code: # This model implementation is heavily based on:
  
  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,
      StableDiffusionControlNetInpaintPipeline,
      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 (
      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
      TEXT_TO_IMAGE_IMAGE_PARAMS,
  )
  from ..test_pipelines_common import (
      PipelineKarrasSchedulerTesterMixin,
      PipelineLatentTesterMixin,
      PipelineTesterMixin,
  )
  
  
  enable_full_determinism()
  
  
  
  
  class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_   ,  UpperCamelCase_   ,  unittest.TestCase		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[Any]     =       StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase_						:      List[str]     =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase_						:      Tuple     =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	UpperCAmelCase_						:      List[str]     =       frozenset({"""control_image"""}		)  # skip `image` and `mask` for now, only test for control_image
  	UpperCAmelCase_						:      List[str]     =       TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	UNetaDConditionModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=9	,    out_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,    cross_attention_dim=32	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	ControlNetModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	DDIMScheduler(
  								    beta_start=0.0_0_0_8_5	,    beta_end=0.0_1_2	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	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       )
  								lowerCAmelCase				=	CLIPTextConfig(
  								    bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
  								lowerCAmelCase				=	CLIPTextModel(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
  
  								lowerCAmelCase				=	{
  								    '''unet''': unet,
  								    '''controlnet''': controlnet,
  								    '''scheduler''': scheduler,
  								    '''vae''': vae,
  								    '''text_encoder''': text_encoder,
  								    '''tokenizer''': tokenizer,
  								    '''safety_checker''': None,
  								    '''feature_extractor''': None,
  								}
  								return components
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=0       )		->Dict:
  								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
  															lowerCAmelCase				=	torch.manual_seed(__SCREAMING_SNAKE_CASE       )
  								else:
  															lowerCAmelCase				=	torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	2
  								lowerCAmelCase				=	randn_tensor(
  								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=__SCREAMING_SNAKE_CASE	,    device=torch.device(__SCREAMING_SNAKE_CASE       )	,    )
  								lowerCAmelCase				=	floats_tensor((1, 3, 32, 32)	,    rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	init_image.cpu().permute(0	,    2	,    3	,    1       )[0]
  
  								lowerCAmelCase				=	Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert('''RGB'''       ).resize((64, 64)       )
  								lowerCAmelCase				=	Image.fromarray(np.uinta(init_image + 4       )       ).convert('''RGB'''       ).resize((64, 64)       )
  
  								lowerCAmelCase				=	{
  								    '''prompt''': '''A painting of a squirrel eating a burger''',
  								    '''generator''': generator,
  								    '''num_inference_steps''': 2,
  								    '''guidance_scale''': 6.0,
  								    '''output_type''': '''numpy''',
  								    '''image''': image,
  								    '''mask_image''': mask_image,
  								    '''control_image''': control_image,
  								}
  
  								return inputs
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->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     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								self._test_inference_batch_single_identical(expected_max_diff=2e-3       )
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Dict     =       StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase_						:      Dict     =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase_						:      Any     =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	UpperCAmelCase_						:      int     =       frozenset([]		)
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	UNetaDConditionModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=4	,    out_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,    cross_attention_dim=32	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	ControlNetModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	DDIMScheduler(
  								    beta_start=0.0_0_0_8_5	,    beta_end=0.0_1_2	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	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       )
  								lowerCAmelCase				=	CLIPTextConfig(
  								    bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
  								lowerCAmelCase				=	CLIPTextModel(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
  
  								lowerCAmelCase				=	{
  								    '''unet''': unet,
  								    '''controlnet''': controlnet,
  								    '''scheduler''': scheduler,
  								    '''vae''': vae,
  								    '''text_encoder''': text_encoder,
  								    '''tokenizer''': tokenizer,
  								    '''safety_checker''': None,
  								    '''feature_extractor''': None,
  								}
  								return components
  
  
  
  
  class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_   ,  unittest.TestCase		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase_						:      Optional[int]     =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase_						:      Any     =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	UNetaDConditionModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=9	,    out_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,    cross_attention_dim=32	,    )
  								torch.manual_seed(0       )
  
  								def init_weights(__SCREAMING_SNAKE_CASE       ):
  															if isinstance(__SCREAMING_SNAKE_CASE	,    torch.nn.Convad       ):
  																						torch.nn.init.normal(m.weight       )
  																						m.bias.data.fill_(1.0       )
  
  								lowerCAmelCase				=	ControlNetModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
  								controlneta.controlnet_down_blocks.apply(__SCREAMING_SNAKE_CASE       )
  
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	ControlNetModel(
  								    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
  								controlneta.controlnet_down_blocks.apply(__SCREAMING_SNAKE_CASE       )
  
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	DDIMScheduler(
  								    beta_start=0.0_0_0_8_5	,    beta_end=0.0_1_2	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )
  								torch.manual_seed(0       )
  								lowerCAmelCase				=	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       )
  								lowerCAmelCase				=	CLIPTextConfig(
  								    bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
  								lowerCAmelCase				=	CLIPTextModel(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
  
  								lowerCAmelCase				=	MultiControlNetModel([controlneta, controlneta]       )
  
  								lowerCAmelCase				=	{
  								    '''unet''': unet,
  								    '''controlnet''': controlnet,
  								    '''scheduler''': scheduler,
  								    '''vae''': vae,
  								    '''text_encoder''': text_encoder,
  								    '''tokenizer''': tokenizer,
  								    '''safety_checker''': None,
  								    '''feature_extractor''': None,
  								}
  								return components
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=0       )		->List[Any]:
  								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
  															lowerCAmelCase				=	torch.manual_seed(__SCREAMING_SNAKE_CASE       )
  								else:
  															lowerCAmelCase				=	torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	2
  
  								lowerCAmelCase				=	[
  								    randn_tensor(
  								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=__SCREAMING_SNAKE_CASE	,    device=torch.device(__SCREAMING_SNAKE_CASE       )	,    ),
  								    randn_tensor(
  								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=__SCREAMING_SNAKE_CASE	,    device=torch.device(__SCREAMING_SNAKE_CASE       )	,    ),
  								]
  								lowerCAmelCase				=	floats_tensor((1, 3, 32, 32)	,    rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	init_image.cpu().permute(0	,    2	,    3	,    1       )[0]
  
  								lowerCAmelCase				=	Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert('''RGB'''       ).resize((64, 64)       )
  								lowerCAmelCase				=	Image.fromarray(np.uinta(init_image + 4       )       ).convert('''RGB'''       ).resize((64, 64)       )
  
  								lowerCAmelCase				=	{
  								    '''prompt''': '''A painting of a squirrel eating a burger''',
  								    '''generator''': generator,
  								    '''num_inference_steps''': 2,
  								    '''guidance_scale''': 6.0,
  								    '''output_type''': '''numpy''',
  								    '''image''': image,
  								    '''mask_image''': mask_image,
  								    '''control_image''': control_image,
  								}
  
  								return inputs
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								lowerCAmelCase				=	self.get_dummy_components()
  								lowerCAmelCase				=	self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
  								pipe.to(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	1_0.0
  								lowerCAmelCase				=	4
  
  								lowerCAmelCase				=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	steps
  								lowerCAmelCase				=	scale
  								lowerCAmelCase				=	pipe(**__SCREAMING_SNAKE_CASE       )[0]
  
  								lowerCAmelCase				=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	steps
  								lowerCAmelCase				=	scale
  								lowerCAmelCase				=	pipe(**__SCREAMING_SNAKE_CASE	,    control_guidance_start=0.1	,    control_guidance_end=0.2       )[0]
  
  								lowerCAmelCase				=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	steps
  								lowerCAmelCase				=	scale
  								lowerCAmelCase				=	pipe(**__SCREAMING_SNAKE_CASE	,    control_guidance_start=[0.1, 0.3]	,    control_guidance_end=[0.2, 0.7]       )[0]
  
  								lowerCAmelCase				=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	steps
  								lowerCAmelCase				=	scale
  								lowerCAmelCase				=	pipe(**__SCREAMING_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     SCREAMING_SNAKE_CASE_       (							self       )		->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     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								self._test_inference_batch_single_identical(expected_max_diff=2e-3       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.get_dummy_components()
  								lowerCAmelCase				=	self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
  								pipe.to(__SCREAMING_SNAKE_CASE       )
  								pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )
  								with tempfile.TemporaryDirectory() as tmpdir:
  															try:
  																						# save_pretrained is not implemented for Multi-ControlNet
  																						pipe.save_pretrained(__SCREAMING_SNAKE_CASE       )
  															except NotImplementedError:
  																						pass
  
  
  
  
  @slow
  @require_torch_gpu
  class      lowercase_						(						unittest.TestCase		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								super().tearDown()
  								gc.collect()
  								torch.cuda.empty_cache()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								lowerCAmelCase				=	ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''       )
  
  								lowerCAmelCase				=	StableDiffusionControlNetInpaintPipeline.from_pretrained(
  								    '''runwayml/stable-diffusion-inpainting'''	,    safety_checker=__SCREAMING_SNAKE_CASE	,    controlnet=__SCREAMING_SNAKE_CASE       )
  								pipe.enable_model_cpu_offload()
  								pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	torch.Generator(device='''cpu'''       ).manual_seed(0       )
  								lowerCAmelCase				=	load_image(
  								    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''       ).resize((512, 512)       )
  
  								lowerCAmelCase				=	load_image(
  								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
  								    '''/stable_diffusion_inpaint/input_bench_mask.png'''       ).resize((512, 512)       )
  
  								lowerCAmelCase				=	'''pitch black hole'''
  
  								lowerCAmelCase				=	load_image(
  								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''       ).resize((512, 512)       )
  
  								lowerCAmelCase				=	pipe(
  								    __SCREAMING_SNAKE_CASE	,    image=__SCREAMING_SNAKE_CASE	,    mask_image=__SCREAMING_SNAKE_CASE	,    control_image=__SCREAMING_SNAKE_CASE	,    generator=__SCREAMING_SNAKE_CASE	,    output_type='''np'''	,    num_inference_steps=3	,    )
  
  								lowerCAmelCase				=	output.images[0]
  
  								assert image.shape == (512, 512, 3)
  
  								lowerCAmelCase				=	load_numpy(
  								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''       )
  
  								assert np.abs(expected_image - image       ).max() < 9e-2
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								lowerCAmelCase				=	ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''       )
  
  								lowerCAmelCase				=	StableDiffusionControlNetInpaintPipeline.from_pretrained(
  								    '''runwayml/stable-diffusion-v1-5'''	,    safety_checker=__SCREAMING_SNAKE_CASE	,    controlnet=__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	DDIMScheduler.from_config(pipe.scheduler.config       )
  								pipe.enable_model_cpu_offload()
  								pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	torch.Generator(device='''cpu'''       ).manual_seed(33       )
  
  								lowerCAmelCase				=	load_image(
  								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''       )
  								lowerCAmelCase				=	init_image.resize((512, 512)       )
  
  								lowerCAmelCase				=	load_image(
  								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''       )
  								lowerCAmelCase				=	mask_image.resize((512, 512)       )
  
  								lowerCAmelCase				=	'''a handsome man with ray-ban sunglasses'''
  
  								def make_inpaint_condition(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  															lowerCAmelCase				=	np.array(image.convert('''RGB'''       )       ).astype(np.floataa       ) / 2_5_5.0
  															lowerCAmelCase				=	np.array(image_mask.convert('''L'''       )       ).astype(np.floataa       ) / 2_5_5.0
  
  															assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
  															lowerCAmelCase				=	-1.0  # set as masked pixel
  															lowerCAmelCase				=	np.expand_dims(__SCREAMING_SNAKE_CASE	,    0       ).transpose(0	,    3	,    1	,    2       )
  															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
  															return image
  
  								lowerCAmelCase				=	make_inpaint_condition(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	pipe(
  								    __SCREAMING_SNAKE_CASE	,    image=__SCREAMING_SNAKE_CASE	,    mask_image=__SCREAMING_SNAKE_CASE	,    control_image=__SCREAMING_SNAKE_CASE	,    guidance_scale=9.0	,    eta=1.0	,    generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=20	,    output_type='''np'''	,    )
  								lowerCAmelCase				=	output.images[0]
  
  								assert image.shape == (512, 512, 3)
  
  								lowerCAmelCase				=	load_numpy(
  								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''       )
  
  								assert np.abs(expected_image - image       ).max() < 9e-2
  
  
  
  
style_context: import argparse
  import os
  import re
  
  import numpy as np
  import PIL
  import torch
  from timm import create_model
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
  
  from accelerate import Accelerator
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
  							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
  								lowerCAmelCase				=	file_names
  								lowerCAmelCase				=	image_transform
  								lowerCAmelCase				=	label_to_id
  
  
  	def __len__(							self       )		->Optional[int]:
  								return len(self.file_names       )
  
  
  
  
  	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
  								lowerCAmelCase				=	self.file_names[idx]
  								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
  								if self.image_transform is not None:
  															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
  								if self.label_to_id is not None:
  															lowerCAmelCase				=	self.label_to_id[label]
  								return {"image": image, "label": label}
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
  							# Initialize accelerator
  							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)
  
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							lowerCAmelCase				=	config['''lr''']
  							lowerCAmelCase				=	int(config['''num_epochs''']							)
  							lowerCAmelCase				=	int(config['''seed''']							)
  							lowerCAmelCase				=	int(config['''batch_size''']							)
  							lowerCAmelCase				=	config['''image_size''']
  							if not isinstance(snake_case__     ,						(list, tuple)							):
  														lowerCAmelCase				=	(image_size, image_size)
  
  							# Grab all the image filenames
  							lowerCAmelCase				=	[os.path.join(args.data_dir     ,						snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith('''.jpg'''							)]
  
  							# Build the label correspondences
  							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
  							lowerCAmelCase				=	list(set(snake_case__							)							)
  							id_to_label.sort()
  							lowerCAmelCase				=	{lbl: i for i, lbl in enumerate(snake_case__							)}
  
  							# Set the seed before splitting the data.
  							np.random.seed(snake_case__							)
  							torch.manual_seed(snake_case__							)
  							torch.cuda.manual_seed_all(snake_case__							)
  
  							# Split our filenames between train and validation
  							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
  							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
  							lowerCAmelCase				=	random_perm[:cut]
  							lowerCAmelCase				=	random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							lowerCAmelCase				=	Compose([RandomResizedCrop(snake_case__     ,						scale=(0.5, 1.0)							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset(
  							    [file_names[i] for i in train_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# For evaluation, we use a deterministic Resize
  							lowerCAmelCase				=	Compose([Resize(snake_case__							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset([file_names[i] for i in eval_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# Instantiate dataloaders.
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  
  							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  							lowerCAmelCase				=	create_model('''resnet50d'''     ,						pretrained=snake_case__     ,						num_classes=len(snake_case__							)							)
  
  							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  							lowerCAmelCase				=	model.to(accelerator.device							)
  
  							# Freezing the base model
  							for param in model.parameters():
  														lowerCAmelCase				=	False
  							for param in model.get_classifier().parameters():
  														lowerCAmelCase				=	True
  
  							# We normalize the batches of images to be a bit faster.
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''mean''']							)[None, :, None, None].to(accelerator.device							)
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''std''']							)[None, :, None, None].to(accelerator.device							)
  
  							# Instantiate optimizer
  							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)
  
  							# Instantiate learning rate scheduler
  							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)
  
  							# Prepare everything
  							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  							# prepare method.
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
  							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)
  
  							# Now we train the model
  							for epoch in range(snake_case__							):
  														model.train()
  														for step, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
  																					accelerator.backward(snake_case__							)
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														lowerCAmelCase				=	0
  														lowerCAmelCase				=	0
  														for _, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					with torch.no_grad():
  																												lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	outputs.argmax(dim=-1							)
  																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''label'''])							)
  																					lowerCAmelCase				=	predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														lowerCAmelCase				=	accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"							)
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
  							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
  							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
  							parser.add_argument(
  							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,						help='''Whether to use mixed precision. Choose'''
  							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
  							    '''and an Nvidia Ampere GPU.'''     ,						)
  							parser.add_argument(
  							    '''--checkpointing_steps'''     ,						type=snake_case__     ,						default=snake_case__     ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,						)
  							parser.add_argument('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''							)
  							lowerCAmelCase				=	parser.parse_args()
  							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
  							training_function(snake_case__     ,						snake_case__							)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  