



"""simple docstring"""





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   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Union[str, Any]					)				->		List[Any]:


             '''simple docstring'''




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



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


             '''simple docstring'''




             UpperCAmelCase_    				=							1
             UpperCAmelCase_    				=							3
             UpperCAmelCase_    				=							(32, 32)

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



       @property
       def   lowercase__       (							self    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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=_UpperCAmelCase	,       only_cross_attention=(True, True, False)	,       num_class_embeds=100	,       )
             return model



       @property
       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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    :						Tuple					)				->		Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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(_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[Any]					)				->		Tuple:


             '''simple docstring'''




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

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

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

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

             UpperCAmelCase_    				=							output.images

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

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

             UpperCAmelCase_    				=							low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)
             UpperCAmelCase_    				=							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   lowercase__       (							self    :						Any					)				->		int:


             '''simple docstring'''




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

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

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

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

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







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


             '''simple docstring'''




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

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

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

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

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

             UpperCAmelCase_    				=							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   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Tuple:


             '''simple docstring'''




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

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

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

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

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



       def   lowercase__       (							self    :						Tuple					)				->		Any:


             '''simple docstring'''




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

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

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

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

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







       def   lowercase__       (							self    :						int					)				->		Optional[Any]:


             '''simple docstring'''




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

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

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

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

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

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









"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      =		{
    """configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Swinv2ForImageClassification""",
      """Swinv2ForMaskedImageModeling""",
      """Swinv2Model""",
      """Swinv2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




