
'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class       _a (nn.Linear):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__				,							A__ = True				,							A__ = "constant"				,							A__ = 0.0				,							A__ = "topK"				,							)			->	str:
          super(A__				,							self					).__init__(in_features=A__				,							out_features=A__				,							bias=A__					)
          assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
          _SCREAMING_SNAKE_CASE          = pruning_method

          if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
             _SCREAMING_SNAKE_CASE          = mask_scale
             _SCREAMING_SNAKE_CASE          = mask_init
             _SCREAMING_SNAKE_CASE          = nn.Parameter(torch.empty(self.weight.size()					)					)
             self.init_mask()






       def 				UpperCamelCase (							self					)			->	Tuple:
          if self.mask_init == "constant":
             init.constant_(self.mask_scores				,							val=self.mask_scale					)
          elif self.mask_init == "uniform":
             init.uniform_(self.mask_scores				,							a=-self.mask_scale				,							b=self.mask_scale					)
          elif self.mask_init == "kaiming":
             init.kaiming_uniform_(self.mask_scores				,							a=math.sqrt(5					)					)






       def 				UpperCamelCase (							self				,							A__				,							A__					)			->	Tuple:
          # Get the mask
          if self.pruning_method == "topK":
             _SCREAMING_SNAKE_CASE          = TopKBinarizer.apply(self.mask_scores				,							A__					)
          elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
             _SCREAMING_SNAKE_CASE          = """sigmoied""" in self.pruning_method
             _SCREAMING_SNAKE_CASE          = ThresholdBinarizer.apply(self.mask_scores				,							A__				,							A__					)
          elif self.pruning_method == "magnitude":
             _SCREAMING_SNAKE_CASE          = MagnitudeBinarizer.apply(self.weight				,							A__					)
          elif self.pruning_method == "l0":
             _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = -0.1, 1.1, 2 / 3
             if self.training:
                _SCREAMING_SNAKE_CASE          = torch.zeros_like(self.mask_scores					).uniform_().clamp(0.0001				,							0.9999					)
                _SCREAMING_SNAKE_CASE          = torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b					)
             else:
                _SCREAMING_SNAKE_CASE          = torch.sigmoid(self.mask_scores					)
             _SCREAMING_SNAKE_CASE          = s * (r - l) + l
             _SCREAMING_SNAKE_CASE          = s_bar.clamp(min=0.0				,							max=1.0					)
          # Mask weights with computed mask
          _SCREAMING_SNAKE_CASE          = mask * self.weight
          # Compute output (linear layer) with masked weights
          return nn.functional.linear(A__				,							A__				,							self.bias					)








'''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,
     )






