'''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

__a										=					logging.get_logger(__name__)





@add_end_docstrings(_lowerCAmelCase   )
class     A__							(       _lowerCAmelCase   ):





   """simple docstring"""





   def __init__( self     :		Union[str, Any]						,     **lowerCAmelCase__     :		int			)       ->    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     :		Union[str, Any]						,     lowerCAmelCase__     :		Union[str, "Image.Image", List[Dict[str, Any]]]						,     lowerCAmelCase__     :		Union[str, List[str]] = None						,     **lowerCAmelCase__     :		Union[str, Any]						,     )       ->    Dict:







      """simple docstring"""




      if "text_queries" in kwargs:
         _UpperCAmelCase							:	Optional[Any]							=   kwargs.pop("text_queries"			)

      if isinstance(lowerCAmelCase__						,     (str, Image.Image)			):
         _UpperCAmelCase							:	Tuple							=   {"""image""": image, """candidate_labels""": candidate_labels}
      else:
         _UpperCAmelCase							:	Dict							=   image
      _UpperCAmelCase							:	Optional[Any]							=   super().__call__(lowerCAmelCase__						,     **lowerCAmelCase__			)
      return results




   def 			_lowerCAmelCase		( self     :		Any						,     **lowerCAmelCase__     :		Dict			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   {}
      if "threshold" in kwargs:
         _UpperCAmelCase							:	Optional[Any]							=   kwargs["""threshold"""]
      if "top_k" in kwargs:
         _UpperCAmelCase							:	Any							=   kwargs["""top_k"""]
      return {}, {}, postprocess_params




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		Any			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   load_image(inputs["image"]			)
      _UpperCAmelCase							:	int							=   inputs["""candidate_labels"""]
      if isinstance(lowerCAmelCase__						,     lowerCAmelCase__			):
         _UpperCAmelCase							:	Union[str, Any]							=   candidate_labels.split(","			)

      _UpperCAmelCase							:	List[str]							=   torch.tensor([[image.height, image.width]]						,     dtype=torch.intaa			)
      for i, candidate_label in enumerate(lowerCAmelCase__			):
         _UpperCAmelCase							:	Dict							=   self.tokenizer(lowerCAmelCase__						,     return_tensors=self.framework			)
         _UpperCAmelCase							:	Optional[int]							=   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 			_lowerCAmelCase		( self     :		Optional[Any]						,     lowerCAmelCase__     :		Optional[int]			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   model_inputs.pop("target_size"			)
      _UpperCAmelCase							:	Any							=   model_inputs.pop("candidate_label"			)
      _UpperCAmelCase							:	Optional[int]							=   model_inputs.pop("is_last"			)

      _UpperCAmelCase							:	Optional[Any]							=   self.model(**lowerCAmelCase__			)

      _UpperCAmelCase							:	Union[str, Any]							=   {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
      return model_outputs




   def 			_lowerCAmelCase		( self     :		List[Any]						,     lowerCAmelCase__     :		List[str]						,     lowerCAmelCase__     :		Optional[int]=0.1						,     lowerCAmelCase__     :		Optional[Any]=None			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   []
      for model_output in model_outputs:
         _UpperCAmelCase							:	List[str]							=   model_output["""candidate_label"""]
         _UpperCAmelCase							:	Any							=   BaseModelOutput(lowerCAmelCase__			)
         _UpperCAmelCase							:	int							=   self.image_processor.post_process_object_detection(
             outputs=lowerCAmelCase__						,     threshold=lowerCAmelCase__						,     target_sizes=model_output["target_size"]			)[0]

         for index in outputs["scores"].nonzero():
            _UpperCAmelCase							:	Union[str, Any]							=   outputs["""scores"""][index].item()
            _UpperCAmelCase							:	Optional[int]							=   self._get_bounding_box(outputs["boxes"][index][0]			)

            _UpperCAmelCase							:	Dict							=   {"""score""": score, """label""": label, """box""": box}
            results.append(lowerCAmelCase__			)

      _UpperCAmelCase							:	List[str]							=   sorted(lowerCAmelCase__						,     key=lambda lowerCAmelCase__			: x["score"]						,     reverse=lowerCAmelCase__			)
      if top_k:
         _UpperCAmelCase							:	Any							=   results[:top_k]

      return results




   def 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		"torch.Tensor"			)       ->    Dict[str, int]:







      """simple docstring"""




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




"""simple docstring"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
