





'''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:
					snake_case_  :							Optional[int]            =	None


snake_case_  :							List[str]            =	logging.get_logger(__name__)

snake_case_  :							Optional[int]            =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :							Union[str, 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'''
        ),
    },
}

snake_case_  :							int            =	{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}


class        __a   (lowerCAmelCase__  ):
							__a       : int          =							VOCAB_FILES_NAMES
							__a       : str          =							PRETRAINED_VOCAB_FILES_MAP
							__a       : Union[str, Any]          =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a       : int          =							['input_ids', 'attention_mask']
							__a       : Optional[int]          =							XLMRobertaTokenizer

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







													"""simple docstring"""
													# Mask token behave like a normal word, i.e. include the space before it
													UpperCAmelCase_      :   Optional[int]      =	AddedToken(lowercase__							,  lstrip=lowercase__							,  rstrip=lowercase__ ) if isinstance(lowercase__							,  lowercase__ ) else mask_token

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

													UpperCAmelCase_      :   Union[str, Any]      =	vocab_file
													UpperCAmelCase_      :   Any      =	False if not self.vocab_file else True

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  str = None )       ->							List[int]:







													"""simple docstring"""

													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase_      :   Tuple      =	[self.cls_token_id]
													UpperCAmelCase_      :   List[Any]      =	[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Dict = None )       ->							List[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	[self.sep_token_id]
													UpperCAmelCase_      :   str      =	[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      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  str = None )       ->							Tuple[str]:







													"""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(lowercase__ ):
																			logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
																			return
													UpperCAmelCase_      :   Dict      =	os.path.join(
													    lowercase__							,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

													return (out_vocab_file,)













def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		__A    =							[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		__A    =							1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(lowerCAmelCase__					,     n + 1	):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

