






"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    tempfile.mkdtemp()

       UpperCamelCase__         =    BlipImageProcessor()
       UpperCamelCase__         =    BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'      )

       UpperCamelCase__         =    BlipProcessor(snake_case_ ,				snake_case_      )

       processor.save_pretrained(self.tmpdirname      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       List[Any]:
       return AutoProcessor.from_pretrained(self.tmpdirname ,				**snake_case_      ).tokenizer

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Any:
       return AutoProcessor.from_pretrained(self.tmpdirname ,				**snake_case_      ).image_processor

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       shutil.rmtree(self.tmpdirname      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    [np.random.randint(255 ,				size=(3, 30, 400) ,				dtype=np.uinta      )]

       UpperCamelCase__         =    [Image.fromarray(np.moveaxis(snake_case_ ,				0 ,				-1      )      ) for x in image_inputs]

       return image_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    BlipProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       UpperCamelCase__         =    self.get_tokenizer(bos_token='(BOS)' ,				eos_token='(EOS)'      )
       UpperCamelCase__         =    self.get_image_processor(do_normalize=snake_case_ ,				padding_value=1.0      )

       UpperCamelCase__         =    BlipProcessor.from_pretrained(
           self.tmpdirname ,				bos_token='(BOS)' ,				eos_token='(EOS)' ,				do_normalize=snake_case_ ,				padding_value=1.0      )

       self.assertEqual(processor.tokenizer.get_vocab() ,				tokenizer_add_kwargs.get_vocab()      )
       self.assertIsInstance(processor.tokenizer ,				snake_case_      )

       self.assertEqual(processor.image_processor.to_json_string() ,				image_processor_add_kwargs.to_json_string()      )
       self.assertIsInstance(processor.image_processor ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.get_image_processor()
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BlipProcessor(tokenizer=snake_case_ ,				image_processor=snake_case_      )

       UpperCamelCase__         =    self.prepare_image_inputs()

       UpperCamelCase__         =    image_processor(snake_case_ ,				return_tensors='np'      )
       UpperCamelCase__         =    processor(images=snake_case_ ,				return_tensors='np'      )

       for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum() ,				input_processor[key].sum() ,				delta=1E-2      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.get_image_processor()
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BlipProcessor(tokenizer=snake_case_ ,				image_processor=snake_case_      )

       UpperCamelCase__         =    'lower newer'

       UpperCamelCase__         =    processor(text=snake_case_      )

       UpperCamelCase__         =    tokenizer(snake_case_ ,				return_token_type_ids=snake_case_      )

       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key] ,				encoded_processor[key]      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.get_image_processor()
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BlipProcessor(tokenizer=snake_case_ ,				image_processor=snake_case_      )

       UpperCamelCase__         =    'lower newer'
       UpperCamelCase__         =    self.prepare_image_inputs()

       UpperCamelCase__         =    processor(text=snake_case_ ,				images=snake_case_      )

       self.assertListEqual(list(inputs.keys()      ) ,				['pixel_values', 'input_ids', 'attention_mask']      )

       # test if it raises when no input is passed
       with pytest.raises(snake_case_      ):
           processor()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.get_image_processor()
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BlipProcessor(tokenizer=snake_case_ ,				image_processor=snake_case_      )

       UpperCamelCase__         =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__         =    processor.batch_decode(snake_case_      )
       UpperCamelCase__         =    tokenizer.batch_decode(snake_case_      )

       self.assertListEqual(snake_case_ ,				snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.get_image_processor()
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BlipProcessor(tokenizer=snake_case_ ,				image_processor=snake_case_      )

       UpperCamelCase__         =    'lower newer'
       UpperCamelCase__         =    self.prepare_image_inputs()

       UpperCamelCase__         =    processor(text=snake_case_ ,				images=snake_case_      )

       # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
       self.assertListEqual(list(inputs.keys()      ) ,				['pixel_values', 'input_ids', 'attention_mask']      )












"""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




