code: 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_:      Tuple        =			[
      'AutomaticSpeechRecognition',
      'AudioClassification',
      'ImageClassification',
      'LanguageModeling',
      'QuestionAnsweringExtractive',
      'Summarization',
      'TaskTemplate',
      'TextClassification',
  ]
  
  A_:      Tuple        =			get_logger(__name__)
  
  
  A_:      int        =			{
      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[int]:
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	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
  							_lowercase				=	NAMEaTEMPLATE.get(_A					)
  							return template.from_dict(_A					)
  
  
  
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
  				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					c_float
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return sizeof(self.value  )
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):
  
  
  
  							"""simple docstring"""
  
  
  							if dimension <= 0:
  														_lowercase				=	fixed_dimension
  
  							dimension -= num_token_to_add
  							return dimension
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  							try:
  														return AutoProcessor.from_pretrained(_A					)
  							except (ValueError, OSError, KeyError):
  														_lowercase				,       _lowercase				=	None, None
  														try:
  																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  														try:
  																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  
  														if tokenizer is not None and feature_extractor is not None:
  																					raise ValueError(
  																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
  														elif tokenizer is None and feature_extractor is None:
  																					return None
  														elif tokenizer is not None:
  																					return tokenizer
  														else:
  																					return feature_extractor
  
  
  