'''simple docstring'''




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 						__snake_case (       lowerCAmelCase       :     Dict      ,  lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     Tuple			):
 # Initialise PyTorch model
 __UpperCAmelCase         =					ElectraConfig.from_json_file(lowerCAmelCase			)
 print(F"""Building PyTorch model from configuration: {config}"""			)

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

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

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


if __name__ == "__main__":
     _UpperCamelCase				:		Dict       			=		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\'.'
         ),
     )
     _UpperCamelCase				:		Union[str, Any]       			=		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
     )




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     list			):

 for insert_index, insert_value in enumerate(collection[1:]			):
  __UpperCAmelCase         =					insert_index
  while insert_index >= 0 and insert_value < collection[insert_index]:
   __UpperCAmelCase         =					collection[insert_index]
   insert_index -= 1
  if insert_index != temp_index:
   __UpperCAmelCase         =					insert_value
 return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _UpperCamelCase				:		List[Any]       			=		input('Enter numbers separated by a comma:\n').strip()
     _UpperCamelCase				:		List[Any]       			=		[int(item) for item in user_input.split(',')]
     print(f"{insertion_sort(unsorted) = }")



