
def 				a__   (   a   :				list[list[float]]		):

  """simple docstring"""







  _snake_case     :						list[list[float]]						=   []
  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   :				list[list[float]]       ,     a   :				list[int]		):

  """simple docstring"""







  _snake_case     :						list[list[float]]						=   []
  for dlist, weight in zip(a       ,     a		):
    _snake_case     :						Optional[Any]						=   min(a		)
    _snake_case     :						Tuple						=   max(a		)

    _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     :						Optional[int]						=   f'Invalid weight of {weight:f} provided'
      raise ValueError(a		)

    score_lists.append(a		)

  return score_lists
def 				a__   (   a   :				list[list[float]]		):

  """simple docstring"""







  _snake_case     :						list[float]						=   [0 for i in range(len(score_lists[0]		)		)]

  for slist in score_lists:
    for j, ele in enumerate(a		):
      _snake_case     :						Union[str, Any]						=   final_scores[j] + ele

  return final_scores



def 				a__   (   a   :				list[list[float]]       ,     a   :				list[int]		):

  """simple docstring"""







  _snake_case     :						List[str]						=   get_data(a		)
  _snake_case     :						List[Any]						=   calculate_each_score(a       ,     a		)
  _snake_case     :						Optional[Any]						=   generate_final_scores(a		)

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

  return source_data



"""simple docstring"""
from typing import TYPE_CHECKING

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


_a      :			str					=							{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Optional[Any]					=							["""YolosFeatureExtractor"""]
    _a      :			List[Any]					=							["""YolosImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Union[str, Any]					=							[
        """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """YolosForObjectDetection""",
        """YolosModel""",
        """YolosPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_yolos import YolosFeatureExtractor
        from .image_processing_yolos import YolosImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yolos import (
            YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
            YolosForObjectDetection,
            YolosModel,
            YolosPreTrainedModel,
        )


else:
    import sys

    _a      :			Union[str, Any]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

