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					)



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


