




import argparse

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


logging.set_verbosity_info()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


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

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

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


if __name__ == "__main__":
	__A						:		str          =						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")
	__A						:		Union[str, Any]          =						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,
	)











from hashlib import mda, shaaaa

__A						:		Optional[int]          =						(shaaaa, mda)

class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		int = 8):


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									__lowerCAmelCase					=  size




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									self.bitarray |= h




					def     _lowercase						(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									return (h & self.bitarray) == h




					def __contains__(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									return self.exists(_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(_lowercase)[2:]
									return res.zfill(self.size)




					@property
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									return self.format_bin(self.bitarray)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									for func in HASH_FUNCTIONS:
													__lowerCAmelCase					=  (
													    int.from_bytes(func(value.encode()).digest(),     """little""") % self.size
													)
													res |= 2**position
									return res




					def     _lowercase						(   self:		Any,     _lowercase:		str):


									'''simple docstring'''


									return self.format_bin(self.hash_(_lowercase))







					@property
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(self.bitarray).count("""1""")
									return (n_ones / self.size) ** len(_lowercase)





