



from __future__ import annotations

from random import choice





def 					__snake_case     (      _UpperCAmelCase    ):
    return choice(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    random_pivot(_UpperCAmelCase    )

    # partition based on pivot
    # linear time
    __a							=    [e for e in lst if e < pivot]
    __a							=    [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(_UpperCAmelCase    ) == k - 1:
        return pivot
    # pivot is in elements bigger than k
    elif len(_UpperCAmelCase    ) < k - 1:
        return kth_number(_UpperCAmelCase       ,			k - len(_UpperCAmelCase    ) - 1    )
    # pivot is in elements smaller than k
    else:
        return kth_number(_UpperCAmelCase       ,			_UpperCAmelCase    )


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

__snake_case						:Tuple						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



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


           '''simple docstring'''

           super().__init__(**__SCREAMING_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(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

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

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



       def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					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 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

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

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



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

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

           __a							=    self.model(**__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None):


           '''simple docstring'''

           __a							=    []
           for model_output in model_outputs:
               __a							=    model_output['''candidate_label''']
               __a							=    BaseModelOutput(__SCREAMING_SNAKE_CASE)
               __a							=    self.image_processor.post_process_object_detection(
                   outputs=__SCREAMING_SNAKE_CASE					,		threshold=__SCREAMING_SNAKE_CASE					,		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(__SCREAMING_SNAKE_CASE)

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

           return results





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					"torch.Tensor"):


           '''simple docstring'''

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





