


'''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    :str				       =     logging.get_logger(__name__)



@add_end_docstrings(_a )
class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	Optional[Any]							,				**snake_case  :	List[str]    )		->							Optional[Any]:
          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  :	int							,				snake_case  :	Union[str, "Image.Image", List[Dict[str, Any]]]							,				snake_case  :	Union[str, List[str]] = None							,				**snake_case  :	Any							,				)		->							Any:
          if "text_queries" in kwargs:
               __UpperCAmelCase			:				Tuple													=    kwargs.pop('''text_queries'''    )

          if isinstance(snake_case							,				(str, Image.Image)    ):
               __UpperCAmelCase			:				List[str]													=    {'''image''': image, '''candidate_labels''': candidate_labels}
          else:
               __UpperCAmelCase			:				Union[str, Any]													=    image
          __UpperCAmelCase			:				Optional[int]													=    super().__call__(snake_case							,				**snake_case    )
          return results

     def 				lowerCamelCase__				(       self  :	str							,				**snake_case  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				Any													=    {}
          if "threshold" in kwargs:
               __UpperCAmelCase			:				List[Any]													=    kwargs['''threshold''']
          if "top_k" in kwargs:
               __UpperCAmelCase			:				Optional[int]													=    kwargs['''top_k''']
          return {}, {}, postprocess_params

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	int    )		->							Optional[int]:
          __UpperCAmelCase			:				int													=    load_image(inputs['''image''']    )
          __UpperCAmelCase			:				Optional[int]													=    inputs['''candidate_labels''']
          if isinstance(snake_case							,				snake_case    ):
               __UpperCAmelCase			:				Optional[Any]													=    candidate_labels.split(''','''    )

          __UpperCAmelCase			:				Optional[Any]													=    torch.tensor([[image.height, image.width]]							,				dtype=torch.intaa    )
          for i, candidate_label in enumerate(snake_case    ):
               __UpperCAmelCase			:				Optional[Any]													=    self.tokenizer(snake_case							,				return_tensors=self.framework    )
               __UpperCAmelCase			:				List[Any]													=    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 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Optional[Any]    )		->							List[str]:
          __UpperCAmelCase			:				str													=    model_inputs.pop('''target_size'''    )
          __UpperCAmelCase			:				Optional[int]													=    model_inputs.pop('''candidate_label'''    )
          __UpperCAmelCase			:				List[Any]													=    model_inputs.pop('''is_last'''    )

          __UpperCAmelCase			:				int													=    self.model(**snake_case    )

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

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[Any]=0.1							,				snake_case  :	List[Any]=None    )		->							Tuple:
          __UpperCAmelCase			:				Any													=    []
          for model_output in model_outputs:
               __UpperCAmelCase			:				Dict													=    model_output['''candidate_label''']
               __UpperCAmelCase			:				Dict													=    BaseModelOutput(snake_case    )
               __UpperCAmelCase			:				Any													=    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():
                    __UpperCAmelCase			:				Tuple													=    outputs['''scores'''][index].item()
                    __UpperCAmelCase			:				Optional[Any]													=    self._get_bounding_box(outputs['''boxes'''][index][0]    )

                    __UpperCAmelCase			:				Any													=    {'''score''': score, '''label''': label, '''box''': box}
                    results.append(snake_case    )

          __UpperCAmelCase			:				List[str]													=    sorted(snake_case							,				key=lambda snake_case    : x["score"]							,				reverse=snake_case    )
          if top_k:
               __UpperCAmelCase			:				Optional[int]													=    results[:top_k]

          return results






     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	"torch.Tensor"    )		->							Dict[str, int]:
          if self.framework != "pt":
               raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''    )
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    box.int().tolist()
          __UpperCAmelCase			:				Dict													=    {
              '''xmin''': xmin,
              '''ymin''': ymin,
              '''xmax''': xmax,
              '''ymax''': ymax,
          }
          return bbox



'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	int    )		->							List[Any]:
          if dst_width < 0 or dst_height < 0:
               raise ValueError('''Destination width/height should be > 0'''    )

          __UpperCAmelCase			:				str													=    img
          __UpperCAmelCase			:				List[Any]													=    img.shape[1]
          __UpperCAmelCase			:				Optional[Any]													=    img.shape[0]
          __UpperCAmelCase			:				Dict													=    dst_width
          __UpperCAmelCase			:				List[str]													=    dst_height

          __UpperCAmelCase			:				Union[str, Any]													=    self.src_w / self.dst_w
          __UpperCAmelCase			:				List[str]													=    self.src_h / self.dst_h

          __UpperCAmelCase			:				Optional[int]													=    (
              np.ones((self.dst_h, self.dst_w, 3)							,				np.uinta    ) * 255
          )

     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          for i in range(self.dst_h    ):
               for j in range(self.dst_w    ):
                    __UpperCAmelCase			:				Any													=    self.img[self.get_y(snake_case    )][self.get_x(snake_case    )]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	int    )		->							int:
          return int(self.ratio_x * x    )






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	int    )		->							int:
          return int(self.ratio_y * y    )


if __name__ == "__main__":
      __UpperCAmelCase       ,					__UpperCAmelCase    :int				       =     8_0_0, 6_0_0
      __UpperCAmelCase    :Dict				       =     imread("image_data/lena.jpg", 1)
      __UpperCAmelCase    :int				       =     NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()