




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
	)






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__)


