





'''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 lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Tuple    )						->Optional[int]:
    # Initialise PyTorch model
    _SCREAMING_SNAKE_CASE									=			MobileBertConfig.from_json_file(__lowerCamelCase    )
    print(F'Building PyTorch model from configuration: {config}'    )
    _SCREAMING_SNAKE_CASE									=			MobileBertForPreTraining(__lowerCamelCase    )
    # Load weights from tf checkpoint
    _SCREAMING_SNAKE_CASE									=			load_tf_weights_in_mobilebert(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'    )
    torch.save(model.state_dict()					,       __lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= 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."""
      )
      lowercase_					= parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)











"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


