

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:
				_a:			List[Any]       							=	None


_a:			Any       							=	logging.get_logger(__name__)

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

_a:			Optional[int]       							=	{
    "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"
        ),
    },
}

_a:			List[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   __UpperCamelCase			(    lowerCAmelCase__  ):
	SCREAMING_SNAKE_CASE__			 =   VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__			 =   PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__			 =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__			 =   ['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE__			 =   XLMRobertaTokenizer




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


		'''simple docstring'''







		UpperCAmelCase_				 =	AddedToken(_A						,		lstrip=_A						,		rstrip=_A							) if isinstance(_A						,		_A							) else mask_token

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

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




	def 					__A    (						self		:  Tuple						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Any = None							):


		'''simple docstring'''







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




	def 					__A    (						self		:  int						,		lowerCAmelCase		:  Optional[int]						,		lowerCAmelCase		:  Any = None							):


		'''simple docstring'''







		UpperCAmelCase_				 =	[self.sep_token_id]
		UpperCAmelCase_				 =	[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		:  Union[str, Any]						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  Optional[Any] = 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(_A							):
			logger.error(F"Vocabulary path ({save_directory}) should be a directory."							)
			return
		UpperCAmelCase_				 =	os.path.join(
		    _A						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

		return (out_vocab_file,)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


    return "".join(chr(ord(_SCREAMING_SNAKE_CASE				) + 32				) if 'A' <= char <= 'Z' else char for char in word				)


if __name__ == "__main__":
       from doctest import testmod

       testmod()


