


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case          =							{
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["MobileNetV2FeatureExtractor"]
   _snake_case          =							["MobileNetV2ImageProcessor"]


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileNetV2ForImageClassification",
       "MobileNetV2ForSemanticSegmentation",
       "MobileNetV2Model",
       "MobileNetV2PreTrainedModel",
       "load_tf_weights_in_mobilenet_v2",
   ]


if TYPE_CHECKING:
   from .configuration_mobilenet_va import (
       MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileNetVaConfig,
       MobileNetVaOnnxConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
      from .image_processing_mobilenet_va import MobileNetVaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilenet_va import (
          MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileNetVaForImageClassification,
          MobileNetVaForSemanticSegmentation,
          MobileNetVaModel,
          MobileNetVaPreTrainedModel,
          load_tf_weights_in_mobilenet_va,
      )


else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""


def   snake_case						(				_a:     list[list[float]]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      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   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for dlist, weight in zip(_a		,   _a							):
            lowerCamelCase__           =		min(_a							)
            lowerCamelCase__           =		max(_a							)

            lowerCamelCase__           =		[]
            # 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:
                  lowerCamelCase__           =		F'Invalid weight of {weight:f} provided'
                  raise ValueError(_a							)

            score_lists.append(_a							)

      return score_lists



def   snake_case						(				_a:     list[list[float]]							)->		list[float]:
      '''simple docstring'''


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

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

      return final_scores






def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		get_data(_a							)
      lowerCamelCase__           =		calculate_each_score(_a		,   _a							)
      lowerCamelCase__           =		generate_final_scores(_a							)

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

      return source_data



