





from functools import lru_cache





@lru_cache
def    _lowercase    (  _UpperCAmelCase )  ->     int:
				if not isinstance(_UpperCAmelCase	,					_UpperCAmelCase ):
								raise TypeError("""k must be an integer.""" )
				if k < 0:
								raise ValueError("""k integer must be greater or equal to zero.""" )
				if k == 0:
								return 0
				elif k == 1:
								return 1
				else:
								return fibonacci(k - 1 ) + fibonacci(k - 2 )





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     int:
				lowerCamelCase		       =len(_UpperCAmelCase )
				# Find m such that F_m >= n where F_i is the i_th fibonacci number.
				lowerCamelCase		       =0
				while True:
								if fibonacci(_UpperCAmelCase ) >= len_list:
												lowerCamelCase		       =i
												break
								i += 1
				lowerCamelCase		       =0
				while fibb_k > 0:
								lowerCamelCase		       =min(
								    offset + fibonacci(fibb_k - 1 )	,					len_list - 1 )  # Prevent out of range
								lowerCamelCase		       =arr[index_k]
								if item_k_1 == val:
												return index_k
								elif val < item_k_1:
												fibb_k -= 1
								elif val > item_k_1:
												offset += fibonacci(fibb_k - 1 )
												fibb_k -= 2
				else:
								return -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()







'''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,
     )






