


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


_snake_case				=			logging.get_logger(__name__)

_snake_case				=			{
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}




class  A_ (			a_  ):







 """simple docstring"""





 _UpperCamelCase					:			List[str]     	=       '''mobilevitv2'''
 def __init__(      self       :			str							,     _a       :			Dict=3							,     _a       :			Tuple=2_5_6							,     _a       :			Tuple=2							,     _a       :			Optional[int]=2.0							,     _a       :			Tuple="swish"							,     _a       :			Optional[Any]=3							,     _a       :			Any=3_2							,     _a       :			int=0.1							,     _a       :			Any=0.0_2							,     _a       :			int=1E-5							,     _a       :			int=5_1_2							,     _a       :			int=[6, 1_2, 1_8]							,     _a       :			Optional[int]=0.1							,     _a       :			str=2_5_5							,     _a       :			str=[2, 4, 3]							,     _a       :			Union[str, Any]=[1_2_8, 1_9_2, 2_5_6]							,     _a       :			List[Any]=1.0							,     _a       :			List[str]=2							,     _a       :			int=0.0							,     _a       :			List[Any]=0.0							,     **_a       :			Any							,     )     ->      Optional[Any]:
      super().__init__(**_a					)

      UpperCAmelCase    :					List[str]			=      num_channels
      UpperCAmelCase    :					int			=      image_size
      UpperCAmelCase    :					int			=      patch_size
      UpperCAmelCase    :					Any			=      expand_ratio
      UpperCAmelCase    :					int			=      hidden_act
      UpperCAmelCase    :					List[str]			=      conv_kernel_size
      UpperCAmelCase    :					Optional[int]			=      output_stride
      UpperCAmelCase    :					Dict			=      initializer_range
      UpperCAmelCase    :					Optional[int]			=      layer_norm_eps
      UpperCAmelCase    :					Dict			=      n_attn_blocks
      UpperCAmelCase    :					Dict			=      base_attn_unit_dims
      UpperCAmelCase    :					Union[str, Any]			=      width_multiplier
      UpperCAmelCase    :					List[Any]			=      ffn_multiplier
      UpperCAmelCase    :					Union[str, Any]			=      ffn_dropout
      UpperCAmelCase    :					List[Any]			=      attn_dropout
      UpperCAmelCase    :					Any			=      classifier_dropout_prob

      # decode head attributes for semantic segmentation
      UpperCAmelCase    :					str			=      aspp_out_channels
      UpperCAmelCase    :					int			=      atrous_rates
      UpperCAmelCase    :					Optional[Any]			=      aspp_dropout_prob
      UpperCAmelCase    :					int			=      semantic_loss_ignore_index




class  A_ (			a_  ):







 """simple docstring"""





 _UpperCamelCase					:			str     	=       version.parse('''1.11'''  )
 @property
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      Mapping[str, Mapping[int, str]]:
      return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]					)
 @property
 def 	_UpperCamelCase     (      self       :			str					)     ->      Mapping[str, Mapping[int, str]]:
      if self.task == "image-classification":
           return OrderedDict([("logits", {0: "batch"})]					)
      else:
           return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]					)

 @property
 def 	_UpperCamelCase     (      self       :			Tuple					)     ->      float:
      return 1E-4







from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



