





"""simple docstring"""





import doctest
from collections import deque

import numpy as np




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						[2, 1, 2, -1]
        __UpperCAmelCase    :						str       =						[1, 2, 3, 4]







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						len(self.first_signal )
        __UpperCAmelCase    :						Union[str, Any]       =						len(self.second_signal )

        __UpperCAmelCase    :						Union[str, Any]       =						max(__a						,				__a )

        # create a zero matrix of max_length x max_length
        __UpperCAmelCase    :						List[str]       =						[[0] * max_length for i in range(__a )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
          self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
          self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__a ):
          __UpperCAmelCase    :						Any       =						deque(self.second_signal )
          rotated_signal.rotate(__a )
          for j, item in enumerate(__a ):
            matrix[i][j] += item

        # multiply the matrix with the first signal
        __UpperCAmelCase    :						Tuple       =						np.matmul(np.transpose(__a )						,				np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__a						,				2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()






"""simple docstring"""





import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						LxmertConfig.from_json_file(UpperCAmelCase_  )
  print(f'''Building PyTorch model from configuration: {config}'''  )
  __UpperCAmelCase    :						Tuple       =						LxmertForPreTraining(UpperCAmelCase_  )

  # Load weights from tf checkpoint
  load_tf_weights_in_lxmert(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
  torch.save(model.state_dict()  ,			UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Optional[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(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase__				:Union[str, Any]				   =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)