


'''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


A__:   str    =	[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

A__:   Any    =	get_logger(__name__)


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


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     dict  )  ->      Optional[TaskTemplate]:
       _a   :			Any					 =task_template_dict.get("""task"""  )
       if task_name is None:
              logger.warning(F"Couldn't find template for task '{task_name}'. Available templates: {list(_UpperCAmelCase  )}"  )
              return None
       _a   :			Optional[int]					 =NAMEaTEMPLATE.get(_UpperCAmelCase  )
       return template.from_dict(_UpperCAmelCase  )






'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             if text is None and images is None:
                    raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

             if text is not None:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if images is not None:
                    _a   :			Any					 =self.image_processor(SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if text is not None and images is not None:
                    _a   :			Dict					 =image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE     )					,						tensor_type=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


