





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  List[str]	,				snake_case_     :  Optional[Any]	)				->    str:



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

			if isinstance(image[0]	,				PIL.Image.Image	):
						__snake_case								=       [np.array(i.resize((w, h)	,				resample=PIL_INTERPOLATION['''lanczos''']	)	)[None, :] for i in image]
						__snake_case								=       np.concatenate(A__	,				axis=0	)
						__snake_case								=       np.array(A__	).astype(np.floataa	) / 255.0
						__snake_case								=       image.transpose(0	,				3	,				1	,				2	)
						__snake_case								=       2.0 * image - 1.0
						__snake_case								=       torch.from_numpy(A__	)
			elif isinstance(image[0]	,				torch.Tensor	):
						__snake_case								=       torch.cat(A__	,				dim=0	)
			return image






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  int	,				snake_case_     :  int	,				snake_case_     :  str=0.9_995	)				->    int:



			if not isinstance(A__	,				np.ndarray	):
						__snake_case								=       True
						__snake_case								=       va.device
						__snake_case								=       va.cpu().numpy()
						__snake_case								=       va.cpu().numpy()

			__snake_case								=       np.sum(va * va / (np.linalg.norm(A__	) * np.linalg.norm(A__	))	)
			if np.abs(A__	) > DOT_THRESHOLD:
						__snake_case								=       (1 - t) * va + t * va
			else:
						__snake_case								=       np.arccos(A__	)
						__snake_case								=       np.sin(A__	)
						__snake_case								=       theta_a * t
						__snake_case								=       np.sin(A__	)
						__snake_case								=       np.sin(theta_a - theta_t	) / sin_theta_a
						__snake_case								=       sin_theta_t / sin_theta_a
						__snake_case								=       sa * va + sa * va

			if inputs_are_torch:
						__snake_case								=       torch.from_numpy(A__	).to(A__	)

			return va






def lowerCamelCase__   (							snake_case_     :  Optional[int]	,				snake_case_     :  Tuple	)				->    List[str]:
			__snake_case								=       F.normalize(A__	,				dim=-1	)
			__snake_case								=       F.normalize(A__	,				dim=-1	)
			return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  int	)				->    Dict:



			for param in model.parameters():
						__snake_case								=       value







class 		SCREAMING_SNAKE_CASE__    (	__lowerCamelCase  ):


						def __init__(self	:				Tuple    ,      a__	:				AutoencoderKL    ,      a__	:				CLIPTextModel    ,      a__	:				CLIPModel    ,      a__	:				CLIPTokenizer    ,      a__	:				UNetaDConditionModel    ,      a__	:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]    ,      a__	:				CLIPFeatureExtractor    ,      a__	:				Optional[int]=None    ,      a__	:				Union[str, Any]=None    ,      a__	:				List[str]=None    ,      ):





									"""simple docstring"""



									super().__init__()
									self.register_modules(
									    vae=UpperCamelCase_    ,      text_encoder=UpperCamelCase_    ,      clip_model=UpperCamelCase_    ,      tokenizer=UpperCamelCase_    ,      unet=UpperCamelCase_    ,      scheduler=UpperCamelCase_    ,      feature_extractor=UpperCamelCase_    ,      coca_model=UpperCamelCase_    ,      coca_tokenizer=UpperCamelCase_    ,      coca_transform=UpperCamelCase_    ,      )
									__snake_case								=       (
									    feature_extractor.size
									    if isinstance(feature_extractor.size    ,      UpperCamelCase_				)
									    else feature_extractor.size['''shortest_edge''']
									)
									__snake_case								=       transforms.Normalize(mean=feature_extractor.image_mean    ,      std=feature_extractor.image_std				)
									set_requires_grad(self.text_encoder    ,      UpperCamelCase_				)
									set_requires_grad(self.clip_model    ,      UpperCamelCase_				)


						def        a			(self	:				List[Any]    ,      a__	:				Optional[Union[str, int]] = "auto"				):





									"""simple docstring"""



									if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												__snake_case								=       self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(UpperCamelCase_				)


						def        a			(self	:				str				):





									"""simple docstring"""



									self.enable_attention_slicing(UpperCamelCase_				)


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									set_requires_grad(self.vae    ,      UpperCamelCase_				)


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									set_requires_grad(self.vae    ,      UpperCamelCase_				)


						def        a			(self	:				List[Any]				):





									"""simple docstring"""



									set_requires_grad(self.unet    ,      UpperCamelCase_				)


						def        a			(self	:				List[Any]				):





									"""simple docstring"""



									set_requires_grad(self.unet    ,      UpperCamelCase_				)


						def        a			(self	:				List[str]    ,      a__	:				Dict    ,      a__	:				List[str]    ,      a__	:				str				):





									"""simple docstring"""



									__snake_case								=       min(int(num_inference_steps * strength				)    ,      UpperCamelCase_				)

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

									return timesteps, num_inference_steps - t_start


						def        a			(self	:				str    ,      a__	:				str    ,      a__	:				int    ,      a__	:				Union[str, Any]    ,      a__	:				Optional[int]    ,      a__	:				Dict    ,      a__	:				Optional[Any]=None				):





									"""simple docstring"""



									if not isinstance(UpperCamelCase_    ,      torch.Tensor				):
												raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase_				)}"""				)

									__snake_case								=       image.to(device=UpperCamelCase_    ,      dtype=UpperCamelCase_				)

									if isinstance(UpperCamelCase_    ,      UpperCamelCase_				):
												__snake_case								=       [
												    self.vae.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(UpperCamelCase_				)
												]
												__snake_case								=       torch.cat(UpperCamelCase_    ,      dim=0				)
									else:
												__snake_case								=       self.vae.encode(UpperCamelCase_				).latent_dist.sample(UpperCamelCase_				)

									# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									__snake_case								=       0.1_8_2_1_5 * init_latents
									__snake_case								=       init_latents.repeat_interleave(UpperCamelCase_    ,      dim=0				)

									__snake_case								=       randn_tensor(init_latents.shape    ,      generator=UpperCamelCase_    ,      device=UpperCamelCase_    ,      dtype=UpperCamelCase_				)

									# get latents
									__snake_case								=       self.scheduler.add_noise(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)
									__snake_case								=       init_latents

									return latents


						def        a			(self	:				int    ,      a__	:				Any				):





									"""simple docstring"""



									__snake_case								=       self.coca_transform(UpperCamelCase_				).unsqueeze(0				)
									with torch.no_grad(), torch.cuda.amp.autocast():
												__snake_case								=       self.coca_model.generate(transformed_image.to(device=self.device    ,      dtype=self.coca_model.dtype				)				)
									__snake_case								=       self.coca_tokenizer.decode(generated[0].cpu().numpy()				)
									return generated.split('''<end_of_text>'''				)[0].replace('''<start_of_text>'''    ,      ''''''				).rstrip(''' .,'''				)


						def        a			(self	:				List[str]    ,      a__	:				Optional[Any]    ,      a__	:				Optional[int]				):





									"""simple docstring"""



									__snake_case								=       self.feature_extractor.preprocess(UpperCamelCase_				)
									__snake_case								=       torch.from_numpy(clip_image_input['''pixel_values'''][0]				).unsqueeze(0				).to(self.device				).half()
									__snake_case								=       self.clip_model.get_image_features(UpperCamelCase_				)
									__snake_case								=       image_embeddings_clip / image_embeddings_clip.norm(p=2    ,      dim=-1    ,      keepdim=UpperCamelCase_				)
									__snake_case								=       image_embeddings_clip.repeat_interleave(UpperCamelCase_    ,      dim=0				)
									return image_embeddings_clip


						@torch.enable_grad()
						def        a			(self	:				int    ,      a__	:				Union[str, Any]    ,      a__	:				List[Any]    ,      a__	:				int    ,      a__	:				List[Any]    ,      a__	:				Optional[Any]    ,      a__	:				Any    ,      a__	:				List[Any]    ,      ):





									"""simple docstring"""



									__snake_case								=       latents.detach().requires_grad_()

									__snake_case								=       self.scheduler.scale_model_input(UpperCamelCase_    ,      UpperCamelCase_				)

									# predict the noise residual
									__snake_case								=       self.unet(UpperCamelCase_    ,      UpperCamelCase_    ,      encoder_hidden_states=UpperCamelCase_				).sample

									if isinstance(self.scheduler    ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
												__snake_case								=       self.scheduler.alphas_cumprod[timestep]
												__snake_case								=       1 - alpha_prod_t
												# compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__snake_case								=       (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

												__snake_case								=       torch.sqrt(UpperCamelCase_				)
												__snake_case								=       pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler    ,      UpperCamelCase_				):
												__snake_case								=       self.scheduler.sigmas[index]
												__snake_case								=       latents - sigma * noise_pred
									else:
												raise ValueError(f"""scheduler type {type(self.scheduler				)} not supported"""				)

									# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									__snake_case								=       1 / 0.1_8_2_1_5 * sample
									__snake_case								=       self.vae.decode(UpperCamelCase_				).sample
									__snake_case								=       (image / 2 + 0.5).clamp(0    ,      1				)

									__snake_case								=       transforms.Resize(self.feature_extractor_size				)(UpperCamelCase_				)
									__snake_case								=       self.normalize(UpperCamelCase_				).to(latents.dtype				)

									__snake_case								=       self.clip_model.get_image_features(UpperCamelCase_				)
									__snake_case								=       image_embeddings_clip / image_embeddings_clip.norm(p=2    ,      dim=-1    ,      keepdim=UpperCamelCase_				)

									__snake_case								=       spherical_dist_loss(UpperCamelCase_    ,      UpperCamelCase_				).mean() * clip_guidance_scale

									__snake_case								=       -torch.autograd.grad(UpperCamelCase_    ,      UpperCamelCase_				)[0]

									if isinstance(self.scheduler    ,      UpperCamelCase_				):
												__snake_case								=       latents.detach() + grads * (sigma**2)
												__snake_case								=       noise_pred_original
									else:
												__snake_case								=       noise_pred_original - torch.sqrt(UpperCamelCase_				) * grads
									return noise_pred, latents


						@torch.no_grad()
						def __call__(self	:				Union[str, Any]    ,      a__	:				Union[torch.FloatTensor, PIL.Image.Image]    ,      a__	:				Union[torch.FloatTensor, PIL.Image.Image]    ,      a__	:				Optional[str] = None    ,      a__	:				Optional[str] = None    ,      a__	:				Optional[int] = 512    ,      a__	:				Optional[int] = 512    ,      a__	:				float = 0.6    ,      a__	:				Optional[int] = 50    ,      a__	:				Optional[float] = 7.5    ,      a__	:				Optional[int] = 1    ,      a__	:				float = 0.0    ,      a__	:				Optional[float] = 100    ,      a__	:				Optional[torch.Generator] = None    ,      a__	:				Optional[str] = "pil"    ,      a__	:				bool = True    ,      a__	:				float = 0.8    ,      a__	:				float = 0.1    ,      a__	:				float = 0.1    ,      ):





									"""simple docstring"""



									if isinstance(UpperCamelCase_    ,      UpperCamelCase_				) and len(UpperCamelCase_				) != batch_size:
												raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(UpperCamelCase_				)} generators."""				)

									if height % 8 != 0 or width % 8 != 0:
												raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""				)

									if isinstance(UpperCamelCase_    ,      torch.Generator				) and batch_size > 1:
												__snake_case								=       [generator] + [None] * (batch_size - 1)

									__snake_case								=       [
									    ('''model''', self.coca_model is None),
									    ('''tokenizer''', self.coca_tokenizer is None),
									    ('''transform''', self.coca_transform is None),
									]
									__snake_case								=       [x[0] for x in coca_is_none if x[1]]
									__snake_case								=       ''', '''.join(UpperCamelCase_				)
									# generate prompts with coca model if prompt is None
									if content_prompt is None:
												if len(UpperCamelCase_				):
															raise ValueError(
															    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
															    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""				)
												__snake_case								=       self.get_image_description(UpperCamelCase_				)
									if style_prompt is None:
												if len(UpperCamelCase_				):
															raise ValueError(
															    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
															    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""				)
												__snake_case								=       self.get_image_description(UpperCamelCase_				)

									# get prompt text embeddings for content and style
									__snake_case								=       self.tokenizer(
									    UpperCamelCase_    ,      padding='''max_length'''    ,      max_length=self.tokenizer.model_max_length    ,      truncation=UpperCamelCase_    ,      return_tensors='''pt'''    ,      )
									__snake_case								=       self.text_encoder(content_text_input.input_ids.to(self.device				)				)[0]

									__snake_case								=       self.tokenizer(
									    UpperCamelCase_    ,      padding='''max_length'''    ,      max_length=self.tokenizer.model_max_length    ,      truncation=UpperCamelCase_    ,      return_tensors='''pt'''    ,      )
									__snake_case								=       self.text_encoder(style_text_input.input_ids.to(self.device				)				)[0]

									__snake_case								=       slerp(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)

									# duplicate text embeddings for each generation per prompt
									__snake_case								=       text_embeddings.repeat_interleave(UpperCamelCase_    ,      dim=0				)

									# set timesteps
									__snake_case								=       '''offset''' in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
									__snake_case								=       {}
									if accepts_offset:
												__snake_case								=       1

									self.scheduler.set_timesteps(UpperCamelCase_    ,      **UpperCamelCase_				)
									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									self.scheduler.timesteps.to(self.device				)

									__snake_case	,     __snake_case								=       self.get_timesteps(UpperCamelCase_    ,      UpperCamelCase_    ,      self.device				)
									__snake_case								=       timesteps[:1].repeat(UpperCamelCase_				)

									# Preprocess image
									__snake_case								=       preprocess(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)
									__snake_case								=       self.prepare_latents(
									    UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      text_embeddings.dtype    ,      self.device    ,      UpperCamelCase_				)

									__snake_case								=       preprocess(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)
									__snake_case								=       self.prepare_latents(
									    UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      text_embeddings.dtype    ,      self.device    ,      UpperCamelCase_				)

									__snake_case								=       slerp(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)

									if clip_guidance_scale > 0:
												__snake_case								=       self.get_clip_image_embeddings(UpperCamelCase_    ,      UpperCamelCase_				)
												__snake_case								=       self.get_clip_image_embeddings(UpperCamelCase_    ,      UpperCamelCase_				)
												__snake_case								=       slerp(
												    UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_				)

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									__snake_case								=       guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
												__snake_case								=       content_text_input.input_ids.shape[-1]
												__snake_case								=       self.tokenizer(['''''']    ,      padding='''max_length'''    ,      max_length=UpperCamelCase_    ,      return_tensors='''pt'''				)
												__snake_case								=       self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
												# duplicate unconditional embeddings for each generation per prompt
												__snake_case								=       uncond_embeddings.repeat_interleave(UpperCamelCase_    ,      dim=0				)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												__snake_case								=       torch.cat([uncond_embeddings, text_embeddings]				)

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									__snake_case								=       (batch_size, self.unet.config.in_channels, height // 8, width // 8)
									__snake_case								=       text_embeddings.dtype
									if latents is None:
												if self.device.type == "mps":
															# randn does not work reproducibly on mps
															__snake_case								=       torch.randn(UpperCamelCase_    ,      generator=UpperCamelCase_    ,      device='''cpu'''    ,      dtype=UpperCamelCase_				).to(
															    self.device				)
												else:
															__snake_case								=       torch.randn(UpperCamelCase_    ,      generator=UpperCamelCase_    ,      device=self.device    ,      dtype=UpperCamelCase_				)
									else:
												if latents.shape != latents_shape:
															raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
												__snake_case								=       latents.to(self.device				)

									# scale the initial noise by the standard deviation required by the scheduler
									__snake_case								=       latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									__snake_case								=       '''eta''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
									__snake_case								=       {}
									if accepts_eta:
												__snake_case								=       eta

									# check if the scheduler accepts generator
									__snake_case								=       '''generator''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
									if accepts_generator:
												__snake_case								=       generator

									with self.progress_bar(total=UpperCamelCase_				):
												for i, t in enumerate(UpperCamelCase_				):
															# expand the latents if we are doing classifier free guidance
															__snake_case								=       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
															__snake_case								=       self.scheduler.scale_model_input(UpperCamelCase_    ,      UpperCamelCase_				)

															# predict the noise residual
															__snake_case								=       self.unet(UpperCamelCase_    ,      UpperCamelCase_    ,      encoder_hidden_states=UpperCamelCase_				).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																		__snake_case	,     __snake_case								=       noise_pred.chunk(2				)
																		__snake_case								=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																		__snake_case								=       (
																		    text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
																		)
																		__snake_case	,     __snake_case								=       self.cond_fn(
																		    UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      )

															# compute the previous noisy sample x_t -> x_t-1
															__snake_case								=       self.scheduler.step(UpperCamelCase_    ,      UpperCamelCase_    ,      UpperCamelCase_    ,      **UpperCamelCase_				).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									__snake_case								=       1 / 0.1_8_2_1_5 * latents
									__snake_case								=       self.vae.decode(UpperCamelCase_				).sample

									__snake_case								=       (image / 2 + 0.5).clamp(0    ,      1				)
									__snake_case								=       image.cpu().permute(0    ,      2    ,      3    ,      1				).numpy()

									if output_type == "pil":
												__snake_case								=       self.numpy_to_pil(UpperCamelCase_				)

									if not return_dict:
												return (image, None)

									return StableDiffusionPipelineOutput(images=UpperCamelCase_    ,      nsfw_content_detected=UpperCamelCase_				)








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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

         __snake_case								=       '''bird'''
         __snake_case								=       jax.device_count()
         __snake_case								=       pipe.prepare_text_inputs([prompts] * num_samples				)

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

         __snake_case								=       '''Chef in the kitchen'''
         __snake_case								=       jax.device_count()
         __snake_case								=       pipe.prepare_text_inputs([prompts] * num_samples				)

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

