




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):
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.dummy_uncond_unet
             __lowerCamelCase   =						KarrasVeScheduler()

             __lowerCamelCase   =						KarrasVePipeline(unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_   )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(num_inference_steps=2    ,       generator=UpperCamelCase_    ,       output_type="""numpy"""   ).images

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(num_inference_steps=2    ,       generator=UpperCamelCase_    ,       output_type="""numpy"""    ,       return_dict=UpperCamelCase_   )[0]

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

             assert image.shape == (1, 32, 32, 3)
             __lowerCamelCase   =						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):
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						"""google/ncsnpp-celebahq-256"""
             __lowerCamelCase   =						UNetaDModel.from_pretrained(UpperCamelCase_   )
             __lowerCamelCase   =						KarrasVeScheduler()

             __lowerCamelCase   =						KarrasVePipeline(unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_   )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(num_inference_steps=20    ,       generator=UpperCamelCase_    ,       output_type="""numpy"""   ).images

             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             assert image.shape == (1, 2_56, 2_56, 3)
             __lowerCamelCase   =						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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase_									=	{}


try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['MLukeTokenizer']

if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mluke import MLukeTokenizer


else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

