


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          torch.manual_seed(0							)
          _a    	=				UNetaDModel(
              block_out_channels=(32, 64)    ,				layers_per_block=2    ,				sample_size=32    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,				)
          return model





     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				self.dummy_uncond_unet
          _a    	=				KarrasVeScheduler()

          _a    	=				KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase							)
          pipe.to(__UpperCAmelCase							)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

          _a    	=				torch.manual_seed(0							)
          _a    	=				pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type='''numpy'''							).images

          _a    	=				torch.manual_seed(0							)
          _a    	=				pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type='''numpy'''    ,				return_dict=__UpperCAmelCase							)[0]

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

          assert image.shape == (1, 32, 32, 3)
          _a    	=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				'''google/ncsnpp-celebahq-256'''
          _a    	=				UNetaDModel.from_pretrained(__UpperCAmelCase							)
          _a    	=				KarrasVeScheduler()

          _a    	=				KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase							)
          pipe.to(__UpperCAmelCase							)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

          _a    	=				torch.manual_seed(0							)
          _a    	=				pipe(num_inference_steps=20    ,				generator=__UpperCAmelCase    ,				output_type='''numpy'''							).images

          _a    	=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          _a    	=				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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



"""simple docstring"""






from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels