



import torch

from transformers import AutoTokenizer



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 def __init__(						self	,    a					)	->							Tuple:




   """simple docstring"""







   _A           =    AutoTokenizer.from_pretrained(a					)
 def lowercase_		(						self	,    a					)	->							Optional[Any]:




   """simple docstring"""







   if isinstance(a	,    a					) and all(isinstance(_x	,    a					) for _x in x					):
     _A           =    None
     for l in x:
       _A           =    self.tokenizer(
           a	,    padding='''max_length'''	,    max_length=3_8_4	,    truncation=a	,    return_tensors='''pt'''	,    )
       _A           =    torch.tensor([len(a					)]					)
       if d is not None:
         for k in d.keys():
           _A           =    torch.cat((d[k], t[k])	,    0					)
       else:
         _A           =    t

     _A           =    torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''					)					)
     _A           =    torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''					)					)

   elif isinstance(a	,    a					) and all(isinstance(_x	,    a					) for _x in x					):
     _A           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=3_8_4	,    truncation=a	,    return_tensors='''pt'''	,    )

   else:
     raise Exception(
         '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
         ''' strings` for supports are supported.'''					)

   return d



 def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a=0.70					)	->							List[str]:




   """simple docstring"""







   _A           =    []
   for idx in range(len(W_query['''input_ids''']					)					):
     _A           =    _A           =    range(p_start.shape[1]					)

     _A           =    []
     for start_id in start_indexes:
       for end_id in end_indexes:
         if start_id < end_id:
           output.append(
               (
                   start_id,
                   end_id,
                   p_start[idx][start_id].item(),
                   p_end[idx][end_id].item(),
               )					)

     output.sort(key=lambda a					: (tup[2] * tup[3])	,    reverse=a					)
     _A           =    []
     for k in range(len(a					)					):
       if output[k][2] * output[k][3] >= thresh:
         _A    ,   _A           =    output[k][0], output[k][1]
         _A           =    self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]					)
         temp.append((decoded, output[k][2] * output[k][3])					)

     final_outputs.append(a					)

   return final_outputs




import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
  # Initialise PyTorch model
  _A           =    ElectraConfig.from_json_file(__snake_case							)
  print(F'''Building PyTorch model from configuration: {config}'''							)

  if discriminator_or_generator == "discriminator":
    _A           =    ElectraForPreTraining(__snake_case							)
  elif discriminator_or_generator == "generator":
    _A           =    ElectraForMaskedLM(__snake_case							)
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''							)

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __snake_case						,				__snake_case						,				__snake_case						,				discriminator_or_generator=__snake_case							)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
  torch.save(model.state_dict()						,				__snake_case							)


if __name__ == "__main__":
    __lowerCamelCase  				=					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 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."""
    )
    parser.add_argument(
        """--discriminator_or_generator""",
        default=None,
        type=str,
        required=True,
        help=(
            """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
            """'generator'."""
        ),
    )
    __lowerCamelCase  				=					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
    )