


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__				:Optional[Any]			   =	{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__				:List[Any]			   =	[
					    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TableTransformerForObjectDetection""",
					    """TableTransformerModel""",
					    """TableTransformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_table_transformer import (
					    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TableTransformerConfig,
					    TableTransformerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_table_transformer import (
										    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TableTransformerForObjectDetection,
										    TableTransformerModel,
										    TableTransformerPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__				:int			   =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""






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      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	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   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					: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      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

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

