




def 							_UpperCAmelCase     (UpperCamelCase_       :       str				):
				'''simple docstring'''
				_lowerCAmelCase			:			str        =					word.upper()
				_lowerCAmelCase			:			List[str]        =					0
				_lowerCAmelCase			:			int        =					{}
				_lowerCAmelCase			:			List[Any]        =					[]

				for letter in word:
								if letter not in letter_nums:
												_lowerCAmelCase			:			Optional[int]        =					str(UpperCamelCase_				)
												next_num += 1
								word_pattern.append(letter_nums[letter]				)
				return ".".join(UpperCamelCase_				)


if __name__ == "__main__":
	import pprint
	import time

	_lowerCamelCase					:     Tuple					  =  time.time()
	with open("dictionary.txt") as in_file:
		_lowerCamelCase					:     Any					  =  in_file.read().splitlines()

	_lowerCamelCase					:     dict					  =  {}
	for word in word_list:
		_lowerCamelCase					:     str					  =  get_word_pattern(word)
		if pattern in all_patterns:
			all_patterns[pattern].append(word)
		else:
			_lowerCamelCase					:     Dict					  =  [word]

	with open("word_patterns.txt", "w") as out_file:
		out_file.write(pprint.pformat(all_patterns))

	_lowerCamelCase					:     int					  =  round(time.time() - start_time, 2)
	print(F'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
	# Done!  9,581 word patterns found in 0.58 seconds.



'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

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


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






