

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:
	lowerCAmelCase__						=	None


lowerCAmelCase__						=	logging.get_logger(__name__)

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

lowerCAmelCase__						=	{
    """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"""
        ),
    },
}

lowerCAmelCase__						=	{
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}

class    a__  (   snake_case     ):
			"""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 , lowercase=None , lowercase=None , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , **lowercase , )					->  List[str]:
							'''simple docstring'''






							A__            =     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 , )

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


			def        UpperCamelCase						(      self , lowercase , lowercase = None    )					->  List[int]:
							'''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 + sep + token_ids_a + sep


			def        UpperCamelCase						(      self , lowercase , lowercase = None    )					->  List[int]:
							'''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 + sep + token_ids_a + sep    ) * [0]


			def        UpperCamelCase						(      self , lowercase , lowercase = 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
							A__            =     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,)




from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowerCAmelCase__						=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowerCAmelCase__						=	get_logger(__name__)


lowerCAmelCase__						=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict )     -> Optional[TaskTemplate]:




				'''simple docstring'''





				A__            =     task_template_dict.get("task" )
				if task_name is None:
								logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(SCREAMING_SNAKE_CASE_ )}' )
								return None
				A__            =     NAMEaTEMPLATE.get(SCREAMING_SNAKE_CASE_ )
				return template.from_dict(SCREAMING_SNAKE_CASE_ )

