code: 
  
  
  
  
  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		(							SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Any			=	ElectraConfig.from_json_file(lowerCAmelCase__      )
  							print(F"""Building PyTorch model from configuration: {config}"""      )
  
  							if discriminator_or_generator == "discriminator":
  														a_       :   Dict			=	ElectraForPreTraining(lowerCAmelCase__      )
  							elif discriminator_or_generator == "generator":
  														a_       :   List[Any]			=	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__":
  	SCREAMING_SNAKE_CASE : Dict          =    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[Any]          =    parser.parse_args()
  	convert_tf_checkpoint_to_pytorch(
  	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
  	)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  