
'''simple docstring'''



import unittest

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


if is_torch_available():
     import torch

     from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class 				_a			(unittest.TestCase):





       """simple docstring"""




       @slow
       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
          model.to(_UpperCAmelCase )

          # encoder-decoder outputs
          _SCREAMING_SNAKE_CASE       	=			torch.tensor([[17, 9_62_08, 10_34_71, 2]] ).to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]] ).to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids=_UpperCAmelCase    ,							attention_mask=_UpperCAmelCase    ,							encoder_outputs=_UpperCAmelCase    ,							decoder_input_ids=_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			output[0]
          _SCREAMING_SNAKE_CASE       	=			torch.Size((1, 14, 25_00_12) )
          self.assertEqual(output_predited_logis.shape    ,							_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]] ).to(_UpperCAmelCase )
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,							_UpperCAmelCase    ,							atol=1E-4 ) )

          # encoder outputs
          _SCREAMING_SNAKE_CASE       	=			model.prophetnet.encoder(_UpperCAmelCase )[0]
          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]] ).to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			torch.Size((1, 4, 10_24) )
          self.assertEqual(encoder_outputs.shape    ,							_UpperCAmelCase )
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,							_UpperCAmelCase    ,							atol=1E-4 ) )

          # decoder outputs
          _SCREAMING_SNAKE_CASE       	=			model.prophetnet.decoder(
              _UpperCAmelCase    ,							encoder_hidden_states=_UpperCAmelCase    ,							)
          _SCREAMING_SNAKE_CASE       	=			decoder_outputs[1].view(1    ,							model.config.ngram    ,							14    ,							-1 )
          _SCREAMING_SNAKE_CASE       	=			model.lm_head(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,							_UpperCAmelCase    ,							atol=1E-4 ) )






       @slow
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )
          model.to(_UpperCAmelCase )

          _SCREAMING_SNAKE_CASE       	=			torch.tensor([[17, 9_62_08, 10_34_71, 2]] ).to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]] ).to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids=_UpperCAmelCase    ,							attention_mask=_UpperCAmelCase    ,							encoder_outputs=_UpperCAmelCase    ,							decoder_input_ids=_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			output[0]
          _SCREAMING_SNAKE_CASE       	=			torch.Size((1, 14, 25_00_12) )
          self.assertEqual(output_predited_logis.shape    ,							_UpperCAmelCase )
          # compare the actual values for a slice.
          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]] ).to(_UpperCAmelCase )

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,							_UpperCAmelCase    ,							atol=1E-4 ) )






       @slow
       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )
          model.to(_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			5_12

          _SCREAMING_SNAKE_CASE       	=			XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )

          _SCREAMING_SNAKE_CASE       	=			(
              "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
              " January 14, 2020, according to the official portal of the organization. From that day, users of this"
              " system will not be able to receive security updates, which could make their computers vulnerable to"
              " cyber attacks."
          )
          _SCREAMING_SNAKE_CASE       	=			(
              "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
              " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
              " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
              " к кибератакам."
          )
          _SCREAMING_SNAKE_CASE       	=			(
              "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
          )

          _SCREAMING_SNAKE_CASE       	=			tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,							padding=_UpperCAmelCase    ,							max_length=2_55    ,							return_tensors="""pt""" ).input_ids
          _SCREAMING_SNAKE_CASE       	=			input_ids.to(_UpperCAmelCase )

          _SCREAMING_SNAKE_CASE       	=			model.generate(
              _UpperCAmelCase    ,							num_beams=10    ,							length_penalty=1.0    ,							no_repeat_ngram_size=3    ,							early_stopping=_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			[tokenizer.decode(_UpperCAmelCase    ,							skip_special_tokens=_UpperCAmelCase ) for g in summary_ids]
          _SCREAMING_SNAKE_CASE       	=			"Microsoft to end Windows 7 free support after January 14, 2020"
          _SCREAMING_SNAKE_CASE       	=			"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
          _SCREAMING_SNAKE_CASE       	=			"微软打算终止对Windows 7操作系统的免费支持"
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]    ,							_UpperCAmelCase    ,							)

          _SCREAMING_SNAKE_CASE       	=			model.generate(
              _UpperCAmelCase    ,							num_beams=1    ,							length_penalty=1.0    ,							no_repeat_ngram_size=3    ,							early_stopping=_UpperCAmelCase )
          _SCREAMING_SNAKE_CASE       	=			[
              tokenizer.convert_ids_to_tokens(_UpperCAmelCase    ,							skip_special_tokens=_UpperCAmelCase ) for g in summary_ids_beama
          ]
          _SCREAMING_SNAKE_CASE       	=			"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(""" """ )
          _SCREAMING_SNAKE_CASE       	=			"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
              """ """ )
          _SCREAMING_SNAKE_CASE       	=			"微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(""" """ )
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]    ,							_UpperCAmelCase    ,							)








'''simple docstring'''



from cva import destroyAllWindows, imread, imshow, waitKey
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			img.shape[0], img.shape[1]

   def to_grayscale(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      return 0.2126 * red + 0.587 * green + 0.114 * blue

   def normalize(SCREAMING_SNAKE_CASE_  ):
      return min(SCREAMING_SNAKE_CASE_      , 2_55  )

   for i in range(SCREAMING_SNAKE_CASE_  ):
      for j in range(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			int(to_grayscale(*img[i][j]  )  )
         _SCREAMING_SNAKE_CASE       	=			[
             normalize(SCREAMING_SNAKE_CASE_  ),
             normalize(greyscale + factor  ),
             normalize(greyscale + 2 * factor  ),
         ]

   return img


if __name__ == "__main__":
     # read original image
     UpperCamelCase__ :						Optional[int]    							=					{
         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()






