






"""simple docstring"""





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=BlenderbotSmallTokenizer
   a      :				Tuple 						=False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       super().setUp()

       UpperCamelCase__         =    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       UpperCamelCase__         =    dict(zip(snake_case_ ,				range(len(snake_case_      )      )      )      )

       UpperCamelCase__         =    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
       UpperCamelCase__         =    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

       UpperCamelCase__         =    os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']      )
       UpperCamelCase__         =    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(snake_case_      ) + '\n'      )
       with open(self.merges_file ,				'w' ,				encoding='utf-8'      ) as fp:
           fp.write('\n'.join(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Optional[Any]:
       kwargs.update(self.special_tokens_map      )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Dict:
       UpperCamelCase__         =    'adapt act apte'
       UpperCamelCase__         =    'adapt act apte'
       return input_text, output_text

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    BlenderbotSmallTokenizer(self.vocab_file ,				self.merges_file ,				**self.special_tokens_map      )
       UpperCamelCase__         =    'adapt act apte'
       UpperCamelCase__         =    ['adapt', 'act', 'ap@@', 'te']
       UpperCamelCase__         =    tokenizer.tokenize(snake_case_      )
       self.assertListEqual(snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       UpperCamelCase__         =    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_      ) ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'      )
       assert tok('sam'      ).input_ids == [1384]
       UpperCamelCase__         =    'I am a small frog.'
       UpperCamelCase__         =    tok([src_text] ,				padding=snake_case_ ,				truncation=snake_case_      )['input_ids']
       UpperCamelCase__         =    tok.batch_decode(snake_case_ ,				skip_special_tokens=snake_case_ ,				clean_up_tokenization_spaces=snake_case_      )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'      )
       UpperCamelCase__         =    'I am a small frog .'
       UpperCamelCase__         =    '.'
       UpperCamelCase__         =    tok(snake_case_      )['input_ids']
       UpperCamelCase__         =    tok(snake_case_      )['input_ids']

       assert encoded[-1] == encoded_dot[0]












"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




