


"""simple docstring"""


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,
)


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

A	:  Union[str, Any]            =     "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=8 ):

			'''simple docstring'''




			__lowerCAmelCase							       =     height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			__lowerCAmelCase							       =     width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=512       ,					_UpperCamelCase=512 ):

			'''simple docstring'''




			__lowerCAmelCase							       =     pil_image.resize((w, h)       ,					resample=Image.BICUBIC       ,					reducing_gap=1 )
			__lowerCAmelCase							       =     np.array(pil_image.convert("RGB" ) )
			__lowerCAmelCase							       =     arr.astype(np.floataa ) / 1_27.5 - 1
			__lowerCAmelCase							       =     np.transpose(_UpperCamelCase       ,					[2, 0, 1] )
			__lowerCAmelCase							       =     torch.from_numpy(_UpperCamelCase ).unsqueeze(0 )
			return image


class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

										self.register_modules(
										    unet=__a       ,  scheduler=__a       ,  movq=__a       ,  )
										__lowerCAmelCase							       =     2 ** (len(self.movq.config.block_out_channels      ) - 1)

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										# get the original timestep using init_timestep
										__lowerCAmelCase							       =     min(int(num_inference_steps * strength      )       ,  __a      )

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

										return timesteps, num_inference_steps - t_start

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a=None      ):
										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      )}"      )

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

										__lowerCAmelCase							       =     batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													__lowerCAmelCase							       =     image

										else:
													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."      )

													elif isinstance(__a       ,  __a      ):
																__lowerCAmelCase							       =     [
																    self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(__a      )
																]
																__lowerCAmelCase							       =     torch.cat(__a       ,  dim=0      )
													else:
																__lowerCAmelCase							       =     self.movq.encode(__a      ).latent_dist.sample(__a      )

													__lowerCAmelCase							       =     self.movq.config.scaling_factor * init_latents

										__lowerCAmelCase							       =     torch.cat([init_latents]       ,  dim=0      )

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

										# get latents
										__lowerCAmelCase							       =     self.scheduler.add_noise(__a       ,  __a       ,  __a      )

										__lowerCAmelCase							       =     init_latents

										return latents

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

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

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

							def      snake_case							(	self       ,  __a=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							       =     torch.device(f"cuda:{gpu_id}"      )

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

										__lowerCAmelCase							       =     None
										for cpu_offloaded_model in [self.unet, self.movq]:
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     cpu_offload_with_hook(__a       ,  __a       ,  prev_module_hook=__a      )

										# We'll offload the last model manually.
										__lowerCAmelCase							       =     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(__a       ,  "_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(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  __a = 5_12       ,  __a = 5_12       ,  __a = 1_00       ,  __a = 4.0       ,  __a = 0.3       ,  __a = 1       ,  __a = None       ,  __a = "pil"       ,  __a = True       ,  ):
										__lowerCAmelCase							       =     self._execution_device

										__lowerCAmelCase							       =     guidance_scale > 1.0

										if isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     torch.cat(__a       ,  dim=0      )
										__lowerCAmelCase							       =     image_embeds.shape[0]
										if isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     torch.cat(__a       ,  dim=0      )

										if do_classifier_free_guidance:
													__lowerCAmelCase							       =     image_embeds.repeat_interleave(__a       ,  dim=0      )
													__lowerCAmelCase							       =     negative_image_embeds.repeat_interleave(__a       ,  dim=0      )

										__lowerCAmelCase							       =     torch.cat([negative_image_embeds, image_embeds]       ,  dim=0      ).to(dtype=self.unet.dtype       ,  device=__a      )

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

										__lowerCAmelCase							       =     torch.cat([prepare_image(__a       ,  __a       ,  __a      ) for i in image]       ,  dim=0      )
										__lowerCAmelCase							       =     image.to(dtype=image_embeds.dtype       ,  device=__a      )

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

													__lowerCAmelCase							       =     {"image_embeds": image_embeds}
													__lowerCAmelCase							       =     self.unet(
													    sample=__a       ,  timestep=__a       ,  encoder_hidden_states=__a       ,  added_cond_kwargs=__a       ,  return_dict=__a       ,  )[0]

													if do_classifier_free_guidance:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     noise_pred.split(latents.shape[1]       ,  dim=1      )
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     noise_pred.chunk(2      )
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     variance_pred.chunk(2      )
																__lowerCAmelCase							       =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																__lowerCAmelCase							       =     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       ,      __lowerCAmelCase							       =     noise_pred.split(latents.shape[1]       ,  dim=1      )

													# compute the previous noisy sample x_t -> x_t-1
													__lowerCAmelCase							       =     self.scheduler.step(
													    __a       ,  __a       ,  __a       ,  generator=__a       ,  )[0]

										# post-processing
										__lowerCAmelCase							       =     self.movq.decode(__a       ,  force_not_quantize=__a      )["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							       =     image * 0.5 + 0.5
													__lowerCAmelCase							       =     image.clamp(0       ,  1      )
													__lowerCAmelCase							       =     image.cpu().permute(0       ,  2       ,  3       ,  1      ).float().numpy()

										if output_type == "pil":
													__lowerCAmelCase							       =     self.numpy_to_pil(__a      )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=__a      )








"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




