


import argparse
from collections import defaultdict

import yaml


a : Any		=				'''docs/source/en/_toctree.yml'''


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   defaultdict(_UpperCamelCase  )
			for doc in model_doc:
						counts[doc["local"]] += 1
			__lowercase 					=   [key for key, value in counts.items() if value > 1]

			__lowercase 					=   []
			for duplicate_key in duplicates:
						__lowercase 					=   list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}  )
						if len(_UpperCamelCase  ) > 1:
									raise ValueError(
									    F'{duplicate_key} is present several times in the documentation table of content at '
									    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
									    '''others.'''  )
						# Only add this once
						new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}  )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]  )

			# Sort
			return sorted(_UpperCamelCase       ,			key=lambda _UpperCamelCase  : s["title"].lower()  )


def 		lowercase_  (     _UpperCamelCase=False  ):


			'''simple docstring'''







			with open(_UpperCamelCase       ,			encoding='''utf-8'''  ) as f:
						__lowercase 					=   yaml.safe_load(f.read()  )

			# Get to the API doc
			__lowercase 					=   0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			__lowercase 					=   content[api_idx]['''sections''']

			# Then to the model doc
			__lowercase 					=   0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			__lowercase 					=   api_doc[model_idx]['''sections''']

			__lowercase 					=   [(idx, section) for idx, section in enumerate(_UpperCamelCase  ) if '''sections''' in section]
			__lowercase 					=   False
			for idx, modality_doc in modalities_docs:
						__lowercase 					=   modality_doc['''sections''']
						__lowercase 					=   clean_model_doc_toc(_UpperCamelCase  )

						if old_modality_doc != new_modality_doc:
									__lowercase 					=   True
									if overwrite:
												__lowercase 					=   new_modality_doc

			if diff:
						if overwrite:
									__lowercase 					=   model_doc
									__lowercase 					=   api_doc
									with open(_UpperCamelCase       ,			'''w'''       ,			encoding='''utf-8'''  ) as f:
												f.write(yaml.dump(_UpperCamelCase       ,			allow_unicode=_UpperCamelCase  )  )
						else:
									raise ValueError(
									    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''  )


if __name__ == "__main__":
	a : Union[str, Any]		=				argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	a : int		=				parser.parse_args()

	check_model_doc(args.fix_and_overwrite)






import argparse

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


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   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__":
	a : Optional[int]		=				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.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


