





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

lowercase      :       int									=  logging.get_logger(__name__)





@add_end_docstrings(__UpperCAmelCase	)
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  *lowercase					,  **lowercase)			->						int:







          '''simple docstring'''

          super().__init__(*lowercase					,  **lowercase)
          self.check_model_type(lowercase)

   def 							__lowercase		(					self					,  lowercase=None					,  lowercase=None					,  lowercase=None					,  **lowercase)			->						int:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				{}, {}
          if padding is not None:
                 a__						:	Tuple =				padding
          if truncation is not None:
                 a__						:	List[str] =				truncation
          if top_k is not None:
                 a__						:	Tuple =				top_k
          return preprocess_params, {}, postprocess_params

   def __call__(					self					,  lowercase					,  lowercase = None					,  **lowercase)			->						str:







          '''simple docstring'''

          if isinstance(lowercase					,  (Image.Image, str)) and isinstance(lowercase					,  lowercase):
                 a__						:	Union[str, Any] =				{'image': image, 'question': question}
          else:
                 a__						:	Optional[Any] =				image
          a__						:	Dict =				super().__call__(lowercase					,  **lowercase)
          return results

   def 							__lowercase		(					self					,  lowercase					,  lowercase=False					,  lowercase=False)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				load_image(inputs['image'])
          a__						:	Tuple =				self.tokenizer(
              inputs['question']					,  return_tensors=self.framework					,  padding=lowercase					,  truncation=lowercase)
          a__						:	Dict =				self.image_processor(images=lowercase					,  return_tensors=self.framework)
          model_inputs.update(lowercase)
          return model_inputs

   def 							__lowercase		(					self					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	int =				self.model(**lowercase)
          return model_outputs

   def 							__lowercase		(					self					,  lowercase					,  lowercase=5)			->						int:







          '''simple docstring'''

          if top_k > self.model.config.num_labels:
                 a__						:	Optional[int] =				self.model.config.num_labels

          if self.framework == "pt":
                 a__						:	Dict =				model_outputs.logits.sigmoid()[0]
                 a__ ,							a__						:	str =				probs.topk(lowercase)
          else:
                 raise ValueError(F'Unsupported framework: {self.framework}')

          a__						:	str =				scores.tolist()
          a__						:	Optional[int] =				ids.tolist()
          return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase					,  lowercase)]









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


