def main()

in tools/create_examples_from_transformers.py [0:0]
59 lines of code
13 McCabe index (conditional complexity)

def main():
    args = parse_args()
    examples = args.examples
    if examples == "all":
        examples = SUPPORTED_EXAMPLES
    download_examples_from_transformers(
        examples, args.dest, predicate=keep_only_examples_with_trainer_and_requirements_predicate, version=args.version
    )

    for example_dir in args.dest.iterdir():
        if example_dir.is_file():
            continue
        for file_path in example_dir.iterdir():
            if file_path.name == "run_generation.py":
                continue
            if "run" in file_path.name and file_path.suffix == ".py":
                if file_path.name == "run_qa.py":
                    trainer_file_path = file_path.parent / "trainer_qa.py"
                elif file_path.name == "run_seq2seq_qa.py":
                    trainer_file_path = file_path.parent / "trainer_seq2seq_qa.py"
                else:
                    trainer_file_path = file_path
                hf_argument_file_path = file_path
                training_argument_file_path = file_path

                print(f"Processing {file_path}")
                with open(trainer_file_path, "r") as fp:
                    file_content = fp.read()
                trainer_cls, processed_content, import_end_index = remove_import(TRAINER_IMPORT_PATTERN, file_content)
                code = generate_new_import_code(AWS_CODE[trainer_cls])
                code = f"\n{code}\n"
                processed_content = insert_code_at_position(code, processed_content, import_end_index)
                with open(trainer_file_path, "w") as fp:
                    fp.write(processed_content)

                with open(hf_argument_file_path, "r") as fp:
                    file_content = fp.read()
                _, processed_content, import_end_index = remove_import(HF_ARGUMENT_PARSER_IMPORT_PATTERN, file_content)
                code = generate_new_import_code(AWS_CODE["HfArgumentParser"])
                code = f"\n{code}\n"
                processed_content = insert_code_at_position(code, processed_content, import_end_index)
                with open(hf_argument_file_path, "w") as fp:
                    fp.write(processed_content)

                with open(training_argument_file_path, "r") as fp:
                    file_content = fp.read()
                training_args_cls, processed_content, import_end_index = remove_import(
                    TRAINING_ARGUMENTS_IMPORT_PATTERN, file_content
                )
                code = generate_new_import_code(AWS_CODE[training_args_cls])
                code = f"\n{code}\n"
                processed_content = insert_code_at_position(code, processed_content, import_end_index)
                with open(training_argument_file_path, "w") as fp:
                    fp.write(processed_content)

            elif file_path.name == "requirements.txt":
                with open(file_path, "r") as fp:
                    file_content = fp.read()
                processed_content = re.sub(TORCH_REQUIREMENT_PATTERN, "", file_content)
                if file_path.parent.name == "image-classification":
                    processed_content += "\nscikit-learn"
                with open(file_path, "w") as fp:
                    fp.write(processed_content)

    # Linting and styling.
    subprocess.run(["ruff", f"{args.dest}", "--fix"])