'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   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 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.dummy_uncond_unet
								_lowerCAmelCase										=PNDMScheduler()

								_lowerCAmelCase										=PNDMPipeline(unet=lowerCamelCase_		,						scheduler=lowerCamelCase_				)
								pndm.to(lowerCamelCase_				)
								pndm.set_progress_bar_config(disable=lowerCamelCase_				)

								_lowerCAmelCase										=torch.manual_seed(0				)
								_lowerCAmelCase										=pndm(generator=lowerCamelCase_		,						num_inference_steps=20		,						output_type='numpy'				).images

								_lowerCAmelCase										=torch.manual_seed(0				)
								_lowerCAmelCase										=pndm(generator=lowerCamelCase_		,						num_inference_steps=20		,						output_type='numpy'		,						return_dict=lowerCamelCase_				)[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([1.0, 1.0, 0.0, 1.0, 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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										='google/ddpm-cifar10-32'

								_lowerCAmelCase										=UNetaDModel.from_pretrained(lowerCamelCase_				)
								_lowerCAmelCase										=PNDMScheduler()

								_lowerCAmelCase										=PNDMPipeline(unet=lowerCamelCase_		,						scheduler=lowerCamelCase_				)
								pndm.to(lowerCamelCase_				)
								pndm.set_progress_bar_config(disable=lowerCamelCase_				)
								_lowerCAmelCase										=torch.manual_seed(0				)
								_lowerCAmelCase										=pndm(generator=lowerCamelCase_		,						output_type='numpy'				).images

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

								assert image.shape == (1, 32, 32, 3)
								_lowerCAmelCase										=np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]				)

								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,
)


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

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





