
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:
 UpperCAmelCase     				=			None


UpperCAmelCase     				=			logging.get_logger(__name__)

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

UpperCAmelCase     				=			{
    '''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'''
        ),
    },
}

UpperCAmelCase     				=			{
    '''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 	A_       (      __lowerCamelCase       ):




 '''simple docstring'''


 _UpperCamelCase					:					Tuple										=	VOCAB_FILES_NAMES
 _UpperCamelCase					:					int										=	PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase					:					List[str]										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase					:					str										=	["""input_ids""", """attention_mask"""]
 _UpperCamelCase					:					Optional[int]										=	XLMRobertaTokenizer





 def __init__(	self		,   snake_case=None		,   snake_case=None		,   snake_case="<s>"		,   snake_case="</s>"		,   snake_case="</s>"		,   snake_case="<s>"		,   snake_case="<unk>"		,   snake_case="<pad>"		,   snake_case="<mask>"		,   **snake_case		,   ):
  # Mask token behave like a normal word, i.e. include the space before it
  lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else mask_token

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

  lowercase					  =   vocab_file
  lowercase					  =   False if not self.vocab_file else True





 def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):

  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  lowercase					  =   [self.cls_token_id]
  lowercase					  =   [self.sep_token_id]
  return cls + token_ids_a + sep + sep + token_ids_a + sep





 def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):
  lowercase					  =   [self.sep_token_id]
  lowercase					  =   [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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = 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(snake_case	):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''	)
   return
  lowercase					  =   os.path.join(
      snake_case		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

  return (out_vocab_file,)



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

