


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


_A			=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     if isinstance(SCREAMING_SNAKE_CASE__      ,					torch.Tensor    ):
          return image
     elif isinstance(SCREAMING_SNAKE_CASE__      ,					PIL.Image.Image    ):
          __UpperCamelCase   							=[image]

     __UpperCamelCase   							=[trans(img.convert('RGB'    )    ) for img in image]
     __UpperCamelCase   							=torch.stack(SCREAMING_SNAKE_CASE__    )
     return image






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_   )  ->	Dict:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __UpperCamelCase   							=DDIMScheduler.from_config(scheduler.config   )

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






    def _a (      self ,       A_   )  ->	Optional[Any]:
         if strength < 0 or strength > 1:
              raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'   )






    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	str:
         # get the original timestep using init_timestep
         __UpperCamelCase   							=min(int(num_inference_steps * strength   ) ,       A_   )

         __UpperCamelCase   							=max(num_inference_steps - init_timestep ,       0   )
         __UpperCamelCase   							=self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       A_=None   )  ->	str:
         if not isinstance(A_ ,       (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(A_   )}'   )

         __UpperCamelCase   							=image.to(device=A_ ,       dtype=A_   )

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

         __UpperCamelCase   							=init_latents.shape
         __UpperCamelCase   							=randn_tensor(A_ ,       generator=A_ ,       device=A_ ,       dtype=A_   )

         # get latents
         print('add noise to latents at timestep' ,       A_   )
         __UpperCamelCase   							=self.scheduler.add_noise(A_ ,       A_ ,       A_   )
         __UpperCamelCase   							=init_latents

         return latents






    @torch.no_grad()
    def __call__(      self ,       A_ = None ,       A_ = 0.8 ,       A_ = 1 ,       A_ = None ,       A_ = 0.0 ,       A_ = 50 ,       A_ = None ,       A_ = "pil" ,       A_ = True ,       )  ->	Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(A_   )

         # 2. Preprocess image
         __UpperCamelCase   							=preprocess(A_   )

         # 3. set timesteps
         self.scheduler.set_timesteps(A_ ,       device=self.device   )
         __UpperCamelCase							,				__UpperCamelCase   							=self.get_timesteps(A_ ,       A_ ,       self.device   )
         __UpperCamelCase   							=timesteps[:1].repeat(A_   )

         # 4. Prepare latent variables
         __UpperCamelCase   							=self.prepare_latents(A_ ,       A_ ,       A_ ,       self.unet.dtype ,       self.device ,       A_   )
         __UpperCamelCase   							=latents

         # 5. Denoising loop
         for t in self.progress_bar(A_   ):
              # 1. predict noise model_output
              __UpperCamelCase   							=self.unet(A_ ,       A_   ).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
              __UpperCamelCase   							=self.scheduler.step(
                  A_ ,       A_ ,       A_ ,       eta=A_ ,       use_clipped_model_output=A_ ,       generator=A_ ,       ).prev_sample

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

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

         return ImagePipelineOutput(images=A_   )









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(A_ ,       size=size['shortest_edge'] ,       default_to_square=A_   )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase   							=make_list_of_images(A_   )

         if not valid_images(A_   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

         if do_resize and size is None:
              raise ValueError('Size must be specified if do_resize is True.'   )

         if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.'   )

         if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

         # All transformations expect numpy arrays.
         __UpperCamelCase   							=[to_numpy_array(A_   ) for image in images]

         if do_resize:
              __UpperCamelCase   							=[self.resize(image=A_ ,       size=A_ ,       resample=A_   ) for image in images]

         if do_center_crop:
              __UpperCamelCase   							=[self.center_crop(image=A_ ,       size=A_   ) for image in images]

         if do_rescale:
              __UpperCamelCase   							=[self.rescale(image=A_ ,       scale=A_   ) for image in images]

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=A_ ,       mean=A_ ,       std=A_   ) for image in images]

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )





