

"""simple docstring"""






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class 						__UpperCamelCase     :

  def __init__(		self     ,_A     ,_A     ,_A   ):
      '''simple docstring'''

      if dst_width < 0 or dst_height < 0:
          raise ValueError('Destination width/height should be > 0'   )

      _lowerCAmelCase							: Dict	=     img
      _lowerCAmelCase							: List[Any]	=     img.shape[1]
      _lowerCAmelCase							: Tuple	=     img.shape[0]
      _lowerCAmelCase							: Tuple	=     dst_width
      _lowerCAmelCase							: List[Any]	=     dst_height

      _lowerCAmelCase							: Any	=     self.src_w / self.dst_w
      _lowerCAmelCase							: Dict	=     self.src_h / self.dst_h

      _lowerCAmelCase							: Tuple	=     (
          np.ones((self.dst_h, self.dst_w, 3)     ,np.uinta   ) * 255
      )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      for i in range(self.dst_h   ):
          for j in range(self.dst_w   ):
              _lowerCAmelCase							: int	=     self.img[self.get_y(_a   )][self.get_x(_a   )]

  def __lowerCamelCase		(		self     ,_A   ):
      '''simple docstring'''

      return int(self.ratio_x * x   )







  def __lowerCamelCase		(		self     ,_A   ):
      '''simple docstring'''

      return int(self.ratio_y * y   )


if __name__ == "__main__":
 _lowerCAmelCase       						=		8_0_0, 6_0_0
 _lowerCAmelCase       						=		imread("""image_data/lena.jpg""", 1)
 _lowerCAmelCase       						=		NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()







"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


