



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


UpperCamelCase__:       Any							     =      logging.get_logger(__name__)

UpperCamelCase__:       Optional[Any]							     =      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__:       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__:       Any							     =      {
    "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     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	VOCAB_FILES_NAMES
	lowerCamelCase__      			=	PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__      			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__      			=	["""input_ids""", """attention_mask"""]
	lowerCamelCase__      			=	XLMRobertaTokenizer







	def __init__(						self      :					List[Any]		,      __snake_case      :					Dict=None		,      __snake_case      :					Any=None		,      __snake_case      :					Optional[int]="<s>"		,      __snake_case      :					List[Any]="</s>"		,      __snake_case      :					List[Any]="</s>"		,      __snake_case      :					Any="<s>"		,      __snake_case      :					Any="<unk>"		,      __snake_case      :					int="<pad>"		,      __snake_case      :					Optional[Any]="<mask>"		,      **__snake_case      :					Union[str, Any]		,      )      ->		Optional[int]:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase			:	Union[str, Any]										=		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		,      )

								UpperCAmelCase			:	List[Any]										=		vocab_file
								UpperCAmelCase			:	Dict										=		False if not self.vocab_file else True







	def 					A     (						self      :					List[Any]		,      __snake_case      :					List[int]		,      __snake_case      :					Optional[List[int]] = None		)      ->		List[int]:

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







	def 					A     (						self      :					int		,      __snake_case      :					List[int]		,      __snake_case      :					Optional[List[int]] = None		)      ->		List[int]:
								UpperCAmelCase			:	Tuple										=		[self.sep_token_id]
								UpperCAmelCase			:	Tuple										=		[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      :					Optional[Any]		,      __snake_case      :					str		,      __snake_case      :					Optional[str] = None		)      ->		Tuple[str]:
								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
								UpperCAmelCase			:	str										=		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,)










'''simple docstring'''






import re


def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       Dict:
							def docstring_decorator(_lowerCAmelCase  :						List[str]  ):
														UpperCAmelCase			:	str										=		fn.__doc__
														UpperCAmelCase			:	List[str]										=		func_doc.split('''\n'''  )
														UpperCAmelCase			:	Optional[Any]										=		0
														while i < len(_lowerCAmelCase  ) and re.search(R'''^\s*Examples?:\s*$'''       ,				lines[i]  ) is None:
																					i += 1
														if i < len(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Union[str, Any]										=		example_docstring
																					UpperCAmelCase			:	Tuple										=		'''\n'''.join(_lowerCAmelCase  )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""  )
														UpperCAmelCase			:	Any										=		func_doc
														return fn

							return docstring_decorator





