

'''simple docstring'''





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()
class        A  (	a  ,    a  ,    a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	int   =   StableDiffusionControlNetImgaImgPipeline
						__UpperCAmelCase				:	Optional[Any]   =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
						__UpperCAmelCase				:	Optional[int]   =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						__UpperCAmelCase				:	List[Any]   =   IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""} )
						__UpperCAmelCase				:	Dict   =   IMAGE_TO_IMAGE_IMAGE_PARAMS





						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										torch.manual_seed(0			)
										_a        =   UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=4	,    out_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,    cross_attention_dim=3_2	,    )
										torch.manual_seed(0			)
										_a        =   ControlNetModel(
										    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    cross_attention_dim=3_2	,    conditioning_embedding_out_channels=(1_6, 3_2)	,    )
										torch.manual_seed(0			)
										_a        =   DDIMScheduler(
										    beta_start=0.00_085	,    beta_end=0.012	,    beta_schedule="scaled_linear"	,    clip_sample=snake_case__	,    set_alpha_to_one=snake_case__	,    )
										torch.manual_seed(0			)
										_a        =   AutoencoderKL(
										    block_out_channels=[3_2, 6_4]	,    in_channels=3	,    out_channels=3	,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,    latent_channels=4	,    )
										torch.manual_seed(0			)
										_a        =   CLIPTextConfig(
										    bos_token_id=0	,    eos_token_id=2	,    hidden_size=3_2	,    intermediate_size=3_7	,    layer_norm_eps=1E-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_0_0_0	,    )
										_a        =   CLIPTextModel(snake_case__			)
										_a        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

										_a        =   {
										    "unet": unet,
										    "controlnet": controlnet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=0			)			->							Tuple:
										if str(snake_case__			).startswith("mps"			):
														_a        =   torch.manual_seed(snake_case__			)
										else:
														_a        =   torch.Generator(device=snake_case__			).manual_seed(snake_case__			)

										_a        =   2
										_a        =   randn_tensor(
										    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__			)	,    )
										_a        =   floats_tensor(control_image.shape	,    rng=random.Random(snake_case__			)			).to(snake_case__			)
										_a        =   image.cpu().permute(0	,    2	,    3	,    1			)[0]
										_a        =   Image.fromarray(np.uinta(snake_case__			)			).convert("RGB"			).resize((6_4, 6_4)			)
										_a        =   {
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										    "image": image,
										    "control_image": control_image,
										}

										return inputs





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)





						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()	,    reason="XFormers attention is only available with CUDA and `xformers` installed"	,    )
						def __lowerCAmelCase   (	self			)			->							int:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)





						def __lowerCAmelCase   (	self			)			->							List[Any]:
										self._test_inference_batch_single_identical(expected_max_diff=2E-3			)



class        A  (	a  ,    a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	int   =   StableDiffusionControlNetImgaImgPipeline
						__UpperCAmelCase				:	List[str]   =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
						__UpperCAmelCase				:	Dict   =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						__UpperCAmelCase				:	str   =   frozenset([] )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										torch.manual_seed(0			)
										_a        =   UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=4	,    out_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,    cross_attention_dim=3_2	,    )
										torch.manual_seed(0			)

										def init_weights(snake_case_			):
														if isinstance(snake_case__	,    torch.nn.Convad			):
																		torch.nn.init.normal(m.weight			)
																		m.bias.data.fill_(1.0			)

										_a        =   ControlNetModel(
										    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    cross_attention_dim=3_2	,    conditioning_embedding_out_channels=(1_6, 3_2)	,    )
										controlneta.controlnet_down_blocks.apply(snake_case__			)

										torch.manual_seed(0			)
										_a        =   ControlNetModel(
										    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,    cross_attention_dim=3_2	,    conditioning_embedding_out_channels=(1_6, 3_2)	,    )
										controlneta.controlnet_down_blocks.apply(snake_case__			)

										torch.manual_seed(0			)
										_a        =   DDIMScheduler(
										    beta_start=0.00_085	,    beta_end=0.012	,    beta_schedule="scaled_linear"	,    clip_sample=snake_case__	,    set_alpha_to_one=snake_case__	,    )
										torch.manual_seed(0			)
										_a        =   AutoencoderKL(
										    block_out_channels=[3_2, 6_4]	,    in_channels=3	,    out_channels=3	,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,    latent_channels=4	,    )
										torch.manual_seed(0			)
										_a        =   CLIPTextConfig(
										    bos_token_id=0	,    eos_token_id=2	,    hidden_size=3_2	,    intermediate_size=3_7	,    layer_norm_eps=1E-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_0_0_0	,    )
										_a        =   CLIPTextModel(snake_case__			)
										_a        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

										_a        =   MultiControlNetModel([controlneta, controlneta]			)

										_a        =   {
										    "unet": unet,
										    "controlnet": controlnet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=0			)			->							List[Any]:
										if str(snake_case__			).startswith("mps"			):
														_a        =   torch.manual_seed(snake_case__			)
										else:
														_a        =   torch.Generator(device=snake_case__			).manual_seed(snake_case__			)

										_a        =   2

										_a        =   [
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__			)	,    ),
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__			)	,    ),
										]

										_a        =   floats_tensor(control_image[0].shape	,    rng=random.Random(snake_case__			)			).to(snake_case__			)
										_a        =   image.cpu().permute(0	,    2	,    3	,    1			)[0]
										_a        =   Image.fromarray(np.uinta(snake_case__			)			).convert("RGB"			).resize((6_4, 6_4)			)
										_a        =   {
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										    "image": image,
										    "control_image": control_image,
										}

										return inputs





						def __lowerCAmelCase   (	self			)			->							str:
										_a        =   self.get_dummy_components()
										_a        =   self.pipeline_class(**snake_case__			)
										pipe.to(snake_case__			)

										_a        =   10.0
										_a        =   4

										_a        =   self.get_dummy_inputs(snake_case__			)
										_a        =   steps
										_a        =   scale
										_a        =   pipe(**snake_case__			)[0]

										_a        =   self.get_dummy_inputs(snake_case__			)
										_a        =   steps
										_a        =   scale
										_a        =   pipe(**snake_case__	,    control_guidance_start=0.1	,    control_guidance_end=0.2			)[0]

										_a        =   self.get_dummy_inputs(snake_case__			)
										_a        =   steps
										_a        =   scale
										_a        =   pipe(**snake_case__	,    control_guidance_start=[0.1, 0.3]	,    control_guidance_end=[0.2, 0.7]			)[0]

										_a        =   self.get_dummy_inputs(snake_case__			)
										_a        =   steps
										_a        =   scale
										_a        =   pipe(**snake_case__	,    control_guidance_start=0.4	,    control_guidance_end=[0.5, 0.8]			)[0]

										# make sure that all outputs are different
										assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
										assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
										assert np.sum(np.abs(output_a - output_a			)			) > 1E-3





						def __lowerCAmelCase   (	self			)			->							List[str]:
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)





						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()	,    reason="XFormers attention is only available with CUDA and `xformers` installed"	,    )
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)





						def __lowerCAmelCase   (	self			)			->							List[str]:
										self._test_inference_batch_single_identical(expected_max_diff=2E-3			)





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   self.get_dummy_components()
										_a        =   self.pipeline_class(**snake_case__			)
										pipe.to(snake_case__			)
										pipe.set_progress_bar_config(disable=snake_case__			)
										with tempfile.TemporaryDirectory() as tmpdir:
														try:
																		# save_pretrained is not implemented for Multi-ControlNet
																		pipe.save_pretrained(snake_case__			)
														except NotImplementedError:
																		pass



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





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





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"			)

										_a        =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"	,    safety_checker=snake_case__	,    controlnet=snake_case__			)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=snake_case__			)

										_a        =   torch.Generator(device="cpu"			).manual_seed(0			)
										_a        =   "evil space-punk bird"
										_a        =   load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"			).resize((5_1_2, 5_1_2)			)
										_a        =   load_image(
										    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"			).resize((5_1_2, 5_1_2)			)

										_a        =   pipe(
										    snake_case__	,    snake_case__	,    control_image=snake_case__	,    generator=snake_case__	,    output_type="np"	,    num_inference_steps=5_0	,    strength=0.6	,    )

										_a        =   output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)

										_a        =   load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"			)

										assert np.abs(expected_image - image			).max() < 9E-2













"""simple docstring"""



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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   ):
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

        UpperCAmelCase__						:    Tuple							=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							jnp.array(
            [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]							)
        print(f'output_slice: {output_slice}'							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


