


'''simple docstring'''







import doctest
from collections import deque

import numpy as np


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]):
           _A					:     Any     = [2, 1, 2, -1]
           _A					:     Optional[int]     = [1, 2, 3, 4]


    def        A   (  self      :							int):
           _A					:     Any     = len(self.first_signal)
           _A					:     Any     = len(self.second_signal)

           _A					:     Union[str, Any]     = max(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           # create a zero matrix of max_length x max_length
           _A					:     Union[str, Any]     = [[0] * max_length for i in range(SCREAMING_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(SCREAMING_SNAKE_CASE):
                  _A					:     str     = deque(self.second_signal)
                  rotated_signal.rotate(SCREAMING_SNAKE_CASE)
                  for j, item in enumerate(SCREAMING_SNAKE_CASE):
                         matrix[i][j] += item

        # multiply the matrix with the first signal
           _A					:     Union[str, Any]     = np.matmul(np.transpose(SCREAMING_SNAKE_CASE)				,							np.transpose(self.first_signal))

           # rounding-off to two decimal places
           return [round(SCREAMING_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_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

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






