






"""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 (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       ElectraConfig.from_json_file(_snake_case							)
      print(F'''Building PyTorch model from configuration: {config}'''							)

      if discriminator_or_generator == "discriminator":
            UpperCAmelCase							=       ElectraForPreTraining(_snake_case							)
      elif discriminator_or_generator == "generator":
            UpperCAmelCase							=       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__":
  _UpperCamelCase     			=  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     			=  parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
  )





from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






