



'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__a          =		logging.get_logger(__name__)  # pylint: disable=invalid-name





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""     ,   _lowerCAmelCase     ,   )
      if isinstance(_lowerCAmelCase     ,   torch.Tensor		):
            return image
      elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image		):
            snake_case__  :    Optional[Any]    						=      [image]

      if isinstance(image[0]     ,   PIL.Image.Image		):
            snake_case__						,				snake_case__  :    Tuple    						=      image[0].size
            snake_case__						,				snake_case__  :    List[Any]    						=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            snake_case__  :    List[str]    						=      [np.array(i.resize((w, h)     ,   resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
            snake_case__  :    Optional[int]    						=      np.concatenate(_lowerCAmelCase     ,   axis=0		)
            snake_case__  :    Tuple    						=      np.array(_lowerCAmelCase		).astype(np.floataa		) / 255.0
            snake_case__  :    Tuple    						=      image.transpose(0     ,   3     ,   1     ,   2		)
            snake_case__  :    Any    						=      2.0 * image - 1.0
            snake_case__  :    Optional[int]    						=      torch.from_numpy(_lowerCAmelCase		)
      elif isinstance(image[0]     ,   torch.Tensor		):
            snake_case__  :    Any    						=      torch.cat(_lowerCAmelCase     ,   dim=0		)
      return image





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      if isinstance(_lowerCAmelCase     ,   torch.Tensor		):
            return mask
      elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image		):
            snake_case__  :    Any    						=      [mask]

      if isinstance(mask[0]     ,   PIL.Image.Image		):
            snake_case__						,				snake_case__  :    Optional[int]    						=      mask[0].size
            snake_case__						,				snake_case__  :    List[Any]    						=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
            snake_case__  :    int    						=      [np.array(m.convert("""L"""		).resize((w, h)     ,   resample=PIL_INTERPOLATION["""nearest"""]		)		)[None, :] for m in mask]
            snake_case__  :    Dict    						=      np.concatenate(_lowerCAmelCase     ,   axis=0		)
            snake_case__  :    int    						=      mask.astype(np.floataa		) / 255.0
            snake_case__  :    Optional[Any]    						=      0
            snake_case__  :    str    						=      1
            snake_case__  :    Dict    						=      torch.from_numpy(_lowerCAmelCase		)
      elif isinstance(mask[0]     ,   torch.Tensor		):
            snake_case__  :    Optional[Any]    						=      torch.cat(_lowerCAmelCase     ,   dim=0		)
      return mask







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		42
    lowercase					=		42




    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     List[Any]						):
          super().__init__()
          self.register_modules(unet=snake_case_ ,     scheduler=snake_case_						)




    @torch.no_grad()
    def __call__(    self					:     Optional[Any] ,     snake_case_					:     Union[torch.Tensor, PIL.Image.Image] ,     snake_case_					:     Union[torch.Tensor, PIL.Image.Image] ,     snake_case_					:     int = 250 ,     snake_case_					:     float = 0.0 ,     snake_case_					:     int = 10 ,     snake_case_					:     int = 10 ,     snake_case_					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None ,     snake_case_					:     Optional[str] = "pil" ,     snake_case_					:     bool = True ,     ):
          snake_case__  :    Optional[Any]    						=      image

          snake_case__  :    Dict    						=      _preprocess_image(snake_case_						)
          snake_case__  :    int    						=      original_image.to(device=self.device ,     dtype=self.unet.dtype						)
          snake_case__  :    int    						=      _preprocess_mask(snake_case_						)
          snake_case__  :    List[str]    						=      mask_image.to(device=self.device ,     dtype=self.unet.dtype						)

          snake_case__  :    Union[str, Any]    						=      original_image.shape[0]

          # sample gaussian noise to begin the loop
          if isinstance(snake_case_ ,     snake_case_						) and len(snake_case_						) != batch_size:
                raise ValueError(
                    f"You have passed a list of generators of length {len(snake_case_						)}, but requested an effective batch"
                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

          snake_case__  :    Any    						=      original_image.shape
          snake_case__  :    Any    						=      randn_tensor(snake_case_ ,     generator=snake_case_ ,     device=self.device ,     dtype=self.unet.dtype						)

          # set step values
          self.scheduler.set_timesteps(snake_case_ ,     snake_case_ ,     snake_case_ ,     self.device						)
          snake_case__  :    Dict    						=      eta

          snake_case__  :    Optional[int]    						=      self.scheduler.timesteps[0] + 1
          snake_case__  :    List[str]    						=      generator[0] if isinstance(snake_case_ ,     snake_case_						) else generator
          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
                if t < t_last:
                      # predict the noise residual
                      snake_case__  :    Optional[int]    						=      self.unet(snake_case_ ,     snake_case_						).sample
                      # compute previous image: x_t -> x_t-1
                      snake_case__  :    Optional[int]    						=      self.scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_						).prev_sample

                else:
                      # compute the reverse: x_t-1 -> x_t
                      snake_case__  :    str    						=      self.scheduler.undo_step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      t

          snake_case__  :    Optional[Any]    						=      (image / 2 + 0.5).clamp(0 ,     1						)
          snake_case__  :    Dict    						=      image.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()
          if output_type == "pil":
                snake_case__  :    str    						=      self.numpy_to_pil(snake_case_						)

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=snake_case_						)





'''simple docstring'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
