


"""simple docstring"""






import sys
import turtle
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[int],     ):






     """simple docstring"""

     my_pen.up()
     my_pen.goto(vertexa[0],     vertexa[1]       )
     my_pen.down()
     my_pen.goto(vertexa[0],     vertexa[1]       )
     my_pen.goto(vertexa[0],     vertexa[1]       )
     my_pen.goto(vertexa[0],     vertexa[1]       )

     if depth == 0:
          return

     triangle(lowerCamelCase_,     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     depth - 1       )
     triangle(lowerCamelCase_,     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     depth - 1       )
     triangle(lowerCamelCase_,     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     get_mid(lowerCamelCase_,     lowerCamelCase_       ),     depth - 1       )


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise ValueError(
          '''Correct format for using this script: '''
          '''python fractals.py <int:depth_for_fractal>'''
      )
   __snake_case		=       turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   __snake_case		=       [(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



"""simple docstring"""






import warnings

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


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['image_processor', 'tokenizer']
     A_       :	Union[str, Any]    =		'ChineseCLIPImageProcessor'
     A_       :	Union[str, Any]    =		('BertTokenizer', 'BertTokenizerFast')


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:
          _a    	=				None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''    ,				__UpperCAmelCase    ,				)
               _a    	=				kwargs.pop('''feature_extractor'''							)

          _a    	=				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__(__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				self.image_processor


     def __call__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:

          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:
               _a    	=				self.tokenizer(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if images is not None:
               _a    	=				self.image_processor(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

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


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[int]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,				__UpperCAmelCase    ,				)
          return self.image_processor_class