



"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class       UpperCamelCase  (					unittest.TestCase				):





     """simple docstring"""






     @property
     def 						A__  (					self :							Dict ):
          torch.manual_seed(0 )
          A__				=   UNetaDModel(
              block_out_channels=(3_2, 6_4)	,      layers_per_block=2	,      sample_size=3_2	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
          return model





     def 						A__  (					self :							Any ):
          A__				=   '''cpu'''
          A__				=   self.dummy_uncond_unet
          A__				=   DDPMScheduler()

          A__				=   DDPMPipeline(unet=_lowerCamelCase	,      scheduler=_lowerCamelCase )
          ddpm.to(_lowerCamelCase )
          ddpm.set_progress_bar_config(disable=_lowerCamelCase )

          A__				=   torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
          A__				=   ddpm(generator=_lowerCamelCase	,      num_inference_steps=2	,      output_type='''numpy''' ).images

          A__				=   torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
          A__				=   ddpm(generator=_lowerCamelCase	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_lowerCamelCase )[0]

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

          assert image.shape == (1, 3_2, 3_2, 3)
          A__				=   np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04] )

          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 						A__  (					self :							List[str] ):
          A__				=   self.dummy_uncond_unet
          A__				=   DDPMScheduler(prediction_type='''sample''' )

          A__				=   DDPMPipeline(unet=_lowerCamelCase	,      scheduler=_lowerCamelCase )
          ddpm.to(_lowerCamelCase )
          ddpm.set_progress_bar_config(disable=_lowerCamelCase )

          A__				=   torch.manual_seed(0 )
          A__				=   ddpm(generator=_lowerCamelCase	,      num_inference_steps=2	,      output_type='''numpy''' ).images

          A__				=   torch.manual_seed(0 )
          A__				=   ddpm(generator=_lowerCamelCase	,      num_inference_steps=2	,      output_type='''numpy''' )[0]

          A__				=   image[0, -3:, -3:, -1]
          A__				=   image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          A__				=   1E-2 if torch_device != '''mps''' else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten() ).max() < tolerance


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





     """simple docstring"""






     def 						A__  (					self :							Dict ):
          A__				=   '''google/ddpm-cifar10-32'''

          A__				=   UNetaDModel.from_pretrained(_lowerCamelCase )
          A__				=   DDPMScheduler.from_pretrained(_lowerCamelCase )

          A__				=   DDPMPipeline(unet=_lowerCamelCase	,      scheduler=_lowerCamelCase )
          ddpm.to(_lowerCamelCase )
          ddpm.set_progress_bar_config(disable=_lowerCamelCase )

          A__				=   torch.manual_seed(0 )
          A__				=   ddpm(generator=_lowerCamelCase	,      output_type='''numpy''' ).images

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

          assert image.shape == (1, 3_2, 3_2, 3)
          A__				=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385] )
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case	:				List[Any]  					=			{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case	:				str  					=			[
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

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



