


'''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					       =							{
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							['''DistilBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DistilBertForMaskedLM''',
		    '''DistilBertForMultipleChoice''',
		    '''DistilBertForQuestionAnswering''',
		    '''DistilBertForSequenceClassification''',
		    '''DistilBertForTokenClassification''',
		    '''DistilBertModel''',
		    '''DistilBertPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDistilBertForMaskedLM''',
		    '''TFDistilBertForMultipleChoice''',
		    '''TFDistilBertForQuestionAnswering''',
		    '''TFDistilBertForSequenceClassification''',
		    '''TFDistilBertForTokenClassification''',
		    '''TFDistilBertMainLayer''',
		    '''TFDistilBertModel''',
		    '''TFDistilBertPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''FlaxDistilBertForMaskedLM''',
		    '''FlaxDistilBertForMultipleChoice''',
		    '''FlaxDistilBertForQuestionAnswering''',
		    '''FlaxDistilBertForSequenceClassification''',
		    '''FlaxDistilBertForTokenClassification''',
		    '''FlaxDistilBertModel''',
		    '''FlaxDistilBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_distilbert import (
		    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DistilBertConfig,
		    DistilBertOnnxConfig,
		)
		from .tokenization_distilbert import DistilBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_distilbert_fast import DistilBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_distilbert import (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				    DistilBertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertMainLayer,
				    TFDistilBertModel,
				    TFDistilBertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				    FlaxDistilBertPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# 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(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

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


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