






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 		snake_case							(     unittest.TestCase   ):





     @property
     def 	lowerCamelCase       (  self     )		->							Optional[Any]:
        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     )		->							Dict:
        lowerCamelCase											="""cpu"""
        lowerCamelCase											=self.dummy_uncond_unet
        lowerCamelCase											=DDPMScheduler()

        lowerCamelCase											=DDPMPipeline(unet=_UpperCAmelCase       ,   scheduler=_UpperCAmelCase     )
        ddpm.to(_UpperCAmelCase     )
        ddpm.set_progress_bar_config(disable=_UpperCAmelCase     )

        lowerCamelCase											=torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
        lowerCamelCase											=ddpm(generator=_UpperCAmelCase       ,   num_inference_steps=2       ,   output_type="""numpy"""     ).images

        lowerCamelCase											=torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
        lowerCamelCase											=ddpm(generator=_UpperCAmelCase       ,   num_inference_steps=2       ,   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(
            [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]     )

        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 	lowerCamelCase       (  self     )		->							Optional[Any]:
        lowerCamelCase											=self.dummy_uncond_unet
        lowerCamelCase											=DDPMScheduler(prediction_type="""sample"""     )

        lowerCamelCase											=DDPMPipeline(unet=_UpperCAmelCase       ,   scheduler=_UpperCAmelCase     )
        ddpm.to(_UpperCAmelCase     )
        ddpm.set_progress_bar_config(disable=_UpperCAmelCase     )

        lowerCamelCase											=torch.manual_seed(0     )
        lowerCamelCase											=ddpm(generator=_UpperCAmelCase       ,   num_inference_steps=2       ,   output_type="""numpy"""     ).images

        lowerCamelCase											=torch.manual_seed(0     )
        lowerCamelCase											=ddpm(generator=_UpperCAmelCase       ,   num_inference_steps=2       ,   output_type="""numpy"""     )[0]

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

        assert image.shape == (1, 32, 32, 3)
        lowerCamelCase											=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 		snake_case							(     unittest.TestCase   ):





     def 	lowerCamelCase       (  self     )		->							Tuple:
        lowerCamelCase											="""google/ddpm-cifar10-32"""

        lowerCamelCase											=UNetaDModel.from_pretrained(_UpperCAmelCase     )
        lowerCamelCase											=DDPMScheduler.from_pretrained(_UpperCAmelCase     )

        lowerCamelCase											=DDPMPipeline(unet=_UpperCAmelCase       ,   scheduler=_UpperCAmelCase     )
        ddpm.to(_UpperCAmelCase     )
        ddpm.set_progress_bar_config(disable=_UpperCAmelCase     )

        lowerCamelCase											=torch.manual_seed(0     )
        lowerCamelCase											=ddpm(generator=_UpperCAmelCase       ,   output_type="""numpy"""     ).images

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

        assert image.shape == (1, 32, 32, 3)
        lowerCamelCase											=np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]     )
        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







from typing import TYPE_CHECKING

from ...utils import _LazyModule


_UpperCAmelCase				:	Dict    						=    {'tokenization_bert_japanese': ['BertJapaneseTokenizer', 'CharacterTokenizer', 'MecabTokenizer']}


if TYPE_CHECKING:
   from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
   import sys

   _UpperCAmelCase				:	Tuple    						=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)