




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					)	->			Optional[Any]:

			'''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__":
				__a			:	List[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'."""
				    ),
				)
				__a			:	List[Any]							     =		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
				)






from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			if not inplace:
						UpperCamelCase_     				= ma + ma
			else:
						ma += ma

			return ma




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= -1 * len(__snake_case					)
			UpperCamelCase_     				= list(range(len(tensor.shape[:zero_index]					)					)					)
			return tensor.permute(first_inds + [zero_index + i for i in inds]					)




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			return t.reshape(t.shape[:-no_dims] + (-1,)					)




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case = 1E-4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= mask.expand(*value.shape					)
			return torch.sum(mask * value     ,		dim=__snake_case					) / (eps + torch.sum(__snake_case     ,		dim=__snake_case					))




def  a_		(							__snake_case     ,		__snake_case = 2.31_25     ,		__snake_case = 21.68_75     ,		__snake_case = 6_4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= torch.linspace(__snake_case     ,		__snake_case     ,		no_bins - 1     ,		device=pts.device					)
			UpperCamelCase_     				= torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2     ,		dim=-1					)					)
			return torch.bucketize(__snake_case     ,		__snake_case					)




def  a_		(							__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			UpperCamelCase_     				= dicts[0]
			UpperCamelCase_     				= {}
			for k, v in first.items():
						UpperCamelCase_     				= [d[k] for d in dicts]
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_multimap(__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= fn(__snake_case					)

			return new_dict




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= v_bins.view(((1,) * len(x.shape					)) + (len(__snake_case					),)					)
			UpperCamelCase_     				= x[..., None] - reshaped_bins
			UpperCamelCase_     				= torch.argmin(torch.abs(__snake_case					)     ,		dim=-1					)
			return nn.functional.one_hot(__snake_case     ,		num_classes=len(__snake_case					)					).float()




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case = 0     ,		__snake_case = 0					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= []
			for i, s in enumerate(data.shape[:no_batch_dims]					):
						UpperCamelCase_     				= torch.arange(__snake_case					)
						UpperCamelCase_     				= r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
						ranges.append(__snake_case					)

			UpperCamelCase_     				= [slice(__snake_case					) for _ in range(len(data.shape					) - no_batch_dims					)]
			UpperCamelCase_     				= inds
			ranges.extend(__snake_case					)
			# Matt note: Editing this to get around the behaviour of using a list as an array index changing
			# in recent Numpy versions
			return data[tuple(__snake_case					)]


__a			:	List[Any]							     =		TypeVar("""T""")




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Dict[Any, Union[dict, list, tuple, Any]]:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for k, v in dic.items():
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_map(__snake_case     ,		__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= tree_map(__snake_case     ,		__snake_case     ,		__snake_case					)

			return new_dict




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			list:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			tuple:

			'''simple docstring'''


			...




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			if isinstance(__snake_case     ,		__snake_case					):
						return dict_map(__snake_case     ,		__snake_case     ,		__snake_case					)
			elif isinstance(__snake_case     ,		__snake_case					):
						return [tree_map(__snake_case     ,		__snake_case     ,		__snake_case					) for x in tree]
			elif isinstance(__snake_case     ,		__snake_case					):
						return tuple(tree_map(__snake_case     ,		__snake_case     ,		__snake_case					) for x in tree					)
			elif isinstance(__snake_case     ,		__snake_case					):
						return fn(__snake_case					)
			else:
						print(type(__snake_case					)					)
						raise ValueError('Not supported'					)





__a			:	Tuple							     =		partial(tree_map, leaf_type=torch.Tensor)
