'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      _lowerCamelCase :				List[str]														= None

_lowerCamelCase :				Tuple														= logging.get_logger(__name__)

_lowerCamelCase :				List[Any]														= {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase :				List[str]														= {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

_lowerCamelCase :				Optional[Any]														= {
    """microsoft/deberta-v2-xlarge""": 5_12,
    """microsoft/deberta-v2-xxlarge""": 5_12,
    """microsoft/deberta-v2-xlarge-mnli""": 5_12,
    """microsoft/deberta-v2-xxlarge-mnli""": 5_12,
}

_lowerCamelCase :				Optional[int]														= {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						VOCAB_FILES_NAMES
   __UpperCAmelCase           =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase           =						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase           =						DebertaVaTokenizer



   def __init__(    self					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[UNK]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[PAD]"					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[MASK]"					,      **lowerCAmelCase__					,      )				->     None:
        """simple docstring"""





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

        _UpperCamelCase  :int       =do_lower_case
        _UpperCamelCase  :Dict       =split_by_punct
        _UpperCamelCase  :Any       =vocab_file
        _UpperCamelCase  :Any       =False if not self.vocab_file else True



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     int:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False     )				->     Optional[Any]:
        """simple docstring"""





        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCAmelCase__					,      token_ids_a=lowerCAmelCase__					,      already_has_special_tokens=lowerCAmelCase__     )

        if token_ids_a is not None:
             return [1] + ([0] * len(lowerCAmelCase__     )) + [1] + ([0] * len(lowerCAmelCase__     )) + [1]
        return [1] + ([0] * len(lowerCAmelCase__     )) + [1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =[self.sep_token_id]
        _UpperCamelCase  :List[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     ) * [0] + len(token_ids_a + sep     ) * [1]




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = 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(lowerCAmelCase__     ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
             return
        _UpperCamelCase  :str       =os.path.join(
            lowerCAmelCase__					,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

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

        return (out_vocab_file,)
'''simple docstring'''

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

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )