



import torch

from transformers import AutoTokenizer



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  AutoTokenizer.from_pretrained(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if isinstance(lowerCamelCase_						,			lowerCamelCase_						) and all(isinstance(_x						,			lowerCamelCase_						) for _x in x						):
											a   	=  None
											for l in x:
																a   	=  self.tokenizer(
																    lowerCamelCase_						,			padding="max_length"						,			max_length=384						,			truncation=lowerCamelCase_						,			return_tensors="pt"						,			)
																a   	=  torch.tensor([len(lowerCamelCase_						)]						)
																if d is not None:
																					for k in d.keys():
																										a   	=  torch.cat((d[k], t[k])						,			0						)
																else:
																					a   	=  t

											a   	=  torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"						)						)
											a   	=  torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"						)						)

						elif isinstance(lowerCamelCase_						,			lowerCamelCase_						) and all(isinstance(_x						,			lowerCamelCase_						) for _x in x						):
											a   	=  self.tokenizer(
											    lowerCamelCase_						,			padding="max_length"						,			max_length=384						,			truncation=lowerCamelCase_						,			return_tensors="pt"						,			)

						else:
											raise Exception(
											    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
											    " strings` for supports are supported."						)

						return d




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_=0.70						):
						"""simple docstring"""

						a   	=  []
						for idx in range(len(W_query["input_ids"]						)						):
											a   	=  range(p_start.shape[1]						)

											a   	=  []
											for start_id in start_indexes:
																for end_id in end_indexes:
																					if start_id < end_id:
																										output.append(
																										    (
																										        start_id,
																										        end_id,
																										        p_start[idx][start_id].item(),
																										        p_end[idx][end_id].item(),
																										    )						)

											output.sort(key=lambda lowerCamelCase_						: (tup[2] * tup[3])						,			reverse=lowerCamelCase_						)
											a   	=  []
											for k in range(len(lowerCamelCase_						)						):
																if output[k][2] * output[k][3] >= thresh:
																					a   	=  output[k][0], output[k][1]
																					a   	=  self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]						)
																					temp.append((decoded, output[k][2] * output[k][3])						)

											final_outputs.append(lowerCamelCase_						)

						return final_outputs








"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()




