
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowercase     							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase     							=						'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Optional[Any]=8 )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			lowerCamelCase__ :      Tuple								=		width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      List[Any]=512      ,			UpperCamelCase		:      Union[str, Any]=512 )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		pil_image.resize((w, h)      ,			resample=Image.BICUBIC      ,			reducing_gap=1 )
			lowerCamelCase__ :      Union[str, Any]								=		np.array(pil_image.convert("""RGB""" ) )
			lowerCamelCase__ :      List[str]								=		arr.astype(np.floataa ) / 127.5 - 1
			lowerCamelCase__ :      str								=		np.transpose(UpperCamelCase      ,			[2, 0, 1] )
			lowerCamelCase__ :      List[str]								=		torch.from_numpy(UpperCamelCase ).unsqueeze(0 )
			return image
class 				__A					( A_      ):





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

						self.register_modules(
						    unet=__magic_name__							,    scheduler=__magic_name__							,    movq=__magic_name__							,    )
						lowerCamelCase__ :      Any								=		2 ** (len(self.movq.config.block_out_channels				) - 1)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						# get the original timestep using init_timestep
						lowerCamelCase__ :      int								=		min(int(num_inference_steps * strength				)							,    __magic_name__				)

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

						return timesteps, num_inference_steps - t_start





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=None				):
						if not isinstance(__magic_name__							,    (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(__magic_name__				)}"				)

						lowerCamelCase__ :      List[str]								=		image.to(device=__magic_name__							,    dtype=__magic_name__				)

						lowerCamelCase__ :      List[Any]								=		batch_size * num_images_per_prompt

						if image.shape[1] == 4:
									lowerCamelCase__ :      Optional[Any]								=		image

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

									elif isinstance(__magic_name__							,    __magic_name__				):
												lowerCamelCase__ :      Dict								=		[
												    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(__magic_name__				)
												]
												lowerCamelCase__ :      List[str]								=		torch.cat(__magic_name__							,    dim=0				)
									else:
												lowerCamelCase__ :      List[Any]								=		self.movq.encode(__magic_name__				).latent_dist.sample(__magic_name__				)

									lowerCamelCase__ :      Optional[Any]								=		self.movq.config.scaling_factor * init_latents

						lowerCamelCase__ :      List[Any]								=		torch.cat([init_latents]							,    dim=0				)

						lowerCamelCase__ :      Dict								=		init_latents.shape
						lowerCamelCase__ :      Dict								=		randn_tensor(__magic_name__							,    generator=__magic_name__							,    device=__magic_name__							,    dtype=__magic_name__				)

						# get latents
						lowerCamelCase__ :      str								=		self.scheduler.add_noise(__magic_name__							,    __magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      Any								=		init_latents

						return latents





			def        _snake_case    (self							,    __magic_name__=0				):
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

						lowerCamelCase__ :      List[str]								=		torch.device(f"cuda:{gpu_id}"				)

						lowerCamelCase__ :      Dict								=		[
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self							,    __magic_name__=0				):
						if is_accelerate_available() and is_accelerate_version(""">="""							,    """0.17.0.dev0"""				):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""				)

						lowerCamelCase__ :      str								=		torch.device(f"cuda:{gpu_id}"				)

						if self.device.type != "cpu":
									self.to("""cpu"""							,    silence_dtype_warnings=__magic_name__				)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						lowerCamelCase__ :      List[Any]								=		None
						for cpu_offloaded_model in [self.unet, self.movq]:
									lowerCamelCase__ :      int								=		cpu_offload_with_hook(__magic_name__							,    __magic_name__							,    prev_module_hook=__magic_name__				)

						# We'll offload the last model manually.
						lowerCamelCase__ :      Optional[int]								=		hook





			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def        _snake_case    (self				):
						if not hasattr(self.unet							,    """_hf_hook"""				):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(__magic_name__							,    """_hf_hook"""				)
									    and hasattr(module._hf_hook							,    """execution_device"""				)
									    and module._hf_hook.execution_device is not None
									):
												return torch.device(module._hf_hook.execution_device				)
						return self.device





			@torch.no_grad()
			@replace_example_docstring(__magic_name__				)
			def __call__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = 512							,    __magic_name__ = 512							,    __magic_name__ = 100							,    __magic_name__ = 4.0							,    __magic_name__ = 0.3							,    __magic_name__ = 1							,    __magic_name__ = None							,    __magic_name__ = "pil"							,    __magic_name__ = True							,    ):
						lowerCamelCase__ :      List[str]								=		self._execution_device

						lowerCamelCase__ :      List[str]								=		guidance_scale > 1.0

						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      int								=		torch.cat(__magic_name__							,    dim=0				)
						lowerCamelCase__ :      Any								=		image_embeds.shape[0]
						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Tuple								=		torch.cat(__magic_name__							,    dim=0				)

						if do_classifier_free_guidance:
									lowerCamelCase__ :      Tuple								=		image_embeds.repeat_interleave(__magic_name__							,    dim=0				)
									lowerCamelCase__ :      str								=		negative_image_embeds.repeat_interleave(__magic_name__							,    dim=0				)

						lowerCamelCase__ :      Any								=		torch.cat([negative_image_embeds, image_embeds]							,    dim=0				).to(dtype=self.unet.dtype							,    device=__magic_name__				)

						if not isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		[image]
						if not all(isinstance(__magic_name__							,    (PIL.Image.Image, torch.Tensor)				) for i in image				):
									raise ValueError(
									    f"Input is in incorrect format: {[type(__magic_name__				) for i in image]}. Currently, we only support  PIL image and pytorch tensor"				)

						lowerCamelCase__ :      List[str]								=		torch.cat([prepare_image(__magic_name__							,    __magic_name__							,    __magic_name__				) for i in image]							,    dim=0				)
						lowerCamelCase__ :      Union[str, Any]								=		image.to(dtype=image_embeds.dtype							,    device=__magic_name__				)

						lowerCamelCase__ :      Any								=		self.movq.encode(__magic_name__				)["""latents"""]
						lowerCamelCase__ :      List[str]								=		latents.repeat_interleave(__magic_name__							,    dim=0				)
						self.scheduler.set_timesteps(__magic_name__							,    device=__magic_name__				)
						lowerCamelCase__ :      str								=		self.get_timesteps(__magic_name__							,    __magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      Dict								=		timesteps[:1].repeat(batch_size * num_images_per_prompt				)
						lowerCamelCase__ :      Optional[Any]								=		downscale_height_and_width(__magic_name__							,    __magic_name__							,    self.movq_scale_factor				)
						lowerCamelCase__ :      Union[str, Any]								=		self.prepare_latents(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    image_embeds.dtype							,    __magic_name__							,    __magic_name__				)
						for i, t in enumerate(self.progress_bar(__magic_name__				)				):
									# expand the latents if we are doing classifier free guidance
									lowerCamelCase__ :      Optional[Any]								=		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

									lowerCamelCase__ :      Any								=		{"""image_embeds""": image_embeds}
									lowerCamelCase__ :      List[str]								=		self.unet(
									    sample=__magic_name__							,    timestep=__magic_name__							,    encoder_hidden_states=__magic_name__							,    added_cond_kwargs=__magic_name__							,    return_dict=__magic_name__							,    )[0]

									if do_classifier_free_guidance:
												lowerCamelCase__ :      List[str]								=		noise_pred.split(latents.shape[1]							,    dim=1				)
												lowerCamelCase__ :      List[str]								=		noise_pred.chunk(2				)
												lowerCamelCase__ :      List[Any]								=		variance_pred.chunk(2				)
												lowerCamelCase__ :      List[str]								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												lowerCamelCase__ :      Union[str, Any]								=		torch.cat([noise_pred, variance_pred_text]							,    dim=1				)

									if not (
									    hasattr(self.scheduler.config							,    """variance_type"""				)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												lowerCamelCase__ :      int								=		noise_pred.split(latents.shape[1]							,    dim=1				)

									# compute the previous noisy sample x_t -> x_t-1
									lowerCamelCase__ :      int								=		self.scheduler.step(
									    __magic_name__							,    __magic_name__							,    __magic_name__							,    generator=__magic_name__							,    )[0]

						# post-processing
						lowerCamelCase__ :      Union[str, Any]								=		self.movq.decode(__magic_name__							,    force_not_quantize=__magic_name__				)["""sample"""]

						if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"				)

						if output_type in ["np", "pil"]:
									lowerCamelCase__ :      Tuple								=		image * 0.5 + 0.5
									lowerCamelCase__ :      List[Any]								=		image.clamp(0							,    1				)
									lowerCamelCase__ :      List[str]								=		image.cpu().permute(0							,    2							,    3							,    1				).float().numpy()

						if output_type == "pil":
									lowerCamelCase__ :      Optional[Any]								=		self.numpy_to_pil(__magic_name__				)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__magic_name__				)








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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return resize(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return center_crop(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									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_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."""				)

						# All transformations expect numpy arrays.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

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

						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		[self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				) for image in images]

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



