






"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Any      				=			{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			["""MobileNetV1FeatureExtractor"""]
    UpperCamelCase_						:					Dict      				=			["""MobileNetV1ImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[str]      				=			[
        """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """MobileNetV1ForImageClassification""",
        """MobileNetV1Model""",
        """MobileNetV1PreTrainedModel""",
        """load_tf_weights_in_mobilenet_v1""",
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_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_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""





import numpy as np



def      __lowercase       (     a			:					Union[str, Any]							,		a			:					Optional[Any]							,		a			:					List[str]			)       ->       Dict:
      __snake_case   :     Dict	      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      __snake_case   :     int	      =np.floor(a			)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      __snake_case   :     Dict	      =int(n_draws - floored.sum()			)
      if need_to_add > 0:
            __snake_case   :     List[str]	      =continuous - floored
            __snake_case   :     str	      =np.sort(np.unique(a			)			)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((__snake_case)      ,					)   :     Tuple	      =np.where(remainder == value			)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  __snake_case   :     int	      =min(len(a			)							,		a			)
                  __snake_case   :     Any	      =rng.choice(a							,		size=a							,		replace=a			)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa			)



def      __lowercase       (     a			:					Any							,		a			:					str							,		a			:					Dict							,		a			:					Union[str, Any]							,		a			:					Union[str, Any]=10			)       ->       Any:
      __snake_case      ,					__snake_case   :     int	      =np.unique(a							,		return_inverse=a			)
      __snake_case   :     Dict	      =classes.shape[0]
      __snake_case   :     int	      =np.bincount(a			)
      if np.min(a			) < 2:
            raise ValueError('''Minimum class count error'''			)
      if n_train < n_classes:
            raise ValueError(
                '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)			)
      if n_test < n_classes:
            raise ValueError(
                '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)			)
      __snake_case   :     str	      =np.split(np.argsort(a							,		kind='''mergesort'''			)							,		np.cumsum(a			)[:-1]			)
      for _ in range(a			):
            __snake_case   :     str	      =approximate_mode(a							,		a							,		a			)
            __snake_case   :     List[str]	      =class_counts - n_i
            __snake_case   :     Union[str, Any]	      =approximate_mode(a							,		a							,		a			)

            __snake_case   :     int	      =[]
            __snake_case   :     Union[str, Any]	      =[]

            for i in range(a			):
                  __snake_case   :     Union[str, Any]	      =rng.permutation(class_counts[i]			)
                  __snake_case   :     Dict	      =class_indices[i].take(a							,		mode='''clip'''			)
                  train.extend(perm_indices_class_i[: n_i[i]]			)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
            __snake_case   :     Any	      =rng.permutation(a			)
            __snake_case   :     int	      =rng.permutation(a			)

            yield train, test


