






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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												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  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.dummy_uncond_unet
												a__          =  KarrasVeScheduler()

												a__          =  KarrasVePipeline(unet=_lowercase				,		scheduler=_lowercase    )
												pipe.to(_lowercase    )
												pipe.set_progress_bar_config(disable=_lowercase    )

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

												a__          =  torch.manual_seed(0    )
												a__          =  pipe(num_inference_steps=2				,		generator=_lowercase				,		output_type='''numpy'''				,		return_dict=_lowercase    )[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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  """google/ncsnpp-celebahq-256"""
												a__          =  UNetaDModel.from_pretrained(_lowercase    )
												a__          =  KarrasVeScheduler()

												a__          =  KarrasVePipeline(unet=_lowercase				,		scheduler=_lowercase    )
												pipe.to(_lowercase    )
												pipe.set_progress_bar_config(disable=_lowercase    )

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

												a__          =  image[0, -3:, -3:, -1]
												assert image.shape == (1, 256, 256, 3)
												a__          =  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]    )

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










"""simple docstring"""







from typing import TYPE_CHECKING

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


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	__a       :	List[Any]             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


