
'''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

UpperCAmelCase_							: Optional[int] =							logging.get_logger(__name__)

@add_end_docstrings(_a			)
class 			UpperCAmelCase__    (							_a			):



    def __init__(						self   :							Optional[int],*__A   :							Optional[int],**__A   :							List[Any]  ):
          super().__init__(*__UpperCamelCase,**__UpperCamelCase  )
          self.check_model_type(__UpperCamelCase  )



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							List[str]=None,__A   :							Dict=None,__A   :							int=None,**__A   :							List[Any]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = {}, {}
          if padding is not None:
                _lowerCamelCase					: Any		    = padding
          if truncation is not None:
                _lowerCamelCase					: Any		    = truncation
          if top_k is not None:
                _lowerCamelCase					: List[Any]		    = top_k
          return preprocess_params, {}, postprocess_params



    def __call__(						self   :							List[str],__A   :							Dict,__A   :							Union[str, Any] = None,**__A   :							Union[str, Any]  ):
          if isinstance(__UpperCamelCase,(Image.Image, str)  ) and isinstance(__UpperCamelCase,__UpperCamelCase  ):
                _lowerCamelCase					: str		    = {"image": image, "question": question}
          else:
                _lowerCamelCase					: Any		    = image
          _lowerCamelCase					: Any		    = super().__call__(__UpperCamelCase,**__UpperCamelCase  )
          return results



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							Any,__A   :							str=False,__A   :							Dict=False  ):
          _lowerCamelCase					: Dict		    = load_image(inputs["image"]  )
          _lowerCamelCase					: List[Any]		    = self.tokenizer(
              inputs["question"],return_tensors=self.framework,padding=__UpperCamelCase,truncation=__UpperCamelCase  )
          _lowerCamelCase					: Any		    = self.image_processor(images=__UpperCamelCase,return_tensors=self.framework  )
          model_inputs.update(__UpperCamelCase  )
          return model_inputs



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							int  ):
          _lowerCamelCase					: int		    = self.model(**__UpperCamelCase  )
          return model_outputs



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							str,__A   :							Tuple=5  ):
          if top_k > self.model.config.num_labels:
                _lowerCamelCase					: Tuple		    = self.model.config.num_labels

          if self.framework == "pt":
                _lowerCamelCase					: Union[str, Any]		    = model_outputs.logits.sigmoid()[0]
                _lowerCamelCase    ,		_lowerCamelCase					: Any		    = probs.topk(__UpperCamelCase  )
          else:
                raise ValueError(f'Unsupported framework: {self.framework}'  )

          _lowerCamelCase					: Union[str, Any]		    = scores.tolist()
          _lowerCamelCase					: List[Any]		    = ids.tolist()
          return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCamelCase,__UpperCamelCase  )]

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
		from .camera import create_pan_cameras
		from .pipeline_shap_e import ShapEPipeline
		from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
		from .renderer import (
		    BoundingBoxVolume,
		    ImportanceRaySampler,
		    MLPNeRFModelOutput,
		    MLPNeRSTFModel,
		    ShapEParamsProjModel,
		    ShapERenderer,
		    StratifiedRaySampler,
		    VoidNeRFModel,
		)