





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	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      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	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					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

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








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

