


'''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 	_A				(lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Union[str, Any]		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							MobileBertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							MobileBertForPreTraining(lowerCAmelCase__		)
     # Load weights from tf checkpoint
     _a			      =							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__":
 a_					:    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."
 )
 a_					:    str          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

