"""simple docstring"""
import doctest
from collections import deque

import numpy as np



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self      ):






										"""simple docstring"""

										snake_case        =     [2, 1, 2, -1]
										snake_case        =     [1, 2, 3, 4]

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     len(self.first_signal      )
										snake_case        =     len(self.second_signal      )

										snake_case        =     max(lowerCAmelCase						,      lowerCAmelCase      )

										# create a zero matrix of max_length x max_length
										snake_case        =     [[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      ):
																snake_case        =     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
										snake_case        =     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()



"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


