



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[list[float]]				)  ->     list[list[float]]:
 _snake_case    :  list[list[float]]					=					[]
 for data in source_data:
  for i, el in enumerate(SCREAMING_SNAKE_CASE__				):
   if len(SCREAMING_SNAKE_CASE__				) < i + 1:
    data_lists.append([]				)
   data_lists[i].append(float(SCREAMING_SNAKE_CASE__				)				)
 return data_lists



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[list[float]]				,       SCREAMING_SNAKE_CASE__       :	list[int]				)  ->     list[list[float]]:
 _snake_case    :  list[list[float]]					=					[]
 for dlist, weight in zip(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  _snake_case    :  Dict					=					min(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Union[str, Any]					=					max(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  list[float]					=					[]
  # for weight 0 score is 1 - actual score
  if weight == 0:
   for item in dlist:
    try:
     score.append(1 - ((item - mind) / (maxd - mind))				)
    except ZeroDivisionError:
     score.append(1				)

  elif weight == 1:
   for item in dlist:
    try:
     score.append((item - mind) / (maxd - mind)				)
    except ZeroDivisionError:
     score.append(0				)

        # weight not 0 or 1
  else:
   _snake_case    :  Union[str, Any]					=					F'''Invalid weight of {weight:f} provided'''
   raise ValueError(SCREAMING_SNAKE_CASE__				)

  score_lists.append(SCREAMING_SNAKE_CASE__				)

 return score_lists



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[list[float]]				)  ->     list[float]:
 _snake_case    :  list[float]					=					[0 for i in range(len(score_lists[0]				)				)]

 for slist in score_lists:
  for j, ele in enumerate(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Tuple					=					final_scores[j] + ele

 return final_scores



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[list[float]]				,       SCREAMING_SNAKE_CASE__       :	list[int]				)  ->     list[list[float]]:
 _snake_case    :  List[Any]					=					get_data(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[Any]					=					calculate_each_score(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Any					=					generate_final_scores(SCREAMING_SNAKE_CASE__				)

 # append scores to source data
 for i, ele in enumerate(SCREAMING_SNAKE_CASE__				):
  source_data[i].append(SCREAMING_SNAKE_CASE__				)

 return source_data





import warnings

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







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					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__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







    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:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







    return self.tokenizer.decode(*lowerCAmelCase				,		**lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
