



'''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

lowercase							       =			logging.get_logger(__name__)







@add_end_docstrings(__a)
class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 def __init__(   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      a_		,						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							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      if isinstance(lowerCAmelCase_							,  (Image.Image, str)) and isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		={"image": image, "question": question}
      else:
           a_       		=image
      a_       		=super().__call__(lowerCAmelCase_							,  **lowerCAmelCase_)
      return results




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False) ->      Optional[int]:



      """simple docstring"""







      a_       		=load_image(inputs["image"])
      a_       		=self.tokenizer(
          inputs["question"]							,  return_tensors=self.framework							,  padding=lowerCAmelCase_							,  truncation=lowerCAmelCase_)
      a_       		=self.image_processor(images=lowerCAmelCase_							,  return_tensors=self.framework)
      model_inputs.update(lowerCAmelCase_)
      return model_inputs




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=self.model(**lowerCAmelCase_)
      return model_outputs




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=5) ->      Any:



      """simple docstring"""







      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_		,						a_       		=probs.topk(lowerCAmelCase_)
      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(lowerCAmelCase_							,  lowerCAmelCase_)]







'''simple docstring'''


from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip


