"""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 			A__						(  lowercase_    :  Tuple			,   lowercase_    :  Optional[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  int	)     ->       List[str]:
   # Initialise PyTorch model
   __lowerCamelCase								=				ElectraConfig.from_json_file(lowercase_	)
   print(F'''Building PyTorch model from configuration: {config}'''	)

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

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

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE  :		str        =      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\'.'''
         ),
     )
     _SCREAMING_SNAKE_CASE  :		Optional[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"""







_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)






