

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    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[str]:
         """simple docstring"""





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

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		1
         A_							       =		3
         A_							       =		(3_2, 3_2)

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

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			str:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		UNetaDConditionModel(
             block_out_channels=(3_2, 3_2, 6_4)			,						layers_per_block=2			,						sample_size=3_2			,						in_channels=7			,						out_channels=4			,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')			,						up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')			,						cross_attention_dim=3_2			,						attention_head_dim=8			,						use_linear_projection=lowerCamelCase__			,						only_cross_attention=(True, True, False)			,						num_class_embeds=1_0_0			,						)
         return model

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4]			,						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     UpperCamelCase					(				self	:    Optional[int]       ) ->			Optional[int]:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		CLIPTextConfig(
             bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						intermediate_size=3_7			,						layer_norm_eps=1e-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_0_0_0			,						hidden_act='''gelu'''			,						projection_dim=5_1_2			,						)
         return CLIPTextModel(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Optional[Any]:
         """simple docstring"""





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

         A_							       =		self.dummy_image.cpu().permute(0			,						2			,						3			,						1       )[0]
         A_							       =		Image.fromarray(np.uinta(lowerCamelCase__       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )

         # make sure here that pndm scheduler skips prk
         A_							       =		StableDiffusionUpscalePipeline(
             unet=lowerCamelCase__			,						low_res_scheduler=lowerCamelCase__			,						scheduler=lowerCamelCase__			,						vae=lowerCamelCase__			,						text_encoder=lowerCamelCase__			,						tokenizer=lowerCamelCase__			,						max_noise_level=3_5_0			,						)
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		'''A painting of a squirrel eating a burger'''
         A_							       =		torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
         A_							       =		sd_pipe(
             [prompt]			,						image=lowerCamelCase__			,						generator=lowerCamelCase__			,						guidance_scale=6.0			,						noise_level=2_0			,						num_inference_steps=2			,						output_type='''np'''			,						)

         A_							       =		output.images

         A_							       =		torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
         A_							       =		sd_pipe(
             [prompt]			,						image=lowerCamelCase__			,						generator=lowerCamelCase__			,						guidance_scale=6.0			,						noise_level=2_0			,						num_inference_steps=2			,						output_type='''np'''			,						return_dict=lowerCamelCase__			,						)[0]

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

         A_							       =		low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         A_							       =		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     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





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

         A_							       =		self.dummy_image.cpu().permute(0			,						2			,						3			,						1       )[0]
         A_							       =		Image.fromarray(np.uinta(lowerCamelCase__       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )

         # make sure here that pndm scheduler skips prk
         A_							       =		StableDiffusionUpscalePipeline(
             unet=lowerCamelCase__			,						low_res_scheduler=lowerCamelCase__			,						scheduler=lowerCamelCase__			,						vae=lowerCamelCase__			,						text_encoder=lowerCamelCase__			,						tokenizer=lowerCamelCase__			,						max_noise_level=3_5_0			,						)
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		'''A painting of a squirrel eating a burger'''
         A_							       =		sd_pipe(
             2 * [prompt]			,						image=2 * [low_res_image]			,						guidance_scale=6.0			,						noise_level=2_0			,						num_inference_steps=2			,						output_type='''np'''			,						)
         A_							       =		output.images
         assert image.shape[0] == 2

         A_							       =		torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
         A_							       =		sd_pipe(
             [prompt]			,						image=lowerCamelCase__			,						generator=lowerCamelCase__			,						num_images_per_prompt=2			,						guidance_scale=6.0			,						noise_level=2_0			,						num_inference_steps=2			,						output_type='''np'''			,						)
         A_							       =		output.images
         assert image.shape[0] == 2





   @unittest.skipIf(torch_device != '''cuda'''			,						'''This test requires a GPU'''       )
   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





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

         A_							       =		self.dummy_image.cpu().permute(0			,						2			,						3			,						1       )[0]
         A_							       =		Image.fromarray(np.uinta(lowerCamelCase__       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )

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

         # make sure here that pndm scheduler skips prk
         A_							       =		StableDiffusionUpscalePipeline(
             unet=lowerCamelCase__			,						low_res_scheduler=lowerCamelCase__			,						scheduler=lowerCamelCase__			,						vae=lowerCamelCase__			,						text_encoder=lowerCamelCase__			,						tokenizer=lowerCamelCase__			,						max_noise_level=3_5_0			,						)
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

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



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

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]       ) ->			int:
         """simple docstring"""





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

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

         A_							       =		'''a cat sitting on a park bench'''

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

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image       ).max() < 1e-3

   def     UpperCamelCase					(				self	:    List[str]       ) ->			List[Any]:
         """simple docstring"""





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

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

         A_							       =		'''a cat sitting on a park bench'''

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

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image       ).max() < 5e-1





   def     UpperCamelCase					(				self	:    Dict       ) ->			Optional[Any]:
         """simple docstring"""





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

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

         A_							       =		'''stabilityai/stable-diffusion-x4-upscaler'''
         A_							       =		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()

         A_							       =		'''a cat sitting on a park bench'''

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

         A_							       =		torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 1_0**9








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      return int((input_a, input_a).count(0   ) != 0   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      assert nand_gate(0		,				0   ) == 1
      assert nand_gate(0		,				1   ) == 1
      assert nand_gate(1		,				0   ) == 1
      assert nand_gate(1		,				1   ) == 0


if __name__ == "__main__":
      print(nand_gate(0, 0))
      print(nand_gate(0, 1))
      print(nand_gate(1, 0))
      print(nand_gate(1, 1))





