
from ...configuration_utils import PretrainedConfig




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''bert-generation'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=5_0_3_5_8						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=2_4						,						__SCREAMING_SNAKE_CASE=1_6						,						__SCREAMING_SNAKE_CASE=4_0_9_6						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE="absolute"						,						__SCREAMING_SNAKE_CASE=True						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Tuple:




           """simple docstring"""







           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE						,						bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				vocab_size
           UpperCamelCase__    :   Optional[int]					=				hidden_size
           UpperCamelCase__    :   Tuple					=				num_hidden_layers
           UpperCamelCase__    :   int					=				num_attention_heads
           UpperCamelCase__    :   Dict					=				hidden_act
           UpperCamelCase__    :   Dict					=				intermediate_size
           UpperCamelCase__    :   Dict					=				hidden_dropout_prob
           UpperCamelCase__    :   List[Any]					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Optional[int]					=				max_position_embeddings
           UpperCamelCase__    :   List[str]					=				initializer_range
           UpperCamelCase__    :   List[str]					=				layer_norm_eps
           UpperCamelCase__    :   str					=				position_embedding_type
           UpperCamelCase__    :   str					=				use_cache




import doctest
from collections import deque

import numpy as np




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				[2, 1, 2, -1]
           UpperCamelCase__    :   Union[str, Any]					=				[1, 2, 3, 4]






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   list[float]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				len(self.first_signal  )
           UpperCamelCase__    :   Any					=				len(self.second_signal  )

           UpperCamelCase__    :   Dict					=				max(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           # create a zero matrix of max_length x max_length
           UpperCamelCase__    :   List[Any]					=				[[0] * max_length for i in range(__SCREAMING_SNAKE_CASE  )]

           # 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(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Optional[int]					=				deque(self.second_signal  )
                 rotated_signal.rotate(__SCREAMING_SNAKE_CASE  )
                 for j, item in enumerate(__SCREAMING_SNAKE_CASE  ):
                       matrix[i][j] += item

        # multiply the matrix with the first signal
           UpperCamelCase__    :   Optional[Any]					=				np.matmul(np.transpose(__SCREAMING_SNAKE_CASE  )						,						np.transpose(self.first_signal  )  )

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


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


