





'''simple docstring'''

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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class     __lowerCAmelCase					(   SCREAMING_SNAKE_CASE__			):






       """simple docstring"""





       def __init__(							self       :		Any	,			lowerCAmelCase__       :		VQModel	,			lowerCAmelCase__       :		UNetaDModel	,			lowerCAmelCase__       :		DDIMScheduler					)   ->					Tuple:

            '''simple docstring'''




            super().__init__()
            self.register_modules(vqvae=a_	,			unet=a_	,			scheduler=a_					)







       @torch.no_grad()
       def __call__(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		int = 1	,			lowerCAmelCase__       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			lowerCAmelCase__       :		float = 0.0	,			lowerCAmelCase__       :		int = 50	,			lowerCAmelCase__       :		Optional[str] = "pil"	,			lowerCAmelCase__       :		bool = True	,			**lowerCAmelCase__       :		Tuple	,			)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	randn_tensor(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,			generator=a_	,			)
            _UpperCamelCase       =	latents.to(self.device					)

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

            self.scheduler.set_timesteps(a_					)

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            _UpperCamelCase       =	'''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)

            _UpperCamelCase       =	{}
            if accepts_eta:
                 _UpperCamelCase       =	eta

            for t in self.progress_bar(self.scheduler.timesteps					):
                 _UpperCamelCase       =	self.scheduler.scale_model_input(a_	,			a_					)
                 # predict the noise residual
                 _UpperCamelCase       =	self.unet(a_	,			a_					).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase       =	self.scheduler.step(a_	,			a_	,			a_	,			**a_					).prev_sample

            # decode the image latents with the VAE
            _UpperCamelCase       =	self.vqvae.decode(a_					).sample

            _UpperCamelCase       =	(image / 2 + 0.5).clamp(0	,			1					)
            _UpperCamelCase       =	image.cpu().permute(0	,			2	,			3	,			1					).numpy()
            if output_type == "pil":
                 _UpperCamelCase       =	self.numpy_to_pil(a_					)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=a_					)













'''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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

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

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

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

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



