


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any  ,   __A			:      Dict  ,   __A			:      Any    )     ->		Dict:

       """simple docstring"""

       a_ :		Optional[int]							=  BertConfig.from_json_file(__A    )
       print(F"""Building PyTorch model from configuration: {config}"""    )
       a_ :		int							=  BertForPreTraining(__A    )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__A  ,   __A  ,   __A    )

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


if __name__ == "__main__":
       UpperCAmelCase_				:			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(
           '--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.'
       )
       UpperCAmelCase_				:			int   =	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_				:			Dict   =	{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMSNModel',
       'ViTMSNForImageClassification',
       'ViTMSNPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

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


