




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class      UpperCAmelCase						(			A   ,					unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					BartTokenizer
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					BartTokenizerFast
   __SCREAMING_SNAKE_CASE		:							Dict							=					True
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					filter_roberta_detectors






   def  _a						(	self   :       List[str]     )->			str:
     super().setUp()
     _lowercase: Union[str, Any]						     =    [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     _lowercase: Optional[Any]						     =    dict(zip(UpperCamelCase_						,					range(len(UpperCamelCase_     )     )     )     )
     _lowercase: List[str]						     =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     _lowercase: List[str]						     =    {'unk_token': '<unk>'}

     _lowercase: Dict						     =    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['vocab_file']     )
     _lowercase: List[Any]						     =    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['merges_file']     )
     with open(self.vocab_file						,					'w'						,					encoding='utf-8'     ) as fp:
       fp.write(json.dumps(UpperCamelCase_     ) + '\n'     )
     with open(self.merges_file						,					'w'						,					encoding='utf-8'     ) as fp:
       fp.write('\n'.join(UpperCamelCase_     )     )






   def  _a						(	self   :       Optional[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Dict:
     kwargs.update(self.special_tokens_map     )
     return self.tokenizer_class.from_pretrained(self.tmpdirname						,					**UpperCamelCase_     )






   def  _a						(	self   :       Dict						,					**UpperCamelCase_   :       Any     )->			Union[str, Any]:
     kwargs.update(self.special_tokens_map     )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,					**UpperCamelCase_     )






   def  _a						(	self   :       str						,					UpperCamelCase_   :       int     )->			Union[str, Any]:
     return "lower newer", "lower newer"






   @cached_property
   def  _a						(	self   :       Optional[int]     )->			Tuple:
     return BartTokenizer.from_pretrained('facebook/bart-large'     )






   @cached_property
   def  _a						(	self   :       Optional[int]     )->			str:
     return BartTokenizerFast.from_pretrained('facebook/bart-large'     )






   @require_torch
   def  _a						(	self   :       Optional[Any]     )->			Tuple:
     _lowercase: int						     =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     _lowercase: Optional[int]						     =    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       _lowercase: Optional[Any]						     =    tokenizer(UpperCamelCase_						,					max_length=len(UpperCamelCase_     )						,					padding=UpperCamelCase_						,					return_tensors='pt'     )
       self.assertIsInstance(UpperCamelCase_						,					UpperCamelCase_     )

       self.assertEqual((2, 9)						,					batch.input_ids.shape     )
       self.assertEqual((2, 9)						,					batch.attention_mask.shape     )
       _lowercase: int						     =    batch.input_ids.tolist()[0]
       self.assertListEqual(UpperCamelCase_						,					UpperCamelCase_     )
       # Test that special tokens are reset






   @require_torch
   def  _a						(	self   :       List[Any]     )->			List[Any]:
     _lowercase: int						     =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       _lowercase: str						     =    tokenizer(UpperCamelCase_						,					padding=UpperCamelCase_						,					return_tensors='pt'     )
       # check if input_ids are returned and no labels
       self.assertIn('input_ids'						,					UpperCamelCase_     )
       self.assertIn('attention_mask'						,					UpperCamelCase_     )
       self.assertNotIn('labels'						,					UpperCamelCase_     )
       self.assertNotIn('decoder_attention_mask'						,					UpperCamelCase_     )






   @require_torch
   def  _a						(	self   :       Optional[int]     )->			Optional[int]:
     _lowercase: Optional[int]						     =    [
         'Summary of the text.',
         'Another summary.',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       _lowercase: Union[str, Any]						     =    tokenizer(text_target=UpperCamelCase_						,					max_length=32						,					padding='max_length'						,					return_tensors='pt'     )
       self.assertEqual(32						,					targets['input_ids'].shape[1]     )






   @require_torch
   def  _a						(	self   :       int     )->			List[str]:
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       _lowercase: int						     =    tokenizer(
           ['I am a small frog' * 10_24, 'I am a small frog']						,					padding=UpperCamelCase_						,					truncation=UpperCamelCase_						,					return_tensors='pt'     )
       self.assertIsInstance(UpperCamelCase_						,					UpperCamelCase_     )
       self.assertEqual(batch.input_ids.shape						,					(2, 10_24)     )






   @require_torch
   def  _a						(	self   :       Optional[int]     )->			Tuple:
     _lowercase: List[str]						     =    ['A long paragraph for summarization.']
     _lowercase: Optional[Any]						     =    [
         'Summary of the text.',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       _lowercase: int						     =    tokenizer(UpperCamelCase_						,					return_tensors='pt'     )
       _lowercase: List[Any]						     =    tokenizer(text_target=UpperCamelCase_						,					return_tensors='pt'     )
       _lowercase: Tuple						     =    inputs['input_ids']
       _lowercase: Tuple						     =    targets['input_ids']
       self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()     )
       self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()     )
       self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()     )
       self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()     )






   def  _a						(	self   :       List[Any]     )->			List[Any]:
     pass






   def  _a						(	self   :       Any     )->			int:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
         _lowercase: Dict						     =    self.rust_tokenizer_class.from_pretrained(UpperCamelCase_						,					**UpperCamelCase_     )
         _lowercase: List[Any]						     =    self.tokenizer_class.from_pretrained(UpperCamelCase_						,					**UpperCamelCase_     )
         _lowercase: Optional[int]						     =    'A, <mask> AllenNLP sentence.'
         _lowercase: Any						     =    tokenizer_r.encode_plus(UpperCamelCase_						,					add_special_tokens=UpperCamelCase_						,					return_token_type_ids=UpperCamelCase_     )
         _lowercase: Optional[Any]						     =    tokenizer_p.encode_plus(UpperCamelCase_						,					add_special_tokens=UpperCamelCase_						,					return_token_type_ids=UpperCamelCase_     )

         # token_type_ids should put 0 everywhere
         self.assertEqual(sum(tokens_r['token_type_ids']     )						,					sum(tokens_p['token_type_ids']     )     )

         # attention_mask should put 1 everywhere, so sum over length should be 1
         self.assertEqual(
             sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )						,					sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )						,					)

         _lowercase: Tuple						     =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
         _lowercase: Tuple						     =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

         # Rust correctly handles the space before the mask while python doesnt
         self.assertSequenceEqual(tokens_p['input_ids']						,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
         self.assertSequenceEqual(tokens_r['input_ids']						,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

         self.assertSequenceEqual(
             UpperCamelCase_						,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
         self.assertSequenceEqual(
             UpperCamelCase_						,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





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

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

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

  return img


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