



a_      :      Union[str, Any]    =     tuple[float, float, float]
a_      :      Tuple    =     tuple[float, float, float]






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= end_pointa[0] - end_pointa[0]
 a__												= end_pointa[1] - end_pointa[1]
 a__												= end_pointa[2] - end_pointa[2]
 return (x, y, z)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ab[1] * ac[2] - ab[2] * ac[1]  # *i
 a__												= (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
 a__												= ab[0] * ac[1] - ab[1] * ac[0]  # *k
 return (x, y, z)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 return tuple(round(__UpperCAmelCase							,  __UpperCAmelCase       ) for x in vector       ) == (0, 0, 0)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase = 10       ):
 a__												= create_vector(__UpperCAmelCase							,  __UpperCAmelCase       )
 a__												= create_vector(__UpperCAmelCase							,  __UpperCAmelCase       )
 return is_zero_vector(get_ad_vectors_cross(__UpperCAmelCase							,  __UpperCAmelCase       )							,  __UpperCAmelCase       )










import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					['''image_processor''', '''tokenizer''']
 _lowercase   :    Optional[int]      						=					'''ChineseCLIPImageProcessor'''
 _lowercase   :    List[Any]      						=					('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  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.'''		,				SCREAMING_SNAKE_CASE		,				)
   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__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			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(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  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(**SCREAMING_SNAKE_CASE      )		,				tensor_type=SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 @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      ) ->			Optional[int]:
  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





