



import doctest
from collections import deque

import numpy as np



class 		_lowercase		:

   def __init__(      self			:       Optional[Any]							)       ->				None:


    """simple docstring"""






    a						=		[2, 1, 2, -1]
    a						=		[1, 2, 3, 4]




   def 		A							(      self			:       Union[str, Any]							)       ->				list[float]:


    """simple docstring"""






    a						=		len(self.first_signal							)
    a						=		len(self.second_signal							)

    a						=		max(__lowerCAmelCase							,   __lowerCAmelCase							)

    # create a zero matrix of max_length x max_length
    a						=		[[0] * max_length for i in range(__lowerCAmelCase							)]

    # 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(__lowerCAmelCase							):
     a						=		deque(self.second_signal							)
     rotated_signal.rotate(__lowerCAmelCase							)
     for j, item in enumerate(__lowerCAmelCase							):
      matrix[i][j] += item

        # multiply the matrix with the first signal
    a						=		np.matmul(np.transpose(__lowerCAmelCase							)							,   np.transpose(self.first_signal							)							)

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


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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, 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(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 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.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

