


"""simple docstring"""







import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 		_A    (    unittest.TestCase    ):







    """simple docstring"""




    def       __snake_case				(   self							:       Any):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    @property
    def       __snake_case				(   self							:       Optional[int]):
      a		:						Optional[int] 				=     1
      a		:						Optional[int] 				=     3
      a		:						Optional[int] 				=     (32, 32)

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






    @property
    def       __snake_case				(   self							:       Dict):
      torch.manual_seed(0)
      a		:						int 				=     UNetaDConditionModel(
          block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=4							,					out_channels=4							,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,					cross_attention_dim=32							,					)
      return model






    @property
    def       __snake_case				(   self							:       List[str]):
      torch.manual_seed(0)
      a		:						Tuple 				=     AutoencoderKL(
          block_out_channels=[32, 64]							,					in_channels=3							,					out_channels=3							,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,					latent_channels=4							,					)
      return model






    @property
    def       __snake_case				(   self							:       Optional[int]):
      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							,					)
      return CLIPTextModel(__UpperCAmelCase)






    @property
    def       __snake_case				(   self							:       Tuple):
      def extract(*__UpperCAmelCase							:       str							,					**__UpperCAmelCase							:       List[str]):






        class 		_A    :







            """simple docstring"""




            def __init__(   self							:       Any):
              a		:						Optional[Any] 				=     torch.ones([0])






            def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       List[Any]):
              self.pixel_values.to(__UpperCAmelCase)
              return self






        return Out()

      return extract






    def       __snake_case				(   self							:       Any):
      a		:						List[str] 				=     "cpu"  # ensure determinism for the device-dependent torch.Generator
      a		:						Any 				=     self.dummy_cond_unet
      a		:						Optional[Any] 				=     DDIMScheduler(
          beta_start=0.00_085							,					beta_end=0.012							,					beta_schedule="scaled_linear"							,					clip_sample=__UpperCAmelCase							,					set_alpha_to_one=__UpperCAmelCase							,					)

      a		:						Any 				=     self.dummy_vae
      a		:						List[Any] 				=     self.dummy_text_encoder
      a		:						List[str] 				=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      # make sure here that pndm scheduler skips prk
      a		:						List[str] 				=     StableDiffusionPipeline(
          unet=__UpperCAmelCase							,					scheduler=__UpperCAmelCase							,					vae=__UpperCAmelCase							,					text_encoder=__UpperCAmelCase							,					tokenizer=__UpperCAmelCase							,					safety_checker=__UpperCAmelCase							,					feature_extractor=self.dummy_extractor							,					)
      a		:						Tuple 				=     sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						int 				=     "A painting of a squirrel eating a burger"

      a		:						Optional[int] 				=     torch.Generator(device=__UpperCAmelCase).manual_seed(0)
      a		:						str 				=     sd_pipe([prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=6.0							,					num_inference_steps=2							,					output_type="np")
      a		:						str 				=     output.images

      a		:						int 				=     torch.Generator(device=__UpperCAmelCase).manual_seed(0)
      a		:						Optional[int] 				=     sd_pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=6.0							,					num_inference_steps=2							,					output_type="np"							,					return_dict=__UpperCAmelCase							,					)[0]

      a		:						List[str] 				=     image[0, -3:, -3:, -1]
      a		:						Dict 				=     image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a		:						List[str] 				=     np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867])

      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       __snake_case				(   self							:       str):
      a		:						Union[str, Any] 				=     "cpu"  # ensure determinism for the device-dependent torch.Generator
      a		:						Union[str, Any] 				=     self.dummy_cond_unet
      a		:						List[str] 				=     PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      a		:						List[Any] 				=     self.dummy_vae
      a		:						List[Any] 				=     self.dummy_text_encoder
      a		:						Tuple 				=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      # make sure here that pndm scheduler skips prk
      a		:						List[str] 				=     StableDiffusionPipeline(
          unet=__UpperCAmelCase							,					scheduler=__UpperCAmelCase							,					vae=__UpperCAmelCase							,					text_encoder=__UpperCAmelCase							,					tokenizer=__UpperCAmelCase							,					safety_checker=__UpperCAmelCase							,					feature_extractor=self.dummy_extractor							,					)
      a		:						Any 				=     sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						Tuple 				=     "A painting of a squirrel eating a burger"
      a		:						Tuple 				=     torch.Generator(device=__UpperCAmelCase).manual_seed(0)
      a		:						Optional[Any] 				=     sd_pipe([prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=6.0							,					num_inference_steps=2							,					output_type="np")

      a		:						Dict 				=     output.images

      a		:						List[str] 				=     torch.Generator(device=__UpperCAmelCase).manual_seed(0)
      a		:						List[Any] 				=     sd_pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=6.0							,					num_inference_steps=2							,					output_type="np"							,					return_dict=__UpperCAmelCase							,					)[0]

      a		:						Optional[int] 				=     image[0, -3:, -3:, -1]
      a		:						Any 				=     image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a		:						List[Any] 				=     np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994])

      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       __snake_case				(   self							:       Optional[Any]):
      a		:						Tuple 				=     StableDiffusionPipeline.from_pretrained(
          "hf-internal-testing/tiny-stable-diffusion-lms-pipe"							,					safety_checker=__UpperCAmelCase)
      assert isinstance(__UpperCAmelCase							,					__UpperCAmelCase)
      assert isinstance(pipe.scheduler							,					__UpperCAmelCase)
      assert pipe.safety_checker is None

      a		:						Optional[Any] 				=     pipe("example prompt"							,					num_inference_steps=2).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(__UpperCAmelCase)
        a		:						Tuple 				=     StableDiffusionPipeline.from_pretrained(__UpperCAmelCase)

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      a		:						Tuple 				=     pipe("example prompt"							,					num_inference_steps=2).images[0]
      assert image is not None






    @unittest.skipIf(torch_device != "cuda"							,					"This test requires a GPU")
    def       __snake_case				(   self							:       Optional[int]):
      a		:						int 				=     self.dummy_cond_unet
      a		:						Optional[int] 				=     PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      a		:						List[str] 				=     self.dummy_vae
      a		:						Union[str, Any] 				=     self.dummy_text_encoder
      a		:						int 				=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      # put models in fp16
      a		:						Union[str, Any] 				=     unet.half()
      a		:						Union[str, Any] 				=     vae.half()
      a		:						Union[str, Any] 				=     bert.half()

      # make sure here that pndm scheduler skips prk
      a		:						Optional[int] 				=     StableDiffusionPipeline(
          unet=__UpperCAmelCase							,					scheduler=__UpperCAmelCase							,					vae=__UpperCAmelCase							,					text_encoder=__UpperCAmelCase							,					tokenizer=__UpperCAmelCase							,					safety_checker=__UpperCAmelCase							,					feature_extractor=self.dummy_extractor							,					)
      a		:						Any 				=     sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						List[str] 				=     "A painting of a squirrel eating a burger"
      a		:						Optional[Any] 				=     sd_pipe([prompt]							,					num_inference_steps=2							,					output_type="np").images

      assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class 		_A    (    unittest.TestCase    ):







    """simple docstring"""




    def       __snake_case				(   self							:       str):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def       __snake_case				(   self							:       Tuple):
      a		:						Dict 				=     "cuda"
      a		:						Tuple 				=     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
      a		:						int 				=     pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						List[Any] 				=     "a photo of a cat"
      a		:						List[str] 				=     {
          "editing_prompt": ["sunglasses"],
          "reverse_editing_direction": [False],
          "edit_warmup_steps": 10,
          "edit_guidance_scale": 6,
          "edit_threshold": 0.95,
          "edit_momentum_scale": 0.5,
          "edit_mom_beta": 0.6,
      }

      a		:						int 				=     3
      a		:						Union[str, Any] 				=     7

      # no sega enabled
      a		:						Any 				=     torch.Generator(__UpperCAmelCase)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Any 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					)

      a		:						Union[str, Any] 				=     output.images
      a		:						str 				=     image[0, -3:, -3:, -1]
      a		:						List[str] 				=     [
          0.34_673_113,
          0.38_492_733,
          0.37_597_352,
          0.34_086_335,
          0.35_650_748,
          0.35_579_205,
          0.3_384_763,
          0.34_340_236,
          0.3_573_271,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

      # with sega enabled
      # generator = torch.manual_seed(seed)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Union[str, Any] 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					**__UpperCAmelCase							,					)

      a		:						List[str] 				=     output.images
      a		:						str 				=     image[0, -3:, -3:, -1]
      a		:						str 				=     [
          0.41_887_826,
          0.37_728_766,
          0.30_138_272,
          0.41_416_335,
          0.41_664_985,
          0.36_283_392,
          0.36_191_246,
          0.43_364_465,
          0.43_001_732,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    def       __snake_case				(   self							:       str):
      a		:						Union[str, Any] 				=     "cuda"
      a		:						Dict 				=     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
      a		:						Union[str, Any] 				=     pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						List[str] 				=     "an image of a crowded boulevard, realistic, 4k"
      a		:						int 				=     {
          "editing_prompt": "crowd, crowded, people",
          "reverse_editing_direction": True,
          "edit_warmup_steps": 10,
          "edit_guidance_scale": 8.3,
          "edit_threshold": 0.9,
          "edit_momentum_scale": 0.5,
          "edit_mom_beta": 0.6,
      }

      a		:						str 				=     9
      a		:						int 				=     7

      # no sega enabled
      a		:						str 				=     torch.Generator(__UpperCAmelCase)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Optional[Any] 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					)

      a		:						List[str] 				=     output.images
      a		:						Union[str, Any] 				=     image[0, -3:, -3:, -1]
      a		:						Union[str, Any] 				=     [
          0.43_497_998,
          0.91_814_065,
          0.7_540_739,
          0.55_580_205,
          0.8_467_265,
          0.5_389_691,
          0.62_574_506,
          0.58_897_763,
          0.50_926_757,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

      # with sega enabled
      # generator = torch.manual_seed(seed)
      generator.manual_seed(__UpperCAmelCase)
      a		:						str 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					**__UpperCAmelCase							,					)

      a		:						Dict 				=     output.images
      a		:						List[str] 				=     image[0, -3:, -3:, -1]
      a		:						List[str] 				=     [
          0.3_089_719,
          0.30_500_144,
          0.29_016_042,
          0.30_630_964,
          0.325_687,
          0.29_419_225,
          0.2_908_091,
          0.28_723_598,
          0.27_696_294,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    def       __snake_case				(   self							:       Union[str, Any]):
      a		:						int 				=     "cuda"
      a		:						str 				=     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
      a		:						Tuple 				=     pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						int 				=     "a castle next to a river"
      a		:						Optional[Any] 				=     {
          "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
          "reverse_editing_direction": False,
          "edit_warmup_steps": [15, 18],
          "edit_guidance_scale": 6,
          "edit_threshold": [0.9, 0.8],
          "edit_momentum_scale": 0.5,
          "edit_mom_beta": 0.6,
      }

      a		:						Union[str, Any] 				=     48
      a		:						Tuple 				=     7

      # no sega enabled
      a		:						Union[str, Any] 				=     torch.Generator(__UpperCAmelCase)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Optional[Any] 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					)

      a		:						str 				=     output.images
      a		:						Union[str, Any] 				=     image[0, -3:, -3:, -1]
      a		:						Optional[Any] 				=     [
          0.75_163_555,
          0.76_037_145,
          0.61_785,
          0.9_189_673,
          0.8_627_701,
          0.85_189_694,
          0.8_512_813,
          0.87_012_076,
          0.8_312_857,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

      # with sega enabled
      # generator = torch.manual_seed(seed)
      generator.manual_seed(__UpperCAmelCase)
      a		:						int 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					**__UpperCAmelCase							,					)

      a		:						Optional[Any] 				=     output.images
      a		:						int 				=     image[0, -3:, -3:, -1]
      a		:						int 				=     [
          0.73_553_365,
          0.7_537_271,
          0.74_341_905,
          0.66_480_356,
          0.6_472_925,
          0.63_039_416,
          0.64_812_905,
          0.6_749_717,
          0.6_517_102,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    def       __snake_case				(   self							:       str):
      a		:						str 				=     "cuda"
      a		:						List[str] 				=     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,					torch_dtype=torch.floataa)
      a		:						Optional[Any] 				=     pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						List[Any] 				=     "a photo of a cat"
      a		:						List[str] 				=     {
          "editing_prompt": ["sunglasses"],
          "reverse_editing_direction": [False],
          "edit_warmup_steps": 10,
          "edit_guidance_scale": 6,
          "edit_threshold": 0.95,
          "edit_momentum_scale": 0.5,
          "edit_mom_beta": 0.6,
      }

      a		:						Optional[Any] 				=     3
      a		:						List[str] 				=     7

      # no sega enabled
      a		:						Tuple 				=     torch.Generator(__UpperCAmelCase)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Optional[Any] 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					)

      a		:						Optional[Any] 				=     output.images
      a		:						Dict 				=     image[0, -3:, -3:, -1]
      a		:						int 				=     [
          0.34_887_695,
          0.3_876_953,
          0.375,
          0.34_423_828,
          0.3_581_543,
          0.35_717_773,
          0.3_383_789,
          0.34_570_312,
          0.359_375,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

      # with sega enabled
      # generator = torch.manual_seed(seed)
      generator.manual_seed(__UpperCAmelCase)
      a		:						Optional[Any] 				=     pipe(
          [prompt]							,					generator=__UpperCAmelCase							,					guidance_scale=__UpperCAmelCase							,					num_inference_steps=50							,					output_type="np"							,					width=512							,					height=512							,					**__UpperCAmelCase							,					)

      a		:						Optional[int] 				=     output.images
      a		:						str 				=     image[0, -3:, -3:, -1]
      a		:						Tuple 				=     [
          0.42_285_156,
          0.36_914_062,
          0.29_077_148,
          0.42_041_016,
          0.41_918_945,
          0.35_498_047,
          0.3_618_164,
          0.4_423_828,
          0.43_115_234,
      ]

      assert image.shape == (1, 512, 512, 3)

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2










"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






