


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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     list[list[float]]										=    []
     for dlist, weight in zip(__UpperCamelCase  ,  __UpperCamelCase ):
          a_    :     Any										=    min(__UpperCamelCase )
          a_    :     List[Any]										=    max(__UpperCamelCase )

          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_    :     Tuple										=    F'''Invalid weight of {weight:f} provided'''
               raise ValueError(__UpperCamelCase )

          score_lists.append(__UpperCamelCase )

     return score_lists



def  _a			(						__UpperCamelCase ):
     a_    :     list[float]										=    [0 for i in range(len(score_lists[0] ) )]

     for slist in score_lists:
          for j, ele in enumerate(__UpperCamelCase ):
               a_    :     int										=    final_scores[j] + ele

     return final_scores



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Any										=    get_data(__UpperCamelCase )
     a_    :     Optional[int]										=    calculate_each_score(__UpperCamelCase  ,  __UpperCamelCase )
     a_    :     Optional[int]										=    generate_final_scores(__UpperCamelCase )

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

     return source_data







from typing import TYPE_CHECKING

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


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



