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 __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      1
     SCREAMING_SNAKE_CASE__										=      3
     SCREAMING_SNAKE_CASE__										=      (32, 32)

     SCREAMING_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      )
     SCREAMING_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=1_00       ,	)
     return model




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_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			:				int      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_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=10_00       ,	hidden_act="""gelu"""       ,	projection_dim=5_12       ,	)
     return CLIPTextModel(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.dummy_image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((64, 64)      )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__										=      StableDiffusionUpscalePipeline(
         unet=_lowercase       ,	low_res_scheduler=_lowercase       ,	scheduler=_lowercase       ,	vae=_lowercase       ,	text_encoder=_lowercase       ,	tokenizer=_lowercase       ,	max_noise_level=3_50       ,	)
     SCREAMING_SNAKE_CASE__										=      sd_pipe.to(_lowercase      )
     sd_pipe.set_progress_bar_config(disable=_lowercase      )

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

     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(0      )
     SCREAMING_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]

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

     SCREAMING_SNAKE_CASE__										=      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]      )

     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			:				int      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.dummy_image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((64, 64)      )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__										=      StableDiffusionUpscalePipeline(
         unet=_lowercase       ,	low_res_scheduler=_lowercase       ,	scheduler=_lowercase       ,	vae=_lowercase       ,	text_encoder=_lowercase       ,	tokenizer=_lowercase       ,	max_noise_level=3_50       ,	)
     SCREAMING_SNAKE_CASE__										=      sd_pipe.to(_lowercase      )
     sd_pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      """A painting of a squirrel eating a burger"""
     SCREAMING_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"""       ,	)
     SCREAMING_SNAKE_CASE__										=      output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(0      )
     SCREAMING_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"""       ,	)
     SCREAMING_SNAKE_CASE__										=      output.images
     assert image.shape[0] == 2




   @unittest.skipIf(torch_device != """cuda"""       ,	"""This test requires a GPU"""      )
   def 	__a						(    self			:				str      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.dummy_image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((64, 64)      )

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

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__										=      StableDiffusionUpscalePipeline(
         unet=_lowercase       ,	low_res_scheduler=_lowercase       ,	scheduler=_lowercase       ,	vae=_lowercase       ,	text_encoder=_lowercase       ,	tokenizer=_lowercase       ,	max_noise_level=3_50       ,	)
     SCREAMING_SNAKE_CASE__										=      sd_pipe.to(_lowercase      )
     sd_pipe.set_progress_bar_config(disable=_lowercase      )

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

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







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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      """stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__										=      StableDiffusionUpscalePipeline.from_pretrained(_lowercase      )
     pipe.to(_lowercase      )
     pipe.set_progress_bar_config(disable=_lowercase      )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__										=      """a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__										=      torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipe(
         prompt=_lowercase       ,	image=_lowercase       ,	generator=_lowercase       ,	output_type="""np"""       ,	)
     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image      ).max() < 1E-3




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      """stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__										=      StableDiffusionUpscalePipeline.from_pretrained(
         _lowercase       ,	torch_dtype=torch.floataa       ,	)
     pipe.to(_lowercase      )
     pipe.set_progress_bar_config(disable=_lowercase      )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__										=      """a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__										=      torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipe(
         prompt=_lowercase       ,	image=_lowercase       ,	generator=_lowercase       ,	output_type="""np"""       ,	)
     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image      ).max() < 5E-1




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__										=      """stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_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()

     SCREAMING_SNAKE_CASE__										=      """a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__										=      torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipe(
         prompt=_lowercase       ,	image=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=5       ,	output_type="""np"""       ,	)

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

from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
