


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_					(lowercase__      ,					lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =StableDiffusionInstructPixaPixPipeline
				snake_case	    =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
				snake_case	    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				snake_case	    =IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case	    =IMAGE_TO_IMAGE_IMAGE_PARAMS





				def 						__UpperCamelCase    (				self)  -> Any:
											torch.manual_seed(0)
											a__      =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       ,  )
											a__      =PNDMScheduler(skip_prk_steps=lowercase_)
											torch.manual_seed(0)
											a__      =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)
											a__      =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       ,  )
											a__      =CLIPTextModel(lowercase_)
											a__      =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=0)  -> Optional[Any]:
											a__      =floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(lowercase_)).to(lowercase_)
											a__      =image.cpu().permute(0       ,  2       ,  3       ,  1)[0]
											a__      =Image.fromarray(np.uinta(lowercase_)).convert('RGB')
											if str(lowercase_).startswith('mps'):
																		a__      =torch.manual_seed(lowercase_)
											else:
																		a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      ={
											    '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 						__UpperCamelCase    (				self)  -> Dict:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator
											a__      =self.get_dummy_components()
											a__      =StableDiffusionInstructPixaPixPipeline(**lowercase_)
											a__      =sd_pipe.to(lowercase_)
											sd_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =sd_pipe(**lowercase_).images
											a__      =image[0, -3:, -3:, -1]
											assert image.shape == (1, 32, 32, 3)
											a__      =np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15])

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





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator
											a__      =self.get_dummy_components()
											a__      =StableDiffusionInstructPixaPixPipeline(**lowercase_)
											a__      =sd_pipe.to(lowercase_)
											sd_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      ='french fries'
											a__      =sd_pipe(**lowercase_       ,  negative_prompt=lowercase_)
											a__      =output.images
											a__      =image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__      =np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31])

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





				def 						__UpperCamelCase    (				self)  -> str:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator
											a__      =self.get_dummy_components()
											a__      =StableDiffusionInstructPixaPixPipeline(**lowercase_)
											a__      =sd_pipe.to(lowercase_)
											sd_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =[inputs['prompt']] * 2

											a__      =np.array(inputs['image']).astype(np.floataa) / 2_55.0
											a__      =torch.from_numpy(lowercase_).unsqueeze(0).to(lowercase_)
											a__      =image / 2 + 0.5
											a__      =image.permute(0       ,  3       ,  1       ,  2)
											a__      =image.repeat(2       ,  1       ,  1       ,  1)

											a__      =sd_pipe(**lowercase_).images
											a__      =image[-1, -3:, -3:, -1]

											assert image.shape == (2, 32, 32, 3)
											a__      =np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79])

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





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator
											a__      =self.get_dummy_components()
											a__      =EulerAncestralDiscreteScheduler(
											    beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  beta_schedule='scaled_linear')
											a__      =StableDiffusionInstructPixaPixPipeline(**lowercase_)
											a__      =sd_pipe.to(lowercase_)
											sd_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =sd_pipe(**lowercase_).images
											a__      =image[0, -3:, -3:, -1]

											a__      =[round(lowercase_       ,  4) for x in image_slice.flatten().tolist()]
											print(','.join([str(lowercase_) for x in slice]))

											assert image.shape == (1, 32, 32, 3)
											a__      =np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86])

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





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3)





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =self.get_dummy_components()
											a__      =StableDiffusionInstructPixaPixPipeline(**lowercase_)
											a__      =VaeImageProcessor(do_resize=lowercase_       ,  do_normalize=lowercase_)
											a__      =pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)

											a__      =pipe(**self.get_dummy_inputs_by_type(lowercase_       ,  input_image_type='pt'))[0]

											a__      =components['vae']
											a__      =self.get_dummy_inputs_by_type(lowercase_       ,  input_image_type='pt')

											for image_param in self.image_latents_params:
																		if image_param in inputs.keys():
																									a__      =vae.encode(inputs[image_param]).latent_dist.mode()

											a__      =pipe(**lowercase_)[0]

											a__      =np.abs(out - out_latents_inputs).max()
											self.assertLess(lowercase_       ,  1e-4       ,  'passing latents as image input generate different result from passing image')







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





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





				def 						__UpperCamelCase    (				self       ,  lowercase_=0)  -> Dict:
											a__      =torch.manual_seed(lowercase_)
											a__      =load_image(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg')
											a__      ={
											    '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 						__UpperCamelCase    (				self)  -> str:
											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    'timbrooks/instruct-pix2pix'       ,  safety_checker=lowercase_)
											pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing()

											a__      =self.get_inputs()
											a__      =pipe(**lowercase_).images
											a__      =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											a__      =np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55])

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





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    'timbrooks/instruct-pix2pix'       ,  safety_checker=lowercase_)
											a__      =LMSDiscreteScheduler.from_config(pipe.scheduler.config)
											pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing()

											a__      =self.get_inputs()
											a__      =pipe(**lowercase_).images
											a__      =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											a__      =np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01])

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





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    'timbrooks/instruct-pix2pix'       ,  safety_checker=lowercase_)
											a__      =DDIMScheduler.from_config(pipe.scheduler.config)
											pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing()

											a__      =self.get_inputs()
											a__      =pipe(**lowercase_).images
											a__      =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											a__      =np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53])

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





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =0

											def callback_fn(lowercase_       ,  lowercase_       ,  lowercase_) -> None:
																		a__      =True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																									a__      =latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									a__      =latents[0, -3:, -3:, -1]
																									a__      =np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83])

																									assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
																		elif step == 2:
																									a__      =latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									a__      =latents[0, -3:, -3:, -1]
																									a__      =np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15])

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

											a__      =False

											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    'timbrooks/instruct-pix2pix'       ,  safety_checker=lowercase_       ,  torch_dtype=torch.floataa)
											a__      =pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing()

											a__      =self.get_inputs()
											pipe(**lowercase_       ,  callback=lowercase_       ,  callback_steps=1)
											assert callback_fn.has_been_called
											assert number_of_steps == 3





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    'timbrooks/instruct-pix2pix'       ,  safety_checker=lowercase_       ,  torch_dtype=torch.floataa)
											a__      =pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing(1)
											pipe.enable_sequential_cpu_offload()

											a__      =self.get_inputs()
											a__      =pipe(**lowercase_)

											a__      =torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 10**9





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											a__      =inputs['image'].resize((504, 504))

											a__      ='timbrooks/instruct-pix2pix'
											a__      =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    lowercase_       ,  safety_checker=lowercase_       ,  )
											pipe.to(lowercase_)
											pipe.set_progress_bar_config(disable=lowercase_)
											pipe.enable_attention_slicing()

											a__      =pipe(**lowercase_)
											a__      =output.images[0]

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

											assert image.shape == (504, 504, 3)
											a__      =np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90])

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





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument(
	    '--original_config_file',
	    type=str,
	    required=True,
	    help='The YAML config file corresponding to the original architecture.',
	)
	parser.add_argument(
	    '--num_in_channels',
	    default=None,
	    type=int,
	    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
	)
	parser.add_argument(
	    '--image_size',
	    default=512,
	    type=int,
	    help=(
	        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
	        ' Base. Use 768 for Stable Diffusion v2.'
	    ),
	)
	parser.add_argument(
	    '--extract_ema',
	    action='store_true',
	    help=(
	        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
	        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
	        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
	    ),
	)
	parser.add_argument(
	    '--upcast_attention',
	    action='store_true',
	    help=(
	        'Whether the attention computation should always be upcasted. This is necessary when running stable'
	        ' diffusion 2.1.'
	    ),
	)
	parser.add_argument(
	    '--from_safetensors',
	    action='store_true',
	    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
	)
	parser.add_argument(
	    '--to_safetensors',
	    action='store_true',
	    help='Whether to store pipeline in safetensors format or not.',
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')






	def   _lowercase(					__a				:						Optional[Any]     ):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(f"""could not parse string as bool {string}"""     )

	parser.add_argument(
	    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
	)

	parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

