

'''simple docstring'''



import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def       snake_case   (	snake_case      : List[str]					,		snake_case      : Optional[Any]					,		snake_case      : Optional[int]			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							MobileBertConfig.from_json_file(snake_case			)
    print(F'Building PyTorch model from configuration: {config}'			)
    lowerCAmelCase 						=							MobileBertForPreTraining(snake_case			)
    # Load weights from tf checkpoint
    lowerCAmelCase 						=							load_tf_weights_in_mobilebert(snake_case					,		snake_case					,		snake_case			)
    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'			)
    torch.save(model.state_dict()					,		snake_case			)


if __name__ == "__main__":
     _UpperCamelCase						:  Optional[Any]							       =				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(
         "--mobilebert_config_file",
         default=None,
         type=str,
         required=True,
         help=(
             "The config json file corresponding to the pre-trained MobileBERT 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						:  Any							       =				parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCamelCase						:  Optional[int]							       =				{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				["VisionTextDualEncoderModel"]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  int							       =				["FlaxVisionTextDualEncoderModel"]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Tuple							       =				["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
     from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
     from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
     import sys

     _UpperCamelCase						:  Optional[int]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure)





