



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[int]:
						# Initialise PyTorch model
						UpperCAmelCase         =     RemBertConfig.from_json_file(lowerCamelCase_    )
						print("Building PyTorch model from configuration: {}".format(str(lowerCamelCase_    )    )    )
						UpperCAmelCase         =     RemBertModel(lowerCamelCase_    )

						# Load weights from tf checkpoint
						load_tf_weights_in_rembert(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    )

						# Save pytorch-model
						print("Save PyTorch model to {}".format(lowerCamelCase_    )    )
						torch.save(model.state_dict()    ,   lowerCamelCase_    )


if __name__ == "__main__":
		__lowerCamelCase     :							int  							=				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(
		    "--rembert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained RemBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__lowerCamelCase     :							int  							=				parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase     :							Optional[int]  							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							List[Any]  							=				[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCamelCase     :							Optional[int]  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



