


"""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					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Dict:
							# Initialise PyTorch model
							snake_case_     							=		ElectraConfig.from_json_file(_SCREAMING_SNAKE_CASE					)
							print(f"""Building PyTorch model from configuration: {config}"""					)

							if discriminator_or_generator == "discriminator":
														snake_case_     							=		ElectraForPreTraining(_SCREAMING_SNAKE_CASE					)
							elif discriminator_or_generator == "generator":
														snake_case_     							=		ElectraForMaskedLM(_SCREAMING_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(
							    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			discriminator_or_generator=_SCREAMING_SNAKE_CASE					)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     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[int]		=     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"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



