



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__			:   Union[str, Any]      			=    None


UpperCAmelCase__			:   int      			=    logging.get_logger(__name__)

UpperCAmelCase__			:   Dict      			=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase__			:   List[Any]      			=    {
    """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__			:   Tuple      			=    {
    """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_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Optional[Any]					       =	VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_       :			Any					       =	PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	["input_ids", "attention_mask"]
		SCREAMING_SNAKE_CASE_       :			Optional[Any]					       =	XLMRobertaTokenizer







		def __init__(    self  :   Optional[int]       ,		UpperCAmelCase_  :   Any=None       ,		UpperCAmelCase_  :   int=None       ,		UpperCAmelCase_  :   str="<s>"       ,		UpperCAmelCase_  :   List[Any]="</s>"       ,		UpperCAmelCase_  :   Optional[int]="</s>"       ,		UpperCAmelCase_  :   Optional[int]="<s>"       ,		UpperCAmelCase_  :   Optional[int]="<unk>"       ,		UpperCAmelCase_  :   Optional[int]="<pad>"       ,		UpperCAmelCase_  :   Optional[int]="<mask>"       ,		**UpperCAmelCase_  :   Tuple       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	AddedToken(UpperCAmelCase_       ,		lstrip=UpperCAmelCase_       ,		rstrip=UpperCAmelCase_					) if isinstance(UpperCAmelCase_       ,		UpperCAmelCase_					) else mask_token

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

						_UpperCAmelCase						:  Tuple     					=	vocab_file
						_UpperCAmelCase						:  List[Any]     					=	False if not self.vocab_file else True







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   List[Any]       ,		UpperCAmelCase_  :   List[Any] = None					)       ->		Tuple:


						'''simple docstring'''







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







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   List[str] = None					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	[self.sep_token_id]
						_UpperCAmelCase						:  Dict     					=	[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       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   List[str] = None					)       ->		Union[str, Any]:


						'''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
						_UpperCAmelCase						:  Union[str, Any]     					=	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,)





"""simple docstring"""







from math import factorial


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		return factorial(__UpperCAmelCase      ) / (factorial(__UpperCAmelCase      ) * factorial(n - r      ))


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Optional[Any]     =   0

		for i in range(1   ,				101      ):
				for j in range(1   ,				i + 1      ):
						if combinations(__UpperCAmelCase   ,				__UpperCAmelCase      ) > 1E6:
								total += 1
		return total


if __name__ == "__main__":
		print(solution())



