'''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


__lowercase        =    transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 							snake_case__				(     _A:   Optional[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


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

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






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					super().__init__()

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

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



			def 				a_			(  self			,       __lowerCAmelCase):




					"""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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    min(int(num_inference_steps * strength)			,       __lowerCAmelCase)

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

					return timesteps, num_inference_steps - t_start



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase=None):




					"""simple docstring"""


					if not isinstance(__lowerCAmelCase			,       (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(__lowerCAmelCase)}")

					lowerCAmelCase     =    image.to(device=__lowerCAmelCase			,       dtype=__lowerCAmelCase)

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

					lowerCAmelCase     =    init_latents.shape
					lowerCAmelCase     =    randn_tensor(__lowerCAmelCase			,       generator=__lowerCAmelCase			,       device=__lowerCAmelCase			,       dtype=__lowerCAmelCase)

					# get latents
					print("""add noise to latents at timestep"""			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.scheduler.add_noise(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    init_latents

					return latents







			@torch.no_grad()
			def __call__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0.8			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0.0			,       __lowerCAmelCase = 50			,       __lowerCAmelCase = None			,       __lowerCAmelCase = "pil"			,       __lowerCAmelCase = True			,       ):




					"""simple docstring"""


					self.check_inputs(__lowerCAmelCase)

					# 2. Preprocess image
					lowerCAmelCase     =    preprocess(__lowerCAmelCase)

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

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

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

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

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

					return ImagePipelineOutput(images=__lowerCAmelCase)





'''simple docstring'''




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
					return resize(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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 or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

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

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

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

					if do_color_quantize:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       ChannelDimension.LAST) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							lowerCAmelCase     =    np.array(__lowerCAmelCase)
							lowerCAmelCase     =    color_quantize(__lowerCAmelCase			,       __lowerCAmelCase).reshape(images.shape[:-1])

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							lowerCAmelCase     =    list(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




