





'''simple docstring'''


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase :					str 							=transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def 							UpperCAmelCase_  (						__lowerCamelCase : Dict			):
   if isinstance(__lowerCamelCase     ,torch.Tensor			):
      return image
   elif isinstance(__lowerCamelCase     ,PIL.Image.Image			):
      lowercase_      :Any         =			[image]

   lowercase_      :Any         =			[trans(img.convert("RGB"			)			) for img in image]
   lowercase_      :Optional[int]         =			torch.stack(__lowerCamelCase			)
   return image


class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: Dict     ,				lowercase		: Any     ,				lowercase		: int						):





        """simple docstring"""




        super().__init__()

        # make sure scheduler can always be converted to DDIM
        lowercase_      :Optional[Any]         =			DDIMScheduler.from_config(scheduler.config						)

        self.register_modules(unet=lowercase     ,				scheduler=lowercase						)





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: List[str]						):





        """simple docstring"""




        if strength < 0 or strength > 1:
           raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'						)





     def 							lowercase__				(			self		: Dict     ,				lowercase		: Dict     ,				lowercase		: Tuple     ,				lowercase		: Tuple						):





        """simple docstring"""




        lowercase_      :Dict         =			min(int(num_inference_steps * strength						)     ,				lowercase						)

        lowercase_      :Optional[Any]         =			max(num_inference_steps - init_timestep     ,				0						)
        lowercase_      :int         =			self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start





     def 							lowercase__				(			self		: Dict     ,				lowercase		: Any     ,				lowercase		: List[Any]     ,				lowercase		: List[str]     ,				lowercase		: Any     ,				lowercase		: Union[str, Any]     ,				lowercase		: Union[str, Any]=None						):





        """simple docstring"""




        if not isinstance(lowercase     ,				(torch.Tensor, PIL.Image.Image, list)						):
           raise ValueError(
               F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase						)}'						)

        lowercase_      :Dict         =			image.to(device=lowercase     ,				dtype=lowercase						)

        if isinstance(lowercase     ,				lowercase						) and len(lowercase						) != batch_size:
           raise ValueError(
               F'You have passed a list of generators of length {len(lowercase						)}, but requested an effective batch'
               F' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

        lowercase_      :Union[str, Any]         =			init_latents.shape
        lowercase_      :Union[str, Any]         =			randn_tensor(lowercase     ,				generator=lowercase     ,				device=lowercase     ,				dtype=lowercase						)

        # get latents
        print("add noise to latents at timestep"     ,				lowercase						)
        lowercase_      :Dict         =			self.scheduler.add_noise(lowercase     ,				lowercase     ,				lowercase						)
        lowercase_      :int         =			init_latents

        return latents







     @torch.no_grad()
     def __call__(			self		: Optional[int]     ,				lowercase		: Union[torch.FloatTensor, PIL.Image.Image] = None     ,				lowercase		: float = 0.8     ,				lowercase		: int = 1     ,				lowercase		: Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,				lowercase		: float = 0.0     ,				lowercase		: int = 50     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "pil"     ,				lowercase		: bool = True     ,				):





        """simple docstring"""




        self.check_inputs(lowercase						)

        # 2. Preprocess image
        lowercase_      :Tuple         =			preprocess(lowercase						)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase     ,				device=self.device						)
        lowercase_      ,							lowercase_      :Any         =			self.get_timesteps(lowercase     ,				lowercase     ,				self.device						)
        lowercase_      :str         =			timesteps[:1].repeat(lowercase						)

        # 4. Prepare latent variables
        lowercase_      :Optional[int]         =			self.prepare_latents(lowercase     ,				lowercase     ,				lowercase     ,				self.unet.dtype     ,				self.device     ,				lowercase						)
        lowercase_      :List[Any]         =			latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase						):
           # 1. predict noise model_output
           lowercase_      :Tuple         =			self.unet(lowercase     ,				lowercase						).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           lowercase_      :Union[str, Any]         =			self.scheduler.step(
               lowercase     ,				lowercase     ,				lowercase     ,				eta=lowercase     ,				use_clipped_model_output=lowercase     ,				generator=lowercase     ,				).prev_sample

        lowercase_      :Tuple         =			(image / 2 + 0.5).clamp(0     ,				1						)
        lowercase_      :Tuple         =			image.cpu().permute(0     ,				2     ,				3     ,				1						).numpy()
        if output_type == "pil":
           lowercase_      :Union[str, Any]         =			self.numpy_to_pil(lowercase						)

        if not return_dict:
           return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase						)









'''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

lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase							)
class 				a_       (    _lowerCAmelCase							):





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





        """simple docstring"""




        super().__init__(**lowercase						)

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

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





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





        """simple docstring"""




        if "text_queries" in kwargs:
           lowercase_      :List[Any]         =			kwargs.pop("text_queries"						)

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





     def 							lowercase__				(			self		: Optional[int]     ,				**lowercase		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Dict						):





        """simple docstring"""




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

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





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :Dict         =			model_inputs.pop("target_size"						)
        lowercase_      :str         =			model_inputs.pop("candidate_label"						)
        lowercase_      :List[Any]         =			model_inputs.pop("is_last"						)

        lowercase_      :Optional[Any]         =			self.model(**lowercase						)

        lowercase_      :str         =			{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
        return model_outputs





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: List[str]     ,				lowercase		: List[str]=0.1     ,				lowercase		: Optional[int]=None						):





        """simple docstring"""




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

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

              lowercase_      :int         =			{"score": score, "label": label, "box": box}
              results.append(lowercase						)

        lowercase_      :Dict         =			sorted(lowercase     ,				key=lambda lowercase						: x["score"]     ,				reverse=lowercase						)
        if top_k:
           lowercase_      :List[str]         =			results[:top_k]

        return results







     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: "torch.Tensor"						):





        """simple docstring"""




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


