
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						StableDiffusionInstructPixaPixPipeline
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __lowerCamelCase				       =						IMAGE_TO_IMAGE_IMAGE_PARAMS
       __lowerCamelCase				       =						IMAGE_TO_IMAGE_IMAGE_PARAMS


       def 	_snake_case				(    self				)						->    Tuple:
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				str		=UNetaDConditionModel(
                  block_out_channels=(32, 64)						,       layers_per_block=2						,       sample_size=32						,       in_channels=8						,       out_channels=4						,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,       cross_attention_dim=32						,       )
              SCREAMING_SNAKE_CASE_							:				Any		=PNDMScheduler(skip_prk_steps=UpperCAmelCase_				)
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=AutoencoderKL(
                  block_out_channels=[32, 64]						,       in_channels=3						,       out_channels=3						,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,       latent_channels=4						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				str		=CLIPTextConfig(
                  bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1e-0_5						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1_000						,       )
              SCREAMING_SNAKE_CASE_							:				int		=CLIPTextModel(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              SCREAMING_SNAKE_CASE_							:				int		={
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''vae''': vae,
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''safety_checker''': None,
                  '''feature_extractor''': None,
              }
              return components


       def 	_snake_case				(    self						,       __A						,       __A=0				)						->    int:
              SCREAMING_SNAKE_CASE_							:				str		=floats_tensor((1, 3, 32, 32)						,       rng=random.Random(UpperCAmelCase_				)				).to(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=image.cpu().permute(0						,       2						,       3						,       1				)[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=Image.fromarray(np.uinta(UpperCAmelCase_				)				).convert('''RGB'''				)
              if str(UpperCAmelCase_				).startswith('''mps'''				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.manual_seed(UpperCAmelCase_				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.Generator(device=UpperCAmelCase_				).manual_seed(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': image,
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''guidance_scale''': 6.0,
                  '''image_guidance_scale''': 1,
                  '''output_type''': '''numpy''',
              }
              return inputs


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				int		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				Any		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=sd_pipe.to(UpperCAmelCase_				)
              sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_inputs(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Any		=sd_pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image[0, -3:, -3:, -1]
              assert image.shape == (1, 32, 32, 3)
              SCREAMING_SNAKE_CASE_							:				str		=np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              SCREAMING_SNAKE_CASE_							:				int		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=sd_pipe.to(UpperCAmelCase_				)
              sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_dummy_inputs(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				List[str]		='''french fries'''
              SCREAMING_SNAKE_CASE_							:				int		=sd_pipe(**UpperCAmelCase_						,       negative_prompt=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				str		=output.images
              SCREAMING_SNAKE_CASE_							:				List[str]		=image[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              SCREAMING_SNAKE_CASE_							:				List[str]		=np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Any		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				Dict		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Dict		=sd_pipe.to(UpperCAmelCase_				)
              sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_dummy_inputs(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=[inputs['''prompt''']] * 2

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=np.array(inputs['''image''']				).astype(np.floataa				) / 255.0
              SCREAMING_SNAKE_CASE_							:				int		=torch.from_numpy(UpperCAmelCase_				).unsqueeze(0				).to(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image / 2 + 0.5
              SCREAMING_SNAKE_CASE_							:				int		=image.permute(0						,       3						,       1						,       2				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image.repeat(2						,       1						,       1						,       1				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=sd_pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image[-1, -3:, -3:, -1]

              assert image.shape == (2, 32, 32, 3)
              SCREAMING_SNAKE_CASE_							:				int		=np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Dict		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				str		=EulerAncestralDiscreteScheduler(
                  beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='''scaled_linear'''				)
              SCREAMING_SNAKE_CASE_							:				str		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=sd_pipe.to(UpperCAmelCase_				)
              sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_inputs(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=sd_pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Dict		=image[0, -3:, -3:, -1]

              SCREAMING_SNAKE_CASE_							:				List[str]		=[round(UpperCAmelCase_						,       4				) for x in image_slice.flatten().tolist()]
              print(''','''.join([str(UpperCAmelCase_				) for x in slice]				)				)

              assert image.shape == (1, 32, 32, 3)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    int:
              super().test_inference_batch_single_identical(expected_max_diff=3e-3				)





       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				Dict		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=VaeImageProcessor(do_resize=UpperCAmelCase_						,       do_normalize=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Dict		=pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Any		=pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase_						,       input_image_type='''pt'''				)				)[0]

              SCREAMING_SNAKE_CASE_							:				List[str]		=components['''vae''']
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_dummy_inputs_by_type(UpperCAmelCase_						,       input_image_type='''pt'''				)

              for image_param in self.image_latents_params:
                     if image_param in inputs.keys():
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=vae.encode(inputs[image_param]				).latent_dist.mode()

              SCREAMING_SNAKE_CASE_							:				Dict		=pipe(**UpperCAmelCase_				)[0]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=np.abs(out - out_latents_inputs				).max()
              self.assertLess(UpperCAmelCase_						,       1e-4						,       '''passing latents as image input generate different result from passing image'''				)






@slow
@require_torch_gpu
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self				)						->    str:
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()


       def 	_snake_case				(    self						,       __A=0				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.manual_seed(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=load_image(
                  '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''prompt''': '''turn him into a cyborg''',
                  '''image''': image,
                  '''generator''': generator,
                  '''num_inference_steps''': 3,
                  '''guidance_scale''': 7.5,
                  '''image_guidance_scale''': 1.0,
                  '''output_type''': '''numpy''',
              }
              return inputs


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  '''timbrooks/instruct-pix2pix'''						,       safety_checker=UpperCAmelCase_				)
              pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_inputs()
              SCREAMING_SNAKE_CASE_							:				Dict		=pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)
              SCREAMING_SNAKE_CASE_							:				Dict		=np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]				)

              assert np.abs(expected_slice - image_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Any		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  '''timbrooks/instruct-pix2pix'''						,       safety_checker=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
              pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing()

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_inputs()
              SCREAMING_SNAKE_CASE_							:				Any		=pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Dict		=image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)
              SCREAMING_SNAKE_CASE_							:				Tuple		=np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]				)

              assert np.abs(expected_slice - image_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  '''timbrooks/instruct-pix2pix'''						,       safety_checker=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=DDIMScheduler.from_config(pipe.scheduler.config				)
              pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing()

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_inputs()
              SCREAMING_SNAKE_CASE_							:				int		=pipe(**UpperCAmelCase_				).images
              SCREAMING_SNAKE_CASE_							:				Tuple		=image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)
              SCREAMING_SNAKE_CASE_							:				Dict		=np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]				)

              assert np.abs(expected_slice - image_slice				).max() < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=0

              def callback_fn(__A						,       __A						,       __A				) -> None:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=True
                     nonlocal number_of_steps
                     number_of_steps += 1
                     if step == 1:
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=latents.detach().cpu().numpy()
                            assert latents.shape == (1, 4, 64, 64)
                            SCREAMING_SNAKE_CASE_							:				str		=latents[0, -3:, -3:, -1]
                            SCREAMING_SNAKE_CASE_							:				Any		=np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]				)

                            assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2
                     elif step == 2:
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=latents.detach().cpu().numpy()
                            assert latents.shape == (1, 4, 64, 64)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=latents[0, -3:, -3:, -1]
                            SCREAMING_SNAKE_CASE_							:				int		=np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]				)

                            assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2

              SCREAMING_SNAKE_CASE_							:				List[str]		=False

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  '''timbrooks/instruct-pix2pix'''						,       safety_checker=UpperCAmelCase_						,       torch_dtype=torch.floataa				)
              SCREAMING_SNAKE_CASE_							:				Dict		=pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing()

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_inputs()
              pipe(**UpperCAmelCase_						,       callback=UpperCAmelCase_						,       callback_steps=1				)
              assert callback_fn.has_been_called
              assert number_of_steps == 3


       def 	_snake_case				(    self				)						->    Any:
              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              SCREAMING_SNAKE_CASE_							:				int		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  '''timbrooks/instruct-pix2pix'''						,       safety_checker=UpperCAmelCase_						,       torch_dtype=torch.floataa				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing(1				)
              pipe.enable_sequential_cpu_offload()

              SCREAMING_SNAKE_CASE_							:				int		=self.get_inputs()
              SCREAMING_SNAKE_CASE_							:				Dict		=pipe(**UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.cuda.max_memory_allocated()
              # make sure that less than 2.2 GB is allocated
              assert mem_bytes < 2.2 * 10**9





       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_inputs()
              # resize to resolution that is divisible by 8 but not 16 or 32
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=inputs['''image'''].resize((504, 504)				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		='''timbrooks/instruct-pix2pix'''
              SCREAMING_SNAKE_CASE_							:				str		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
                  UpperCAmelCase_						,       safety_checker=UpperCAmelCase_						,       )
              pipe.to(UpperCAmelCase_				)
              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
              pipe.enable_attention_slicing()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=pipe(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=output.images[0]

              SCREAMING_SNAKE_CASE_							:				Any		=image[255:258, 383:386, -1]

              assert image.shape == (504, 504, 3)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3





'''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 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

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

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

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

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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




