



"""simple docstring"""






import doctest
from collections import deque

import numpy as np






class 			__snake_case			:




  def __init__(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					[2, 1, 2, -1]
         _lowerCamelCase		:							int            =					[1, 2, 3, 4]




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					len(self.first_signal						)
         _lowerCamelCase		:							Any            =					len(self.second_signal						)

         _lowerCamelCase		:							Optional[Any]            =					max(_lowerCAmelCase		,   _lowerCAmelCase						)

         # create a zero matrix of max_length x max_length
         _lowerCamelCase		:							Optional[Any]            =					[[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						):
                _lowerCamelCase		:							Optional[int]            =					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
         _lowerCamelCase		:							Tuple            =					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()






from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizer
  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizerFast
  _SCREAMING_SNAKE_CASE		     =							True



  @slow
  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       DistilBertTokenizer.from_pretrained("distilbert-base-uncased"	)

     snake_case_            =       tokenizer.encode("sequence builders"     ,					add_special_tokens=_lowerCAmelCase	)
     snake_case_            =       tokenizer.encode("multi-sequence build"     ,					add_special_tokens=_lowerCAmelCase	)

     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase     ,					_lowerCAmelCase	)

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]




