

"""simple docstring"""




from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					:  str             =      {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  int             =      [
           'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GroupViTModel',
           'GroupViTPreTrainedModel',
           'GroupViTTextModel',
           'GroupViTVisionModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  int             =      [
           'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFGroupViTModel',
           'TFGroupViTPreTrainedModel',
           'TFGroupViTTextModel',
           'TFGroupViTVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_groupvit import (
           GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           GroupViTConfig,
           GroupViTOnnxConfig,
           GroupViTTextConfig,
           GroupViTVisionConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_groupvit import (
                  GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GroupViTModel,
                  GroupViTPreTrainedModel,
                  GroupViTTextModel,
                  GroupViTVisionModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_groupvit import (
                  TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFGroupViTModel,
                  TFGroupViTPreTrainedModel,
                  TFGroupViTTextModel,
                  TFGroupViTVisionModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE					:  int             =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import doctest
from collections import deque

import numpy as np






class 	UpperCAmelCase__		:






  """simple docstring"""


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







  def 				lowercase_		(							self						:		Optional[int] )		->							list[float]:
       SCREAMING_SNAKE_CASE__   =   len(self.first_signal )
       SCREAMING_SNAKE_CASE__   =   len(self.second_signal )

       SCREAMING_SNAKE_CASE__   =   max(__lowerCamelCase     ,       __lowerCamelCase )

       # create a zero matrix of max_length x max_length
       SCREAMING_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 ):
            SCREAMING_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
       SCREAMING_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()


