


'''simple docstring'''
import doctest
from collections import deque

import numpy as np
class        lowercase     :

		"""simple docstring"""

		def __init__(		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			[2, 1, 2, -1]
							UpperCamelCase__       :List[Any]							 =			[1, 2, 3, 4]




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			len(self.first_signal							)
							UpperCamelCase__       :int							 =			len(self.second_signal							)

							UpperCamelCase__       :int							 =			max(UpperCamelCase_   ,       UpperCamelCase_							)

							# create a zero matrix of max_length x max_length
							UpperCamelCase__       :Optional[int]							 =			[[0] * max_length for i in range(UpperCamelCase_							)]

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

        # multiply the matrix with the first signal
							UpperCamelCase__       :Dict							 =			np.matmul(np.transpose(UpperCamelCase_							)   ,       np.transpose(self.first_signal							)							)

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


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



'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case											=					logging.getLogger(__name__)
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'masked_bert'




		def __init__(		self   ,       UpperCamelCase_=30522   ,       UpperCamelCase_=768   ,       UpperCamelCase_=12   ,       UpperCamelCase_=12   ,       UpperCamelCase_=3072   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=512   ,       UpperCamelCase_=2   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=1e-12   ,       UpperCamelCase_=0   ,       UpperCamelCase_="topK"   ,       UpperCamelCase_="constant"   ,       UpperCamelCase_=0.0   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_   ,       **UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			vocab_size
							UpperCamelCase__       :Dict							 =			hidden_size
							UpperCamelCase__       :Optional[int]							 =			num_hidden_layers
							UpperCamelCase__       :Any							 =			num_attention_heads
							UpperCamelCase__       :Optional[Any]							 =			hidden_act
							UpperCamelCase__       :str							 =			intermediate_size
							UpperCamelCase__       :List[Any]							 =			hidden_dropout_prob
							UpperCamelCase__       :List[str]							 =			attention_probs_dropout_prob
							UpperCamelCase__       :List[Any]							 =			max_position_embeddings
							UpperCamelCase__       :int							 =			type_vocab_size
							UpperCamelCase__       :List[Any]							 =			initializer_range
							UpperCamelCase__       :Any							 =			layer_norm_eps
							UpperCamelCase__       :str							 =			pruning_method
							UpperCamelCase__       :int							 =			mask_init
							UpperCamelCase__       :Optional[Any]							 =			mask_scale