from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			['''image_processor''', '''tokenizer''']
  _SCREAMING_SNAKE_CASE    				=			'''BlipImageProcessor'''
  _SCREAMING_SNAKE_CASE    				=			'''AutoTokenizer'''

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   __a       =  False
   super().__init__(A__     ,							A__       )
   __a       =  self.image_processor

  def __call__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      List[str] = None     ,							__SCREAMING_SNAKE_CASE :      Any = True     ,							__SCREAMING_SNAKE_CASE :      Any = False     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      str = 0     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any] = None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any] = False     ,							__SCREAMING_SNAKE_CASE :      Dict = False     ,							__SCREAMING_SNAKE_CASE :      List[str] = False     ,							__SCREAMING_SNAKE_CASE :      str = False     ,							__SCREAMING_SNAKE_CASE :      Dict = False     ,							__SCREAMING_SNAKE_CASE :      int = True     ,							__SCREAMING_SNAKE_CASE :      Any = None     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   if images is None and text is None:
    raise ValueError("You have to specify either images or text."       )

   # Get only text
   if images is None:
    __a       =  self.tokenizer
    __a       =  self.tokenizer(
        text=A__     ,							add_special_tokens=A__     ,							padding=A__     ,							truncation=A__     ,							max_length=A__     ,							stride=A__     ,							pad_to_multiple_of=A__     ,							return_attention_mask=A__     ,							return_overflowing_tokens=A__     ,							return_special_tokens_mask=A__     ,							return_offsets_mapping=A__     ,							return_token_type_ids=A__     ,							return_length=A__     ,							verbose=A__     ,							return_tensors=A__     ,							**A__     ,							)
    return text_encoding

   # add pixel_values
   __a       =  self.image_processor(A__     ,							return_tensors=A__       )

   if text is not None:
    __a       =  self.tokenizer(
        text=A__     ,							add_special_tokens=A__     ,							padding=A__     ,							truncation=A__     ,							max_length=A__     ,							stride=A__     ,							pad_to_multiple_of=A__     ,							return_attention_mask=A__     ,							return_overflowing_tokens=A__     ,							return_special_tokens_mask=A__     ,							return_offsets_mapping=A__     ,							return_token_type_ids=A__     ,							return_length=A__     ,							verbose=A__     ,							return_tensors=A__     ,							**A__     ,							)
   else:
    __a       =  None

   if text_encoding is not None:
    encoding_image_processor.update(A__       )

   return encoding_image_processor

  def 		_UpperCAmelCase   (		self :      Tuple     ,							*__SCREAMING_SNAKE_CASE :      Dict     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
   return self.tokenizer.batch_decode(*A__     ,							**A__       )

  def 		_UpperCAmelCase   (		self :      str     ,							*__SCREAMING_SNAKE_CASE :      Tuple     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   return self.tokenizer.decode(*A__     ,							**A__       )

  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.tokenizer.model_input_names
   __a       =  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



def   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 for data in source_data:
  for i, el in enumerate(_A					):
   if len(_A					) < i + 1:
    data_lists.append([]					)
   data_lists[i].append(float(_A					)					)
 return data_lists



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 for dlist, weight in zip(_A							,				_A					):
  __a       =  min(_A					)
  __a       =  max(_A					)

  __a       =  []
  # 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:
   __a       =  f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A					)

  score_lists.append(_A					)

 return score_lists



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [0 for i in range(len(score_lists[0]					)					)]

 for slist in score_lists:
  for j, ele in enumerate(_A					):
   __a       =  final_scores[j] + ele

 return final_scores






def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_data(_A					)
 __a       =  calculate_each_score(_A							,				_A					)
 __a       =  generate_final_scores(_A					)

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

 return source_data


