






import doctest
from collections import deque

import numpy as np




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self				) ->				None:
								snake_case_				:							str						 =       [2, 1, 2, -1]
								snake_case_				:							int						 =       [1, 2, 3, 4]






						def       _lowerCAmelCase       (	self				) ->				list[float]:
								snake_case_				:							Dict						 =       len(self.first_signal				)
								snake_case_				:							Union[str, Any]						 =       len(self.second_signal				)

								snake_case_				:							Optional[Any]						 =       max(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								# create a zero matrix of max_length x max_length
								snake_case_				:							Tuple						 =       [[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				):
										snake_case_				:							Any						 =       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
								snake_case_				:							Dict						 =       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()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :							Optional[int]			  =    {
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NllbMoeForConditionalGeneration''',
							    '''NllbMoeModel''',
							    '''NllbMoePreTrainedModel''',
							    '''NllbMoeTop2Router''',
							    '''NllbMoeSparseMLP''',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
