

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






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

    return image_array




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	List[Any]:



    """simple docstring"""






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

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

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


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE		:int				  =    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		:Optional[int]				  =    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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
