


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros



def 			_lowerCAmelCase						(							lowerCAmelCase_							:int		,						lowerCAmelCase_							:List[str]							)->Optional[int]:
   '''simple docstring'''






   snake_case_    ,    snake_case_					    =					image.shape
   snake_case_					    =					cols - block_size[1] + 1
   snake_case_					    =					rows - block_size[0] + 1
   snake_case_					    =					zeros((dst_height * dst_width, block_size[1] * block_size[0])							)
   snake_case_					    =					0
   for i in range(0		,						lowerCAmelCase_							):
      for j in range(0		,						lowerCAmelCase_							):
         snake_case_					    =					ravel(image[i : i + block_size[0], j : j + block_size[1]]							)
         snake_case_					    =					window
         row += 1

   return image_array



def 			_lowerCAmelCase						(							lowerCAmelCase_							:str		,						lowerCAmelCase_							:str							)->Optional[int]:
   '''simple docstring'''






   snake_case_    ,    snake_case_					    =					image.shape[0], image.shape[1]
   snake_case_					    =					filter_kernel.shape[0]
   snake_case_					    =					k_size // 2
   # Pads image with the edge values of array.
   snake_case_					    =					pad(lowerCAmelCase_		,						lowerCAmelCase_		,						mode="edge"							)

   # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
   snake_case_					    =					imacol(lowerCAmelCase_		,						(k_size, k_size)							)

   #  turn the kernel into shape(k*k, 1)
   snake_case_					    =					ravel(lowerCAmelCase_							)
   # reshape and get the dst image
   snake_case_					    =					dot(lowerCAmelCase_		,						lowerCAmelCase_							).reshape(lowerCAmelCase_		,						lowerCAmelCase_							)
   return dst


if __name__ == "__main__":
   # read original image
   SCREAMING_SNAKE_CASE	:Any  		=     imread(R'''../image_data/lena.jpg''')
   # turn image in gray scale value
   SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     cvtColor(img, COLOR_BGR2GRAY)
   # Laplace operator
   SCREAMING_SNAKE_CASE	:List[str]  		=     array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
   SCREAMING_SNAKE_CASE	:str  		=     img_convolve(gray, Laplace_kernel).astype(uinta)
   imshow('''Laplacian''', out)
   waitKey(0)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE	:List[Any]  		=     {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:int  		=     [
       '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Speech2Text2ForCausalLM''',
       '''Speech2Text2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
   from .processing_speech_to_text_a import SpeechaTextaProcessor
   from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_to_text_a import (
          SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechaTextaForCausalLM,
          SpeechaTextaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE	:str  		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




