


"""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

__snake_case   			=    logging.get_logger(__name__)







@add_end_docstrings(snake_case__	)
class _lowerCAmelCase				(      snake_case__	):







   def __init__(	self						,	**UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       super().__init__(**_A						)

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

       requires_backends(self						,	"vision"						)
       self.check_model_type(_A						)







   def __call__(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		Union[str, Any]:

       '''simple docstring'''




       if "text_queries" in kwargs:
           snake_case    :      Dict      					=    kwargs.pop("text_queries"						)

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







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




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

       snake_case    :      List[str]      					=    torch.tensor([[image.height, image.width]]						,	dtype=torch.intaa						)
       for i, candidate_label in enumerate(_A						):
           snake_case    :      Union[str, Any]      					=    self.tokenizer(_A						,	return_tensors=self.framework						)
           snake_case    :      Optional[int]      					=    self.image_processor(_A						,	return_tensors=self.framework						)
           yield {
               "is_last": i == len(_A						) - 1,
               "target_size": target_size,
               "candidate_label": candidate_label,
               **text_inputs,
               **image_features,
           }







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




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

       snake_case    :      str      					=    self.model(**_A						)

       snake_case    :      Dict      					=    {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=0.1						,	UpperCamelCase__=None						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    []
       for model_output in model_outputs:
           snake_case    :      Optional[Any]      					=    model_output["candidate_label"]
           snake_case    :      Dict      					=    BaseModelOutput(_A						)
           snake_case    :      int      					=    self.image_processor.post_process_object_detection(
               outputs=_A						,	threshold=_A						,	target_sizes=model_output["target_size"]						)[0]

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

               snake_case    :      Optional[int]      					=    {"score": score, "label": label, "box": box}
               results.append(_A						)

       snake_case    :      Optional[Any]      					=    sorted(_A						,	key=lambda UpperCamelCase__						: x["score"]						,	reverse=_A						)
       if top_k:
           snake_case    :      int      					=    results[:top_k]

       return results







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




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











import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)




