


'''simple docstring'''

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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Any   )		->					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    ,		_UpperCamelCase   ):
						for j in range(0    ,		_UpperCamelCase   ):
									UpperCamelCase__ 						=						ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
									UpperCamelCase__ 						=						window
									row += 1

			return image_array



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]   )		->					str:





			'''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(_UpperCamelCase    ,		_UpperCamelCase    ,		mode="edge"   )

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

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


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



'''simple docstring'''

from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch







class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
 _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
 _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):





    """simple docstring"""



    if image_processor is None:
       raise ValueError("You need to specify an `image_processor`."					)
    if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`."					)

    UpperCamelCase__ 						=						max_seq_length
    UpperCamelCase__ 						=						task_seq_length

    super().__init__(a_,					a_					)

 def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):





    """simple docstring"""



    if text_list is None:
       raise ValueError("tokens cannot be None."					)

    UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)

    UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]

    UpperCamelCase__ 						=						[]
    for attn_mask, input_id in zip(a_,					a_					):
       UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
       token_inputs.append(token.unsqueeze(0					)					)

    UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
    return token_inputs

 def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):





    """simple docstring"""



    return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)