





from __future__ import annotations

from random import choice


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							List[str]:
 '''simple docstring'''


 return choice(lowerCamelCase_	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  int	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			random_pivot(lowerCamelCase_	)

 # partition based on pivot
 # linear time
 SCREAMING_SNAKE_CASE								=			[e for e in lst if e < pivot]
 SCREAMING_SNAKE_CASE								=			[e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(lowerCamelCase_	) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(lowerCamelCase_	) < k - 1:
  return kth_number(lowerCamelCase_		,							k - len(lowerCamelCase_	) - 1	)
 # pivot is in elements smaller than k
 else:
  return kth_number(lowerCamelCase_		,							lowerCamelCase_	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




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

a_    :						Dict    		=      logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE   )
class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Any							,    **snake_case__ :      Optional[int]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )

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

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

    def __call__(			self :      List[Any]							,    snake_case__ :      Union[str, "Image.Image", List[Dict[str, Any]]]							,    snake_case__ :      Union[str, List[str]] = None							,    **snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






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

     if isinstance(snake_case__							,    (str, Image.Image)    ):
      SCREAMING_SNAKE_CASE								=			{'image': image, 'candidate_labels': candidate_labels}
     else:
      SCREAMING_SNAKE_CASE								=			image
     SCREAMING_SNAKE_CASE								=			super().__call__(snake_case__							,    **snake_case__    )
     return results

    def UpperCamelCase			(			self :      Union[str, Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			load_image(inputs['image']    )
     SCREAMING_SNAKE_CASE								=			inputs['candidate_labels']
     if isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			candidate_labels.split(','    )

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

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Dict    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			self.model(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
     return model_outputs

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      str=0.1							,    snake_case__ :      Union[str, Any]=None    ):

     """simple docstring"""






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

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

       SCREAMING_SNAKE_CASE								=			{'score': score, 'label': label, 'box': box}
       results.append(snake_case__    )

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

     return results




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      "torch.Tensor"    ):

     """simple docstring"""






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

