



'''simple docstring'''



import unittest
from timeit import timeit



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    int       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	first_num if first_num >= second_num else second_num
	__lowerCAmelCase       				=	max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    int       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    int       ):






	'''simple docstring'''




	return first_num // greatest_common_divisor(UpperCAmelCase_					,		UpperCAmelCase_       ) * second_num



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	(
	    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
	)
	print(
	    "least_common_multiple_slow():"					,		timeit("least_common_multiple_slow(1000, 999)"					,		setup=UpperCAmelCase_       )					,		)
	print(
	    "least_common_multiple_fast():"					,		timeit("least_common_multiple_fast(1000, 999)"					,		setup=UpperCAmelCase_       )					,		)






class        UpperCAmelCase__						(		unittest.TestCase			):
					a   :  List[Any]   =				(
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					a   :  Any   =				(2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)



					def 							UpperCAmelCase_			(     self  )  ->      Dict:


						for i, (first_num, second_num) in enumerate(self.test_inputs  ):
							__lowerCAmelCase       				=	least_common_multiple_slow(UpperCamelCase      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	least_common_multiple_fast(UpperCamelCase      ,					UpperCamelCase  )
							with self.subTest(i=UpperCamelCase  ):
								self.assertEqual(UpperCamelCase      ,					self.expected_results[i]  )
								self.assertEqual(UpperCamelCase      ,					self.expected_results[i]  )


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


"""simple docstring"""



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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    None

SCREAMING_SNAKE_CASE_       :						int					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Any					=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_       :						Optional[int]					=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       VOCAB_FILES_NAMES
    UpperCAmelCase    			=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase    			=       PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase    			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase    			=       DebertaVaTokenizer

    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			List[str]="[CLS]"     ,    UpperCamelCase:			Optional[Any]="[SEP]"     ,    UpperCamelCase:			int="[UNK]"     ,    UpperCamelCase:			List[Any]="[SEP]"     ,    UpperCamelCase:			Optional[Any]="[PAD]"     ,    UpperCamelCase:			Any="[CLS]"     ,    UpperCamelCase:			List[Any]="[MASK]"     ,    **UpperCamelCase:			List[str]     ,    ):






         """simple docstring"""


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

         A__				      =	do_lower_case
         A__				      =	split_by_punct
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


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

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Tuple=False							):






         """simple docstring"""


         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase     ,    token_ids_a=UpperCamelCase     ,    already_has_special_tokens=UpperCamelCase							)

         if token_ids_a is not None:
              return [1] + ([0] * len(UpperCamelCase							)) + [1] + ([0] * len(UpperCamelCase							)) + [1]
         return [1] + ([0] * len(UpperCamelCase							)) + [1]

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Dict=None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[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							) * [0] + len(token_ids_a + sep							) * [1]





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Optional[str] = None							):






         """simple docstring"""


         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(UpperCamelCase							):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
              return
         A__				      =	os.path.join(
             UpperCamelCase     ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

         return (out_vocab_file,)






