

'''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


__lowerCamelCase									=     logging.get_logger(__name__)  # pylint: disable=invalid-name
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Dict:
       warnings.warn(
           """The preprocess method is deprecated and will be removed in a future version. Please"""
           """ use VaeImageProcessor.preprocess instead""",							UpperCAmelCase__,							)
       if isinstance(UpperCAmelCase__,							torch.Tensor					):
              return image
       elif isinstance(UpperCAmelCase__,							PIL.Image.Image					):
              A_						       =   [image]

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

              A_						       =   [np.array(i.resize((w, h),							resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
              A_						       =   np.concatenate(UpperCAmelCase__,							axis=0					)
              A_						       =   np.array(UpperCAmelCase__					).astype(np.floataa					) / 255.0
              A_						       =   image.transpose(0,							3,							1,							2					)
              A_						       =   2.0 * image - 1.0
              A_						       =   torch.from_numpy(UpperCAmelCase__					)
       elif isinstance(image[0],							torch.Tensor					):
              A_						       =   torch.cat(UpperCAmelCase__,							dim=0					)
       return image
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[str]:
       if isinstance(UpperCAmelCase__,							torch.Tensor					):
              return mask
       elif isinstance(UpperCAmelCase__,							PIL.Image.Image					):
              A_						       =   [mask]

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







class        A__   (   _snake_case      ):
    lowercase						       =		42
    lowercase						       =		42



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





           '''simple docstring'''
           super().__init__()
           self.register_modules(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)







    @torch.no_grad()
    def __call__(       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = 250		,					UpperCamelCase__ = 0.0		,					UpperCamelCase__ = 10		,					UpperCamelCase__ = 10		,					UpperCamelCase__ = None		,					UpperCamelCase__ = "pil"		,					UpperCamelCase__ = True		,					)    ->   Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''
           A_						       =   image

           A_						       =   _preprocess_image(UpperCamelCase__							)
           A_						       =   original_image.to(device=self.device		,					dtype=self.unet.dtype							)
           A_						       =   _preprocess_mask(UpperCamelCase__							)
           A_						       =   mask_image.to(device=self.device		,					dtype=self.unet.dtype							)

           A_						       =   original_image.shape[0]

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

           A_						       =   original_image.shape
           A_						       =   randn_tensor(UpperCamelCase__		,					generator=UpperCamelCase__		,					device=self.device		,					dtype=self.unet.dtype							)

           # set step values
           self.scheduler.set_timesteps(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					self.device							)
           A_						       =   eta

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

                  else:
                         # compute the reverse: x_t-1 -> x_t
                         A_						       =   self.scheduler.undo_step(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)
                  A_						       =   t

           A_						       =   (image / 2 + 0.5).clamp(0		,					1							)
           A_						       =   image.cpu().permute(0		,					2		,					3		,					1							).numpy()
           if output_type == "pil":
                  A_						       =   self.numpy_to_pil(UpperCamelCase__							)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCamelCase__							)



'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}


class        A__   (   _snake_case    ,    _snake_case      ):
    lowercase						       =		"resnet"
    lowercase						       =		["basic", "bottleneck"]



    def __init__(       self		,					UpperCamelCase__=3		,					UpperCamelCase__=64		,					UpperCamelCase__=[256, 512, 1024, 2048]		,					UpperCamelCase__=[3, 4, 6, 3]		,					UpperCamelCase__="bottleneck"		,					UpperCamelCase__="relu"		,					UpperCamelCase__=False		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)
           if layer_type not in self.layer_types:
                  raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types							)}'''							)
           A_						       =   num_channels
           A_						       =   embedding_size
           A_						       =   hidden_sizes
           A_						       =   depths
           A_						       =   layer_type
           A_						       =   hidden_act
           A_						       =   downsample_in_first_stage
           A_						       =   ["""stem"""] + [f'''stage{idx}''' for idx in range(1		,					len(UpperCamelCase__							) + 1							)]
           A_       ,      A_						       =   get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__		,					out_indices=UpperCamelCase__		,					stage_names=self.stage_names							)


class        A__   (   _snake_case      ):
    lowercase						       =		version.parse("1.11"      )



    @property
    def        snake_case_      (       self							)    ->   Mapping[str, Mapping[int, str]]:





           '''simple docstring'''
           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ]							)







    @property
    def        snake_case_      (       self							)    ->   float:





           '''simple docstring'''
           return 1e-3
