
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      ["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__         =      """ChineseCLIPImageProcessor"""
  SCREAMING_SNAKE_CASE__         =      ("""BertTokenizer""", """BertTokenizerFast""")







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    None
       if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""	,      SCREAMING_SNAKE_CASE_	,      )
            UpperCamelCase__				=    kwargs.pop("""feature_extractor"""						)

       UpperCamelCase__				=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""						)
       if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""						)

       super().__init__(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor







  def __call__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):

       if text is None and images is None:
            raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

       if text is not None:
            UpperCamelCase__				=    self.tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

       if images is not None:
            UpperCamelCase__				=    self.image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

       if text is not None and images is not None:
            UpperCamelCase__				=    image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_						)	,      tensor_type=SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  @property
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.tokenizer.model_input_names
       UpperCamelCase__				=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







  @property
  def 	UpperCAmelCase_ (self						):
       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,      SCREAMING_SNAKE_CASE_	,      )
       return self.image_processor_class







from PIL import Image






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    image.size
     UpperCamelCase__				=    0
     UpperCamelCase__				=    image.load()
     for i in range(__a						):
          for j in range(__a						):
               UpperCamelCase__				=    pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__a						):
          for i in range(__a						):
               UpperCamelCase__				=    255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
    lowerCamelCase_						     =					mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')





