


"""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

UpperCAmelCase__  							=logging.get_logger(__name__)





@add_end_docstrings(__UpperCAmelCase    )
class    lowerCamelCase__						(		__UpperCAmelCase    ):

      '''simple docstring'''





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






       '''simple docstring'''


       super().__init__(**lowerCAmelCase_							)

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

       requires_backends(self      ,       """vision"""							)
       self.check_model_type(lowerCAmelCase_							)
      def __call__(		self	:	Dict      ,       A_	:	Union[str, "Image.Image", List[Dict[str, Any]]]      ,       A_	:	Union[str, List[str]] = None      ,       **A_	:	Dict      ,       ):






       '''simple docstring'''


       if "text_queries" in kwargs:
        __lowercase									=	kwargs.pop("""text_queries"""							)

       if isinstance(lowerCAmelCase_      ,       (str, Image.Image)							):
        __lowercase									=	{"""image""": image, """candidate_labels""": candidate_labels}
       else:
        __lowercase									=	image
       __lowercase									=	super().__call__(lowerCAmelCase_      ,       **lowerCAmelCase_							)
       return results
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       **A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	{}
       if "threshold" in kwargs:
        __lowercase									=	kwargs["""threshold"""]
       if "top_k" in kwargs:
        __lowercase									=	kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	load_image(inputs["""image"""]							)
       __lowercase									=	inputs["""candidate_labels"""]
       if isinstance(lowerCAmelCase_      ,       lowerCAmelCase_							):
        __lowercase									=	candidate_labels.split(""","""							)

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






       '''simple docstring'''


       __lowercase									=	model_inputs.pop("""target_size"""							)
       __lowercase									=	model_inputs.pop("""candidate_label"""							)
       __lowercase									=	model_inputs.pop("""is_last"""							)

       __lowercase									=	self.model(**lowerCAmelCase_							)

       __lowercase									=	{"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Optional[int]      ,       A_	:	Tuple=0.1      ,       A_	:	List[str]=None							):






       '''simple docstring'''


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

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

         __lowercase									=	{"""score""": score, """label""": label, """box""": box}
         results.append(lowerCAmelCase_							)

       __lowercase									=	sorted(lowerCAmelCase_      ,       key=lambda A_							: x["score"]      ,       reverse=lowerCAmelCase_							)
       if top_k:
        __lowercase									=	results[:top_k]

       return results






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	"torch.Tensor"							):






       '''simple docstring'''


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




"""simple docstring"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
