



'''simple docstring'''




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)  # pylint: disable=invalid-name


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[List, PIL.Image.Image, torch.Tensor]      ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead""" ,   _lowerCamelCase ,   )
		if isinstance(_lowerCamelCase ,   torch.Tensor      ):
				return image
		elif isinstance(_lowerCamelCase ,   PIL.Image.Image      ):
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[image]

		if isinstance(image[0] ,   PIL.Image.Image      ):
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	image[0].size
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				__SCREAMING_SNAKE_CASE :	int					 =	[np.array(i.resize((w, h) ,   resample=PIL_INTERPOLATION["""lanczos"""]      )      )[None, :] for i in image]
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	np.concatenate(_lowerCamelCase ,   axis=0      )
				__SCREAMING_SNAKE_CASE :	str					 =	np.array(_lowerCamelCase      ).astype(np.floataa      ) / 2_55.0
				__SCREAMING_SNAKE_CASE :	List[str]					 =	image.transpose(0 ,   3 ,   1 ,   2      )
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	2.0 * image - 1.0
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	torch.from_numpy(_lowerCamelCase      )
		elif isinstance(image[0] ,   torch.Tensor      ):
				__SCREAMING_SNAKE_CASE :	Any					 =	torch.cat(_lowerCamelCase ,   dim=0      )
		return image


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[List, PIL.Image.Image, torch.Tensor]      ):
		if isinstance(_lowerCamelCase ,   torch.Tensor      ):
				return mask
		elif isinstance(_lowerCamelCase ,   PIL.Image.Image      ):
				__SCREAMING_SNAKE_CASE :	Dict					 =	[mask]

		if isinstance(mask[0] ,   PIL.Image.Image      ):
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[Any]					 =	mask[0].size
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				__SCREAMING_SNAKE_CASE :	Tuple					 =	[np.array(m.convert("""L"""      ).resize((w, h) ,   resample=PIL_INTERPOLATION["""nearest"""]      )      )[None, :] for m in mask]
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	np.concatenate(_lowerCamelCase ,   axis=0      )
				__SCREAMING_SNAKE_CASE :	Dict					 =	mask.astype(np.floataa      ) / 2_55.0
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	0
				__SCREAMING_SNAKE_CASE :	int					 =	1
				__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.from_numpy(_lowerCamelCase      )
		elif isinstance(mask[0] ,   torch.Tensor      ):
				__SCREAMING_SNAKE_CASE :	Any					 =	torch.cat(_lowerCamelCase ,   dim=0      )
		return mask






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	UNetaDModel
			_A   :	RePaintScheduler

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					super().__init__()
					self.register_modules(unet=lowerCAmelCase__							,  scheduler=lowerCAmelCase__		)



			@torch.no_grad()
			def __call__(							self  :				int							,  lowerCAmelCase__  :				Union[torch.Tensor, PIL.Image.Image]							,  lowerCAmelCase__  :				Union[torch.Tensor, PIL.Image.Image]							,  lowerCAmelCase__  :				int = 2_5_0							,  lowerCAmelCase__  :				float = 0.0							,  lowerCAmelCase__  :				int = 1_0							,  lowerCAmelCase__  :				int = 1_0							,  lowerCAmelCase__  :				Optional[Union[torch.Generator, List[torch.Generator]]] = None							,  lowerCAmelCase__  :				Optional[str] = "pil"							,  lowerCAmelCase__  :				bool = True							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	image

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	_preprocess_image(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	original_image.to(device=self.device							,  dtype=self.unet.dtype		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	_preprocess_mask(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	mask_image.to(device=self.device							,  dtype=self.unet.dtype		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	original_image.shape[0]

					# sample gaussian noise to begin the loop
					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."		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	original_image.shape
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	randn_tensor(lowerCAmelCase__							,  generator=lowerCAmelCase__							,  device=self.device							,  dtype=self.unet.dtype		)

					# set step values
					self.scheduler.set_timesteps(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  self.device		)
					__SCREAMING_SNAKE_CASE :	str					 =	eta

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler.timesteps[0] + 1
					__SCREAMING_SNAKE_CASE :	Any					 =	generator[0] if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) else generator
					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
							if t < t_last:
									# predict the noise residual
									__SCREAMING_SNAKE_CASE :	int					 =	self.unet(lowerCAmelCase__							,  lowerCAmelCase__		).sample
									# compute previous image: x_t -> x_t-1
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		).prev_sample

							else:
									# compute the reverse: x_t-1 -> x_t
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.scheduler.undo_step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	t

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

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__		)




'''simple docstring'''




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard






@slow
@require_flax
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().tearDown()
					gc.collect()

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	str					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	str					 =	"""bird"""
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	str					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe.prepare_image_inputs([canny_image] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.array(
					    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
					print(F"output_slice: {output_slice}"		)
					assert jnp.abs(output_slice - expected_slice		).max() < 1E-2



			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	List[str]					 =	"""Chef in the kitchen"""
					__SCREAMING_SNAKE_CASE :	int					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Any					 =	load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	pipe.prepare_image_inputs([pose_image] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.array(
					    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
					print(F"output_slice: {output_slice}"		)
					assert jnp.abs(output_slice - expected_slice		).max() < 1E-2