
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        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  Any    ):




			"""simple docstring"""


			lowerCAmelCase_      =     ElectraConfig.from_json_file(__lowerCAmelCase    )
			print(F"""Building PyTorch model from configuration: {config}"""    )

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

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

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


if __name__ == "__main__":
			_A       =					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'."
			    ),
			)
			_A       =					parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
			)





class      _lowerCAmelCase :




				def __init__( self					)    ->				None:
							# numbermap keys are the number and its values are lists of indexes sorted
							# in ascending order
							lowerCAmelCase_      =     {}
							# indexmap keys are an index and it's values are the number at that index
							lowerCAmelCase_      =     {}




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_delete() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == item:
													array.pop(_UpperCamelCase					)
													return array
										elif array[mid] < item:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1
							raise ValueError(
							    "Either the item is not in the array or the array was unsorted"					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_insert() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == index:
													# If the item already exists in the array,
													# insert it after the existing item
													array.insert(mid + 1    ,    _UpperCamelCase					)
													return array
										elif array[mid] < index:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
							array.insert(_UpperCamelCase    ,    _UpperCamelCase					)
							return array




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				None:
							# Remove previous index
							if index in self.indexmap:
										lowerCAmelCase_      =     self.indexmap[index]
										if len(self.numbermap[n]					) == 1:
													del self.numbermap[n]
										else:
													lowerCAmelCase_      =     self.binary_search_delete(self.numbermap[n]    ,    _UpperCamelCase					)

        # Set new index
							lowerCAmelCase_      =     number

							# Number not seen before or empty so insert number value
							if number not in self.numbermap:
										lowerCAmelCase_      =     [index]

							# Here we need to perform a binary search insertion in order to insert
							# The item in the correct place
							else:
										lowerCAmelCase_      =     self.binary_search_insert(
										    self.numbermap[number]    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							return self.numbermap.get(_UpperCamelCase    ,    [-1]					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



