






import doctest
from collections import deque

import numpy as np





class   UpperCamelCase_ :



  def __init__(      self				)			->			List[Any]:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





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

         UpperCAmelCase	    =			max(snake_case__ ,		snake_case__				)

         # create a zero matrix of max_length x max_length
         UpperCAmelCase	    =			[[0] * max_length for i in range(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(snake_case__				):
                UpperCAmelCase	    =			deque(self.second_signal				)
                rotated_signal.rotate(snake_case__				)
                for j, item in enumerate(snake_case__				):
                       matrix[i][j] += item

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

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


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








"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

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