
"""simple docstring"""




import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	ConvBertModel(SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE_,     from_pt=SCREAMING_SNAKE_CASE_			)
					tf_model.save_pretrained(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	snake_case    =		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(
	    '--convbert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ConvBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	snake_case    =		parser.parse_args()
	convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)



from hashlib import mda, shaaaa

SCREAMING_SNAKE_CASE_						=					(shaaaa, mda)

class  a     :


						def __init__(				self  ,    A_ = 8		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			0B0
										_UpperCAmelCase				:						Optional[Any]       						=			size


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.hash_(A_		)
										self.bitarray |= h


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			self.hash_(A_		)
										return (h & self.bitarray) == h


						def __contains__(				self  ,    A_		):







										'''simple docstring'''


										return self.exists(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			bin(A_		)[2:]
										return res.zfill(self.size		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.format_bin(self.bitarray		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			0B0
										for func in HASH_FUNCTIONS:
														_UpperCAmelCase				:						str       						=			(
														    int.from_bytes(func(value.encode()		).digest()  ,    "little"		) % self.size
														)
														res |= 2**position
										return res


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										return self.format_bin(self.hash_(A_		)		)





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			bin(self.bitarray		).count("1"		)
										return (n_ones / self.size) ** len(A_		)


