


'''simple docstring'''







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






def 				lowerCAmelCase__	(    lowerCamelCase					:      list[list[float]]  ,lowerCamelCase					:      list[int]       ):
       _A					:     list[list[float]]     = []
       for dlist, weight in zip(lowerCamelCase  ,lowerCamelCase       ):
              _A					:     str     = min(lowerCamelCase       )
              _A					:     Tuple     = max(lowerCamelCase       )

              _A					:     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:
                     _A					:     Union[str, Any]     = F'Invalid weight of {weight:f} provided'
                     raise ValueError(lowerCamelCase       )

              score_lists.append(lowerCamelCase       )

       return score_lists






def 				lowerCAmelCase__	(    lowerCamelCase					:      list[list[float]]       ):
       _A					:     list[float]     = [0 for i in range(len(score_lists[0]       )       )]

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

       return final_scores






def 				lowerCAmelCase__	(    lowerCamelCase					:      list[list[float]]  ,lowerCamelCase					:      list[int]       ):
       _A					:     Optional[Any]     = get_data(lowerCamelCase       )
       _A					:     Dict     = calculate_each_score(lowerCamelCase  ,lowerCamelCase       )
       _A					:     Optional[int]     = generate_final_scores(lowerCamelCase       )

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

       return source_data










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






