





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 					A__       (				unittest.TestCase      ):




   """simple docstring"""



   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

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

   @property
   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      List[str]					=							1
          a__		:      int					=							3
          a__		:      Dict					=							(32, 32)

          a__		:      Tuple					=							floats_tensor((batch_size, num_channels) + sizes   ,    rng=random.Random(0)).to(_a)
          return image

   @property
   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          torch.manual_seed(0)
          a__		:      List[Any]					=							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=_a   ,    only_cross_attention=(True, True, False)   ,    num_class_embeds=100   ,    )
          return model

   @property
   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          torch.manual_seed(0)
          a__		:      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 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          torch.manual_seed(0)
          a__		:      Optional[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=1000   ,    hidden_act='gelu'   ,    projection_dim=512   ,    )
          return CLIPTextModel(_a)

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

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

          a__		:      str					=							self.dummy_image.cpu().permute(0   ,    2   ,    3   ,    1)[0]
          a__		:      Tuple					=							Image.fromarray(np.uinta(_a)).convert('RGB').resize((64, 64))

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

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

          a__		:      Dict					=							output.images

          a__		:      Tuple					=							torch.Generator(device=_a).manual_seed(0)
          a__		:      str					=							sd_pipe(
              [prompt]   ,    image=_a   ,    generator=_a   ,    guidance_scale=6.0   ,    noise_level=20   ,    num_inference_steps=2   ,    output_type='np'   ,    return_dict=_a   ,    )[0]

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

          a__		:      Union[str, Any]					=							low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          a__		:      Tuple					=							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 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

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

          a__		:      Tuple					=							self.dummy_image.cpu().permute(0   ,    2   ,    3   ,    1)[0]
          a__		:      Optional[int]					=							Image.fromarray(np.uinta(_a)).convert('RGB').resize((64, 64))

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

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

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

   @unittest.skipIf(torch_device != 'cuda'   ,    'This test requires a GPU')
   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							self.dummy_cond_unet_upscale
          a__		:      Tuple					=							DDPMScheduler()
          a__		:      Dict					=							DDIMScheduler(prediction_type='v_prediction')
          a__		:      List[Any]					=							self.dummy_vae
          a__		:      Any					=							self.dummy_text_encoder
          a__		:      List[Any]					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          a__		:      Dict					=							self.dummy_image.cpu().permute(0   ,    2   ,    3   ,    1)[0]
          a__		:      Optional[Any]					=							Image.fromarray(np.uinta(_a)).convert('RGB').resize((64, 64))

          # put models in fp16, except vae as it overflows in fp16
          a__		:      Dict					=							unet.half()
          a__		:      Tuple					=							text_encoder.half()

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

          a__		:      Union[str, Any]					=							"A painting of a squirrel eating a burger"
          a__		:      Dict					=							torch.manual_seed(0)
          a__		:      Tuple					=							sd_pipe(
              [prompt]   ,    image=_a   ,    generator=_a   ,    num_inference_steps=2   ,    output_type='np'   ,    ).images

          a__		:      Optional[int]					=							low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)





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




   """simple docstring"""



   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

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

   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      Dict					=							load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png')
          a__		:      Optional[int]					=							load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat.npy')

          a__		:      str					=							"stabilityai/stable-diffusion-x4-upscaler"
          a__		:      Dict					=							StableDiffusionUpscalePipeline.from_pretrained(_a)
          pipe.to(_a)
          pipe.set_progress_bar_config(disable=_a)
          pipe.enable_attention_slicing()

          a__		:      Dict					=							"a cat sitting on a park bench"

          a__		:      str					=							torch.manual_seed(0)
          a__		:      str					=							pipe(
              prompt=_a   ,    image=_a   ,    generator=_a   ,    output_type='np'   ,    )
          a__		:      List[str]					=							output.images[0]

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

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          a__		:      List[str]					=							load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png')
          a__		:      Optional[Any]					=							load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat_fp16.npy')

          a__		:      Any					=							"stabilityai/stable-diffusion-x4-upscaler"
          a__		:      str					=							StableDiffusionUpscalePipeline.from_pretrained(
              _a   ,    torch_dtype=torch.floataa   ,    )
          pipe.to(_a)
          pipe.set_progress_bar_config(disable=_a)
          pipe.enable_attention_slicing()

          a__		:      Union[str, Any]					=							"a cat sitting on a park bench"

          a__		:      Optional[int]					=							torch.manual_seed(0)
          a__		:      Optional[int]					=							pipe(
              prompt=_a   ,    image=_a   ,    generator=_a   ,    output_type='np'   ,    )
          a__		:      List[str]					=							output.images[0]

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

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

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

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

          a__		:      Union[str, Any]					=							"stabilityai/stable-diffusion-x4-upscaler"
          a__		:      int					=							StableDiffusionUpscalePipeline.from_pretrained(
              _a   ,    torch_dtype=torch.floataa   ,    )
          pipe.to(_a)
          pipe.set_progress_bar_config(disable=_a)
          pipe.enable_attention_slicing(1)
          pipe.enable_sequential_cpu_offload()

          a__		:      List[str]					=							"a cat sitting on a park bench"

          a__		:      Optional[Any]					=							torch.manual_seed(0)
          a__		:      Dict					=							pipe(
              prompt=_a   ,    image=_a   ,    generator=_a   ,    num_inference_steps=5   ,    output_type='np'   ,    )

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




"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


