



from __future__ import annotations

lowercase_  =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class  _snake_case	:




				def __init__(  self    :  Dict,    __lowercase    :  int  ):
							lowercase__         = -1
							lowercase__         = -1
							lowercase__         = [-1] * length
							lowercase__         = None
							lowercase__         = None




				def __repr__(  self    :  Tuple  ):
							return F'''Node(min_value={self.minn} max_value={self.maxx})'''



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_     ):
			lowercase__         = Node(len(SCREAMING_SNAKE_CASE_     )     )
			lowercase__         = min(SCREAMING_SNAKE_CASE_     ), max(SCREAMING_SNAKE_CASE_     )
			# Leaf node case where the node contains only one unique value
			if root.minn == root.maxx:
						return root
			lowercase__         = (root.minn + root.maxx) // 2

			lowercase__         = []
			lowercase__         = []

			for index, num in enumerate(SCREAMING_SNAKE_CASE_     ):
						if num <= pivot:
									left_arr.append(SCREAMING_SNAKE_CASE_     )
						else:
									right_arr.append(SCREAMING_SNAKE_CASE_     )
						lowercase__         = len(SCREAMING_SNAKE_CASE_     )
			lowercase__         = build_tree(SCREAMING_SNAKE_CASE_     )
			lowercase__         = build_tree(SCREAMING_SNAKE_CASE_     )
			return root



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			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
			lowercase__         = (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  ,     SCREAMING_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  ,     SCREAMING_SNAKE_CASE_  ,     index - node.map_left[index]     )



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			if start > end:
						return 0
			lowercase__         = rank_till_index(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     )
			lowercase__         = rank_till_index(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     start - 1     )
			return rank_till_end - rank_before_start



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			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]
			lowercase__         = node.map_left[end] - (
			    node.map_left[start - 1] if start else 0
			)
			if num_elements_in_left_tree > index:
						return quantile(
						    node.left  ,     SCREAMING_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 __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			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
			lowercase__         = range_counting(
			    node.left  ,     (node.map_left[start - 1] if start else 0)  ,     node.map_left[end] - 1  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     )
			lowercase__         = range_counting(
			    node.right  ,     start - (node.map_left[start - 1] if start else 0)  ,     end - node.map_left[end]  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     )
			return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()








'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   A      :     Optional[int]										= None


A      :     Optional[Any]										= logging.get_logger(__name__)

A      :     Dict										= {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A      :     Optional[int]										= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A      :     List[str]										= {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}


class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    VOCAB_FILES_NAMES
    a													=    PRETRAINED_VOCAB_FILES_MAP
    a													=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=    ["input_ids", "attention_mask"]
    a													=    XLMRobertaTokenizer


    def __init__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Dict=None      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							Any="<s>"      ,							SCREAMING_SNAKE_CASE			:							Tuple="</s>"      ,							SCREAMING_SNAKE_CASE			:							str="</s>"      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]="<s>"      ,							SCREAMING_SNAKE_CASE			:							Any="<unk>"      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]="<pad>"      ,							SCREAMING_SNAKE_CASE			:							List[Any]="<mask>"      ,							**SCREAMING_SNAKE_CASE			:							int      ,							):
           # Mask token behave like a normal word, i.e. include the space before it
           _A     :	str      =					AddedToken(SCREAMING_SNAKE_CASE      ,							lstrip=SCREAMING_SNAKE_CASE      ,							rstrip=SCREAMING_SNAKE_CASE) if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE) else mask_token

           super().__init__(
               SCREAMING_SNAKE_CASE      ,							tokenizer_file=SCREAMING_SNAKE_CASE      ,							bos_token=SCREAMING_SNAKE_CASE      ,							eos_token=SCREAMING_SNAKE_CASE      ,							sep_token=SCREAMING_SNAKE_CASE      ,							cls_token=SCREAMING_SNAKE_CASE      ,							unk_token=SCREAMING_SNAKE_CASE      ,							pad_token=SCREAMING_SNAKE_CASE      ,							mask_token=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE      ,							)

           _A     :	Dict      =					vocab_file
           _A     :	Optional[Any]      =					False if not self.vocab_file else True


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A     :	Optional[Any]      =					[self.cls_token_id]
           _A     :	Optional[int]      =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):
           _A     :	Any      =					[self.sep_token_id]
           _A     :	Optional[Any]      =					[self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = None):
           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.')

           if not os.path.isdir(SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
                  return
           _A     :	Any      =					os.path.join(
               SCREAMING_SNAKE_CASE      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE):
                  copyfile(self.vocab_file      ,							SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)






