 +13,38 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 """
-This script will provide you with a short example of how to perform quantization aware training in TensorFlow using the
+This script will provide you with a short example of how to perform
+quantization aware training in TensorFlow using the
 TensorFlow Model Optimization Toolkit.
 
-The output from this example will be a TensorFlow Lite model file where weights and activations are quantized to 8bit
-integer values.
+The output from this example will be a TensorFlow Lite model file
+where weights and activations are quantized to 8bit integer values.
 
-Quantization helps reduce the size of your models and is necessary for running models on certain hardware such as Arm
-Ethos NPU.
+Quantization helps reduce the size of your models and is necessary
+for running models on certain hardware such as Arm Ethos NPU.
 
-In quantization aware training (QAT), the error introduced with quantizing from fp32 to int8 is simulated using
-fake quantization nodes. By simulating this quantization error when training, the model can learn better adapted
-weights and minimize accuracy losses caused by the reduced precision.
+In quantization aware training (QAT), the error introduced with
+quantizing from fp32 to int8 is simulated using fake quantization nodes.
+By simulating this quantization error when training,
+the model can learn better adapted weights and minimize accuracy losses
+caused by the reduced precision.
 
-Minimum and maximum values for activations are also captured during training so activations for every layer can be
-quantized along with the weights later.
+Minimum and maximum values for activations are also captured
+during training so activations for every layer can be quantized
+along with the weights later.
 
-Quantization is only simulated during training and the training backward passes are still performed in full float
-precision. Actual quantization happens when generating a TensorFlow Lite model.
+Quantization is only simulated during training and the
+training backward passes are still performed in full float precision.
+Actual quantization happens when generating a TensorFlow Lite model.
 
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targeting an Arm Ethos-U55 NPU then the output
+TensorFlow Lite file will also need to be passed through the Vela
 compiler for further optimizations before it can be used.
 
-For more information on using vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
-For more information on quantization aware training
-see: https://www.tensorflow.org/model_optimization/guide/quantization/training
+For more information on using vela see:
+    https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on quantization aware training see:
+    https://www.tensorflow.org/model_optimization/guide/quantization/training
 """
 import pathlib
 
@@ -64,13 +71,15 @@ def quantize_and_convert_to_tflite(keras_model):
 
     # After doing quantization aware training all the information for creating a fully quantized
     # TensorFlow Lite model is already within the quantization aware Keras model.
-    # This means we only need to call convert with default optimizations to generate the quantized TensorFlow Lite model.
+    # This means we only need to call convert with default optimizations to
+    # generate the quantized TensorFlow Lite model.
     converter.optimizations = [tf.lite.Optimize.DEFAULT]
     tflite_model = converter.convert()
 
     return tflite_model
 
 
+# pylint: disable=duplicate-code
 def evaluate_tflite_model(tflite_save_path, x_test, y_test):
     """Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter.
 
@@ -101,13 +110,19 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test):
 
 
 def main():
+    """
+    Run quantization aware training
+    """
     x_train, y_train, x_test, y_test = get_data()
     model = create_model()
 
-    # When working with the TensorFlow Keras API and the TF Model Optimization Toolkit we can make our
-    # model quantization aware in one line. Once this is done we compile the model and train as normal.
-    # It is important to note that the model is only quantization aware and is not quantized yet. The weights are
-    # still floating point and will only be converted to int8 when we generate the TensorFlow Lite model later on.
+    # When working with the TensorFlow Keras API and theTF Model Optimization Toolkit
+    # we can make our model quantization aware in one line.
+    # Once this is done we compile the model and train as normal.
+    # It is important to note that the model is only quantization aware
+    # and is not quantized yet.
+    # The weights are still floating point and will only be converted
+    # to int8 when we generate the TensorFlow Lite model later on.
     quant_aware_model = tfmot.quantization.keras.quantize_model(model)
 
     quant_aware_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
@@ -117,7 +132,7 @@ def main():
     quant_aware_model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
 
     # Test the quantization aware model accuracy.
-    test_loss, test_acc = quant_aware_model.evaluate(x_test, y_test)
+    test_loss, test_acc = quant_aware_model.evaluate(x_test, y_test)  # pylint: disable=unused-variable
     print(f"Test accuracy quant aware: {test_acc:.3f}")
 
     # Quantize and save the resulting TensorFlow Lite model to file.
@@ -132,7 +147,12 @@ def main():
 
     # Test quantized model accuracy. Save time by only testing a subset of the whole data.
     num_test_samples = 1000
-    evaluate_tflite_model(quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+    evaluate_tflite_model(
+        quant_model_save_path,
+        x_test[0:num_test_samples],
+        y_test[0:num_test_samples]
+    )
+# pylint: enable=duplicate-code
 
 
 if __name__ == "__main__":
diff --git a/model_conditioning_examples/setup.sh b/model_conditioning_examples/setup.sh
index 92de78a..678f9d3 100644
--- a/model_conditioning_examples/setup.sh
+++ b/model_conditioning_examples/setup.sh
@@ -1,5 +1,7 @@
+#!/bin/bash
+
 #----------------------------------------------------------------------------
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,8 +16,9 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #----------------------------------------------------------------------------
-#!/bin/bash
+
 python3 -m venv ./env
+# shellcheck disable=SC1091
 source ./env/bin/activate
 pip install -U pip
-pip install -r requirements.txt
\ No newline at end of file
+pip install -r requirements.txt
diff --git a/model_conditioning_examples/training_utils.py b/model_conditioning_examples/training_utils.py
index a022bd1..2ce94b8 100644
--- a/model_conditioning_examples/training_utils.py
+++ b/model_conditioning_examples/training_utils.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -49,7 +49,8 @@ def create_model():
     """
 
     keras_model = tf.keras.models.Sequential([
-        tf.keras.layers.Conv2D(32, 3, padding='same', input_shape=(28, 28, 1), activation=tf.nn.relu),
+        tf.keras.layers.Conv2D(32, 3, padding='same',
+                               input_shape=(28, 28, 1), activation=tf.nn.relu),
         tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu),
         tf.keras.layers.MaxPool2D(),
         tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu),
diff --git a/model_conditioning_examples/weight_clustering.py b/model_conditioning_examples/weight_clustering.py
index 6672d53..e966336 100644
--- a/model_conditioning_examples/weight_clustering.py
+++ b/model_conditioning_examples/weight_clustering.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,22 +13,29 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 """
-This script will provide you with a short example of how to perform clustering of weights (weight sharing) in
-TensorFlow using the TensorFlow Model Optimization Toolkit.
+This script will provide you with a short example of how to perform
+clustering of weights (weight sharing) in TensorFlow
+using the TensorFlow Model Optimization Toolkit.
 
-The output from this example will be a TensorFlow Lite model file where weights in each layer have been 'clustered' into
-16 clusters during training - quantization has then been applied on top of this.
+The output from this example will be a TensorFlow Lite model file
+where weights in each layer have been 'clustered' into 16 clusters
+during training - quantization has then been applied on top of this.
 
-By clustering the model we can improve compression of the model file. This can be essential for deploying certain
-models on systems with limited resources - such as embedded systems using an Arm Ethos NPU.
+By clustering the model we can improve compression of the model file.
+This can be essential for deploying certain models on systems with
+limited resources - such as embedded systems using an Arm Ethos NPU.
 
-After performing clustering we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+After performing clustering we do post-training quantization
+to quantize the model and then generate a TensorFlow Lite file.
 
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targeting an Arm Ethos-U55 NPU then the output
+TensorFlow Lite file will also need to be passed through the Vela
 compiler for further optimizations before it can be used.
 
-For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
-For more information on clustering see: https://www.tensorflow.org/model_optimization/guide/clustering
+For more information on using Vela see:
+    https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on clustering see:
+    https://www.tensorflow.org/model_optimization/guide/clustering
 """
 import pathlib
 
@@ -42,39 +49,52 @@ from post_training_quantization import post_training_quantize, evaluate_tflite_m
 def prepare_for_clustering(keras_model):
     """Prepares a Keras model for clustering."""
 
-    # Choose the number of clusters to use and how to initialize them. Using more clusters will generally
-    # reduce accuracy so you will need to find the optimal number for your use-case.
+    # Choose the number of clusters to use and how to initialize them.
+    # Using more clusters will generally reduce accuracy,
+    # so you will need to find the optimal number for your use-case.
     number_of_clusters = 16
     cluster_centroids_init = tfmot.clustering.keras.CentroidInitialization.LINEAR
 
-    # Apply the clustering wrapper to the whole model so weights in every layer will get clustered. You may find that
-    # to avoid too much accuracy loss only certain non-critical layers in your model should be clustered.
-    clustering_ready_model = tfmot.clustering.keras.cluster_weights(keras_model,
-                                                                    number_of_clusters=number_of_clusters,
-                                                                    cluster_centroids_init=cluster_centroids_init)
+    # Apply the clustering wrapper to the whole model so weights in
+    # every layer will get clustered. You may find that to avoid
+    # too much accuracy loss only certain non-critical layers in
+    # your model should be clustered.
+    clustering_ready_model = tfmot.clustering.keras.cluster_weights(
+        keras_model,
+        number_of_clusters=number_of_clusters,
+        cluster_centroids_init=cluster_centroids_init
+    )
 
     # We must recompile the model after making it ready for clustering.
-    clustering_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-                                   loss=tf.keras.losses.sparse_categorical_crossentropy,
-                                   metrics=['accuracy'])
+    clustering_ready_model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        loss=tf.keras.losses.sparse_categorical_crossentropy,
+        metrics=['accuracy']
+    )
 
     return clustering_ready_model
 
 
 def main():
+    """
+    Run weight clustering
+    """
     x_train, y_train, x_test, y_test = get_data()
     model = create_model()
 
     # Compile and train the model first.
-    # In general it is easier to do clustering as a fine-tuning step after the model is fully trained.
-    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-                  loss=tf.keras.losses.sparse_categorical_crossentropy,
-                  metrics=['accuracy'])
+    # In general, it is easier to do clustering as a
+    # fine-tuning step after the model is fully trained.
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        loss=tf.keras.losses.sparse_categorical_crossentropy,
+        metrics=['accuracy']
+    )
 
     model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
 
     # Test the trained model accuracy.
-    test_loss, test_acc = model.evaluate(x_test, y_test)
+    test_loss, test_acc = model.evaluate(x_test, y_test)  # pylint: disable=unused-variable
     print(f"Test accuracy before clustering: {test_acc:.3f}")
 
     # Prepare the model for clustering.
@@ -88,19 +108,26 @@ def main():
     # Remove all variables that clustering only needed in the training phase.
     model_for_export = tfmot.clustering.keras.strip_clustering(clustered_model)
 
-    # Apply post-training quantization on top of the clustering and save the resulting TensorFlow Lite model to file.
+    # Apply post-training quantization on top of the clustering
+    # and save the resulting TensorFlow Lite model to file.
     tflite_model = post_training_quantize(model_for_export, x_train)
 
     tflite_models_dir = pathlib.Path('./conditioned_models/')
     tflite_models_dir.mkdir(exist_ok=True, parents=True)
 
-    clustered_quant_model_save_path = tflite_models_dir / 'clustered_post_training_quant_model.tflite'
+    clustered_quant_model_save_path = \
+        tflite_models_dir / 'clustered_post_training_quant_model.tflite'
     with open(clustered_quant_model_save_path, 'wb') as f:
         f.write(tflite_model)
 
-    # Test the clustered quantized model accuracy. Save time by only testing a subset of the whole data.
+    # Test the clustered quantized model accuracy.
+    # Save time by only testing a subset of the whole data.
     num_test_samples = 1000
-    evaluate_tflite_model(clustered_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+    evaluate_tflite_model(
+        clustered_quant_model_save_path,
+        x_test[0:num_test_samples],
+        y_test[0:num_test_samples]
+    )
 
 
 if __name__ == "__main__":
diff --git a/model_conditioning_examples/weight_pruning.py b/model_conditioning_examples/weight_pruning.py
index cbf9cf9..303b6df 100644
--- a/model_conditioning_examples/weight_pruning.py
+++ b/model_conditioning_examples/weight_pruning.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,23 +13,31 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 """
-This script will provide you with a short example of how to perform magnitude-based weight pruning in TensorFlow
+This script will provide you with a short example of how to perform
+magnitude-based weight pruning in TensorFlow
 using the TensorFlow Model Optimization Toolkit.
 
-The output from this example will be a TensorFlow Lite model file where ~75% percent of the weights have been 'pruned' to the
+The output from this example will be a TensorFlow Lite model file
+where ~75% percent of the weights have been 'pruned' to the
 value 0 during training - quantization has then been applied on top of this.
 
-By pruning the model we can improve compression of the model file. This can be essential for deploying certain models
-on systems with limited resources - such as embedded systems using Arm Ethos NPU. Also, if the pruned model is run
-on an Arm Ethos NPU then this pruning can improve the execution time of the model.
+By pruning the model we can improve compression of the model file.
+This can be essential for deploying certain models on systems
+with limited resources - such as embedded systems using Arm Ethos NPU.
+Also, if the pruned model is run on an Arm Ethos NPU then
+this pruning can improve the execution time of the model.
 
-After pruning is complete we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+After pruning is complete we do post-training quantization
+to quantize the model and then generate a TensorFlow Lite file.
 
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targeting an Arm Ethos-U55 NPU then the output
+TensorFlow Lite file will also need to be passed through the Vela
 compiler for further optimizations before it can be used.
 
-For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
-For more information on weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning
+For more information on using Vela see:
+    https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on weight pruning see:
+    https://www.tensorflow.org/model_optimization/guide/pruning
 """
 import pathlib
 
@@ -43,13 +51,20 @@ from post_training_quantization import post_training_quantize, evaluate_tflite_m
 def prepare_for_pruning(keras_model):
     """Prepares a Keras model for pruning."""
 
-    # We use a constant sparsity schedule so the amount of sparsity in the model is kept at the same percent throughout
-    # training. An alternative is PolynomialDecay where sparsity can be gradually increased during training.
+    # We use a constant sparsity schedule so the amount of sparsity
+    # in the model is kept at the same percent throughout training.
+    # An alternative is PolynomialDecay where sparsity
+    # can be gradually increased during training.
     pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.75, begin_step=0)
 
-    # Apply the pruning wrapper to the whole model so weights in every layer will get pruned. You may find that to avoid
-    # too much accuracy loss only certain non-critical layers in your model should be pruned.
-    pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(keras_model, pruning_schedule=pruning_schedule)
+    # Apply the pruning wrapper to the whole model
+    # so weights in every layer will get pruned.
+    # You may find that to avoid too much accuracy loss only
+    # certain non-critical layers in your model should be pruned.
+    pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(
+        keras_model,
+        pruning_schedule=pruning_schedule
+    )
 
     # We must recompile the model after making it ready for pruning.
     pruning_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
@@ -60,11 +75,15 @@ def prepare_for_pruning(keras_model):
 
 
 def main():
+    """
+    Run weight pruning
+    """
     x_train, y_train, x_test, y_test = get_data()
     model = create_model()
 
     # Compile and train the model first.
-    # In general it is easier to do pruning as a fine-tuning step after the model is fully trained.
+    # In general, it is easier to do pruning as a fine-tuning step
+    # after the model is fully trained.
     model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
                   loss=tf.keras.losses.sparse_categorical_crossentropy,
                   metrics=['accuracy'])
@@ -72,7 +91,7 @@ def main():
     model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
 
     # Test the trained model accuracy.
-    test_loss, test_acc = model.evaluate(x_test, y_test)
+    test_loss, test_acc = model.evaluate(x_test, y_test)  # pylint: disable=unused-variable
     print(f"Test accuracy before pruning: {test_acc:.3f}")
 
     # Prepare the model for pruning and add the pruning update callback needed in training.
@@ -80,14 +99,23 @@ def main():
     callbacks = [tfmot.sparsity.keras.UpdatePruningStep()]
 
     # Continue training the model but now with pruning applied - remember to pass in the callbacks!
-    pruned_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True, callbacks=callbacks)
+    pruned_model.fit(
+        x=x_train,
+        y=y_train,
+        batch_size=128,
+        epochs=1,
+        verbose=1,
+        shuffle=True,
+        callbacks=callbacks
+    )
     test_loss, test_acc = pruned_model.evaluate(x_test, y_test)
     print(f"Test accuracy after pruning: {test_acc:.3f}")
 
     # Remove all variables that pruning only needed in the training phase.
     model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model)
 
-    # Apply post-training quantization on top of the pruning and save the resulting TensorFlow Lite model to file.
+    # Apply post-training quantization on top of the pruning
+    # and save the resulting TensorFlow Lite model to file.
     tflite_model = post_training_quantize(model_for_export, x_train)
 
     tflite_models_dir = pathlib.Path('./conditioned_models/')
@@ -97,9 +125,14 @@ def main():
     with open(pruned_quant_model_save_path, 'wb') as f:
         f.write(tflite_model)
 
-    # Test the pruned quantized model accuracy. Save time by only testing a subset of the whole data.
+    # Test the pruned quantized model accuracy.
+    # Save time by only testing a subset of the whole data.
     num_test_samples = 1000
-    evaluate_tflite_model(pruned_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+    evaluate_tflite_model(
+        pruned_quant_model_save_path,
+        x_test[0:num_test_samples],
+        y_test[0:num_test_samples]
+    )
 
 
 if __name__ == "__main__":
diff --git a/scripts/py/check_update_resources_downloaded.py b/scripts/py/check_update_resources_downloaded.py
index 6e4da21..bdd9d62 100644
--- a/scripts/py/check_update_resources_downloaded.py
+++ b/scripts/py/check_update_resources_downloaded.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-#  SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,15 +13,20 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-
+"""
+Contains methods to check if the downloaded resources need to be refreshed
+"""
+import hashlib
 import json
 import sys
-import hashlib
+import typing
 from argparse import ArgumentParser
 from pathlib import Path
 
 
-def get_md5sum_for_file(filepath: str) -> str:
+def get_md5sum_for_file(
+        filepath: typing.Union[str, Path]
+) -> str:
     """
     Function to calculate md5sum for contents of a given file.
 
@@ -41,7 +46,7 @@ def get_md5sum_for_file(filepath: str) -> str:
 
 
 def check_update_resources_downloaded(
-    resource_downloaded_dir: str, set_up_script_path: str
+        resource_downloaded_dir: str, set_up_script_path: str
 ):
     """
     Function that check if the resources downloaded need to be refreshed.
@@ -55,27 +60,27 @@ def check_update_resources_downloaded(
     metadata_file_path = Path(resource_downloaded_dir) / "resources_downloaded_metadata.json"
 
     if metadata_file_path.is_file():
-        with open(metadata_file_path) as metadata_json:
-
+        with open(metadata_file_path, encoding="utf8") as metadata_json:
             metadata_dict = json.load(metadata_json)
-            md5_key = 'set_up_script_md5sum'
-            set_up_script_md5sum_metadata = ''
 
-            if md5_key in metadata_dict.keys():
-                set_up_script_md5sum_metadata = metadata_dict["set_up_script_md5sum"]
+        md5_key = 'set_up_script_md5sum'
+        set_up_script_md5sum_metadata = ''
+
+        if md5_key in metadata_dict.keys():
+            set_up_script_md5sum_metadata = metadata_dict["set_up_script_md5sum"]
 
-            set_up_script_md5sum_current = get_md5sum_for_file(set_up_script_path)
+        set_up_script_md5sum_current = get_md5sum_for_file(set_up_script_path)
 
-            if set_up_script_md5sum_current == set_up_script_md5sum_metadata:
-                return 0
+        if set_up_script_md5sum_current == set_up_script_md5sum_metadata:
+            return 0
 
-            # Return code 1 if the resources need to be refreshed.
-            print('Error: hash mismatch!')
-            print(f'Metadata: {set_up_script_md5sum_metadata}')
-            print(f'Current : {set_up_script_md5sum_current}')
-            return 1
+        # Return code 1 if the resources need to be refreshed.
+        print('Error: hash mismatch!')
+        print(f'Metadata: {set_up_script_md5sum_metadata}')
+        print(f'Current : {set_up_script_md5sum_current}')
+        return 1
 
-    # Return error code 2 if the file doesn't exists.
+    # Return error code 2 if the file doesn't exist.
     print(f'Error: could not find {metadata_file_path}')
     return 2
 
@@ -99,7 +104,8 @@ if __name__ == "__main__":
         raise ValueError(f'Invalid script path: {args.setup_script_path}')
 
     # Check the resources are downloaded as expected
-    status = check_update_resources_downloaded(
-                args.resource_downloaded_dir,
-                args.setup_script_path)
-    sys.exit(status)
+    STATUS = check_update_resources_downloaded(
+        args.resource_downloaded_dir,
+        args.setup_script_path
+    )
+    sys.exit(STATUS)
diff --git a/scripts/py/dependency_urls.json b/scripts/py/dependency_urls.json
new file mode 100644
index 0000000..33a84f7
--- /dev/null
+++ b/scripts/py/dependency_urls.json
@@ -0,0 +1,8 @@
+{
+  "cmsis": "https://github.com/ARM-software/CMSIS_5/archive/a75f01746df18bb5b929dfb8dc6c9407fac3a0f3.zip",
+  "cmsis-dsp": "https://github.com/ARM-software/CMSIS-DSP/archive/refs/tags/v1.15.0.zip",
+  "cmsis-nn": "https://github.com/ARM-software/CMSIS-NN/archive/refs/85164a811917770d7027a12a57ed3b469dac6537.zip",
+  "core-driver": "https://git.mlplatform.org/ml/ethos-u/ethos-u-core-driver.git/snapshot/ethos-u-core-driver-23.08.tar.gz",
+  "core-platform": "https://git.mlplatform.org/ml/ethos-u/ethos-u-core-platform.git/snapshot/ethos-u-core-platform-23.08.tar.gz",
+  "tensorflow": "https://github.com/tensorflow/tflite-micro/archive/568d181ccc1f60e49742fd43b7f97141ee8d45fc.zip"
+}
diff --git a/scripts/py/gen_audio.py b/scripts/py/gen_audio.py
index ff33bfb..4d7318c 100644
--- a/scripts/py/gen_audio.py
+++ b/scripts/py/gen_audio.py
@@ -1,6 +1,6 @@
 #!env/bin/python3
 
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,34 +17,99 @@
 """
 Utility script to convert an audio clip into eval platform desired spec.
 """
-import soundfile as sf
-
 from argparse import ArgumentParser
 from os import path
 
-from gen_utils import AudioUtils
+import soundfile as sf
+
+from gen_utils import GenUtils
 
 parser = ArgumentParser()
-parser.add_argument("--audio_path", help="Audio file path", required=True)
-parser.add_argument("--output_dir", help="Output directory", required=True)
-parser.add_argument("--sampling_rate", type=int, help="target sampling rate.", default=16000)
-parser.add_argument("--mono", type=bool, help="convert signal to mono.", default=True)
-parser.add_argument("--offset", type=float, help="start reading after this time (in seconds).", default=0)
-parser.add_argument("--duration", type=float, help="only load up to this much audio (in seconds).", default=0)
-parser.add_argument("--res_type", type=AudioUtils.res_data_type, help=f"Resample type: {AudioUtils.res_type_list()}.", default='kaiser_best')
-parser.add_argument("--min_samples", type=int, help="Minimum sample number.", default=16000)
-parser.add_argument("-v", "--verbosity", action="store_true")
-args = parser.parse_args()
+
+# pylint: disable=duplicate-code
+parser.add_argument(
+    "--audio_path",
+    help="Audio file path",
+    required=True
+)
+
+parser.add_argument(
+    "--output_dir",
+    help="Output directory",
+    required=True
+)
+
+parser.add_argument(
+    "--sampling_rate",
+    type=int,
+    help="target sampling rate.",
+    default=16000
+)
+
+parser.add_argument(
+    "--mono",
+    type=bool,
+    help="convert signal to mono.",
+    default=True
+)
+
+parser.add_argument(
+    "--offset",
+    type=float,
+    help="start reading after this time (in seconds).",
+    default=0
+)
+
+parser.add_argument(
+    "--duration",
+    type=float,
+    help="only load up to this much audio (in seconds).",
+    default=0
+)
+
+parser.add_argument(
+    "--res_type",
+    type=GenUtils.res_data_type,
+    help=f"Resample type: {GenUtils.res_type_list()}.",
+    default='kaiser_best'
+)
+
+parser.add_argument(
+    "--min_samples",
+    type=int,
+    help="Minimum sample number.",
+    default=16000
+)
+
+parser.add_argument(
+    "-v",
+    "--verbosity",
+    action="store_true"
+)
+# pylint: enable=duplicate-code
+
+parsed_args = parser.parse_args()
 
 
 def main(args):
-    audio_data, samplerate = AudioUtils.load_resample_audio_clip(args.audio_path,
-                                                args.sampling_rate,
-                                                args.mono,  args.offset,
-                                                args.duration, args.res_type,
-                                                args.min_samples)
-    sf.write(path.join(args.output_dir, path.basename(args.audio_path)), audio_data, samplerate)
+    """
+    Generate the new audio file
+    @param args:    Parsed args
+    """
+    audio_sample = GenUtils.read_audio_file(
+        args.audio_path, args.offset, args.duration
+    )
+
+    resampled_audio = GenUtils.resample_audio_clip(
+        audio_sample, args.sampling_rate, args.mono, args.res_type, args.min_samples
+    )
+
+    sf.write(
+        path.join(args.output_dir, path.basename(args.audio_path)),
+        resampled_audio.data,
+        resampled_audio.sample_rate
+    )
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_audio_cpp.py b/scripts/py/gen_audio_cpp.py
index 850a871..89d9ae1 100644
--- a/scripts/py/gen_audio_cpp.py
+++ b/scripts/py/gen_audio_cpp.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,93 +21,217 @@ from the cpp files.
 import datetime
 import glob
 import math
-from pathlib import Path
 from argparse import ArgumentParser
+from pathlib import Path
 
 import numpy as np
 from jinja2 import Environment, FileSystemLoader
 
-from gen_utils import AudioUtils
+from gen_utils import GenUtils, AudioSample
 
+# pylint: disable=duplicate-code
 parser = ArgumentParser()
-parser.add_argument("--audio_path", type=str, help="path to audio folder to convert.")
-parser.add_argument("--source_folder_path", type=str, help="path to source folder to be generated.")
-parser.add_argument("--header_folder_path", type=str, help="path to header folder to be generated.")
-parser.add_argument("--sampling_rate", type=int, help="target sampling rate.", default=16000)
-parser.add_argument("--mono", type=bool, help="convert signal to mono.", default=True)
-parser.add_argument("--offset", type=float, help="start reading after this time (in seconds).", default=0)
-parser.add_argument("--duration", type=float, help="only load up to this much audio (in seconds).", default=0)
-parser.add_argument("--res_type", type=AudioUtils.res_data_type, help=f"Resample type: {AudioUtils.res_type_list()}.",
-                    default='kaiser_best')
-parser.add_argument("--min_samples", type=int, help="Minimum sample number.", default=16000)
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
-parser.add_argument("-v", "--verbosity", action="store_true")
-args = parser.parse_args()
+
+parser.add_argument(
+    "--audio_path",
+    type=str,
+    help="path to audio folder to convert."
+)
+
+parser.add_argument(
+    "--source_folder_path",
+    type=str,
+    help="path to source folder to be generated."
+)
+
+parser.add_argument(
+    "--header_folder_path",
+    type=str,
+    help="path to header folder to be generated."
+)
+
+parser.add_argument(
+    "--sampling_rate",
+    type=int,
+    help="target sampling rate.",
+    default=16000
+)
+
+parser.add_argument(
+    "--mono",
+    type=bool,
+    help="convert signal to mono.",
+    default=True
+)
+
+parser.add_argument(
+    "--offset",
+    type=float,
+    help="start reading after this time (in seconds).",
+    default=0
+)
+
+parser.add_argument(
+    "--duration",
+    type=float,
+    help="only load up to this much audio (in seconds).",
+    default=0
+)
+
+parser.add_argument(
+    "--res_type",
+    type=GenUtils.res_data_type,
+    help=f"Resample type: {GenUtils.res_type_list()}.",
+    default='kaiser_best'
+)
+
+parser.add_argument(
+    "--min_samples",
+    type=int,
+    help="Minimum sample number.",
+    default=16000
+)
+
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+)
+
+parser.add_argument(
+    "-v",
+    "--verbosity",
+    action="store_true"
+)
+
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
-def write_hpp_file(header_filepath, cc_filepath, header_template_file, num_audios, audio_filenames, audio_array_namesizes):
+# pylint: enable=duplicate-code
+def write_hpp_file(
+        header_filepath,
+        header,
+        num_audios,
+        audio_array_namesizes
+):
+    """
+    Write audio hpp file
+
+    @param header_filepath:         .hpp filepath
+    @param header:                  Rendered header
+    @param num_audios:              Audio file index
+    @param audio_array_namesizes:   Audio array name sizes
+    """
     print(f"++ Generating {header_filepath}")
 
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 year=datetime.datetime.now().year)
-    env.get_template('AudioClips.hpp.template').stream(common_template_header=hdr,
-                                                       clips_count=num_audios,
-                                                       varname_size=audio_array_namesizes
-                                                       ) \
+    env \
+        .get_template('AudioClips.hpp.template') \
+        .stream(common_template_header=header,
+                clips_count=num_audios,
+                varname_size=audio_array_namesizes) \
         .dump(str(header_filepath))
 
+
+def write_cc_file(
+        cc_filepath,
+        header,
+        num_audios,
+        audio_filenames,
+        audio_array_namesizes
+):
+    """
+    Write cc file
+
+    @param cc_filepath:             .cc filepath
+    @param header:                  Rendered header
+    @param num_audios:              Audio file index
+    @param audio_filenames:         Audio filenames
+    @param audio_array_namesizes:   Audio array name sizes
+    """
     print(f"++ Generating {cc_filepath}")
 
-    env.get_template('AudioClips.cc.template').stream(common_template_header=hdr,
-                                                       clips_count=num_audios,
-                                                       var_names=(name for name, _ in audio_array_namesizes),
-                                                       clip_sizes=(size for _, size in audio_array_namesizes),
-                                                       clip_names=audio_filenames) \
+    env \
+        .get_template('AudioClips.cc.template') \
+        .stream(common_template_header=header,
+                clips_count=num_audios,
+                var_names=(name for name, _ in audio_array_namesizes),
+                clip_sizes=(size for _, size in audio_array_namesizes),
+                clip_names=audio_filenames) \
         .dump(str(cc_filepath))
 
 
-def write_individual_audio_cc_file(clip_dirpath, clip_filename,
-                                   cc_filename, header_template_file, array_name,
-                                   sampling_rate_value, mono_value, offset_value,
-                                   duration_value, res_type_value, min_len):
+def write_individual_audio_cc_file(
+        resampled_audio: AudioSample,
+        clip_filename,
+        cc_filename,
+        header_template_file,
+        array_name
+):
+    """
+    Writes the provided audio sample to a .cc file
+
+    @param resampled_audio:         Audio sample to write
+    @param clip_filename:           File name of the clip
+    @param cc_filename:             File name of the .cc file
+    @param header_template_file:    Header template
+    @param array_name:              Name of the array to write
+    @return:                        Array length of the audio data written
+    """
     print(f"++ Converting {clip_filename} to {Path(cc_filename).name}")
-    audio_filepath = Path(clip_dirpath) / clip_filename
-    clip_data, samplerate = AudioUtils.load_resample_audio_clip(audio_filepath,
-                                                                sampling_rate_value, mono_value,
-                                                                offset_value, duration_value,
-                                                                res_type_value, min_len)
 
     # Change from [-1, 1] fp32 range to int16 range.
-    clip_data = np.clip((clip_data * (1 << 15)),
+    clip_data = np.clip((resampled_audio.data * (1 << 15)),
                         np.iinfo(np.int16).min,
                         np.iinfo(np.int16).max).flatten().astype(np.int16)
 
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 file_name=clip_filename,
-                                 year=datetime.datetime.now().year)
+    hdr = GenUtils.gen_header(env, header_template_file, clip_filename)
 
     hex_line_generator = (', '.join(map(hex, sub_arr))
-                          for sub_arr in np.array_split(clip_data, math.ceil(len(clip_data)/20)))
+                          for sub_arr in np.array_split(clip_data, math.ceil(len(clip_data) / 20)))
 
-    env.get_template('audio.cc.template').stream(common_template_header=hdr,
-                                                 size=len(clip_data),
-                                                 var_name=array_name,
-                                                 audio_data=hex_line_generator) \
+    env \
+        .get_template('audio.cc.template') \
+        .stream(common_template_header=hdr,
+                size=len(clip_data),
+                var_name=array_name,
+                audio_data=hex_line_generator) \
         .dump(str(cc_filename))
 
     return len(clip_data)
 
 
+def create_audio_cc_file(args, filename, array_name, clip_dirpath):
+    """
+    Create an individual audio cpp file
+
+    @param args:            User-specified args
+    @param filename:        Audio filename
+    @param array_name:      Name of the array in the audio .cc file
+    @param clip_dirpath:    Audio file directory path
+    @return:                Array length of the audio data written
+    """
+    cc_filename = (Path(args.source_folder_path) /
+                   (Path(filename).stem.replace(" ", "_") + ".cc"))
+    audio_filepath = Path(clip_dirpath) / filename
+    audio_sample = GenUtils.read_audio_file(audio_filepath, args.offset, args.duration)
+    resampled_audio = GenUtils.resample_audio_clip(
+        audio_sample, args.sampling_rate, args.mono, args.res_type, args.min_samples
+    )
+    return write_individual_audio_cc_file(
+        resampled_audio, filename, cc_filename, args.license_template, array_name,
+    )
+
+
 def main(args):
+    """
+    Convert audio files to .cc + .hpp files
+    @param args:    Parsed args
+    """
     # Keep the count of the audio files converted
     audioclip_idx = 0
     audioclip_filenames = []
@@ -131,25 +255,41 @@ def main(args):
             audioclip_filenames.append(filename)
 
             # Save the cc file
-            cc_filename = Path(args.source_folder_path) / (Path(filename).stem.replace(" ", "_") + ".cc")
             array_name = "audio" + str(audioclip_idx)
-            array_size = write_individual_audio_cc_file(clip_dirpath, filename, cc_filename, args.license_template, array_name,
-                                                        args.sampling_rate, args.mono, args.offset,
-                                                        args.duration, args.res_type, args.min_samples)
+            array_size = create_audio_cc_file(args, filename, array_name, clip_dirpath)
 
             audioclip_array_names.append((array_name, array_size))
             # Increment audio index
             audioclip_idx = audioclip_idx + 1
-        except:
+        except OSError:
             if args.verbosity:
                 print(f"Failed to open {filename} as an audio.")
 
     if len(audioclip_filenames) > 0:
-        write_hpp_file(header_filepath, common_cc_filepath, args.license_template,
-                    audioclip_idx, audioclip_filenames, audioclip_array_names)
+        header = env \
+            .get_template(args.license_template) \
+            .render(script_name=Path(__file__).name,
+                    gen_time=datetime.datetime.now(),
+                    year=datetime.datetime.now().year)
+
+        write_hpp_file(
+            header_filepath,
+            header,
+            audioclip_idx,
+            audioclip_array_names
+        )
+
+        write_cc_file(
+            common_cc_filepath,
+            header,
+            audioclip_idx,
+            audioclip_filenames,
+            audioclip_array_names
+        )
+
     else:
         raise FileNotFoundError("No valid audio clip files found.")
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_default_input_cpp.py b/scripts/py/gen_default_input_cpp.py
index 093a606..6056dc1 100644
--- a/scripts/py/gen_default_input_cpp.py
+++ b/scripts/py/gen_default_input_cpp.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,38 +16,61 @@
 """
 Utility script to generate the minimum InputFiles.hpp and cpp files required by an application.
 """
-import datetime
-from pathlib import Path
 from argparse import ArgumentParser
+from pathlib import Path
 
 from jinja2 import Environment, FileSystemLoader
 
+from gen_utils import GenUtils
+
 parser = ArgumentParser()
-parser.add_argument("--header_folder_path", type=str, help="path to header folder to be generated.")
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
-args = parser.parse_args()
+
+# pylint: disable=duplicate-code
+parser.add_argument(
+    "--header_folder_path",
+    type=str,
+    help="path to header folder to be generated."
+)
+
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+
+)
+
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
+# pylint: enable=duplicate-code
 def write_hpp_file(header_file_path, header_template_file):
+    """
+    Write .hpp file
+    @param header_file_path:        Header file path
+    @param header_template_file:    Header template file
+    """
     print(f"++ Generating {header_file_path}")
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 year=datetime.datetime.now().year)
-    env.get_template('default.hpp.template').stream(common_template_header=hdr) \
+    hdr = GenUtils.gen_header(env, header_template_file)
+    env \
+        .get_template('default.hpp.template') \
+        .stream(common_template_header=hdr) \
         .dump(str(header_file_path))
 
 
 def main(args):
+    """
+    Generate InputFiles.hpp + .cpp
+    @param args:    Parsed args
+    """
     header_filename = "InputFiles.hpp"
     header_filepath = Path(args.header_folder_path) / header_filename
     write_hpp_file(header_filepath, args.license_template)
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_labels_cpp.py b/scripts/py/gen_labels_cpp.py
index 065ed5d..11d5040 100644
--- a/scripts/py/gen_labels_cpp.py
+++ b/scripts/py/gen_labels_cpp.py
@@ -1,6 +1,6 @@
 #!env/bin/python3
 
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,47 +21,83 @@ NN model output vector) into a vector list initialiser. The intention is for
 this script to be called as part of the build framework to auto-generate the
 cpp file with labels that can be used in the application without modification.
 """
-import datetime
-from pathlib import Path
 from argparse import ArgumentParser
+from pathlib import Path
 
 from jinja2 import Environment, FileSystemLoader
 
+from gen_utils import GenUtils
+
+# pylint: disable=duplicate-code
 parser = ArgumentParser()
 
 # Label file path
-parser.add_argument("--labels_file", type=str, help="Path to the label text file", required=True)
+parser.add_argument(
+    "--labels_file",
+    type=str,
+    help="Path to the label text file",
+    required=True
+)
+
 # Output file to be generated
-parser.add_argument("--source_folder_path", type=str, help="path to source folder to be generated.", required=True)
-parser.add_argument("--header_folder_path", type=str, help="path to header folder to be generated.", required=True)
-parser.add_argument("--output_file_name", type=str, help="Required output file name", required=True)
+parser.add_argument(
+    "--source_folder_path",
+    type=str,
+    help="path to source folder to be generated.",
+    required=True
+)
+
+parser.add_argument(
+    "--header_folder_path",
+    type=str,
+    help="path to header folder to be generated.",
+    required=True
+)
+
+parser.add_argument(
+    "--output_file_name",
+    type=str,
+    help="Required output file name",
+    required=True
+)
+
 # Namespaces
-parser.add_argument("--namespaces", action='append', default=[])
+parser.add_argument(
+    "--namespaces",
+    action='append',
+    default=[]
+)
+
 # License template
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+)
 
-args = parser.parse_args()
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
+# pylint: enable=duplicate-code
 def main(args):
+    """
+    Generate labels .cpp
+    @param args:    Parsed args
+    """
     # Get the labels from text file
-    with open(args.labels_file, "r") as f:
+    with open(args.labels_file, "r", encoding="utf8") as f:
         labels = f.read().splitlines()
 
     # No labels?
     if len(labels) == 0:
-        raise Exception(f"no labels found in {args.label_file}")
+        raise ValueError(f"no labels found in {args.label_file}")
 
-    header_template = env.get_template(args.license_template)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 file_name=Path(args.labels_file).name,
-                                 year=datetime.datetime.now().year)
+    hdr = GenUtils.gen_header(env, args.license_template, Path(args.labels_file).name)
 
     hpp_filename = Path(args.header_folder_path) / (args.output_file_name + ".hpp")
     env.get_template('Labels.hpp.template').stream(common_template_header=hdr,
@@ -78,4 +114,4 @@ def main(args):
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_model_cpp.py b/scripts/py/gen_model_cpp.py
index e4933b5..933c189 100644
--- a/scripts/py/gen_model_cpp.py
+++ b/scripts/py/gen_model_cpp.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,29 +18,67 @@ Utility script to generate model c file that can be included in the
 project directly. This should be called as part of cmake framework
 should the models need to be generated at configuration stage.
 """
-import datetime
+import binascii
 from argparse import ArgumentParser
 from pathlib import Path
 
 from jinja2 import Environment, FileSystemLoader
-import binascii
 
+from gen_utils import GenUtils
+
+# pylint: disable=duplicate-code
 parser = ArgumentParser()
 
-parser.add_argument("--tflite_path", help="Model (.tflite) path", required=True)
-parser.add_argument("--output_dir", help="Output directory", required=True)
-parser.add_argument('-e', '--expression', action='append', default=[], dest="expr")
-parser.add_argument('--header', action='append', default=[], dest="headers")
-parser.add_argument('-ns', '--namespaces', action='append', default=[], dest="namespaces")
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
-args = parser.parse_args()
+parser.add_argument(
+    "--tflite_path",
+    help="Model (.tflite) path",
+    required=True
+)
+
+parser.add_argument(
+    "--output_dir",
+    help="Output directory",
+    required=True
+)
+
+parser.add_argument(
+    '-e',
+    '--expression',
+    action='append',
+    default=[],
+    dest="expr"
+)
+
+parser.add_argument(
+    '--header',
+    action='append',
+    default=[],
+    dest="headers"
+)
+
+parser.add_argument(
+    '-ns',
+    '--namespaces',
+    action='append',
+    default=[],
+    dest="namespaces"
+)
+
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+)
+
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
+# pylint: enable=duplicate-code
 def get_tflite_data(tflite_path: str) -> list:
     """
     Reads a binary file and returns a C style array as a
@@ -63,15 +101,19 @@ def get_tflite_data(tflite_path: str) -> list:
     for i in range(0, len(hexstream), 2):
         if 0 == (i % hex_digits_per_line):
             hexstring += "\n"
-        hexstring += '0x' + hexstream[i:i+2] + ", "
+        hexstring += '0x' + hexstream[i:i + 2] + ", "
 
     hexstring += '};\n'
     return [hexstring]
 
 
 def main(args):
+    """
+    Generate models .cpp
+    @param args:    Parsed args
+    """
     if not Path(args.tflite_path).is_file():
-        raise Exception(f"{args.tflite_path} not found")
+        raise ValueError(f"{args.tflite_path} not found")
 
     # Cpp filename:
     cpp_filename = (Path(args.output_dir) / (Path(args.tflite_path).name + ".cc")).resolve()
@@ -80,19 +122,16 @@ def main(args):
 
     cpp_filename.parent.mkdir(exist_ok=True)
 
-    header_template = env.get_template(args.license_template)
-
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 file_name=Path(args.tflite_path).name,
-                                 gen_time=datetime.datetime.now(),
-                                 year=datetime.datetime.now().year)
+    hdr = GenUtils.gen_header(env, args.license_template, Path(args.tflite_path).name)
 
-    env.get_template('tflite.cc.template').stream(common_template_header=hdr,
-                                                  model_data=get_tflite_data(args.tflite_path),
-                                                  expressions=args.expr,
-                                                  additional_headers=args.headers,
-                                                  namespaces=args.namespaces).dump(str(cpp_filename))
+    env \
+        .get_template('tflite.cc.template') \
+        .stream(common_template_header=hdr,
+                model_data=get_tflite_data(args.tflite_path),
+                expressions=args.expr,
+                additional_headers=args.headers,
+                namespaces=args.namespaces).dump(str(cpp_filename))
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_rgb_cpp.py b/scripts/py/gen_rgb_cpp.py
index b8d85ee..e1c93bb 100644
--- a/scripts/py/gen_rgb_cpp.py
+++ b/scripts/py/gen_rgb_cpp.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,87 +18,179 @@ Utility script to convert a set of RGB images in a given location into
 corresponding cpp files and a single hpp file referencing the vectors
 from the cpp files.
 """
-import datetime
 import glob
 import math
-from pathlib import Path
+import typing
 from argparse import ArgumentParser
+from dataclasses import dataclass
+from pathlib import Path
 
 import numpy as np
 from PIL import Image, UnidentifiedImageError
 from jinja2 import Environment, FileSystemLoader
 
+from gen_utils import GenUtils
+
+# pylint: disable=duplicate-code
 parser = ArgumentParser()
-parser.add_argument("--image_path", type=str, help="path to images folder or image file  to convert.")
-parser.add_argument("--source_folder_path", type=str, help="path to source folder to be generated.")
-parser.add_argument("--header_folder_path", type=str, help="path to header folder to be generated.")
-parser.add_argument("--image_size", type=int, nargs=2, help="Size (width and height) of the converted images.")
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
-args = parser.parse_args()
+
+parser.add_argument(
+    "--image_path",
+    type=str,
+    help="path to images folder or image file to convert."
+)
+
+parser.add_argument(
+    "--source_folder_path",
+    type=str,
+    help="path to source folder to be generated."
+)
+
+parser.add_argument(
+    "--header_folder_path",
+    type=str,
+    help="path to header folder to be generated."
+)
+
+parser.add_argument(
+    "--image_size",
+    type=int,
+    nargs=2,
+    help="Size (width and height) of the converted images."
+)
+
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+)
+
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
-def write_hpp_file(header_file_path, cc_file_path, header_template_file, num_images, image_filenames,
-                   image_array_names, image_size):
+# pylint: enable=duplicate-code
+@dataclass
+class ImagesParams:
+    """
+    Template params for Images.hpp and Images.cc
+    """
+    num_images: int
+    image_size: typing.Sequence
+    image_array_names: typing.List[str]
+    image_filenames: typing.List[str]
+
+
+def write_hpp_file(
+        images_params: ImagesParams,
+        header_file_path: Path,
+        cc_file_path: Path,
+        header_template_file: str,
+):
+    """
+    Write Images.hpp and Images.cc
+
+    @param images_params:           Template params
+    @param header_file_path:        Images.hpp path
+    @param cc_file_path:            Images.cc path
+    @param header_template_file:    Header template file name
+    """
     print(f"++ Generating {header_file_path}")
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 year=datetime.datetime.now().year)
-    env.get_template('Images.hpp.template').stream(common_template_header=hdr,
-                                                   imgs_count=num_images,
-                                                   img_size=str(image_size[0] * image_size[1] * 3),
-                                                   var_names=image_array_names) \
+    hdr = GenUtils.gen_header(env, header_template_file)
+
+    image_size = str(images_params.image_size[0] * images_params.image_size[1] * 3)
+
+    env \
+        .get_template('Images.hpp.template') \
+        .stream(common_template_header=hdr,
+                imgs_count=images_params.num_images,
+                img_size=image_size,
+                var_names=images_params.image_array_names) \
         .dump(str(header_file_path))
 
-    env.get_template('Images.cc.template').stream(common_template_header=hdr,
-                                                  var_names=image_array_names,
-                                                  img_names=image_filenames) \
+    env \
+        .get_template('Images.cc.template') \
+        .stream(common_template_header=hdr,
+                var_names=images_params.image_array_names,
+                img_names=images_params.image_filenames) \
         .dump(str(cc_file_path))
 
 
-def write_individual_img_cc_file(image_filename, cc_filename, header_template_file, original_image,
-                                 image_size, array_name):
-    print(f"++ Converting {image_filename} to {cc_filename.name}")
+def resize_crop_image(
+        original_image: Image.Image,
+        image_size: typing.Sequence
+) -> np.ndarray:
+    """
+    Resize and crop input image
 
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 file_name=image_filename,
-                                 year=datetime.datetime.now().year)
+    @param original_image:  Image to resize and crop
+    @param image_size:      New image size
+    @return:                Resized and cropped image
+    """
     # IFM size
     ifm_width = image_size[0]
     ifm_height = image_size[1]
 
     # Aspect ratio resize
-    scale_ratio = (float)(max(ifm_width, ifm_height)) / (float)(min(original_image.size[0], original_image.size[1]))
-    resized_width = (int)(original_image.size[0] * scale_ratio)
-    resized_height = (int)(original_image.size[1] * scale_ratio)
-    resized_image = original_image.resize([resized_width,resized_height], Image.Resampling.BILINEAR)
+    scale_ratio = (float(max(ifm_width, ifm_height))
+                   / float(min(original_image.size[0], original_image.size[1])))
+    resized_width = int(original_image.size[0] * scale_ratio)
+    resized_height = int(original_image.size[1] * scale_ratio)
+    resized_image = original_image.resize(
+        size=(resized_width, resized_height),
+        resample=Image.Resampling.BILINEAR
+    )
 
     # Crop the center of the image
     resized_image = resized_image.crop((
-        (resized_width - ifm_width) / 2,   # left
-        (resized_height - ifm_height) / 2, # top
-        (resized_width + ifm_width) / 2,   # right
+        (resized_width - ifm_width) / 2,  # left
+        (resized_height - ifm_height) / 2,  # top
+        (resized_width + ifm_width) / 2,  # right
         (resized_height + ifm_height) / 2  # bottom
-        ))
+    ))
+
+    return np.array(resized_image, dtype=np.uint8).flatten()
+
+
+def write_individual_img_cc_file(
+        rgb_data: np.ndarray,
+        image_filename: str,
+        cc_filename: Path,
+        header_template_file: str,
+        array_name: str
+):
+    """
+    Write image.cc
+
+    @param rgb_data:                Image data
+    @param image_filename:          Image file name
+    @param cc_filename:             image.cc path
+    @param header_template_file:    Header template file name
+    @param array_name:              C++ array name
+    """
+    print(f"++ Converting {image_filename} to {cc_filename.name}")
+
+    hdr = GenUtils.gen_header(env, header_template_file, image_filename)
 
-    # Convert the image and write it to the cc file
-    rgb_data = np.array(resized_image, dtype=np.uint8).flatten()
     hex_line_generator = (', '.join(map(hex, sub_arr))
                           for sub_arr in np.array_split(rgb_data, math.ceil(len(rgb_data) / 20)))
-    env.get_template('image.cc.template').stream(common_template_header=hdr,
-                                                 var_name=array_name,
-                                                 img_data=hex_line_generator) \
+    env \
+        .get_template('image.cc.template') \
+        .stream(common_template_header=hdr,
+                var_name=array_name,
+                img_data=hex_line_generator) \
         .dump(str(cc_filename))
 
 
 def main(args):
+    """
+    Convert images
+    @param args:    Parsed args
+    """
     # Keep the count of the images converted
     image_idx = 0
     image_filenames = []
@@ -123,26 +215,29 @@ def main(args):
         image_filenames.append(filename)
 
         # Save the cc file
-        cc_filename = Path(args.source_folder_path) / (Path(filename).stem.replace(" ", "_") + ".cc")
+        cc_filename = (Path(args.source_folder_path) /
+                       (Path(filename).stem.replace(" ", "_") + ".cc"))
         array_name = "im" + str(image_idx)
         image_array_names.append(array_name)
-        write_individual_img_cc_file(filename, cc_filename, args.license_template,
-                                     original_image, args.image_size, array_name)
+
+        rgb_data = resize_crop_image(original_image, args.image_size)
+        write_individual_img_cc_file(
+            rgb_data, filename, cc_filename, args.license_template, array_name
+        )
 
         # Increment image index
         image_idx = image_idx + 1
 
-    header_filename = "InputFiles.hpp"
-    header_filepath = Path(args.header_folder_path) / header_filename
-    common_cc_filename = "InputFiles.cc"
-    common_cc_filepath = Path(args.source_folder_path) / common_cc_filename
+    header_filepath = Path(args.header_folder_path) / "InputFiles.hpp"
+    common_cc_filepath = Path(args.source_folder_path) / "InputFiles.cc"
+
+    images_params = ImagesParams(image_idx, args.image_size, image_array_names, image_filenames)
 
     if len(image_filenames) > 0:
-        write_hpp_file(header_filepath, common_cc_filepath, args.license_template,
-                    image_idx, image_filenames, image_array_names, args.image_size)
+        write_hpp_file(images_params, header_filepath, common_cc_filepath, args.license_template)
     else:
         raise FileNotFoundError("No valid images found.")
 
 
 if __name__ == '__main__':
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/gen_test_data_cpp.py b/scripts/py/gen_test_data_cpp.py
index a9e2b75..1ee55ff 100644
--- a/scripts/py/gen_test_data_cpp.py
+++ b/scripts/py/gen_test_data_cpp.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 - 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,81 +18,170 @@ Utility script to convert a set of pairs of npy files in a given location into
 corresponding cpp files and a single hpp file referencing the vectors
 from the cpp files.
 """
-import datetime
 import math
-import os
-import numpy as np
+import typing
+from argparse import ArgumentParser
+from dataclasses import dataclass
 from pathlib import Path
 
-from argparse import ArgumentParser
+import numpy as np
 from jinja2 import Environment, FileSystemLoader
 
+from gen_utils import GenUtils
+
+# pylint: disable=duplicate-code
 parser = ArgumentParser()
-parser.add_argument("--data_folder_path", type=str, help="path to ifm-ofm npy folder to convert.")
-parser.add_argument("--source_folder_path", type=str, help="path to source folder to be generated.")
-parser.add_argument("--header_folder_path", type=str, help="path to header folder to be generated.")
-parser.add_argument("--usecase", type=str, default="", help="Test data file suffix.")
-parser.add_argument("--namespaces", action='append', default=[])
-parser.add_argument("--license_template", type=str, help="Header template file",
-                    default="header_template.txt")
-parser.add_argument("-v", "--verbosity", action="store_true")
 
-args = parser.parse_args()
+parser.add_argument(
+    "--data_folder_path",
+    type=str,
+    help="path to ifm-ofm npy folder to convert."
+)
+
+parser.add_argument(
+    "--source_folder_path",
+    type=str,
+    help="path to source folder to be generated."
+)
+
+parser.add_argument(
+    "--header_folder_path",
+    type=str,
+    help="path to header folder to be generated."
+)
+
+parser.add_argument(
+    "--usecase",
+    type=str,
+    default="",
+    help="Test data file suffix."
+)
+
+parser.add_argument(
+    "--namespaces",
+    action='append',
+    default=[]
+)
+
+parser.add_argument(
+    "--license_template",
+    type=str,
+    help="Header template file",
+    default="header_template.txt"
+)
+
+parser.add_argument(
+    "-v",
+    "--verbosity",
+    action="store_true"
+)
+
+parsed_args = parser.parse_args()
 
 env = Environment(loader=FileSystemLoader(Path(__file__).parent / 'templates'),
                   trim_blocks=True,
                   lstrip_blocks=True)
 
 
-def write_hpp_file(header_filename, cc_file_path, header_template_file, num_ifms, num_ofms,
-                   ifm_array_names, ifm_sizes, ofm_array_names, ofm_sizes, iofm_data_type):
-    header_file_path = Path(args.header_folder_path) / header_filename
+# pylint: enable=duplicate-code
+@dataclass
+class TestDataParams:
+    """
+    Template params for TestData.hpp + TestData.ccc
+    """
+    ifm_count: int
+    ofm_count: int
+    ifm_var_names: typing.List[str]
+    ifm_var_sizes: typing.List[int]
+    ofm_var_names: typing.List[str]
+    ofm_var_sizes: typing.List[int]
+    data_type: str
+
+
+@dataclass
+class IofmParams:
+    """
+    Template params for iofmdata.cc
+    """
+    var_name: str
+    data_type: str
+
+
+def write_hpp_file(
+        template_params: TestDataParams,
+        header_filename: str,
+        cc_file_path: str,
+        header_template_file: str
+):
+    """
+    Write TestData.hpp and TestData.cc
+
+    @param template_params:         Template parameters
+    @param header_filename:         TestData.hpp path
+    @param cc_file_path:            TestData.cc path
+    @param header_template_file:    Header template file name
+    """
+    header_file_path = Path(parsed_args.header_folder_path) / header_filename
 
     print(f"++ Generating {header_file_path}")
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 year=datetime.datetime.now().year)
-    env.get_template('TestData.hpp.template').stream(common_template_header=hdr,
-                                                   ifm_count=num_ifms,
-                                                   ofm_count=num_ofms,
-                                                   ifm_var_names=ifm_array_names,
-                                                   ifm_var_sizes=ifm_sizes,
-                                                   ofm_var_names=ofm_array_names,
-                                                   ofm_var_sizes=ofm_sizes,
-                                                   data_type=iofm_data_type,
-                                                   namespaces=args.namespaces) \
+    hdr = GenUtils.gen_header(env, header_template_file)
+    env \
+        .get_template('TestData.hpp.template') \
+        .stream(common_template_header=hdr,
+                ifm_count=template_params.ifm_count,
+                ofm_count=template_params.ofm_count,
+                ifm_var_names=template_params.ifm_var_names,
+                ifm_var_sizes=template_params.ifm_var_sizes,
+                ofm_var_names=template_params.ofm_var_names,
+                ofm_var_sizes=template_params.ofm_var_sizes,
+                data_type=template_params.data_type,
+                namespaces=parsed_args.namespaces) \
         .dump(str(header_file_path))
 
-    env.get_template('TestData.cc.template').stream(common_template_header=hdr,
-                                                  include_h=header_filename,
-                                                  ifm_var_names=ifm_array_names,
-                                                  ofm_var_names=ofm_array_names,
-                                                  data_type=iofm_data_type,
-                                                  namespaces=args.namespaces) \
+    env \
+        .get_template('TestData.cc.template') \
+        .stream(common_template_header=hdr,
+                include_h=header_filename,
+                ifm_var_names=template_params.ifm_var_names,
+                ofm_var_names=template_params.ofm_var_names,
+                data_type=template_params.data_type,
+                namespaces=parsed_args.namespaces) \
         .dump(str(cc_file_path))
 
 
-def write_individual_cc_file(filename, cc_filename, header_filename, header_template_file, array_name, iofm_data_type):
+def write_individual_cc_file(
+        template_params: IofmParams,
+        header_filename: str,
+        filename: str,
+        cc_filename: Path,
+        header_template_file: str
+):
+    """
+    Write iofmdata.cc
+
+    @param template_params:         Template parameters
+    @param header_filename:         Header file name
+    @param filename:                Input file name
+    @param cc_filename:             iofmdata.cc file name
+    @param header_template_file:    Header template file name
+    """
     print(f"++ Converting {filename} to {cc_filename.name}")
-    header_template = env.get_template(header_template_file)
-    hdr = header_template.render(script_name=Path(__file__).name,
-                                 gen_time=datetime.datetime.now(),
-                                 file_name=filename,
-                                 year=datetime.datetime.now().year)
+    hdr = GenUtils.gen_header(env, header_template_file, filename)
 
     # Convert the image and write it to the cc file
-    fm_data = (np.load(Path(args.data_folder_path) / filename)).flatten()
+    fm_data = (np.load(Path(parsed_args.data_folder_path) / filename)).flatten()
     type(fm_data.dtype)
     hex_line_generator = (', '.join(map(hex, sub_arr))
                           for sub_arr in np.array_split(fm_data, math.ceil(len(fm_data) / 20)))
 
-    env.get_template('iofmdata.cc.template').stream(common_template_header=hdr,
-                                                 include_h=header_filename,
-                                                 var_name=array_name,
-                                                 fm_data=hex_line_generator,
-                                                 data_type=iofm_data_type,
-                                                 namespaces=args.namespaces) \
+    env \
+        .get_template('iofmdata.cc.template') \
+        .stream(common_template_header=hdr,
+                include_h=header_filename,
+                var_name=template_params.var_name,
+                fm_data=hex_line_generator,
+                data_type=template_params.data_type,
+                namespaces=parsed_args.namespaces) \
         .dump(str(cc_filename))
 
 
@@ -104,59 +193,117 @@ def get_npy_vec_size(filename: str) -> int:
     Return:
         size in bytes
     """
-    data = np.load(Path(args.data_folder_path) / filename)
+    data = np.load(Path(parsed_args.data_folder_path) / filename)
     return data.size * data.dtype.itemsize
 
 
-def main(args):
-    # Keep the count of the images converted
-    ifm_array_names = []
-    ofm_array_names = []
+def write_cc_files(args, count, iofm_data_type, add_usecase_fname, prefix):
+    """
+    Write all cc files
+
+    @param args:                User-provided args
+    @param count:               File count
+    @param iofm_data_type:      Data type
+    @param add_usecase_fname:   Use case suffix
+    @param prefix:              Prefix (ifm/ofm)
+    @return:                    Names and sizes of generated C++ arrays
+    """
+    array_names = []
+    sizes = []
+
+    header_filename = get_header_filename(add_usecase_fname)
 
+    # In the data_folder_path there should be pairs of ifm-ofm
+    # It's assumed the ifm-ofm naming convention: ifm0.npy-ofm0.npy, ifm1.npy-ofm1.npy
+    # count = int(len(list(Path(args.data_folder_path).glob(f'{prefix}*.npy'))))
+
+    for idx in range(count):
+        # Save the fm cc file
+        base_name = prefix + str(idx)
+        filename = base_name + ".npy"
+        array_name = base_name + add_usecase_fname
+        cc_filename = Path(args.source_folder_path) / (array_name + ".cc")
+        array_names.append(array_name)
+
+        template_params = IofmParams(
+            var_name=array_name,
+            data_type=iofm_data_type,
+        )
+
+        write_individual_cc_file(
+            template_params, header_filename, filename, cc_filename, args.license_template
+        )
+        sizes.append(get_npy_vec_size(filename))
+
+    return array_names, sizes
+
+
+def get_header_filename(use_case_filename):
+    """
+    Get the header file name from the use case file name
+
+    @param use_case_filename:   The use case file name
+    @return:                    The header file name
+    """
+    return "TestData" + use_case_filename + ".hpp"
+
+
+def get_cc_filename(use_case_filename):
+    """
+    Get the cc file name from the use case file name
+
+    @param use_case_filename:   The use case file name
+    @return:                    The cc file name
+    """
+    return "TestData" + use_case_filename + ".cc"
+
+
+def main(args):
+    """
+    Generate test data
+    @param args:    Parsed args
+    """
     add_usecase_fname = ("_" + args.usecase) if (args.usecase != "") else ""
-    header_filename = "TestData" + add_usecase_fname + ".hpp"
-    common_cc_filename = "TestData" + add_usecase_fname + ".cc"
+    header_filename = get_header_filename(add_usecase_fname)
+    common_cc_filename = get_cc_filename(add_usecase_fname)
 
     # In the data_folder_path there should be pairs of ifm-ofm
     # It's assumed the ifm-ofm naming convention: ifm0.npy-ofm0.npy, ifm1.npy-ofm1.npy
     ifms_count = int(len(list(Path(args.data_folder_path).glob('ifm*.npy'))))
     ofms_count = int(len(list(Path(args.data_folder_path).glob('ofm*.npy'))))
 
-    #i_ofms_count = int(len([name for name in os.listdir(os.path.join(args.data_folder_path)) if name.lower().endswith('.npy')]) / 2)
-
     iofm_data_type = "int8_t"
     if ifms_count > 0:
-        iofm_data_type = "int8_t" if (np.load(Path(args.data_folder_path) / "ifm0.npy").dtype == np.int8) else "uint8_t"
-
-    ifm_sizes = []
-    ofm_sizes = []
+        iofm_data_type = "int8_t" \
+            if (np.load(str(Path(args.data_folder_path) / "ifm0.npy")).dtype == np.int8) \
+            else "uint8_t"
 
-    for idx in range(ifms_count):
-        # Save the fm cc file
-        base_name = "ifm" + str(idx)
-        filename = base_name+".npy"
-        array_name = base_name + add_usecase_fname
-        cc_filename = Path(args.source_folder_path) / (array_name + ".cc")
-        ifm_array_names.append(array_name)
-        write_individual_cc_file(filename, cc_filename, header_filename, args.license_template, array_name, iofm_data_type)
-        ifm_sizes.append(get_npy_vec_size(filename))
+    ifm_array_names, ifm_sizes = write_cc_files(
+        args, ifms_count, iofm_data_type, add_usecase_fname, prefix="ifm"
+    )
 
-    for idx in range(ofms_count):
-        # Save the fm cc file
-        base_name = "ofm" + str(idx)
-        filename = base_name+".npy"
-        array_name = base_name + add_usecase_fname
-        cc_filename = Path(args.source_folder_path) / (array_name + ".cc")
-        ofm_array_names.append(array_name)
-        write_individual_cc_file(filename, cc_filename, header_filename, args.license_template, array_name, iofm_data_type)
-        ofm_sizes.append(get_npy_vec_size(filename))
+    ofm_array_names, ofm_sizes = write_cc_files(
+        args, ofms_count, iofm_data_type, add_usecase_fname, prefix="ofm"
+    )
 
     common_cc_filepath = Path(args.source_folder_path) / common_cc_filename
-    write_hpp_file(header_filename, common_cc_filepath, args.license_template,
-                   ifms_count, ofms_count, ifm_array_names, ifm_sizes, ofm_array_names, ofm_sizes, iofm_data_type)
+
+    template_params = TestDataParams(
+        ifm_count=ifms_count,
+        ofm_count=ofms_count,
+        ifm_var_names=ifm_array_names,
+        ifm_var_sizes=ifm_sizes,
+        ofm_var_names=ofm_array_names,
+        ofm_var_sizes=ofm_sizes,
+        data_type=iofm_data_type,
+    )
+
+    write_hpp_file(
+        template_params, header_filename, common_cc_filepath, args.license_template
+    )
 
 
 if __name__ == '__main__':
-    if args.verbosity:
-        print("Running gen_test_data_cpp with args: "+str(args))
-    main(args)
+    if parsed_args.verbosity:
+        print("Running gen_test_data_cpp with args: " + str(parsed_args))
+    main(parsed_args)
diff --git a/scripts/py/gen_utils.py b/scripts/py/gen_utils.py
index ee33705..6bb4760 100644
--- a/scripts/py/gen_utils.py
+++ b/scripts/py/gen_utils.py
@@ -1,6 +1,6 @@
 #!env/bin/python3
 
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,21 +14,43 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-
-import soundfile as sf
-import resampy
+"""
+Utility functions for .cc + .hpp file generation
+"""
+import argparse
+import datetime
+from dataclasses import dataclass
+from pathlib import Path
+
+import jinja2
 import numpy as np
+import resampy
+import soundfile as sf
 
 
-class AudioUtils:
+@dataclass
+class AudioSample:
+    """
+    Represents an audio sample with its sample rate
+    """
+    data: np.ndarray
+    sample_rate: int
+
+
+class GenUtils:
+    """
+    Class with utility functions for audio and other .cc + .hpp file generation
+    """
+
     @staticmethod
     def res_data_type(res_type_value):
         """
         Returns the input string if is one of the valid resample type
         """
-        import argparse
-        if res_type_value not in AudioUtils.res_type_list():
-            raise argparse.ArgumentTypeError(f"{res_type_value} not valid. Supported only {AudioUtils.res_type_list()}")
+        if res_type_value not in GenUtils.res_type_list():
+            raise argparse.ArgumentTypeError(
+                f"{res_type_value} not valid. Supported only {GenUtils.res_type_list()}"
+            )
         return res_type_value
 
     @staticmethod
@@ -39,27 +61,18 @@ class AudioUtils:
         return ['kaiser_best', 'kaiser_fast']
 
     @staticmethod
-    def load_resample_audio_clip(path, target_sr=16000, mono=True, offset=0.0, duration=0, res_type='kaiser_best',
-                                 min_len=16000):
+    def read_audio_file(
+            path,
+            offset,
+            duration
+    ) -> AudioSample:
         """
-        Load and resample an audio clip with the given desired specs.
+        Reads an audio file to an array
 
-        Parameters:
-        ----------
-        path (string):             Path to the input audio clip.
-        target_sr (int, optional): Target sampling rate. Positive number are considered valid, 
-                                    if zero or negative the native sampling rate of the file will be preserved. Default is 16000. 
-        mono (bool, optional):     Specify if the audio file needs to be converted to mono. Default is True.
-        offset (float, optional):  Target sampling rate. Default is 0.0.
-        duration (int, optional):  Target duration. Positive number are considered valid, 
-                                    if zero or negative the duration of the file will be preserved. Default is 0.
-        res_type (int, optional):  Resample type to use,  Default is 'kaiser_best'.
-        min_len (int, optional):   Minimun lenght of the output audio time series. Default is 16000.
-
-        Returns:
-        ----------
-        y (np.ndarray): Output audio time series of shape shape=(n,) or (2, n).
-        sr (int):       A scalar number > 0 that represent the sampling rate of `y`
+        @param path:        Path to audio file
+        @param offset:      Offset to read from
+        @param duration:    Duration to read
+        @return:            The audio data and the sample rate
         """
         try:
             with sf.SoundFile(path) as audio_file:
@@ -76,40 +89,115 @@ class AudioUtils:
 
                 # Load the target number of frames
                 y = audio_file.read(frames=num_frame_duration, dtype=np.float32, always_2d=False).T
-
-        except:
+        except OSError as err:
             print(f"Failed to open {path} as an audio.")
+            raise err
+
+        return AudioSample(y, origin_sr)
+
+    @staticmethod
+    def _resample_audio(
+            y,
+            target_sr,
+            origin_sr,
+            res_type
+    ):
+        """
+        Resamples audio to a different sample rate
+
+        @param y:           Audio to resample
+        @param target_sr:   Target sample rate
+        @param origin_sr:   Original sample rate
+        @param res_type:    Resample type
+        @return:            The resampled audio
+        """
+        ratio = float(target_sr) / origin_sr
+        axis = -1
+        n_samples = int(np.ceil(y.shape[axis] * ratio))
+
+        # Resample using resampy
+        y_rs = resampy.resample(y, origin_sr, target_sr, filter=res_type, axis=axis)
+        n_rs_samples = y_rs.shape[axis]
+
+        # Adjust the size
+        if n_rs_samples > n_samples:
+            slices = [slice(None)] * y_rs.ndim
+            slices[axis] = slice(0, n_samples)
+            y = y_rs[tuple(slices)]
+        elif n_rs_samples < n_samples:
+            lengths = [(0, 0)] * y_rs.ndim
+            lengths[axis] = (0, n_samples - n_rs_samples)
+            y = np.pad(y_rs, lengths, 'constant', constant_values=0)
+
+        return y
+
+    @staticmethod
+    def resample_audio_clip(
+            audio_sample: AudioSample,
+            target_sr=16000,
+            mono=True,
+            res_type='kaiser_best',
+            min_len=16000
+    ) -> AudioSample:
+        """
+        Load and resample an audio clip with the given desired specs.
+
+        Parameters:
+        ----------
+        path (string):              Path to the input audio clip.
+        target_sr (int, optional):  Target sampling rate. Positive number are considered valid,
+                                    if zero or negative the native sampling rate of the file
+                                    will be preserved. Default is 16000.
+        mono (bool, optional):      Specify if the audio file needs to be converted to mono.
+                                    Default is True.
+        offset (float, optional):   Target sampling rate. Default is 0.0.
+        duration (int, optional):   Target duration. Positive number are considered valid,
+                                    if zero or negative the duration of the file
+                                    will be preserved. Default is 0.
+        res_type (int, optional):   Resample type to use,  Default is 'kaiser_best'.
+        min_len (int, optional):    Minimum length of the output audio time series.
+                                    Default is 16000.
+
+        Returns:
+        ----------
+        y (np.ndarray):     Output audio time series of shape=(n,) or (2, n).
+        sample_rate (int):  A scalar number > 0 that represent the sampling rate of `y`
+        """
+        y = audio_sample.data.copy()
 
         # Convert to mono if requested and if audio has more than one dimension
-        if mono and (y.ndim > 1):
+        if mono and (audio_sample.data.ndim > 1):
             y = np.mean(y, axis=0)
 
-        if not (origin_sr == target_sr) and (target_sr > 0):
-            ratio = float(target_sr) / origin_sr
-            axis = -1
-            n_samples = int(np.ceil(y.shape[axis] * ratio))
-
-            # Resample using resampy
-            y_rs = resampy.resample(y, origin_sr, target_sr, filter=res_type, axis=axis)
-            n_rs_samples = y_rs.shape[axis]
-
-            # Adjust the size
-            if n_rs_samples > n_samples:
-                slices = [slice(None)] * y_rs.ndim
-                slices[axis] = slice(0, n_samples)
-                y = y_rs[tuple(slices)]
-            elif n_rs_samples < n_samples:
-                lengths = [(0, 0)] * y_rs.ndim
-                lengths[axis] = (0, n_samples - n_rs_samples)
-                y = np.pad(y_rs, lengths, 'constant', constant_values=(0))
-
-            sr = target_sr
+        if not (audio_sample.sample_rate == target_sr) and (target_sr > 0):
+            y = GenUtils._resample_audio(y, target_sr, audio_sample.sample_rate, res_type)
+            sample_rate = target_sr
         else:
-            sr = origin_sr
+            sample_rate = audio_sample.sample_rate
 
         # Pad if necessary and min lenght is setted (min_len> 0)
         if (y.shape[0] < min_len) and (min_len > 0):
             sample_to_pad = min_len - y.shape[0]
-            y = np.pad(y, (0, sample_to_pad), 'constant', constant_values=(0))
+            y = np.pad(y, (0, sample_to_pad), 'constant', constant_values=0)
+
+        return AudioSample(data=y, sample_rate=sample_rate)
 
-        return y, sr
+    @staticmethod
+    def gen_header(
+            env: jinja2.Environment,
+            header_template_file: str,
+            file_name: str = None
+    ) -> str:
+        """
+        Generate common licence header
+
+        :param env:                     Jinja2 environment
+        :param header_template_file:    Path to the licence header template
+        :param file_name:               Optional generating script file name
+        :return:                        Generated licence header as a string
+        """
+        header_template = env.get_template(header_template_file)
+        return header_template.render(script_name=Path(__file__).name,
+                                      gen_time=datetime.datetime.now(),
+                                      file_name=file_name,
+                                      year=datetime.datetime.now().year)
diff --git a/scripts/py/git_pre_push_hooks.sh b/scripts/py/git_pre_push_hooks.sh
new file mode 100755
index 0000000..db5706f
--- /dev/null
+++ b/scripts/py/git_pre_push_hooks.sh
@@ -0,0 +1,48 @@
+#!/bin/sh
+#  SPDX-FileCopyrightText: Copyright 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-License-Identifier: Apache-2.0
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+# Called by "git push" with no arguments.  The hook should
+# exit with non-zero status after issuing an appropriate message if
+# it wants to stop the push.
+
+# shellcheck disable=SC2034,SC2162
+while read local_ref local_sha remote_ref remote_sha; do
+  # We should pass only added or modified C/C++ source files to cppcheck.
+  changed_files=$(git diff --name-only HEAD~1 HEAD | grep -iE "\.(c|cpp|cxx|cc|h|hpp|hxx)$" | cut -f 2)
+  if [ -n "$changed_files" ]; then
+    # shellcheck disable=SC2086
+    clang-format -style=file --dry-run --Werror $changed_files
+
+    exitcode1=$?
+    if [ $exitcode1 -ne 0 ]; then
+      echo "Formatting errors found in file: $changed_files. \
+                Please run:
+                    \"clang-format -style=file -i $changed_files\"
+                to correct these errors"
+      exit $exitcode1
+    fi
+
+    # shellcheck disable=SC2086
+    cppcheck --enable=performance,portability --error-exitcode=1 --suppress=*:tests* $changed_files
+    exitcode2=$?
+    if [ $exitcode2 -ne 0 ]; then
+      exit $exitcode2
+    fi
+  fi
+  exit 0
+done
+
+exit 0
diff --git a/scripts/py/rnnoise_dump_extractor.py b/scripts/py/rnnoise_dump_extractor.py
index 715b922..9e6ff1f 100644
--- a/scripts/py/rnnoise_dump_extractor.py
+++ b/scripts/py/rnnoise_dump_extractor.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,47 +20,84 @@ Example use:
 python rnnoise_dump_extractor.py --dump_file output.bin --output_dir ./denoised_wavs/
 """
 
-import soundfile as sf
-import numpy as np
-
 import argparse
-from os import path
 import struct
+import typing
+from os import path
+
+import numpy as np
+import soundfile as sf
 
 
-def extract(fp, output_dir, export_npy):
+def extract(
+        dump_file: typing.IO,
+        output_dir: str,
+        export_npy: bool
+):
+    """
+    Extract audio file from RNNoise output dump
+
+    @param dump_file:   Audio dump file location
+    @param output_dir:  Output direction
+    @param export_npy:  Whether to export the audio as .npy
+    """
     while True:
-        filename_length = struct.unpack("i", fp.read(4))[0]
+        filename_length = struct.unpack("i", dump_file.read(4))[0]
 
         if filename_length == -1:
             return
 
-        filename = struct.unpack("{}s".format(filename_length), fp.read(filename_length))[0].decode('ascii')
-        audio_clip_length = struct.unpack("I", fp.read(4))[0]
-        output_file_name = path.join(output_dir, "denoised_{}".format(filename))
-        audio_clip = fp.read(audio_clip_length)
-        
-        with sf.SoundFile(output_file_name, 'w', channels=1, samplerate=48000, subtype="PCM_16", endian="LITTLE") as wav_file:
+        filename = struct \
+            .unpack(f"{filename_length}s", dump_file.read(filename_length))[0] \
+            .decode('ascii')
+
+        audio_clip_length = struct.unpack("I", dump_file.read(4))[0]
+        output_file_name = path.join(output_dir, f"denoised_{filename}")
+        audio_clip = dump_file.read(audio_clip_length)
+
+        with sf.SoundFile(output_file_name, 'w', channels=1, samplerate=48000, subtype="PCM_16",
+                          endian="LITTLE") as wav_file:
             wav_file.buffer_write(audio_clip, dtype='int16')
-            print("{} written to disk".format(output_file_name))
+            print(f"{output_file_name} written to disk")
 
         if export_npy:
             output_file_name += ".npy"
-            pack_format = "{}h".format(int(audio_clip_length/2))
+            pack_format = f"{int(audio_clip_length / 2)}h"
             npdata = np.array(struct.unpack(pack_format, audio_clip)).astype(np.int16)
             np.save(output_file_name, npdata)
-            print("{} written to disk".format(output_file_name))
+            print(f"{output_file_name} written to disk")
 
 
 def main(args):
+    """
+    Run RNNoise audio dump extraction
+    @param args:    Parsed args
+    """
     extract(args.dump_file, args.output_dir, args.export_npy)
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--dump_file", type=argparse.FileType('rb'), help="Dump file with audio files to extract.", required=True)
-parser.add_argument("--output_dir", help="Output directory, Warning: Duplicated file names will be overwritten.", required=True)
-parser.add_argument("--export_npy", help="Export the audio buffer in NumPy format", action="store_true")
-args = parser.parse_args()
+
+parser.add_argument(
+    "--dump_file",
+    type=argparse.FileType('rb'),
+    help="Dump file with audio files to extract.",
+    required=True
+)
+
+parser.add_argument(
+    "--output_dir",
+    help="Output directory, Warning: Duplicated file names will be overwritten.",
+    required=True
+)
+
+parser.add_argument(
+    "--export_npy",
+    help="Export the audio buffer in NumPy format",
+    action="store_true"
+)
+
+parsed_args = parser.parse_args()
 
 if __name__ == "__main__":
-    main(args)
+    main(parsed_args)
diff --git a/scripts/py/setup_hooks.py b/scripts/py/setup_hooks.py
index ead5e1f..dc3156c 100644
--- a/scripts/py/setup_hooks.py
+++ b/scripts/py/setup_hooks.py
@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
-#  SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
-#  SPDX-License-Identifier: Apache-2.0
+#  SPDX-FileCopyrightText:  Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
 #  you may not use this file except in compliance with the License.
@@ -13,84 +12,56 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-
-import os
-import sys
+"""
+Adds the git hooks script into the appropriate location
+"""
 import argparse
+import os
+import shutil
 import subprocess
-import stat
+import sys
+from pathlib import Path
+
+HOOKS_SCRIPT = "git_pre_push_hooks.sh"
+
 
-def set_hooks_dir(hooks_dir):
-    command = 'git config core.hooksPath {}'.format(hooks_dir)
-    subprocess.Popen(command.split(), stdout=subprocess.PIPE)
+def set_hooks_dir(hooks_dir: str):
+    """
+    Set the hooks path in the git configuration
+    @param hooks_dir:   The hooks directory
+    """
+    command = f'git config core.hooksPath {hooks_dir}'
+    with subprocess.Popen(command.split(), stdout=subprocess.PIPE) as process:
+        process.communicate()
+        return_code = process.returncode
 
-def add_pre_push_hooks(hooks_dir):
+    if return_code != 0:
+        raise RuntimeError(f"Could not configure git hooks path, exited with code {return_code}")
+
+
+def add_pre_push_hooks(hooks_dir: str):
+    """
+    Copies the git hooks scripts into the specified location
+    @param hooks_dir:   The specified git hooks directory
+    """
     pre_push = "pre-push"
     file_path = os.path.join(hooks_dir, pre_push)
     file_exists = os.path.exists(file_path)
     if file_exists:
         os.remove(file_path)
-    f = open(file_path, "a")
-    f.write(
-'''#!/bin/sh
-#  SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
-#  SPDX-License-Identifier: Apache-2.0
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-# Called by "git push" with no arguments.  The hook should
-# exit with non-zero status after issuing an appropriate message if
-# it wants to stop the push.
-
-while read local_ref local_sha remote_ref remote_sha
-do
-    # We should pass only added or modified C/C++ source files to cppcheck.
-    changed_files=$(git diff --name-only HEAD~1 HEAD | grep -iE "\.(c|cpp|cxx|cc|h|hpp|hxx)$" | cut -f 2)
-        if [ -n "$changed_files" ]; then
-            clang-format -style=file --dry-run --Werror $changed_files
-
-            exitcode1=$?
-            if [ $exitcode1 -ne 0 ]; then
-                echo "Formatting errors found in file: $changed_files.
-                \nPlease run:\n\    \"clang-format -style=file -i $changed_files\"
-                \nto correct these errors"
-                exit $exitcode1
-            fi
-
-            cppcheck --enable=performance,portability  --error-exitcode=1 --suppress=*:tests* $changed_files
-            exitcode2=$?
-            if [ $exitcode2 -ne 0 ]; then
-                exit $exitcode2
-            fi
-        fi
-    exit 0
-done
 
-exit 0'''
-)
+    script_path = Path(__file__).resolve().parent / HOOKS_SCRIPT
+    shutil.copy(script_path, hooks_dir)
 
-    f.close()
-    s = os.stat(file_path)
-    os.chmod(file_path, s.st_mode | stat.S_IEXEC)
 
-parser = argparse.ArgumentParser()
-parser.add_argument("git_hooks_path")
-args = parser.parse_args()
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("git_hooks_path")
+    args = parser.parse_args()
 
-dir_exists = os.path.exists(args.git_hooks_path)
-if not dir_exists:
-    print('Error! The Git hooks directory you supplied does not exist.')
-    sys.exit()
+    if not os.path.exists(args.git_hooks_path):
+        print('Error! The Git hooks directory you supplied does not exist.')
+        sys.exit()
 
-add_pre_push_hooks(args.git_hooks_path)
-set_hooks_dir(args.git_hooks_path)
+    add_pre_push_hooks(args.git_hooks_path)
+    set_hooks_dir(args.git_hooks_path)
diff --git a/scripts/py/templates/header_template.txt b/scripts/py/templates/header_template.txt
index f6e3bdb..32bf71a 100644
--- a/scripts/py/templates/header_template.txt
+++ b/scripts/py/templates/header_template.txt
@@ -16,6 +16,6 @@
  */
 
 /*********************    Autogenerated file. DO NOT EDIT *******************
- * Generated from {{script_name}} tool {% if file_name %}and {{file_name}}{% endif %} file.
+ * Generated from {{script_name}} tool {% if file_name %}and {{file_name}} {% endif %}file.
  * Date: {{gen_time}}
  ***************************************************************************/
diff --git a/scripts/py/use_case_resources.json b/scripts/py/use_case_resources.json
new file mode 100644
index 0000000..80fa28d
--- /dev/null
+++ b/scripts/py/use_case_resources.json
@@ -0,0 +1,190 @@
+[
+  {
+    "name": "ad",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/"
+    ],
+    "resources": [
+      {
+        "name": "ad_medium_int8.tflite",
+        "url": "{url_prefix:0}ad_medium_int8.tflite"
+      },
+      {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+      {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"}
+    ]
+  },
+  {
+    "name": "asr",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/"
+    ],
+    "resources": [
+      {
+        "name": "wav2letter_pruned_int8.tflite",
+        "url": "{url_prefix:0}wav2letter_pruned_int8.tflite"
+      },
+      {
+        "name": "ifm0.npy",
+        "url": "{url_prefix:0}testing_input/input_2_int8/0.npy"
+      },
+      {
+        "name": "ofm0.npy",
+        "url": "{url_prefix:0}testing_output/Identity_int8/0.npy"
+      }
+    ]
+  },
+  {
+    "name": "img_class",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/"
+    ],
+    "resources": [
+      {
+        "name": "mobilenet_v2_1.0_224_INT8.tflite",
+        "url": "{url_prefix:0}mobilenet_v2_1.0_224_INT8.tflite"
+      },
+      {
+        "name": "ifm0.npy",
+        "url": "{url_prefix:0}testing_input/tfl.quantize/0.npy"
+      },
+      {
+        "name": "ofm0.npy",
+        "url": "{url_prefix:0}testing_output/MobilenetV2/Predictions/Reshape_11/0.npy"
+      }
+    ]
+  },
+  {
+    "name": "object_detection",
+    "url_prefix": [
+      "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/"
+    ],
+    "resources": [
+      {
+        "name": "yolo-fastest_192_face_v4.tflite",
+        "url": "{url_prefix:0}yolo-fastest_192_face_v4.tflite?raw=true"
+      }
+    ]
+  },
+  {
+    "name": "kws",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/"
+    ],
+    "resources": [
+      {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+      {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
+      {
+        "name": "kws_micronet_m.tflite",
+        "url": "{url_prefix:0}kws_micronet_m.tflite"
+      }
+    ]
+  },
+  {
+    "name": "vww",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/"
+    ],
+    "resources": [
+      {
+        "name": "vww4_128_128_INT8.tflite",
+        "url": "{url_prefix:0}vww4_128_128_INT8.tflite"
+      },
+      {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+      {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"}
+    ]
+  },
+  {
+    "name": "kws_asr",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/",
+      "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/"
+    ],
+    "resources": [
+      {
+        "name": "wav2letter_pruned_int8.tflite",
+        "url": "{url_prefix:0}wav2letter_pruned_int8.tflite"
+      },
+      {
+        "sub_folder": "asr",
+        "name": "ifm0.npy",
+        "url": "{url_prefix:0}testing_input/input_2_int8/0.npy"
+      },
+      {
+        "sub_folder": "asr",
+        "name": "ofm0.npy",
+        "url": "{url_prefix:0}testing_output/Identity_int8/0.npy"
+      },
+      {
+        "sub_folder": "kws",
+        "name": "ifm0.npy",
+        "url": "{url_prefix:1}testing_input/input/0.npy"
+      },
+      {
+        "sub_folder": "kws",
+        "name": "ofm0.npy",
+        "url": "{url_prefix:1}testing_output/Identity/0.npy"
+      },
+      {
+        "name": "kws_micronet_m.tflite",
+        "url": "{url_prefix:1}kws_micronet_m.tflite"
+      }
+    ]
+  },
+  {
+    "name": "noise_reduction",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/"
+    ],
+    "resources": [
+      {"name": "rnnoise_INT8.tflite", "url": "{url_prefix:0}rnnoise_INT8.tflite"},
+      {
+        "name": "ifm0.npy",
+        "url": "{url_prefix:0}testing_input/main_input_int8/0.npy"
+      },
+      {
+        "name": "ifm1.npy",
+        "url": "{url_prefix:0}testing_input/vad_gru_prev_state_int8/0.npy"
+      },
+      {
+        "name": "ifm2.npy",
+        "url": "{url_prefix:0}testing_input/noise_gru_prev_state_int8/0.npy"
+      },
+      {
+        "name": "ifm3.npy",
+        "url": "{url_prefix:0}testing_input/denoise_gru_prev_state_int8/0.npy"
+      },
+      {
+        "name": "ofm0.npy",
+        "url": "{url_prefix:0}testing_output/Identity_int8/0.npy"
+      },
+      {
+        "name": "ofm1.npy",
+        "url": "{url_prefix:0}testing_output/Identity_1_int8/0.npy"
+      },
+      {
+        "name": "ofm2.npy",
+        "url": "{url_prefix:0}testing_output/Identity_2_int8/0.npy"
+      },
+      {
+        "name": "ofm3.npy",
+        "url": "{url_prefix:0}testing_output/Identity_3_int8/0.npy"
+      },
+      {
+        "name": "ofm4.npy",
+        "url": "{url_prefix:0}testing_output/Identity_4_int8/0.npy"
+      }
+    ]
+  },
+  {
+    "name": "inference_runner",
+    "url_prefix": [
+      "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/"
+    ],
+    "resources": [
+      {
+        "name": "dnn_s_quantized.tflite",
+        "url": "{url_prefix:0}dnn_s_quantized.tflite"
+      }
+    ]
+  }
+]
diff --git a/set_up_default_resources.py b/set_up_default_resources.py
index f983508..a3987bc 100755
--- a/set_up_default_resources.py
+++ b/set_up_default_resources.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-#  SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,6 +13,10 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+"""
+Script to set up default resources for ML Embedded Evaluation Kit
+"""
+import dataclasses
 import errno
 import fnmatch
 import json
@@ -22,207 +26,21 @@ import re
 import shutil
 import subprocess
 import sys
+import typing
 import urllib.request
 import venv
 from argparse import ArgumentParser
 from argparse import ArgumentTypeError
 from collections import namedtuple
-from urllib.error import URLError
+from dataclasses import dataclass
 from pathlib import Path
+from urllib.error import URLError
 
 from scripts.py.check_update_resources_downloaded import get_md5sum_for_file
 
-
-json_uc_res = [
-    {
-        "use_case_name": "ad",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/"
-        ],
-        "resources": [
-            {
-                "name": "ad_medium_int8.tflite",
-                "url": "{url_prefix:0}ad_medium_int8.tflite",
-            },
-            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
-            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
-        ],
-    },
-    {
-        "use_case_name": "asr",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/"
-        ],
-        "resources": [
-            {
-                "name": "wav2letter_pruned_int8.tflite",
-                "url": "{url_prefix:0}wav2letter_pruned_int8.tflite",
-            },
-            {
-                "name": "ifm0.npy",
-                "url": "{url_prefix:0}testing_input/input_2_int8/0.npy",
-            },
-            {
-                "name": "ofm0.npy",
-                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
-            },
-        ],
-    },
-    {
-        "use_case_name": "img_class",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/"
-        ],
-        "resources": [
-            {
-                "name": "mobilenet_v2_1.0_224_INT8.tflite",
-                "url": "{url_prefix:0}mobilenet_v2_1.0_224_INT8.tflite",
-            },
-            {
-                "name": "ifm0.npy",
-                "url": "{url_prefix:0}testing_input/tfl.quantize/0.npy",
-            },
-            {
-                "name": "ofm0.npy",
-                "url": "{url_prefix:0}testing_output/MobilenetV2/Predictions/Reshape_11/0.npy",
-            },
-        ],
-    },
-    {
-        "use_case_name": "object_detection",
-        "url_prefix": [
-            "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/"
-        ],
-        "resources": [
-            {
-                "name": "yolo-fastest_192_face_v4.tflite",
-                "url": "{url_prefix:0}yolo-fastest_192_face_v4.tflite?raw=true",
-            }
-        ],
-    },
-    {
-        "use_case_name": "kws",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/"
-        ],
-        "resources": [
-            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
-            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
-            {
-                "name": "kws_micronet_m.tflite",
-                "url": "{url_prefix:0}kws_micronet_m.tflite",
-            },
-        ],
-    },
-    {
-        "use_case_name": "vww",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/"
-        ],
-        "resources": [
-            {
-                "name": "vww4_128_128_INT8.tflite",
-                "url": "{url_prefix:0}vww4_128_128_INT8.tflite",
-            },
-            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
-            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
-        ],
-    },
-    {
-        "use_case_name": "kws_asr",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/",
-            "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/",
-        ],
-        "resources": [
-            {
-                "name": "wav2letter_pruned_int8.tflite",
-                "url": "{url_prefix:0}wav2letter_pruned_int8.tflite",
-            },
-            {
-                "sub_folder": "asr",
-                "name": "ifm0.npy",
-                "url": "{url_prefix:0}testing_input/input_2_int8/0.npy",
-            },
-            {
-                "sub_folder": "asr",
-                "name": "ofm0.npy",
-                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
-            },
-            {
-                "sub_folder": "kws",
-                "name": "ifm0.npy",
-                "url": "{url_prefix:1}testing_input/input/0.npy",
-            },
-            {
-                "sub_folder": "kws",
-                "name": "ofm0.npy",
-                "url": "{url_prefix:1}testing_output/Identity/0.npy",
-            },
-            {
-                "name": "kws_micronet_m.tflite",
-                "url": "{url_prefix:1}kws_micronet_m.tflite",
-            },
-        ],
-    },
-    {
-        "use_case_name": "noise_reduction",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/"
-        ],
-        "resources": [
-            {"name": "rnnoise_INT8.tflite", "url": "{url_prefix:0}rnnoise_INT8.tflite"},
-            {
-                "name": "ifm0.npy",
-                "url": "{url_prefix:0}testing_input/main_input_int8/0.npy",
-            },
-            {
-                "name": "ifm1.npy",
-                "url": "{url_prefix:0}testing_input/vad_gru_prev_state_int8/0.npy",
-            },
-            {
-                "name": "ifm2.npy",
-                "url": "{url_prefix:0}testing_input/noise_gru_prev_state_int8/0.npy",
-            },
-            {
-                "name": "ifm3.npy",
-                "url": "{url_prefix:0}testing_input/denoise_gru_prev_state_int8/0.npy",
-            },
-            {
-                "name": "ofm0.npy",
-                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
-            },
-            {
-                "name": "ofm1.npy",
-                "url": "{url_prefix:0}testing_output/Identity_1_int8/0.npy",
-            },
-            {
-                "name": "ofm2.npy",
-                "url": "{url_prefix:0}testing_output/Identity_2_int8/0.npy",
-            },
-            {
-                "name": "ofm3.npy",
-                "url": "{url_prefix:0}testing_output/Identity_3_int8/0.npy",
-            },
-            {
-                "name": "ofm4.npy",
-                "url": "{url_prefix:0}testing_output/Identity_4_int8/0.npy",
-            },
-        ],
-    },
-    {
-        "use_case_name": "inference_runner",
-        "url_prefix": [
-            "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/"
-        ],
-        "resources": [
-            {
-                "name": "dnn_s_quantized.tflite",
-                "url": "{url_prefix:0}dnn_s_quantized.tflite",
-            }
-        ],
-    },
-]
+# Supported version of Python and Vela
+VELA_VERSION = "3.9.0"
+py3_version_minimum = (3, 9)
 
 # Valid NPU configurations:
 valid_npu_config_names = [
@@ -250,10 +68,57 @@ NPUConfig = namedtuple(
     ],
 )
 
+
+@dataclass(frozen=True)
+class UseCaseResource:
+    """
+    Represent a use case's resource
+    """
+    name: str
+    url: str
+    sub_folder: typing.Optional[str] = None
+
+
+@dataclass(frozen=True)
+class UseCase:
+    """
+    Represent a use case
+    """
+    name: str
+    url_prefix: str
+    resources: typing.List[UseCaseResource]
+
+
 # The internal SRAM size for Corstone-300 implementation on MPS3 specified by AN552
 # The internal SRAM size for Corstone-310 implementation on MPS3 specified by AN555
 # is 4MB, but we are content with the 2MB specified below.
-mps3_max_sram_sz = 2 * 1024 * 1024  # 2 MiB (2 banks of 1 MiB each)
+MPS3_MAX_SRAM_SZ = 2 * 1024 * 1024  # 2 MiB (2 banks of 1 MiB each)
+
+
+def load_use_case_resources(current_file_dir: Path) -> typing.List[UseCase]:
+    """
+    Load use case metadata resources
+
+    Parameters
+    ----------
+    current_file_dir:   Directory of the current script
+
+    Returns
+    -------
+    The use cases resources object parsed to a dict
+    """
+
+    resources_path = current_file_dir / "scripts" / "py" / "use_case_resources.json"
+    with open(resources_path, encoding="utf8") as f:
+        use_cases = json.load(f)
+        return [
+            UseCase(
+                name=u["name"],
+                url_prefix=u["url_prefix"],
+                resources=[UseCaseResource(**r) for r in u["resources"]],
+            )
+            for u in use_cases
+        ]
 
 
 def call_command(command: str, verbose: bool = True) -> str:
@@ -266,21 +131,22 @@ def call_command(command: str, verbose: bool = True) -> str:
     """
     if verbose:
         logging.info(command)
-    proc = subprocess.run(
-        command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True
-    )
-    log = proc.stdout.decode("utf-8")
-    if proc.returncode == 0 and verbose:
+    try:
+        proc = subprocess.run(
+            command, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True
+        )
+        log = proc.stdout.decode("utf-8")
         logging.info(log)
-    else:
+        return log
+    except subprocess.CalledProcessError as err:
+        log = err.stdout.decode("utf-8")
         logging.error(log)
-    proc.check_returncode()
-    return log
+        raise err
 
 
 def get_default_npu_config_from_name(
-    config_name: str, arena_cache_size: int = 0
-) -> NPUConfig:
+        config_name: str, arena_cache_size: int = 0
+) -> typing.Optional[NPUConfig]:
     """
     Gets the file suffix for the TFLite file from the
     `accelerator_config` string.
@@ -314,15 +180,15 @@ def get_default_npu_config_from_name(
     system_configs = ["Ethos_U55_High_End_Embedded", "Ethos_U65_High_End"]
     memory_modes_arena = {
         # For shared SRAM memory mode, we use the MPS3 SRAM size by default.
-        "Shared_Sram": mps3_max_sram_sz if arena_cache_size <= 0 else arena_cache_size,
+        "Shared_Sram": MPS3_MAX_SRAM_SZ if arena_cache_size <= 0 else arena_cache_size,
         # For dedicated SRAM memory mode, we do not override the arena size. This is expected to
         # be defined in the Vela configuration file instead.
         "Dedicated_Sram": None if arena_cache_size <= 0 else arena_cache_size,
     }
 
-    for i in range(len(strings_ids)):
-        if config_name.startswith(strings_ids[i]):
-            npu_config_id = config_name.replace(strings_ids[i], prefix_ids[i])
+    for i, string_id in enumerate(strings_ids):
+        if config_name.startswith(string_id):
+            npu_config_id = config_name.replace(string_id, prefix_ids[i])
             return NPUConfig(
                 config_name=config_name,
                 memory_mode=memory_modes[i],
@@ -335,97 +201,316 @@ def get_default_npu_config_from_name(
     return None
 
 
-def remove_tree_dir(dir_path):
+def remove_tree_dir(dir_path: Path):
+    """
+    Delete and re-create a directory
+
+    Parameters
+    ----------
+    dir_path    : The directory path
+    """
     try:
         # Remove the full directory.
         shutil.rmtree(dir_path)
         # Re-create an empty one.
         os.mkdir(dir_path)
-    except Exception as e:
-        logging.error(f"Failed to delete {dir_path}.")
+    except OSError:
+        logging.error("Failed to delete %s.", dir_path)
 
 
-def set_up_resources(
-    run_vela_on_models: bool = False,
-    additional_npu_config_names: tuple = (),
-    arena_cache_size: int = 0,
-    check_clean_folder: bool = False,
-    additional_requirements_file: str = "") -> (Path, Path):
+def initialize_use_case_resources_directory(
+        use_case: UseCase,
+        metadata: typing.Dict,
+        download_dir: Path,
+        check_clean_folder: bool,
+        setup_script_hash_verified: bool,
+):
     """
-    Helpers function that retrieve the output from a command.
+    Initialize the resources_downloaded directory for a use case
 
-    Parameters:
-    ----------
-    run_vela_on_models (bool):  Specifies if run vela on downloaded models.
-    additional_npu_config_names(list):  list of strings of Ethos-U NPU configs.
-    arena_cache_size (int): Specifies arena cache size in bytes. If a value
-                            greater than 0 is provided, this will be taken
-                            as the cache size. If 0, the default values, as per
-                            the NPU config requirements, are used.
-    check_clean_folder (bool): Indicates whether the resources folder needs to
-                               be checked for updates and cleaned.
-    additional_requirements_file (str): Path to a requirements.txt file if
-                                        additional packages need to be
-                                        installed.
+    @param use_case:                    The use case
+    @param metadata:                    The metadata
+    @param download_dir:                The parent directory
+    @param check_clean_folder:          Whether to clean the folder
+    @param setup_script_hash_verified:  Whether the hash of this script is verified
+    """
+    try:
+        #  Does the usecase_name download dir exist?
+        (download_dir / use_case.name).mkdir()
+    except OSError as err:
+        if err.errno == errno.EEXIST:
+            # The usecase_name download dir exist.
+            if check_clean_folder and not setup_script_hash_verified:
+                for idx, metadata_uc_url_prefix in enumerate(
+                        [
+                            f
+                            for f in metadata["resources_info"]
+                            if f["name"] == use_case.name
+                        ][0]["url_prefix"]
+                ):
+                    if metadata_uc_url_prefix != use_case.url_prefix[idx]:
+                        logging.info("Removing %s resources.", use_case.name)
+                        remove_tree_dir(download_dir / use_case.name)
+                        break
+        elif err.errno != errno.EEXIST:
+            logging.error("Error creating %s directory.", use_case.name)
+            raise
 
-    Returns
-    -------
 
-    Tuple of pair of Paths: (download_directory_path,  virtual_env_path)
+def download_file(url: str, dest: Path):
+    """
+    Download a file
 
-    download_directory_path: Root of the directory where the resources have been downloaded to.
-    virtual_env_path: Path to the root of virtual environment.
+    @param url:     The URL of the file to download
+    @param dest:    The destination of downloaded file
     """
-    # Paths.
-    current_file_dir = Path(__file__).parent.resolve()
-    download_dir = current_file_dir / "resources_downloaded"
-    metadata_file_path = download_dir / "resources_downloaded_metadata.json"
+    try:
+        with urllib.request.urlopen(url) as g:
+            with open(dest, "b+w") as f:
+                f.write(g.read())
+                logging.info("- Downloaded %s to %s.", url, dest)
+    except URLError:
+        logging.error("URLError while downloading %s.", url)
+        raise
+
+
+def download_resources(
+        use_case: UseCase,
+        metadata: typing.Dict,
+        download_dir: Path,
+        check_clean_folder: bool,
+        setup_script_hash_verified: bool,
+):
+    """
+    Download the resources associated with a use case
 
-    metadata_dict = dict()
-    vela_version = "3.9.0"
-    py3_version_minimum = (3, 9)
+    @param use_case:                    The use case
+    @param metadata:                    The metadata
+    @param download_dir:                The parent directory
+    @param check_clean_folder:          Whether to clean the folder
+    @param setup_script_hash_verified:  Whether the hash is already verified
+    """
+    initialize_use_case_resources_directory(
+        use_case,
+        metadata,
+        download_dir,
+        check_clean_folder,
+        setup_script_hash_verified
+    )
 
-    # Is Python minimum requirement matched?
-    py3_version = sys.version_info
-    if py3_version < py3_version_minimum:
-        raise Exception(
-            "ERROR: Python3.9+ is required, please see the documentation on how to update it."
+    reg_expr_str = r"{url_prefix:(.*\d)}"
+    reg_expr_pattern = re.compile(reg_expr_str)
+    for res in use_case.resources:
+        res_name = res.name
+        url_prefix_idx = int(reg_expr_pattern.search(res.url).group(1))
+        res_url = use_case.url_prefix[url_prefix_idx] + re.sub(
+            reg_expr_str, "", res.url
+        )
+
+        sub_folder = ""
+        if res.sub_folder is not None:
+            try:
+                #  Does the usecase_name/sub_folder download dir exist?
+                (download_dir / use_case.name / res.sub_folder).mkdir()
+            except OSError as err:
+                if err.errno != errno.EEXIST:
+                    logging.error(
+                        "Error creating %s/%s directory.",
+                        use_case.name,
+                        res.sub_folder
+                    )
+                    raise
+            sub_folder = res.sub_folder
+
+        res_dst = download_dir / use_case.name / sub_folder / res_name
+
+        if res_dst.is_file():
+            logging.info("File %s exists, skipping download.", res_dst)
+        else:
+            download_file(res_url, res_dst)
+
+
+def run_vela(
+        config: NPUConfig,
+        env_activate_cmd: str,
+        model: Path,
+        config_file: Path,
+        output_dir: Path
+) -> bool:
+    """
+    Run vela on the specified model
+    @param config:              The NPU configuration
+    @param env_activate_cmd:    The Python venv activation command
+    @param model:               The model
+    @param config_file:         The vela config file
+    @param output_dir:          The output directory
+    @return:                    True if the optimisation was skipped, false otherwise
+    """
+    # model name after compiling with vela is an initial model name + _vela suffix
+    vela_optimised_model_path = model.parent / (model.stem + "_vela.tflite")
+
+    vela_command_arena_cache_size = ""
+
+    if config.arena_cache_size:
+        vela_command_arena_cache_size = (
+            f"--arena-cache-size={config.arena_cache_size}"
+        )
+
+    vela_command = (
+            f"{env_activate_cmd} && vela {model} "
+            + f"--accelerator-config={config.config_name} "
+            + "--optimise Performance "
+            + f"--config {config_file} "
+            + f"--memory-mode={config.memory_mode} "
+            + f"--system-config={config.system_config} "
+            + f"--output-dir={output_dir} "
+            + f"{vela_command_arena_cache_size}"
+    )
+
+    # We want the name to include the configuration suffix. For example: vela_H128,
+    # vela_Y512 etc.
+    new_suffix = "_vela_" + config.ethos_u_config_id + ".tflite"
+    new_vela_optimised_model_path = model.parent / (model.stem + new_suffix)
+
+    skip_optimisation = new_vela_optimised_model_path.is_file()
+
+    if skip_optimisation:
+        logging.info(
+            "File %s exists, skipping optimisation.",
+            new_vela_optimised_model_path
         )
     else:
-        logging.info(f"Using Python version: {py3_version}")
+        call_command(vela_command)
+
+        # Rename default vela model.
+        vela_optimised_model_path.rename(new_vela_optimised_model_path)
+        logging.info(
+            "Renaming %s to %s.",
+            vela_optimised_model_path,
+            new_vela_optimised_model_path
+        )
 
+    return skip_optimisation
+
+
+def run_vela_on_all_models(
+        current_file_dir: Path,
+        download_dir: Path,
+        env_activate_cmd: str,
+        arena_cache_size: int,
+        npu_config_names: typing.List[str]
+):
+    """
+    Run vela on downloaded models for the specified NPU configurations
+
+    @param current_file_dir:    Path to the current directory
+    @param download_dir:        Path to the downloaded resources directory
+    @param env_activate_cmd:    Command used to activate Python venv
+    @param npu_config_names:    Names of NPU configurations for which to run Vela
+    @param arena_cache_size:    The arena cache size
+    """
+    config_file = current_file_dir / "scripts" / "vela" / "default_vela.ini"
+    models = [
+        Path(dirpath) / f
+        for dirpath, dirnames, files in os.walk(download_dir)
+        for f in fnmatch.filter(files, "*.tflite")
+        if "vela" not in f
+    ]
+
+    # Get npu config tuple for each config name in a list:
+    npu_configs = [
+        get_default_npu_config_from_name(name, arena_cache_size)
+        for name in npu_config_names
+    ]
+
+    logging.info("All models will be optimised for these configs:")
+    for config in npu_configs:
+        logging.info(config)
+
+    optimisation_skipped = False
+
+    for model in models:
+        for config in npu_configs:
+            optimisation_skipped = run_vela(
+                config,
+                env_activate_cmd,
+                model,
+                config_file,
+                output_dir=model.parent
+            ) or optimisation_skipped
+
+    # If any optimisation was skipped, show how to regenerate:
+    if optimisation_skipped:
+        logging.warning("One or more optimisations were skipped.")
+        logging.warning(
+            "To optimise all the models, please remove the directory %s.",
+            download_dir
+        )
+
+
+def initialize_resources_directory(
+        download_dir: Path,
+        check_clean_folder: bool,
+        metadata_file_path: Path,
+        setup_script_hash: str
+) -> typing.Tuple[typing.Dict, bool]:
+    """
+    Sets up the resources_downloaded directory and checks to see if this script
+    has been modified since the last time resources were downloaded
+
+    @param download_dir:        Path to the resources_downloaded directory
+    @param check_clean_folder:  Determines whether to clean the downloads directory
+    @param metadata_file_path:  Path to the metadata file
+    @param setup_script_hash:   The md5 hash of this script
+    @return:                    The metadata and a boolean to indicate whether this
+                                script has changed since it was last run
+    """
+    metadata_dict = {}
     setup_script_hash_verified = False
-    setup_script_hash = get_md5sum_for_file(Path(__file__).resolve())
 
-    try:
-        #   1.1 Does the download dir exist?
+    if download_dir.is_dir():
+        logging.info("'resources_downloaded' directory exists.")
+        # Check and clean?
+        if check_clean_folder and metadata_file_path.is_file():
+            with open(metadata_file_path, encoding="utf8") as metadata_file:
+                metadata_dict = json.load(metadata_file)
+
+            vela_in_metadata = metadata_dict["ethosu_vela_version"]
+            if vela_in_metadata != VELA_VERSION:
+                # Check if all the resources needs to be removed and regenerated.
+                # This can happen when the Vela version has changed.
+                logging.info(
+                    ("Vela version in metadata is %s, current %s."
+                     " Removing the resources and re-download them.",
+                     vela_in_metadata,
+                     VELA_VERSION
+                     )
+                )
+                remove_tree_dir(download_dir)
+                metadata_dict = {}
+            else:
+                # Check if the set_up_default_resorces.py has changed from last setup
+                setup_script_hash_verified = (
+                        metadata_dict.get("set_up_script_md5sum")
+                        == setup_script_hash
+                )
+    else:
         download_dir.mkdir()
-    except OSError as e:
-        if e.errno == errno.EEXIST:
-            logging.info("'resources_downloaded' directory exists.")
-            # Check and clean?
-            if check_clean_folder and metadata_file_path.is_file():
-                with open(metadata_file_path) as metadata_file:
-                    metadata_dict = json.load(metadata_file)
-                    vela_in_metadata = metadata_dict["ethosu_vela_version"]
-                    if vela_in_metadata != vela_version:
-                        # Check if all the resources needs to be removed and regenerated.
-                        # This can happen when the Vela version has changed.
-                        logging.info(
-                            f"Vela version in metadata is {vela_in_metadata}, current {vela_version}. Removing the resources and re-download them."
-                        )
-                        remove_tree_dir(download_dir)
-                        metadata_dict = dict()
-                    else:
-                        # Check if the set_up_default_resorces.py has changed from last setup
-                        setup_script_hash_verified = (
-                            metadata_dict.get("set_up_script_md5sum")
-                            == setup_script_hash
-                        )
-        else:
-            raise
 
-    # 1.2 Does the virtual environment exist?
+    return metadata_dict, setup_script_hash_verified
+
+
+def set_up_python_venv(
+        download_dir: Path,
+        additional_requirements_file: Path = ""
+):
+    """
+    Set up the Python environment with which to set up the resources
+
+    @param download_dir:                    Path to the resources_downloaded directory
+    @param additional_requirements_file:    Optional additional requirements file
+    @return:                                Path to the venv Python binary + activate command
+    """
     env_dirname = "env"
     env_path = download_dir / env_dirname
 
@@ -434,23 +519,28 @@ def set_up_resources(
 
     env_python = Path(venv_context.env_exe)
 
-    if sys.platform == "win32":
-        env_activate = f"{venv_context.bin_path}/activate.bat"
-    else:
-        env_activate = f". {venv_context.bin_path}/activate"
-
     if not env_python.is_file():
         # Create the virtual environment using current interpreter's venv
         # (not necessarily the system's Python3)
         venv_builder.create(env_dir=env_path)
 
+    if sys.platform == "win32":
+        env_activate = Path(f"{venv_context.bin_path}/activate.bat")
+        env_activate_cmd = str(env_activate)
+    else:
+        env_activate = Path(f"{venv_context.bin_path}/activate")
+        env_activate_cmd = f". {env_activate}"
+
+    if not env_activate.is_file():
+        venv_builder.install_scripts(venv_context, venv_context.bin_path)
+
     # 1.3 Install additional requirements first, if a valid file has been provided
     if additional_requirements_file and os.path.isfile(additional_requirements_file):
         command = f"{env_python} -m pip install -r {additional_requirements_file}"
         call_command(command)
 
     # 1.4 Make sure to have all the main requirements
-    requirements = [f"ethos-u-vela=={vela_version}"]
+    requirements = [f"ethos-u-vela=={VELA_VERSION}"]
     command = f"{env_python} -m pip freeze"
     packages = call_command(command)
     for req in requirements:
@@ -458,162 +548,130 @@ def set_up_resources(
             command = f"{env_python} -m pip install {req}"
             call_command(command)
 
-    # 2. Download models
-    logging.info("Downloading resources.")
-    for uc in json_uc_res:
-        use_case_name = uc["use_case_name"]
-        res_url_prefix = uc["url_prefix"]
-        try:
-            #  Does the usecase_name download dir exist?
-            (download_dir / use_case_name).mkdir()
-        except OSError as e:
-            if e.errno == errno.EEXIST:
-                # The usecase_name download dir exist.
-                if check_clean_folder and not setup_script_hash_verified:
-                    for idx, metadata_uc_url_prefix in enumerate(
-                        [
-                            f
-                            for f in metadata_dict["resources_info"]
-                            if f["use_case_name"] == use_case_name
-                        ][0]["url_prefix"]
-                    ):
-                        if metadata_uc_url_prefix != res_url_prefix[idx]:
-                            logging.info(f"Removing {use_case_name} resources.")
-                            remove_tree_dir(download_dir / use_case_name)
-                            break
-            elif e.errno != errno.EEXIST:
-                logging.error(f"Error creating {use_case_name} directory.")
-                raise
-
-        reg_expr_str = r"{url_prefix:(.*\d)}"
-        reg_expr_pattern = re.compile(reg_expr_str)
-        for res in uc["resources"]:
-            res_name = res["name"]
-            url_prefix_idx = int(reg_expr_pattern.search(res["url"]).group(1))
-            res_url = res_url_prefix[url_prefix_idx] + re.sub(
-                reg_expr_str, "", res["url"]
-            )
+    return env_path, env_activate_cmd
 
-            sub_folder = ""
-            if "sub_folder" in res:
-                try:
-                    #  Does the usecase_name/sub_folder download dir exist?
-                    (download_dir / use_case_name / res["sub_folder"]).mkdir()
-                except OSError as e:
-                    if e.errno != errno.EEXIST:
-                        logging.error(
-                            f"Error creating {use_case_name} / {res['sub_folder']} directory."
-                        )
-                        raise
-                sub_folder = res["sub_folder"]
-
-            res_dst = download_dir / use_case_name / sub_folder / res_name
-
-            if res_dst.is_file():
-                logging.info(f"File {res_dst} exists, skipping download.")
-            else:
-                try:
-                    g = urllib.request.urlopen(res_url)
-                    with open(res_dst, "b+w") as f:
-                        f.write(g.read())
-                        logging.info(f"- Downloaded {res_url} to {res_dst}.")
-                except URLError:
-                    logging.error(f"URLError while downloading {res_url}.")
-                    raise
 
-    # 3. Run vela on models in resources_downloaded
-    # New models will have same name with '_vela' appended.
-    # For example:
-    # original model:    kws_micronet_m.tflite
-    # after vela model:  kws_micronet_m_vela_H128.tflite
-    #
-    # Note: To avoid to run vela twice on the same model, it's supposed that
-    # downloaded model names don't contain the 'vela' word.
-    if run_vela_on_models is True:
-        config_file = current_file_dir / "scripts" / "vela" / "default_vela.ini"
-        models = [
-            Path(dirpath) / f
-            for dirpath, dirnames, files in os.walk(download_dir)
-            for f in fnmatch.filter(files, "*.tflite")
-            if "vela" not in f
-        ]
+def update_metadata(
+        metadata_dict: typing.Dict,
+        setup_script_hash: str,
+        json_uc_res: typing.List[UseCase],
+        metadata_file_path: Path
+):
+    """
+    Update the metadata file
 
-        # Consolidate all config names while discarding duplicates:
-        config_names = list(set(default_npu_config_names + additional_npu_config_names))
+    @param metadata_dict:       The metadata dictionary to update
+    @param setup_script_hash:   The setup script hash
+    @param json_uc_res:         The use case resources metadata
+    @param metadata_file_path   The metadata file path
+    """
+    metadata_dict["ethosu_vela_version"] = VELA_VERSION
+    metadata_dict["set_up_script_md5sum"] = setup_script_hash.strip("\n")
+    metadata_dict["resources_info"] = [dataclasses.asdict(uc) for uc in json_uc_res]
 
-        # Get npu config tuple for each config name in a list:
-        npu_configs = [
-            get_default_npu_config_from_name(name, arena_cache_size)
-            for name in config_names
-        ]
+    with open(metadata_file_path, "w", encoding="utf8") as metadata_file:
+        json.dump(metadata_dict, metadata_file, indent=4)
 
-        logging.info(f"All models will be optimised for these configs:")
-        for config in npu_configs:
-            logging.info(config)
 
-        optimisation_skipped = False
+def set_up_resources(
+        run_vela_on_models: bool = False,
+        additional_npu_config_names: tuple = (),
+        arena_cache_size: int = 0,
+        check_clean_folder: bool = False,
+        additional_requirements_file: Path = ""
+) -> Path:
+    """
+    Helpers function that retrieve the output from a command.
 
-        for model in models:
-            output_dir = model.parent
-            # model name after compiling with vela is an initial model name + _vela suffix
-            vela_optimised_model_path = model.parent / (model.stem + "_vela.tflite")
+    Parameters:
+    ----------
+    run_vela_on_models (bool):  Specifies if run vela on downloaded models.
+    additional_npu_config_names(list):  list of strings of Ethos-U NPU configs.
+    arena_cache_size (int): Specifies arena cache size in bytes. If a value
+                            greater than 0 is provided, this will be taken
+                            as the cache size. If 0, the default values, as per
+                            the NPU config requirements, are used.
+    check_clean_folder (bool): Indicates whether the resources folder needs to
+                               be checked for updates and cleaned.
+    additional_requirements_file (str): Path to a requirements.txt file if
+                                        additional packages need to be
+                                        installed.
 
-            for config in npu_configs:
-                vela_command_arena_cache_size = ""
+    Returns
+    -------
 
-                if config.arena_cache_size:
-                    vela_command_arena_cache_size = (
-                        f"--arena-cache-size={config.arena_cache_size}"
-                    )
+    Tuple of pair of Paths: (download_directory_path,  virtual_env_path)
 
-                vela_command = (
-                    f"{env_activate} && vela {model} "
-                    + f"--accelerator-config={config.config_name} "
-                    + "--optimise Performance "
-                    + f"--config {config_file} "
-                    + f"--memory-mode={config.memory_mode} "
-                    + f"--system-config={config.system_config} "
-                    + f"--output-dir={output_dir} "
-                    + f"{vela_command_arena_cache_size}"
-                )
+    download_directory_path: Root of the directory where the resources have been downloaded to.
+    virtual_env_path: Path to the root of virtual environment.
+    """
+    # Paths.
+    current_file_dir = Path(__file__).parent.resolve()
+    download_dir = current_file_dir / "resources_downloaded"
+    metadata_file_path = download_dir / "resources_downloaded_metadata.json"
 
-                # We want the name to include the configuration suffix. For example: vela_H128,
-                # vela_Y512 etc.
-                new_suffix = "_vela_" + config.ethos_u_config_id + ".tflite"
-                new_vela_optimised_model_path = model.parent / (model.stem + new_suffix)
+    # Is Python minimum requirement matched?
+    if sys.version_info < py3_version_minimum:
+        raise RuntimeError(
+            f"ERROR: Python{'.'.join(str(i) for i in py3_version_minimum)}+ is required,"
+            f" please see the documentation on how to update it."
+        )
+    logging.info("Using Python version: %s", sys.version_info)
 
-                if new_vela_optimised_model_path.is_file():
-                    logging.info(
-                        f"File {new_vela_optimised_model_path} exists, skipping optimisation."
-                    )
-                    optimisation_skipped = True
-                    continue
+    json_uc_res = load_use_case_resources(current_file_dir)
+    setup_script_hash = get_md5sum_for_file(Path(__file__).resolve())
 
-                call_command(vela_command)
+    metadata_dict, setup_script_hash_verified = initialize_resources_directory(
+        download_dir,
+        check_clean_folder,
+        metadata_file_path,
+        setup_script_hash
+    )
 
-                # Rename default vela model.
-                vela_optimised_model_path.rename(new_vela_optimised_model_path)
-                logging.info(
-                    f"Renaming {vela_optimised_model_path} to {new_vela_optimised_model_path}."
-                )
+    env_path, env_activate = set_up_python_venv(
+        download_dir,
+        additional_requirements_file
+    )
 
-        # If any optimisation was skipped, show how to regenerate:
-        if optimisation_skipped:
-            logging.warning("One or more optimisations were skipped.")
-            logging.warning(
-                f"To optimise all the models, please remove the directory {download_dir}."
-            )
+    # 2. Download models
+    logging.info("Downloading resources.")
+    for use_case in json_uc_res:
+        download_resources(
+            use_case,
+            metadata_dict,
+            download_dir,
+            check_clean_folder,
+            setup_script_hash_verified
+        )
+
+    # 3. Run vela on models in resources_downloaded
+    # New models will have same name with '_vela' appended.
+    # For example:
+    # original model:    kws_micronet_m.tflite
+    # after vela model:  kws_micronet_m_vela_H128.tflite
+    #
+    # Note: To avoid to run vela twice on the same model, it's supposed that
+    # downloaded model names don't contain the 'vela' word.
+    if run_vela_on_models is True:
+        # Consolidate all config names while discarding duplicates:
+        run_vela_on_all_models(
+            current_file_dir,
+            download_dir,
+            env_activate,
+            arena_cache_size,
+            npu_config_names=list(set(default_npu_config_names + list(additional_npu_config_names)))
+        )
 
     # 4. Collect and write metadata
     logging.info("Collecting and write metadata.")
-    metadata_dict["ethosu_vela_version"] = vela_version
-    metadata_dict["set_up_script_md5sum"] = setup_script_hash.strip("\n")
-    metadata_dict["resources_info"] = json_uc_res
-
-    with open(metadata_file_path, "w") as metadata_file:
-        json.dump(metadata_dict, metadata_file, indent=4)
+    update_metadata(
+        metadata_dict,
+        setup_script_hash.strip("\n"),
+        json_uc_res,
+        metadata_file_path
+    )
 
-    return download_dir, env_path
+    return env_path
 
 
 if __name__ == "__main__":