




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     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			Any ):
    # Initialise PyTorch model
    UpperCamelCase :Optional[int]										=     ElectraConfig.from_json_file(lowercase__ )
    print(F'''Building PyTorch model from configuration: {config}''' )

    if discriminator_or_generator == "discriminator":
        UpperCamelCase :str										=     ElectraForPreTraining(lowercase__ )
    elif discriminator_or_generator == "generator":
        UpperCamelCase :Optional[int]										=     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__":
 __snake_case							      =						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\'."""
     ),
 )
 __snake_case							      =						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )













from __future__ import annotations

from typing import Generic, TypeVar

__snake_case							      =						TypeVar("""T""")







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      pass







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      pass







class     UpperCAmelCase_		(      Generic[T]				):



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_ = 10				)       ->   List[str]:
          UpperCamelCase :list[T]										=     []
          UpperCamelCase :Any										=     limit
      def __bool__(       self				)       ->   bool:
          return bool(self.stack				)
      def __str__(       self				)       ->   str:
          return str(self.stack				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
          if len(self.stack				) >= self.limit:
              raise StackOverflowError
          self.stack.append(SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   T:
          if not self.stack:
              raise StackUnderflowError
          return self.stack.pop()
      def 					UpperCAmelCase		(       self				)       ->   T:
          if not self.stack:
              raise StackUnderflowError
          return self.stack[-1]
      def 					UpperCAmelCase		(       self				)       ->   bool:
          return not bool(self.stack				)
      def 					UpperCAmelCase		(       self				)       ->   bool:
          return self.size() == self.limit
      def 					UpperCAmelCase		(       self				)       ->   int:
          return len(self.stack				)




      def __contains__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   bool:
          return item in self.stack







def 						_A     (       ):
    UpperCamelCase :Stack[int]										=     Stack(10 )
    assert bool(SCREAMING_SNAKE_CASE__ ) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(SCREAMING_SNAKE_CASE__ ) == "[]"

    try:
        UpperCamelCase :Optional[int]										=     stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        UpperCamelCase :Union[str, Any]										=     stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10 ):
        assert stack.size() == i
        stack.push(SCREAMING_SNAKE_CASE__ )

    assert bool(SCREAMING_SNAKE_CASE__ )
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(SCREAMING_SNAKE_CASE__ ) == str(list(range(10 ) ) )
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100 )
    assert str(SCREAMING_SNAKE_CASE__ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )

    try:
        stack.push(200 )
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()





