
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 ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      1
				_snake_case          =      3
				_snake_case          =      (32, 32)

				_snake_case          =      floats_tensor((batch_size, num_channels) + sizes      ,  rng=random.Random(0     )     ).to(lowercase     )
				return image



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      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=lowercase      ,  only_cross_attention=(True, True, False)      ,  num_class_embeds=100      ,  )
				return model



	@property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      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     :   Union[str, Any]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      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(lowercase     )



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.dummy_cond_unet_upscale
				_snake_case          =      DDPMScheduler()
				_snake_case          =      DDIMScheduler(prediction_type='v_prediction'     )
				_snake_case          =      self.dummy_vae
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

				_snake_case          =      self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1     )[0]
				_snake_case          =      Image.fromarray(np.uinta(lowercase     )     ).convert('RGB'     ).resize((64, 64)     )

				# make sure here that pndm scheduler skips prk
				_snake_case          =      StableDiffusionUpscalePipeline(
				    unet=lowercase      ,  low_res_scheduler=lowercase      ,  scheduler=lowercase      ,  vae=lowercase      ,  text_encoder=lowercase      ,  tokenizer=lowercase      ,  max_noise_level=350      ,  )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      'A painting of a squirrel eating a burger'
				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      sd_pipe(
				    [prompt]      ,  image=lowercase      ,  generator=lowercase      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type='np'      ,  )

				_snake_case          =      output.images

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      sd_pipe(
				    [prompt]      ,  image=lowercase      ,  generator=lowercase      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type='np'      ,  return_dict=lowercase      ,  )[0]

				_snake_case          =      image[0, -3:, -3:, -1]
				_snake_case          =      image_from_tuple[0, -3:, -3:, -1]

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


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.dummy_cond_unet_upscale
				_snake_case          =      DDPMScheduler()
				_snake_case          =      DDIMScheduler(prediction_type='v_prediction'     )
				_snake_case          =      self.dummy_vae
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

				_snake_case          =      self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1     )[0]
				_snake_case          =      Image.fromarray(np.uinta(lowercase     )     ).convert('RGB'     ).resize((64, 64)     )

				# make sure here that pndm scheduler skips prk
				_snake_case          =      StableDiffusionUpscalePipeline(
				    unet=lowercase      ,  low_res_scheduler=lowercase      ,  scheduler=lowercase      ,  vae=lowercase      ,  text_encoder=lowercase      ,  tokenizer=lowercase      ,  max_noise_level=350      ,  )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      'A painting of a squirrel eating a burger'
				_snake_case          =      sd_pipe(
				    2 * [prompt]      ,  image=2 * [low_res_image]      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type='np'      ,  )
				_snake_case          =      output.images
				assert image.shape[0] == 2

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      sd_pipe(
				    [prompt]      ,  image=lowercase      ,  generator=lowercase      ,  num_images_per_prompt=2      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type='np'      ,  )
				_snake_case          =      output.images
				assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,  'This test requires a GPU'     )
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_cond_unet_upscale
				_snake_case          =      DDPMScheduler()
				_snake_case          =      DDIMScheduler(prediction_type='v_prediction'     )
				_snake_case          =      self.dummy_vae
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

				_snake_case          =      self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1     )[0]
				_snake_case          =      Image.fromarray(np.uinta(lowercase     )     ).convert('RGB'     ).resize((64, 64)     )

				# put models in fp16, except vae as it overflows in fp16
				_snake_case          =      unet.half()
				_snake_case          =      text_encoder.half()

				# make sure here that pndm scheduler skips prk
				_snake_case          =      StableDiffusionUpscalePipeline(
				    unet=lowercase      ,  low_res_scheduler=lowercase      ,  scheduler=lowercase      ,  vae=lowercase      ,  text_encoder=lowercase      ,  tokenizer=lowercase      ,  max_noise_level=350      ,  )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      'A painting of a squirrel eating a burger'
				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      sd_pipe(
				    [prompt]      ,  image=lowercase      ,  generator=lowercase      ,  num_inference_steps=2      ,  output_type='np'      ,  ).images

				_snake_case          =      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 ):





	'''simple docstring'''



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/sd2-upscale/low_res_cat.png'     )
				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
				    '/upsampled_cat.npy'     )

				_snake_case          =      'stabilityai/stable-diffusion-x4-upscaler'
				_snake_case          =      StableDiffusionUpscalePipeline.from_pretrained(lowercase     )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

				_snake_case          =      'a cat sitting on a park bench'

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(
				    prompt=lowercase      ,  image=lowercase      ,  generator=lowercase      ,  output_type='np'      ,  )
				_snake_case          =      output.images[0]

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



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/sd2-upscale/low_res_cat.png'     )
				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
				    '/upsampled_cat_fp16.npy'     )

				_snake_case          =      'stabilityai/stable-diffusion-x4-upscaler'
				_snake_case          =      StableDiffusionUpscalePipeline.from_pretrained(
				    lowercase      ,  torch_dtype=torch.floataa      ,  )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

				_snake_case          =      'a cat sitting on a park bench'

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(
				    prompt=lowercase      ,  image=lowercase      ,  generator=lowercase      ,  output_type='np'      ,  )
				_snake_case          =      output.images[0]

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





	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

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

				_snake_case          =      'stabilityai/stable-diffusion-x4-upscaler'
				_snake_case          =      StableDiffusionUpscalePipeline.from_pretrained(
				    lowercase      ,  torch_dtype=torch.floataa      ,  )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing(1     )
				pipe.enable_sequential_cpu_offload()

				_snake_case          =      'a cat sitting on a park bench'

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(
				    prompt=lowercase      ,  image=lowercase      ,  generator=lowercase      ,  num_inference_steps=5      ,  output_type='np'      ,  )

				_snake_case          =      torch.cuda.max_memory_allocated()
				# make sure that less than 2.9 GB is allocated
				assert mem_bytes < 2.9 * 10**9

import baseaa






def 	a_		(	__lowercase       :     str			)			->  bytes:
			return baseaa.aaaencode(string.encode('utf-8'			)			)






def 	a_		(	__lowercase       :     bytes			)			->  str:
			return baseaa.aaadecode(__lowercase			).decode('utf-8'			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()