
"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
			import PIL

SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      List[List[ImageInput]]:






				"""simple docstring"""




				if isinstance(lowerCAmelCase ,				(list, tuple) ) and isinstance(videos[0] ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(lowerCAmelCase ,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(lowerCAmelCase ):
								return [[videos]]

				raise ValueError(F"Could not make batched video from {videos}" )







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
						__SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	["pixel_values"]




						def __init__(							self   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = True   ,       lowercase_ = 1 / 2_5_5   ,       lowercase_ = True   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					None:
										super().__init__(**lowercase_							)
										UpperCAmelCase									=					size if size is not None else {'shortest_edge': 2_5_6}
										UpperCAmelCase									=					get_size_dict(lowercase_   ,       default_to_square=lowercase_							)
										UpperCAmelCase									=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										UpperCAmelCase									=					get_size_dict(lowercase_   ,       param_name='crop_size'							)

										UpperCAmelCase									=					do_resize
										UpperCAmelCase									=					size
										UpperCAmelCase									=					do_center_crop
										UpperCAmelCase									=					crop_size
										UpperCAmelCase									=					resample
										UpperCAmelCase									=					do_rescale
										UpperCAmelCase									=					rescale_factor
										UpperCAmelCase									=					offset
										UpperCAmelCase									=					do_normalize
										UpperCAmelCase									=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase									=					image_std if image_std is not None else IMAGENET_STANDARD_STD




						def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					np.ndarray:
										UpperCAmelCase									=					get_size_dict(lowercase_   ,       default_to_square=lowercase_							)
										if "shortest_edge" in size:
														UpperCAmelCase									=					get_resize_output_image_size(lowercase_   ,       size['shortest_edge']   ,       default_to_square=lowercase_							)
										elif "height" in size and "width" in size:
														UpperCAmelCase									=					(size['height'], size['width'])
										else:
														raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
										return resize(lowercase_   ,       size=lowercase_   ,       resample=lowercase_   ,       data_format=lowercase_   ,       **lowercase_							)




						def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					np.ndarray:
										UpperCAmelCase									=					get_size_dict(lowercase_							)
										if "height" not in size or "width" not in size:
														raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"							)
										return center_crop(lowercase_   ,       size=(size['height'], size['width'])   ,       data_format=lowercase_   ,       **lowercase_							)




						def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = True   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					str:
										UpperCAmelCase									=					image.astype(np.floataa							)
										if offset:
														UpperCAmelCase									=					image - (scale / 2)
										return rescale(lowercase_   ,       scale=lowercase_   ,       data_format=lowercase_   ,       **lowercase_							)




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




						def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = ChannelDimension.FIRST   ,       )						->					np.ndarray:
										if do_resize and size is None or resample is None:
														raise ValueError('Size and resample 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.'							)

										if offset and not do_rescale:
														raise ValueError('For offset, do_rescale must also be set to True.'							)

										# All transformations expect numpy arrays.
										UpperCAmelCase									=					to_numpy_array(lowercase_							)

										if do_resize:
														UpperCAmelCase									=					self.resize(image=lowercase_   ,       size=lowercase_   ,       resample=lowercase_							)

										if do_center_crop:
														UpperCAmelCase									=					self.center_crop(lowercase_   ,       size=lowercase_							)

										if do_rescale:
														UpperCAmelCase									=					self.rescale(image=lowercase_   ,       scale=lowercase_   ,       offset=lowercase_							)

										if do_normalize:
														UpperCAmelCase									=					self.normalize(image=lowercase_   ,       mean=lowercase_   ,       std=lowercase_							)

										UpperCAmelCase									=					to_channel_dimension_format(lowercase_   ,       lowercase_							)
										return image




						def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = ChannelDimension.FIRST   ,       **lowercase_   ,       )						->					PIL.Image.Image:
										UpperCAmelCase									=					do_resize if do_resize is not None else self.do_resize
										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									=					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									=					offset if offset is not None else self.offset
										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									=					size if size is not None else self.size
										UpperCAmelCase									=					get_size_dict(lowercase_   ,       default_to_square=lowercase_							)
										UpperCAmelCase									=					crop_size if crop_size is not None else self.crop_size
										UpperCAmelCase									=					get_size_dict(lowercase_   ,       param_name='crop_size'							)

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

										UpperCAmelCase									=					make_batched(lowercase_							)

										UpperCAmelCase									=					[
										    [
										        self._preprocess_image(
										            image=lowercase_   ,       do_resize=lowercase_   ,       size=lowercase_   ,       resample=lowercase_   ,       do_center_crop=lowercase_   ,       crop_size=lowercase_   ,       do_rescale=lowercase_   ,       rescale_factor=lowercase_   ,       offset=lowercase_   ,       do_normalize=lowercase_   ,       image_mean=lowercase_   ,       image_std=lowercase_   ,       data_format=lowercase_   ,       )
										        for img in video
										    ]
										    for video in videos
										]

										UpperCAmelCase									=					{'pixel_values': videos}
										return BatchFeature(data=lowercase_   ,       tensor_type=lowercase_							)





"""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


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




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

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







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

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

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




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          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   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

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

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          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_							)}"							)

          UpperCAmelCase									=					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."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

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

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

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

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

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

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

          return ImagePipelineOutput(images=lowercase_							)



