code: 
  
  
  """simple docstring"""
  from __future__ import annotations
  
  UpperCAmelCase   =	[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
      _lowerCamelCase  :				Tuple      =		-1
      _lowerCamelCase  :				Union[str, Any]      =		-1
      _lowerCamelCase  :				Optional[Any]      =		[-1] * length
      _lowerCamelCase  :				Union[str, Any]      =		None
      _lowerCamelCase  :				Optional[int]      =		None
  
  
  
  
  
     def __repr__(       self)     ->     int:
      return F'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Optional[int]      ):
  
  
  
  
   """simple docstring"""
  
  
  
  
  
  
  
   _lowerCamelCase  :				Dict      =		Node(len(__snake_case      )      )
   _lowerCamelCase      ,						_lowerCamelCase  :				Any      =		min(__snake_case      ), max(__snake_case      )
   # Leaf node case where the node contains only one unique value
   if root.minn == root.maxx:
    return root
   _lowerCamelCase  :				Optional[int]      =		(root.minn + root.maxx) // 2
  
   _lowerCamelCase  :				str      =		[]
   _lowerCamelCase  :				str      =		[]
  
   for index, num in enumerate(__snake_case      ):
    if num <= pivot:
     left_arr.append(__snake_case      )
    else:
     right_arr.append(__snake_case      )
    _lowerCamelCase  :				str      =		len(__snake_case      )
   _lowerCamelCase  :				int      =		build_tree(__snake_case      )
   _lowerCamelCase  :				Any      =		build_tree(__snake_case      )
   return root
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Any	,   __snake_case       :			Optional[Any]      ):
  
  
  
  
   """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
   _lowerCamelCase  :				Optional[int]      =		(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	,   __snake_case	,   node.map_left[index] - 1      )
   else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right	,   __snake_case	,   index - node.map_left[index]      )
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			Union[str, Any]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
   """simple docstring"""
  
  
  
  
  
  
  
   if start > end:
    return 0
   _lowerCamelCase  :				Optional[Any]      =		rank_till_index(__snake_case	,   __snake_case	,   __snake_case      )
   _lowerCamelCase  :				Tuple      =		rank_till_index(__snake_case	,   __snake_case	,   start - 1      )
   return rank_till_end - rank_before_start
  
  def 							_snake_case			(      __snake_case       :			Optional[Any]	,   __snake_case       :			Dict	,   __snake_case       :			int	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
   """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]
   _lowerCamelCase  :				List[str]      =		node.map_left[end] - (
       node.map_left[start - 1] if start else 0
   )
   if num_elements_in_left_tree > index:
    return quantile(
        node.left	,   __snake_case	,   (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 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			str	,   __snake_case       :			int	,   __snake_case       :			Any	,   __snake_case       :			Tuple      ):
  
  
  
  
   """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
   _lowerCamelCase  :				Optional[int]      =		range_counting(
       node.left	,   (node.map_left[start - 1] if start else 0)	,   node.map_left[end] - 1	,   __snake_case	,   __snake_case	,   )
   _lowerCamelCase  :				Union[str, Any]      =		range_counting(
       node.right	,   start - (node.map_left[start - 1] if start else 0)	,   end - node.map_left[end]	,   __snake_case	,   __snake_case	,   )
   return left + right
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
style_context: 
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  UpperCAmelCase   =	{
      """vocab_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
      },
      """tokenizer_file""": {
          """openai/clip-vit-base-patch32""": (
              """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  UpperCAmelCase   =	{
      """openai/clip-vit-base-patch32""": 77,
  }
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				VOCAB_FILES_NAMES
     __UpperCAmelCase       					=				PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase       					=				['''input_ids''', '''attention_mask''']
     __UpperCAmelCase       					=				CLIPTokenizer
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|startoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      tokenizer_file=SCREAMING_SNAKE_CASE			,      unk_token=SCREAMING_SNAKE_CASE			,      bos_token=SCREAMING_SNAKE_CASE			,      eos_token=SCREAMING_SNAKE_CASE			,      pad_token=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        if not isinstance(self.backend_tokenizer.pre_tokenizer			,      pre_tokenizers.Sequence):
           raise ValueError(
               """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
               """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
               """ to be compatible with this version.The easiest way to do so is"""
               """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
               """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
               """ transformers.""")
  
        self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		self.backend_tokenizer.decode
  
        def new_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		orig_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		text.replace(self.backend_tokenizer.model.end_of_word_suffix			,      """ """).strip()
           return text
  
        _lowerCamelCase  :				List[str]      =		new_decode_method
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				str      =		[self.bos_token_id]
        _lowerCamelCase  :				Union[str, Any]      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				Any      =		[self.bos_token_id]
        _lowerCamelCase  :				int      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Tuple[str]:
        _lowerCamelCase  :				Dict      =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,      name=SCREAMING_SNAKE_CASE)
        return tuple(SCREAMING_SNAKE_CASE)
  
  