



'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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







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







    def       lowercase_	(				self			:  Any     ):
      a						: Tuple					       =					StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'     )
      a						: Dict					       =					sd_pipe.to(__snake_case     )
      sd_pipe.set_progress_bar_config(disable=__snake_case     )

      sd_pipe.set_scheduler('sample_euler'     )

      a						: Dict					       =					'A painting of a squirrel eating a burger'
      a						: List[Any]					       =					torch.manual_seed(0     )
      a						: List[Any]					       =					sd_pipe([prompt]						,      generator=__snake_case						,      guidance_scale=9.0						,      num_inference_steps=20						,      output_type='np'     )

      a						: Union[str, Any]					       =					output.images

      a						: Tuple					       =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      a						: List[str]					       =					np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]     )

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







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Optional[Any]					       =					StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'     )
      a						: List[Any]					       =					sd_pipe.to(__snake_case     )
      sd_pipe.set_progress_bar_config(disable=__snake_case     )

      sd_pipe.set_scheduler('sample_euler'     )

      a						: str					       =					'A painting of a squirrel eating a burger'
      a						: List[Any]					       =					torch.manual_seed(0     )
      a						: List[str]					       =					sd_pipe([prompt]						,      generator=__snake_case						,      guidance_scale=9.0						,      num_inference_steps=20						,      output_type='np'     )

      a						: int					       =					output.images

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

      assert image.shape == (1, 5_12, 5_12, 3)
      a						: str					       =					np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-1







    def       lowercase_	(				self			:  str     ):
      a						: Any					       =					StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'     )
      a						: str					       =					sd_pipe.to(__snake_case     )
      sd_pipe.set_progress_bar_config(disable=__snake_case     )

      sd_pipe.set_scheduler('sample_dpmpp_2m'     )

      a						: Dict					       =					'A painting of a squirrel eating a burger'
      a						: Union[str, Any]					       =					torch.manual_seed(0     )
      a						: List[str]					       =					sd_pipe(
          [prompt]						,      generator=__snake_case						,      guidance_scale=7.5						,      num_inference_steps=15						,      output_type='np'						,      use_karras_sigmas=__snake_case						,      )

      a						: List[str]					       =					output.images

      a						: List[Any]					       =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      a						: Optional[int]					       =					np.array(
          [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]     )

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase: Optional[int]							=						{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: int							=						[
         'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GraphormerForGraphClassification',
         'GraphormerModel',
         'GraphormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_graphormer import (
              GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              GraphormerForGraphClassification,
              GraphormerModel,
              GraphormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: str							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)