






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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					Union[str, Any]	):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@property
	def 						_A (		self				:					Dict	):
		UpperCamelCase     :Tuple          =	1
		UpperCamelCase     :Optional[Any]          =	3
		UpperCamelCase     :Union[str, Any]          =	(32, 32)

		UpperCamelCase     :List[Any]          =	floats_tensor((batch_size, num_channels) + sizes ,  rng=random.Random(0	)	).to(__lowerCamelCase	)
		return image





	@property
	def 						_A (		self				:					Optional[Any]	):
		torch.manual_seed(0	)
		UpperCamelCase     :str          =	UNetaDConditionModel(
		    block_out_channels=(32, 32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=7 ,  out_channels=4 ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""") ,  up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""") ,  cross_attention_dim=32 ,  attention_head_dim=8 ,  use_linear_projection=__lowerCamelCase ,  only_cross_attention=(True, True, False) ,  num_class_embeds=100 ,  )
		return model





	@property
	def 						_A (		self				:					Optional[Any]	):
		torch.manual_seed(0	)
		UpperCamelCase     :Dict          =	AutoencoderKL(
		    block_out_channels=[32, 32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,  latent_channels=4 ,  )
		return model





	@property
	def 						_A (		self				:					str	):
		torch.manual_seed(0	)
		UpperCamelCase     :Union[str, Any]          =	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=1_000 ,  hidden_act="""gelu""" ,  projection_dim=512 ,  )
		return CLIPTextModel(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :List[str]          =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		UpperCamelCase     :Optional[Any]          =	self.dummy_cond_unet_upscale
		UpperCamelCase     :Optional[Any]          =	DDPMScheduler()
		UpperCamelCase     :Optional[int]          =	DDIMScheduler(prediction_type="""v_prediction"""	)
		UpperCamelCase     :List[str]          =	self.dummy_vae
		UpperCamelCase     :Optional[Any]          =	self.dummy_text_encoder
		UpperCamelCase     :Tuple          =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

		UpperCamelCase     :List[str]          =	self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1	)[0]
		UpperCamelCase     :int          =	Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("""RGB"""	).resize((64, 64)	)

		# make sure here that pndm scheduler skips prk
		UpperCamelCase     :List[Any]          =	StableDiffusionUpscalePipeline(
		    unet=__lowerCamelCase ,  low_res_scheduler=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  max_noise_level=350 ,  )
		UpperCamelCase     :Any          =	sd_pipe.to(__lowerCamelCase	)
		sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	"""A painting of a squirrel eating a burger"""
		UpperCamelCase     :Optional[Any]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
		UpperCamelCase     :List[str]          =	sd_pipe(
		    [prompt] ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  guidance_scale=6.0 ,  noise_level=20 ,  num_inference_steps=2 ,  output_type="""np""" ,  )

		UpperCamelCase     :Tuple          =	output.images

		UpperCamelCase     :Optional[Any]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
		UpperCamelCase     :Optional[int]          =	sd_pipe(
		    [prompt] ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  guidance_scale=6.0 ,  noise_level=20 ,  num_inference_steps=2 ,  output_type="""np""" ,  return_dict=__lowerCamelCase ,  )[0]

		UpperCamelCase     :Union[str, Any]          =	image[0, -3:, -3:, -1]
		UpperCamelCase     :List[str]          =	image_from_tuple[0, -3:, -3:, -1]

		UpperCamelCase     :Dict          =	low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		UpperCamelCase     :str          =	np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]	)

		assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :Dict          =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		UpperCamelCase     :Optional[Any]          =	self.dummy_cond_unet_upscale
		UpperCamelCase     :Dict          =	DDPMScheduler()
		UpperCamelCase     :int          =	DDIMScheduler(prediction_type="""v_prediction"""	)
		UpperCamelCase     :List[str]          =	self.dummy_vae
		UpperCamelCase     :Optional[int]          =	self.dummy_text_encoder
		UpperCamelCase     :str          =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

		UpperCamelCase     :List[str]          =	self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1	)[0]
		UpperCamelCase     :Tuple          =	Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("""RGB"""	).resize((64, 64)	)

		# make sure here that pndm scheduler skips prk
		UpperCamelCase     :str          =	StableDiffusionUpscalePipeline(
		    unet=__lowerCamelCase ,  low_res_scheduler=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  max_noise_level=350 ,  )
		UpperCamelCase     :List[str]          =	sd_pipe.to(__lowerCamelCase	)
		sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Union[str, Any]          =	"""A painting of a squirrel eating a burger"""
		UpperCamelCase     :Dict          =	sd_pipe(
		    2 * [prompt] ,  image=2 * [low_res_image] ,  guidance_scale=6.0 ,  noise_level=20 ,  num_inference_steps=2 ,  output_type="""np""" ,  )
		UpperCamelCase     :List[str]          =	output.images
		assert image.shape[0] == 2

		UpperCamelCase     :Any          =	torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
		UpperCamelCase     :Dict          =	sd_pipe(
		    [prompt] ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  num_images_per_prompt=2 ,  guidance_scale=6.0 ,  noise_level=20 ,  num_inference_steps=2 ,  output_type="""np""" ,  )
		UpperCamelCase     :Optional[Any]          =	output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != """cuda""" ,  """This test requires a GPU"""	)
	def 						_A (		self				:					Any	):
		UpperCamelCase     :Union[str, Any]          =	self.dummy_cond_unet_upscale
		UpperCamelCase     :Tuple          =	DDPMScheduler()
		UpperCamelCase     :str          =	DDIMScheduler(prediction_type="""v_prediction"""	)
		UpperCamelCase     :int          =	self.dummy_vae
		UpperCamelCase     :Union[str, Any]          =	self.dummy_text_encoder
		UpperCamelCase     :List[str]          =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

		UpperCamelCase     :int          =	self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1	)[0]
		UpperCamelCase     :Tuple          =	Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("""RGB"""	).resize((64, 64)	)

		# put models in fp16, except vae as it overflows in fp16
		UpperCamelCase     :str          =	unet.half()
		UpperCamelCase     :Optional[int]          =	text_encoder.half()

		# make sure here that pndm scheduler skips prk
		UpperCamelCase     :str          =	StableDiffusionUpscalePipeline(
		    unet=__lowerCamelCase ,  low_res_scheduler=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  max_noise_level=350 ,  )
		UpperCamelCase     :Tuple          =	sd_pipe.to(__lowerCamelCase	)
		sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[int]          =	"""A painting of a squirrel eating a burger"""
		UpperCamelCase     :Tuple          =	torch.manual_seed(0	)
		UpperCamelCase     :List[Any]          =	sd_pipe(
		    [prompt] ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  num_inference_steps=2 ,  output_type="""np""" ,  ).images

		UpperCamelCase     :str          =	low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)

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





	def 						_A (		self				:					Any	):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[Any]          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/sd2-upscale/low_res_cat.png"""	)
		UpperCamelCase     :str          =	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
		    """/upsampled_cat.npy"""	)

		UpperCamelCase     :List[Any]          =	"""stabilityai/stable-diffusion-x4-upscaler"""
		UpperCamelCase     :List[str]          =	StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase	)
		pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)
		pipe.enable_attention_slicing()

		UpperCamelCase     :Optional[int]          =	"""a cat sitting on a park bench"""

		UpperCamelCase     :str          =	torch.manual_seed(0	)
		UpperCamelCase     :List[Any]          =	pipe(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  output_type="""np""" ,  )
		UpperCamelCase     :int          =	output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image	).max() < 1E-3





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :Dict          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/sd2-upscale/low_res_cat.png"""	)
		UpperCamelCase     :Union[str, Any]          =	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
		    """/upsampled_cat_fp16.npy"""	)

		UpperCamelCase     :Any          =	"""stabilityai/stable-diffusion-x4-upscaler"""
		UpperCamelCase     :Union[str, Any]          =	StableDiffusionUpscalePipeline.from_pretrained(
		    __lowerCamelCase ,  torch_dtype=torch.floataa ,  )
		pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)
		pipe.enable_attention_slicing()

		UpperCamelCase     :Optional[int]          =	"""a cat sitting on a park bench"""

		UpperCamelCase     :str          =	torch.manual_seed(0	)
		UpperCamelCase     :Tuple          =	pipe(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  output_type="""np""" ,  )
		UpperCamelCase     :Any          =	output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image	).max() < 5E-1





	def 						_A (		self				:					str	):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		UpperCamelCase     :Tuple          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/sd2-upscale/low_res_cat.png"""	)

		UpperCamelCase     :Any          =	"""stabilityai/stable-diffusion-x4-upscaler"""
		UpperCamelCase     :Tuple          =	StableDiffusionUpscalePipeline.from_pretrained(
		    __lowerCamelCase ,  torch_dtype=torch.floataa ,  )
		pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)
		pipe.enable_attention_slicing(1	)
		pipe.enable_sequential_cpu_offload()

		UpperCamelCase     :Optional[Any]          =	"""a cat sitting on a park bench"""

		UpperCamelCase     :Any          =	torch.manual_seed(0	)
		UpperCamelCase     :Dict          =	pipe(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  generator=__lowerCamelCase ,  num_inference_steps=5 ,  output_type="""np""" ,  )

		UpperCamelCase     :List[Any]          =	torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9








def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  list[int]					)		->			bool:






	"""simple docstring"""



	if graph[path[curr_ind - 1]][next_ver] == 0:
		return False

	# 2. Validate that next vertex is not already in path
	return not any(vertex == next_ver for vertex in path					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  list[int]	,			__magic_name__					:  int					)		->			bool:






	"""simple docstring"""



	if curr_ind == len(__magic_name__					):
		# return whether path exists between current and starting vertices
		return graph[path[curr_ind - 1]][path[0]] == 1

	# Recursive Step
	for next_ver in range(0	,			len(__magic_name__					)					):
		if valid_connection(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					):
			# Insert current vertex  into path as next transition
			UpperCamelCase     :str          =	next_ver
			# Validate created path
			if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			curr_ind + 1					):
				return True
			# Backtrack
			UpperCamelCase     :Union[str, Any]          =	-1
	return False

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int = 0					)		->			list[int]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[-1] * (len(__magic_name__					) + 1)
	# initialize start and end of path with starting index
	UpperCamelCase     :Any          =	start_index
	# evaluate and if we find answer return path either return empty array
	return path if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			1					) else []
