

import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir







@dataclass
class  UpperCamelCase_      :


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			str







class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					Any						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    get_tests_dir("fixtures/test_sentencepiece.model"						)
      SCREAMING_SNAKE_CASE__         =    get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"						)

      SCREAMING_SNAKE_CASE__         =    FakeOriginalTokenizer(vocab_file=A_						)

      with warnings.catch_warnings(record=A_						) as w:
          SCREAMING_SNAKE_CASE__         =    SpmConverter(A_						)
      self.assertEqual(len(A_						)  ,	0						)

      SCREAMING_SNAKE_CASE__         =    FakeOriginalTokenizer(vocab_file=A_						)

      with warnings.catch_warnings(record=A_						) as w:
          SCREAMING_SNAKE_CASE__         =    SpmConverter(A_						)
      self.assertEqual(len(A_						)  ,	1						)

      self.assertIn(
          "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
          " which is not implemented in the fast tokenizers."  ,	str(w[0].message						)  ,	)





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


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

A__										=      get_logger(__name__)


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





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    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
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


