



import torch
from torch import autograd




class 				lowerCAmelCase__	(						autograd.Function    ):







					@staticmethod
					def 			_lowercase      (							_A   :  List[str]      ,		_A   :  torch.tensor      ,		_A   :  float      ,		_A   :  bool):
											A__						:				Optional[int]         =			inputs.numel()
											A__						:				Optional[Any]         =			int(0.0_05 * nb_elems) + 1
											if sigmoid:
																	A__						:				int         =			(torch.sigmoid(_A) > threshold).type(inputs.type())
											else:
																	A__						:				List[Any]         =			(inputs > threshold).type(inputs.type())
											if mask.sum() < nb_min:
																	# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																	A__						:				str         =			inputs.flatten().kthvalue(max(nb_elems - nb_min      ,		1)).values
																	A__						:				str         =			(inputs > k_threshold).type(inputs.type())
											return mask







					@staticmethod
					def 			_lowercase      (							_A   :  Dict      ,		_A   :  Optional[int]):
											return gradOutput, None, None




class 				lowerCAmelCase__	(						autograd.Function    ):







					@staticmethod
					def 			_lowercase      (							_A   :  str      ,		_A   :  torch.tensor      ,		_A   :  float):
											A__						:				Tuple         =			inputs.clone()
											A__     ,							A__						:				Dict         =			inputs.flatten().sort(descending=_A)
											A__						:				str         =			int(threshold * inputs.numel())

											# flat_out and mask access the same memory.
											A__						:				Optional[Any]         =			mask.flatten()
											A__						:				str         =			0
											A__						:				str         =			1
											return mask







					@staticmethod
					def 			_lowercase      (							_A   :  Optional[int]      ,		_A   :  Optional[Any]):
											return gradOutput, None




class 				lowerCAmelCase__	(						UpperCamelCase    ):







					@staticmethod
					def 			_lowercase      (							_A   :  torch.tensor      ,		_A   :  float):
											A__						:				Dict         =			inputs.clone()
											A__     ,							A__						:				Dict         =			inputs.abs().flatten().sort(descending=_A)
											A__						:				Any         =			int(threshold * inputs.numel())

											# flat_out and mask access the same memory.
											A__						:				Optional[int]         =			mask.flatten()
											A__						:				Optional[int]         =			0
											A__						:				List[str]         =			1
											return mask




import argparse

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


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

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

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


if __name__ == "__main__":
		snake_case     :    List[Any]						=    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=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[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,
		)