



'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    # Initialise PyTorch model
    UpperCamelCase__	     =      RoFormerConfig.from_json_file(A__						)
    print(F'Building PyTorch model from configuration: {config}'						)
    UpperCamelCase__	     =      RoFormerForMaskedLM(A__						)

    # Load weights from tf checkpoint
    load_tf_weights_in_roformer(A__       ,	A__       ,	A__						)

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


if __name__ == "__main__":
      lowercase__  :	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(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT 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.'
      )
      lowercase__  :	Tuple            =      parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						UpperCAmelCase_			      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





