



'''simple docstring'''





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from transformers.modeling_outputs import BaseModelOutput

  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase_		       =  logging.get_logger(__name__)






@add_end_docstrings(__lowerCamelCase						)
class __A			(  __lowerCamelCase						):



 '''simple docstring'''







 def __init__(self ,   **A       )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )

      if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''       )

      requires_backends(self ,   '''vision'''       )
      self.check_model_type(A       )



 def __call__(self ,   A ,   A = None ,   **A ,   )    ->     Optional[int]:


      """simple docstring"""



      if "text_queries" in kwargs:
           _a      =  kwargs.pop('''text_queries'''       )

      if isinstance(A ,   (str, Image.Image)       ):
           _a      =  {"""image""": image, """candidate_labels""": candidate_labels}
      else:
           _a      =  image
      _a      =  super().__call__(A ,   **A       )
      return results



 def 					a__  (self ,   **A       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  {}
      if "threshold" in kwargs:
           _a      =  kwargs["""threshold"""]
      if "top_k" in kwargs:
           _a      =  kwargs["""top_k"""]
      return {}, {}, postprocess_params



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  load_image(inputs['''image''']       )
      _a      =  inputs["""candidate_labels"""]
      if isinstance(A ,   A       ):
           _a      =  candidate_labels.split(''','''       )

      _a      =  torch.tensor([[image.height, image.width]] ,   dtype=torch.intaa       )
      for i, candidate_label in enumerate(A       ):
           _a      =  self.tokenizer(A ,   return_tensors=self.framework       )
           _a      =  self.image_processor(A ,   return_tensors=self.framework       )
           yield {
               "is_last": i == len(A       ) - 1,
               "target_size": target_size,
               "candidate_label": candidate_label,
               **text_inputs,
               **image_features,
           }



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  model_inputs.pop('''target_size'''       )
      _a      =  model_inputs.pop('''candidate_label'''       )
      _a      =  model_inputs.pop('''is_last'''       )

      _a      =  self.model(**A       )

      _a      =  {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
      return model_outputs



 def 					a__  (self ,   A ,   A=0.1 ,   A=None       )    ->     List[str]:


      """simple docstring"""



      _a      =  []
      for model_output in model_outputs:
           _a      =  model_output["""candidate_label"""]
           _a      =  BaseModelOutput(A       )
           _a      =  self.image_processor.post_process_object_detection(
               outputs=A ,   threshold=A ,   target_sizes=model_output['''target_size''']       )[0]

           for index in outputs["scores"].nonzero():
                _a      =  outputs["""scores"""][index].item()
                _a      =  self._get_bounding_box(outputs['''boxes'''][index][0]       )

                _a      =  {"""score""": score, """label""": label, """box""": box}
                results.append(A       )

      _a      =  sorted(A ,   key=lambda A       : x["score"] ,   reverse=A       )
      if top_k:
           _a      =  results[:top_k]

      return results



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      if self.framework != "pt":
           raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''       )
      _a      =  box.int().tolist()
      _a      =  {
          """xmin""": xmin,
          """ymin""": ymin,
          """xmax""": xmax,
          """ymax""": ymax,
      }
      return bbox










import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






