



'''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


lowerCamelCase							:str       = transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if isinstance(a_						,				torch.Tensor   ):
						return image
			elif isinstance(a_						,				PIL.Image.Image   ):
						A_     :						List[Any]     =					[image]

			A_     :						Optional[Any]     =					[trans(img.convert("""RGB"""   )   ) for img in image]
			A_     :						Dict     =					torch.stack(a_   )
			return image







class      _lowerCAmelCase							(      a_			):


	def __init__(self	,				lowercase	,				lowercase							):
				super().__init__()

				# make sure scheduler can always be converted to DDIM
				A_     :						Optional[Any]     =					DDIMScheduler.from_config(scheduler.config							)

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


	def    _a     (self	,				lowercase							):

				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							):
				A_     :						Union[str, Any]     =					min(int(num_inference_steps * strength							)	,				lowercase_							)

				A_     :						List[Any]     =					max(num_inference_steps - init_timestep	,				0							)
				A_     :						Dict     =					self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase=None							):

				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_							)}'							)

				A_     :						str     =					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.'							)

				A_     :						List[Any]     =					init_latents.shape
				A_     :						Union[str, Any]     =					randn_tensor(lowercase_	,				generator=lowercase_	,				device=lowercase_	,				dtype=lowercase_							)

				# get latents
				print("""add noise to latents at timestep"""	,				lowercase_							)
				A_     :						List[Any]     =					self.scheduler.add_noise(lowercase_	,				lowercase_	,				lowercase_							)
				A_     :						Optional[int]     =					init_latents

				return latents


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

				# 2. Preprocess image
				A_     :						List[str]     =					preprocess(lowercase_							)

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

				# 4. Prepare latent variables
				A_     :						List[str]     =					self.prepare_latents(lowercase_	,				lowercase_	,				lowercase_	,				self.unet.dtype	,				self.device	,				lowercase_							)
				A_     :						Union[str, Any]     =					latents

				# 5. Denoising loop
				for t in self.progress_bar(lowercase_							):
							# 1. predict noise model_output
							A_     :						List[str]     =					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
							A_     :						Union[str, Any]     =					self.scheduler.step(
							    lowercase_	,				lowercase_	,				lowercase_	,				eta=lowercase_	,				use_clipped_model_output=lowercase_	,				generator=lowercase_	,				).prev_sample

				A_     :						Union[str, Any]     =					(image / 2 + 0.5).clamp(0	,				1							)
				A_     :						Any     =					image.cpu().permute(0	,				2	,				3	,				1							).numpy()
				if output_type == "pil":
							A_     :						List[Any]     =					self.numpy_to_pil(lowercase_							)

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

				return ImagePipelineOutput(images=lowercase_							)




'''simple docstring'''




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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					get_resize_output_image_size(lowercase	,				size=size["""shortest_edge"""]	,				default_to_square=lowercase							)
				return resize(lowercase	,				size=lowercase	,				resample=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				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."""							)

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

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

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

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowercase							) for image in images]

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

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

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

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(lowercase							) != len(lowercase							):
										raise ValueError(
										    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation