


"""simple docstring"""






import doctest
from collections import deque

import numpy as np





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					[2, 1, 2, -1]
												__UpperCAmelCase   :						List[Any]      							=					[1, 2, 3, 4]






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					len(self.first_signal  )
												__UpperCAmelCase   :						Tuple      							=					len(self.second_signal  )

												__UpperCAmelCase   :						Optional[Any]      							=					max(UpperCamelCase ,       UpperCamelCase  )

												# create a zero matrix of max_length x max_length
												__UpperCAmelCase   :						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  ):
																			__UpperCAmelCase   :						Tuple      							=					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   :						List[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()




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase    :     Any   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
