




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase						:							Dict			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							str			     =						{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Any									=   '''convnext'''





				def __init__( self							:	Dict   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	List[Any]=4   , __lowerCamelCase							:	Union[str, Any]=4   , __lowerCamelCase							:	Any=None   , __lowerCamelCase							:	Dict=None   , __lowerCamelCase							:	List[str]="gelu"   , __lowerCamelCase							:	Tuple=0.02   , __lowerCamelCase							:	int=1e-12   , __lowerCamelCase							:	List[str]=1e-6   , __lowerCamelCase							:	str=0.0   , __lowerCamelCase							:	List[Any]=2_24   , __lowerCamelCase							:	Optional[int]=None   , __lowerCamelCase							:	str=None   , **__lowerCamelCase							:	List[str]   , )   ->	int:
							super().__init__(**__lowerCamelCase				)

							a       =     num_channels
							a       =     patch_size
							a       =     num_stages
							a       =     [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
							a       =     [3, 3, 9, 3] if depths is None else depths
							a       =     hidden_act
							a       =     initializer_range
							a       =     layer_norm_eps
							a       =     layer_scale_init_value
							a       =     drop_path_rate
							a       =     image_size
							a       =     ['''stem'''] + [f"""stage{idx}""" for idx in range(1   , len(self.depths				) + 1				)]
							a       =     get_aligned_output_features_output_indices(
							    out_features=__lowerCamelCase   , out_indices=__lowerCamelCase   , stage_names=self.stage_names				)




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				List[str]									=   version.parse("""1.11"""  )





				@property
				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Tuple:
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]				)





				@property
				def 				__UpperCAmelCase				( self							:	int				)   ->	Optional[Any]:
							return 1e-5














"""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


