



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 				__A					( A_					, A_					, A_					, unittest.TestCase      ):
			UpperCamelCase					:Dict     =			StableDiffusionInstructPixaPixPipeline
			UpperCamelCase					:str     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
			UpperCamelCase					:Union[str, Any]     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase					:str     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase					:Union[str, Any]     =			IMAGE_TO_IMAGE_IMAGE_PARAMS





			def        _snake_case    (self				):
						torch.manual_seed(0				)
						lowerCamelCase__ :      Tuple								=		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							,    )
						lowerCamelCase__ :      int								=		PNDMScheduler(skip_prk_steps=__magic_name__				)
						torch.manual_seed(0				)
						lowerCamelCase__ :      List[Any]								=		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__ :      Tuple								=		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__ :      str								=		CLIPTextModel(__magic_name__				)
						lowerCamelCase__ :      List[str]								=		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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





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

						lowerCamelCase__ :      List[Any]								=		self.get_dummy_inputs(__magic_name__				)
						lowerCamelCase__ :      Any								=		sd_pipe(**__magic_name__				).images
						lowerCamelCase__ :      List[Any]								=		image[0, -3:, -3:, -1]
						assert image.shape == (1, 32, 32, 3)
						lowerCamelCase__ :      int								=		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        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase__ :      Tuple								=		self.get_dummy_components()
						lowerCamelCase__ :      Tuple								=		StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
						lowerCamelCase__ :      Dict								=		sd_pipe.to(__magic_name__				)
						sd_pipe.set_progress_bar_config(disable=__magic_name__				)

						lowerCamelCase__ :      Any								=		self.get_dummy_inputs(__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		"""french fries"""
						lowerCamelCase__ :      Optional[int]								=		sd_pipe(**__magic_name__							,    negative_prompt=__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		output.images
						lowerCamelCase__ :      Dict								=		image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCamelCase__ :      int								=		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        _snake_case    (self				):
						lowerCamelCase__ :      int								=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase__ :      Union[str, Any]								=		self.get_dummy_components()
						lowerCamelCase__ :      Optional[Any]								=		StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		sd_pipe.to(__magic_name__				)
						sd_pipe.set_progress_bar_config(disable=__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		self.get_dummy_inputs(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		[inputs["""prompt"""]] * 2

						lowerCamelCase__ :      List[Any]								=		np.array(inputs["""image"""]				).astype(np.floataa				) / 2_55.0
						lowerCamelCase__ :      List[str]								=		torch.from_numpy(__magic_name__				).unsqueeze(0				).to(__magic_name__				)
						lowerCamelCase__ :      int								=		image / 2 + 0.5
						lowerCamelCase__ :      str								=		image.permute(0							,    3							,    1							,    2				)
						lowerCamelCase__ :      List[str]								=		image.repeat(2							,    1							,    1							,    1				)

						lowerCamelCase__ :      Optional[Any]								=		sd_pipe(**__magic_name__				).images
						lowerCamelCase__ :      Union[str, Any]								=		image[-1, -3:, -3:, -1]

						assert image.shape == (2, 32, 32, 3)
						lowerCamelCase__ :      int								=		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        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase__ :      int								=		self.get_dummy_components()
						lowerCamelCase__ :      Optional[int]								=		EulerAncestralDiscreteScheduler(
						    beta_start=0.0_00_85							,    beta_end=0.0_12							,    beta_schedule="""scaled_linear"""				)
						lowerCamelCase__ :      List[Any]								=		StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		sd_pipe.to(__magic_name__				)
						sd_pipe.set_progress_bar_config(disable=__magic_name__				)

						lowerCamelCase__ :      List[str]								=		self.get_dummy_inputs(__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		sd_pipe(**__magic_name__				).images
						lowerCamelCase__ :      Tuple								=		image[0, -3:, -3:, -1]

						lowerCamelCase__ :      Union[str, Any]								=		[round(__magic_name__							,    4				) for x in image_slice.flatten().tolist()]
						print(""",""".join([str(__magic_name__				) for x in slice]				)				)

						assert image.shape == (1, 32, 32, 3)
						lowerCamelCase__ :      Optional[int]								=		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        _snake_case    (self				):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.get_dummy_components()
						lowerCamelCase__ :      Any								=		StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		VaeImageProcessor(do_resize=__magic_name__							,    do_normalize=__magic_name__				)
						lowerCamelCase__ :      int								=		pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		pipe(**self.get_dummy_inputs_by_type(__magic_name__							,    input_image_type="""pt"""				)				)[0]

						lowerCamelCase__ :      Union[str, Any]								=		components["""vae"""]
						lowerCamelCase__ :      int								=		self.get_dummy_inputs_by_type(__magic_name__							,    input_image_type="""pt"""				)

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

						lowerCamelCase__ :      Optional[Any]								=		pipe(**__magic_name__				)[0]

						lowerCamelCase__ :      List[Any]								=		np.abs(out - out_latents_inputs				).max()
						self.assertLess(__magic_name__							,    1E-4							,    """passing latents as image input generate different result from passing image"""				)







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





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





			def        _snake_case    (self							,    __magic_name__=0				):
						lowerCamelCase__ :      Optional[Any]								=		torch.manual_seed(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""				)
						lowerCamelCase__ :      List[str]								=		{
						    """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				):
						lowerCamelCase__ :      str								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""							,    safety_checker=__magic_name__				)
						pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing()

						lowerCamelCase__ :      int								=		self.get_inputs()
						lowerCamelCase__ :      Optional[int]								=		pipe(**__magic_name__				).images
						lowerCamelCase__ :      Tuple								=		image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase__ :      Optional[int]								=		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        _snake_case    (self				):
						lowerCamelCase__ :      int								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""							,    safety_checker=__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing()

						lowerCamelCase__ :      str								=		self.get_inputs()
						lowerCamelCase__ :      Optional[Any]								=		pipe(**__magic_name__				).images
						lowerCamelCase__ :      Any								=		image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase__ :      Union[str, Any]								=		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        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""							,    safety_checker=__magic_name__				)
						lowerCamelCase__ :      Dict								=		DDIMScheduler.from_config(pipe.scheduler.config				)
						pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase__ :      str								=		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        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		0

						def callback_fn(__magic_name__							,    __magic_name__							,    __magic_name__				) -> None:
									lowerCamelCase__ :      Tuple								=		True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
												lowerCamelCase__ :      List[Any]								=		latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												lowerCamelCase__ :      int								=		latents[0, -3:, -3:, -1]
												lowerCamelCase__ :      int								=		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:
												lowerCamelCase__ :      Tuple								=		latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												lowerCamelCase__ :      List[Any]								=		latents[0, -3:, -3:, -1]
												lowerCamelCase__ :      Tuple								=		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

						lowerCamelCase__ :      Optional[int]								=		False

						lowerCamelCase__ :      List[str]								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""							,    safety_checker=__magic_name__							,    torch_dtype=torch.floataa				)
						lowerCamelCase__ :      int								=		pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing()

						lowerCamelCase__ :      Tuple								=		self.get_inputs()
						pipe(**__magic_name__							,    callback=__magic_name__							,    callback_steps=1				)
						assert callback_fn.has_been_called
						assert number_of_steps == 3





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

						lowerCamelCase__ :      List[Any]								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""							,    safety_checker=__magic_name__							,    torch_dtype=torch.floataa				)
						lowerCamelCase__ :      List[str]								=		pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing(1				)
						pipe.enable_sequential_cpu_offload()

						lowerCamelCase__ :      int								=		self.get_inputs()
						lowerCamelCase__ :      Dict								=		pipe(**__magic_name__				)

						lowerCamelCase__ :      List[str]								=		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				):
						lowerCamelCase__ :      Dict								=		self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						lowerCamelCase__ :      List[str]								=		inputs["""image"""].resize((504, 504)				)

						lowerCamelCase__ :      List[str]								=		"""timbrooks/instruct-pix2pix"""
						lowerCamelCase__ :      Tuple								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    __magic_name__							,    safety_checker=__magic_name__							,    )
						pipe.to(__magic_name__				)
						pipe.set_progress_bar_config(disable=__magic_name__				)
						pipe.enable_attention_slicing()

						lowerCamelCase__ :      List[str]								=		pipe(**__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		output.images[0]

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

						assert image.shape == (504, 504, 3)
						lowerCamelCase__ :      List[str]								=		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








def      _A				(UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Dict )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		[1]
			for i in range(2      ,			UpperCamelCase ):
						factorials.append(factorials[-1] * i )
			assert 0 <= k < factorials[-1] * n, "k out of bounds"

			lowerCamelCase__ :      Union[str, Any]								=		[]
			lowerCamelCase__ :      Optional[Any]								=		list(range(UpperCamelCase ) )

			# Find permutation
			while factorials:
						lowerCamelCase__ :      Union[str, Any]								=		factorials.pop()
						lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		divmod(UpperCamelCase      ,			UpperCamelCase )
						permutation.append(elements[number] )
						elements.remove(elements[number] )
			permutation.append(elements[0] )

			return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()



