


"""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 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Tuple	,  _snake_case		: Union[str, Any]	,  _snake_case		: Tuple	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							ElectraConfig.from_json_file(_snake_case	)
	print(f"""Building PyTorch model from configuration: {config}"""	)

	if discriminator_or_generator == "discriminator":
		__snake_case      :		Optional[Any]         =							ElectraForPreTraining(_snake_case	)
	elif discriminator_or_generator == "generator":
		__snake_case      :		Optional[int]         =							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__":
			SCREAMING_SNAKE_CASE					:       List[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					:       str							   =							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					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





