code: 
  
  
  """simple docstring"""
  
  
  
  
  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
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
      """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
      """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
      """apple/deeplabv3-mobilevit-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
      ),
      """apple/deeplabv3-mobilevit-x-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
      ),
      """apple/deeplabv3-mobilevit-xx-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Dict     	=			'mobilevit'
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Tuple=3				,  UpperCamelCase__							:Optional[int]=256				,  UpperCamelCase__							:Union[str, Any]=2				,  UpperCamelCase__							:List[Any]=[144, 192, 240]				,  UpperCamelCase__							:Optional[Any]=[16, 32, 64, 96, 128, 160, 640]				,  UpperCamelCase__							:Any=4				,  UpperCamelCase__							:int=2.0				,  UpperCamelCase__							:Dict=4.0				,  UpperCamelCase__							:Dict="silu"				,  UpperCamelCase__							:List[Any]=3				,  UpperCamelCase__							:List[str]=32				,  UpperCamelCase__							:Union[str, Any]=0.1				,  UpperCamelCase__							:List[Any]=0.0				,  UpperCamelCase__							:int=0.1				,  UpperCamelCase__							:List[Any]=0.02				,  UpperCamelCase__							:List[str]=1E-5				,  UpperCamelCase__							:List[str]=True				,  UpperCamelCase__							:List[Any]=256				,  UpperCamelCase__							:Optional[int]=[6, 12, 18]				,  UpperCamelCase__							:Any=0.1				,  UpperCamelCase__							:List[str]=255				,  **UpperCamelCase__							:Any				,  ):
            super().__init__(**UpperCamelCase__       )
  
            _a          =				num_channels
            _a          =				image_size
            _a          =				patch_size
            _a          =				hidden_sizes
            _a          =				neck_hidden_sizes
            _a          =				num_attention_heads
            _a          =				mlp_ratio
            _a          =				expand_ratio
            _a          =				hidden_act
            _a          =				conv_kernel_size
            _a          =				output_stride
            _a          =				hidden_dropout_prob
            _a          =				attention_probs_dropout_prob
            _a          =				classifier_dropout_prob
            _a          =				initializer_range
            _a          =				layer_norm_eps
            _a          =				qkv_bias
  
            # decode head attributes for semantic segmentation
            _a          =				aspp_out_channels
            _a          =				atrous_rates
            _a          =				aspp_dropout_prob
            _a          =				semantic_loss_ignore_index
  
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   int     	=			version.parse('1.11'      )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]       )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
            if self.task == "image-classification":
                   return OrderedDict([("logits", {0: "batch"})]       )
            else:
                   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]       )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            return 1E-4
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  __SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
            _a          =				-1
            _a          =				-1
            _a          =				[-1] * length
            _a          =				None
            _a          =				None
  
  
  
  
     def __repr__(		self							:Optional[Any]       ):
            return f'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				Node(len(a					)					)
         _a   , _a          =				min(a					), max(a					)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         _a          =				(root.minn + root.maxx) // 2
  
         _a          =				[]
         _a          =				[]
  
         for index, num in enumerate(a					):
                if num <= pivot:
                       left_arr.append(a					)
                else:
                       right_arr.append(a					)
                _a          =				len(a					)
         _a          =				build_tree(a					)
         _a          =				build_tree(a					)
         return root
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         _a          =				(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right,		a,		index - node.map_left[index]					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if start > end:
                return 0
         _a          =				rank_till_index(a,		a,		a					)
         _a          =				rank_till_index(a,		a,		start - 1					)
         return rank_till_end - rank_before_start
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         _a          =				node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
         else:
                return quantile(
                    node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)
  
  
  
  def 						__a      (  a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         _a          =				range_counting(
             node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
         _a          =				range_counting(
             node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  