
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
				import transformer_engine.pytorch as te




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: Dict=True			,   snake_case				: Optional[Any]=True			,   snake_case				: Tuple=True			)   ->     int:



						'''simple docstring'''

						if not is_fpa_available():
												raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''			)
						for name, module in model.named_children():
												if isinstance(snake_case			,   nn.Linear			) and to_transformer_engine and _convert_linear:
																		# Return early if the linear layer weights are not multiples of 16
																		if any(p % 16 != 0 for p in module.weight.shape			):
																								return
																		__UpperCamelCase  	=      module.bias is not None
																		__UpperCamelCase  	=      te.Linear(
																		    module.in_features			,   module.out_features			,   bias=snake_case			,   params_dtype=module.weight.dtype			)
																		__UpperCamelCase  	=      module.weight.data.clone()
																		if has_bias:
																								__UpperCamelCase  	=      module.bias.data.clone()

																		setattr(snake_case			,   snake_case			,   snake_case			)
												elif isinstance(snake_case			,   nn.LayerNorm			) and to_transformer_engine and _convert_ln:
																		__UpperCamelCase  	=      te.LayerNorm(module.normalized_shape[0]			,   eps=module.eps			,   params_dtype=module.weight.dtype			)
																		__UpperCamelCase  	=      module.weight.data.clone()
																		__UpperCamelCase  	=      module.bias.data.clone()

																		setattr(snake_case			,   snake_case			,   snake_case			)
												elif isinstance(snake_case			,   te.Linear			) and not to_transformer_engine and _convert_linear:
																		__UpperCamelCase  	=      module.bias is not None
																		__UpperCamelCase  	=      nn.Linear(
																		    module.in_features			,   module.out_features			,   bias=snake_case			,   params_dtype=module.weight.dtype			)
																		__UpperCamelCase  	=      module.weight.data.clone()
																		if has_bias:
																								__UpperCamelCase  	=      module.bias.data.clone()

																		setattr(snake_case			,   snake_case			,   snake_case			)
												elif isinstance(snake_case			,   te.LayerNorm			) and not to_transformer_engine and _convert_ln:
																		__UpperCamelCase  	=      nn.LayerNorm(module.normalized_shape[0]			,   eps=module.eps			,   params_dtype=module.weight.dtype			)
																		__UpperCamelCase  	=      module.weight.data.clone()
																		__UpperCamelCase  	=      module.bias.data.clone()

																		setattr(snake_case			,   snake_case			,   snake_case			)
												else:
																		convert_model(
																		    snake_case			,   to_transformer_engine=snake_case			,   _convert_linear=_convert_linear			,   _convert_ln=_convert_ln			,   )




def 						A_			(				snake_case				: List[Any]			)   ->     List[Any]:



						'''simple docstring'''

						if not is_fpa_available():
												raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''			)
						for m in model.modules():
												if isinstance(snake_case			,   (te.LayerNorm, te.Linear)			):
																		return True
						return False












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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

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

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


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