






'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 							_SCREAMING_SNAKE_CASE	(					A	:							Optional[Any]	,		A	:							Union[str, Any]	,		A	:							int	)     ->			str:







   """simple docstring"""
   # Initialise PyTorch model
   __snake_case					:   Optional[Any]  				=			TaConfig.from_json_file(__lowerCAmelCase	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   __snake_case					:   Union[str, Any]  				=			TaForConditionalGeneration(__lowerCAmelCase	)

   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   model.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
      __A     						= 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(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __A     						= parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							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
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

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






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

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



