

'''simple docstring'''





import doctest
from collections import deque

import numpy as np





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=[2, 1, 2, -1]
										lowercase :							Tuple    		=[1, 2, 3, 4]







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		=len(self.first_signal   )
										lowercase :							Any    		=len(self.second_signal   )

										lowercase :							Optional[Any]    		=max(UpperCAmelCase__		,					UpperCAmelCase__   )

										# create a zero matrix of max_length x max_length
										lowercase :							Any    		=[[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__   ):
																lowercase :							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
										lowercase :							Optional[Any]    		=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()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






