
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE			  =  {
    '''configuration_clap''': [
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapAudioConfig''',
        '''ClapConfig''',
        '''ClapTextConfig''',
    ],
    '''processing_clap''': ['''ClapProcessor'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE			  =  [
          '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ClapModel''',
          '''ClapPreTrainedModel''',
          '''ClapTextModel''',
          '''ClapTextModelWithProjection''',
          '''ClapAudioModel''',
          '''ClapAudioModelWithProjection''',
      ]
      SCREAMING_SNAKE_CASE			  =  ['''ClapFeatureExtractor''']

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

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








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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_dpmpp_2m'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe(
                 [prompt] ,   generator=lowercase__ ,   guidance_scale=7.5 ,   num_inference_steps=1_5 ,   output_type='''np''' ,   use_karras_sigmas=lowercase__ ,   )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array(
                 [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]     )

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


