


'''simple docstring'''






from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a_    :			int      				=			logging.get_logger(__name__)




@add_end_docstrings(__UpperCAmelCase				)
class        a   (   __UpperCAmelCase				):
 def __init__(       self					, *__magic_name__					, **__magic_name__  )    ->							int:
      super().__init__(*__magic_name__					, **__magic_name__  )
      self.check_model_type(__magic_name__  )
 def 							__UpperCAmelCase						(       self					, __magic_name__=None					, __magic_name__=None					, __magic_name__=None					, **__magic_name__  )    ->							int:
      _a						=							{}, {}
      if padding is not None:
           _a						=							padding
      if truncation is not None:
           _a						=							truncation
      if top_k is not None:
           _a						=							top_k
      return preprocess_params, {}, postprocess_params
 def __call__(       self					, __magic_name__					, __magic_name__ = None					, **__magic_name__  )    ->							str:

      if isinstance(__magic_name__					, (Image.Image, str)  ) and isinstance(__magic_name__					, __magic_name__  ):
           _a						=							{'image': image, 'question': question}
      else:
           _a						=							image
      _a						=							super().__call__(__magic_name__					, **__magic_name__  )
      return results
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__=False					, __magic_name__=False  )    ->							Union[str, Any]:
      _a						=							load_image(inputs['image']  )
      _a						=							self.tokenizer(
          inputs['question']					, return_tensors=self.framework					, padding=__magic_name__					, truncation=__magic_name__  )
      _a						=							self.image_processor(images=__magic_name__					, return_tensors=self.framework  )
      model_inputs.update(__magic_name__  )
      return model_inputs
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							Dict:
      _a						=							self.model(**__magic_name__  )
      return model_outputs




 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__=5  )    ->							int:

      if top_k > self.model.config.num_labels:
           _a						=							self.model.config.num_labels

      if self.framework == "pt":
           _a						=							model_outputs.logits.sigmoid()[0]
           _a						=							probs.topk(__magic_name__  )
      else:
           raise ValueError(f'Unsupported framework: {self.framework}'  )

      _a						=							scores.tolist()
      _a						=							ids.tolist()
      return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__magic_name__					, __magic_name__  )]








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


