



import re
from pathlib import Path
from unittest import TestCase

import pytest







@pytest.mark.integration
class     __lowerCAmelCase					(    A		):



			def 					_lowerCamelCase (		self     :						int				,	A     :						str)     ->					str:



						"""simple docstring"""






						with open(A				,	encoding='utf-8') as input_file:
									_UpperCAmelCase	 =     re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)')
									_UpperCAmelCase	 =     input_file.read()
									_UpperCAmelCase	 =     regexp.search(A)

						return match



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						str)     ->					List[str]:



						"""simple docstring"""






						with open(A				,	encoding='utf-8') as input_file:
									_UpperCAmelCase	 =     re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'				,	re.DOTALL)
									_UpperCAmelCase	 =     input_file.read()
									# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
									_UpperCAmelCase	 =     regexp.finditer(A)

						_UpperCAmelCase	 =     [match for match in matches if match is not None and match.group(1) is not None]
						return matches[0] if matches else None



			def 					_lowerCamelCase (		self     :						List[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     Path('./datasets')
						_UpperCAmelCase	 =     list(dataset_paths.absolute().glob('**/*.py'))

						for dataset in dataset_files:
									if self._no_encoding_on_file_open(str(A)):
												raise AssertionError(F"open(...) must use utf-8 encoding in {dataset}")



			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     Path('./datasets')
						_UpperCAmelCase	 =     list(dataset_paths.absolute().glob('**/*.py'))

						for dataset in dataset_files:
									if self._no_print_statements(str(A)):
												raise AssertionError(F"print statement found in {dataset}. Use datasets.logger/logging instead.")





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
