


'''simple docstring'''



import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_lowercase    :    Dict			     =  logging.get_logger(__name__)

enable_full_determinism()






class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Union[str, Any]							= UNetaDModel
				__magic_name__       :   Union[str, Any]							= "sample"




				@property
				def   a__(	self    :				Dict   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			4
										UpperCAmelCase         =			3
										UpperCAmelCase         =			(32, 32)

										UpperCAmelCase         =			floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor([10]   ).to(lowerCAmelCase   )

										return {"sample": noise, "timestep": time_step}




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

										"""simple docstring"""




										return (3, 32, 32)




				@property
				def   a__(	self    :				int   )->  str:

										"""simple docstring"""




										return (3, 32, 32)





				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''block_out_channels''': (32, 64),
										    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
										    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
										    '''attention_head_dim''': 3,
										    '''out_channels''': 3,
										    '''in_channels''': 3,
										    '''layers_per_block''': 2,
										    '''sample_size''': 32,
										}
										UpperCAmelCase         =			self.dummy_input
										return init_dict, inputs_dict







class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Any							= UNetaDModel
				__magic_name__       :   str							= "sample"




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

										"""simple docstring"""




										UpperCAmelCase         =			4
										UpperCAmelCase         =			4
										UpperCAmelCase         =			(32, 32)

										UpperCAmelCase         =			floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor([10]   ).to(lowerCAmelCase   )

										return {"sample": noise, "timestep": time_step}




				@property
				def   a__(	self    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										return (4, 32, 32)




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

										"""simple docstring"""




										return (4, 32, 32)




				def   a__(	self    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''sample_size''': 32,
										    '''in_channels''': 4,
										    '''out_channels''': 4,
										    '''layers_per_block''': 2,
										    '''block_out_channels''': (32, 64),
										    '''attention_head_dim''': 32,
										    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
										    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
										}
										UpperCAmelCase         =			self.dummy_input
										return init_dict, inputs_dict




				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   ,					output_loading_info=lowerCAmelCase   )

										self.assertIsNotNone(lowerCAmelCase   )
										self.assertEqual(len(loading_info['''missing_keys''']   )   ,					0   )

										model.to(lowerCAmelCase   )
										UpperCAmelCase         =			model(**self.dummy_input   ).sample

										assert image is not None, "Make sure output is not None"




				@unittest.skipIf(torch_device != '''cuda'''   ,					'''This test is supposed to run on GPU'''   )
				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   ,					output_loading_info=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										UpperCAmelCase         =			model(**self.dummy_input   ).sample

										assert image is not None, "Make sure output is not None"




				@unittest.skipIf(torch_device != '''cuda'''   ,					'''This test is supposed to run on GPU'''   )
				def   a__(	self    :				Tuple   )->  int:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   ,					output_loading_info=lowerCAmelCase   )
										model_accelerate.to(lowerCAmelCase   )
										model_accelerate.eval()

										UpperCAmelCase         =			torch.randn(
										    1   ,					model_accelerate.config.in_channels   ,					model_accelerate.config.sample_size   ,					model_accelerate.config.sample_size   ,					generator=torch.manual_seed(0   )   ,					)
										UpperCAmelCase         =			noise.to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor([10] * noise.shape[0]   ).to(lowerCAmelCase   )

										UpperCAmelCase         =			model_accelerate(lowerCAmelCase   ,					lowerCAmelCase   )['''sample''']

										# two models don't need to stay in the device at the same time
										del model_accelerate
										torch.cuda.empty_cache()
										gc.collect()

										UpperCAmelCase       ,       UpperCAmelCase         =			UNetaDModel.from_pretrained(
										    '''fusing/unet-ldm-dummy-update'''   ,					output_loading_info=lowerCAmelCase   ,					low_cpu_mem_usage=lowerCAmelCase   )
										model_normal_load.to(lowerCAmelCase   )
										model_normal_load.eval()
										UpperCAmelCase         =			model_normal_load(lowerCAmelCase   ,					lowerCAmelCase   )['''sample''']

										assert torch_all_close(lowerCAmelCase   ,					lowerCAmelCase   ,					rtol=1E-3   )





				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   )
										model.eval()
										model.to(lowerCAmelCase   )

										UpperCAmelCase         =			torch.randn(
										    1   ,					model.config.in_channels   ,					model.config.sample_size   ,					model.config.sample_size   ,					generator=torch.manual_seed(0   )   ,					)
										UpperCAmelCase         =			noise.to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor([10] * noise.shape[0]   ).to(lowerCAmelCase   )

										with torch.no_grad():
																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   ).sample

										UpperCAmelCase         =			output[0, -1, -3:, -3:].flatten().cpu()
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]   )
										# fmt: on

										self.assertTrue(torch_all_close(lowerCAmelCase   ,					lowerCAmelCase   ,					rtol=1E-3   )   )







class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   int							= UNetaDModel
				__magic_name__       :   List[Any]							= "sample"




				@property
				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Union[str, Any]=(32, 32)   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			4
										UpperCAmelCase         =			3

										UpperCAmelCase         =			floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor(batch_size * [10]   ).to(dtype=torch.intaa   ,					device=lowerCAmelCase   )

										return {"sample": noise, "timestep": time_step}




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

										"""simple docstring"""




										return (3, 32, 32)




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

										"""simple docstring"""




										return (3, 32, 32)




				def   a__(	self    :				Dict   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''block_out_channels''': [32, 64, 64, 64],
										    '''in_channels''': 3,
										    '''layers_per_block''': 1,
										    '''out_channels''': 3,
										    '''time_embedding_type''': '''fourier''',
										    '''norm_eps''': 1E-6,
										    '''mid_block_scale_factor''': math.sqrt(2.0   ),
										    '''norm_num_groups''': None,
										    '''down_block_types''': [
										        '''SkipDownBlock2D''',
										        '''AttnSkipDownBlock2D''',
										        '''SkipDownBlock2D''',
										        '''SkipDownBlock2D''',
										    ],
										    '''up_block_types''': [
										        '''SkipUpBlock2D''',
										        '''SkipUpBlock2D''',
										        '''AttnSkipUpBlock2D''',
										        '''SkipUpBlock2D''',
										    ],
										}
										UpperCAmelCase         =			self.dummy_input
										return init_dict, inputs_dict




				@slow
				def   a__(	self    :				Any   )->  str:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   ,					output_loading_info=lowerCAmelCase   )
										self.assertIsNotNone(lowerCAmelCase   )
										self.assertEqual(len(loading_info['''missing_keys''']   )   ,					0   )

										model.to(lowerCAmelCase   )
										UpperCAmelCase         =			self.dummy_input
										UpperCAmelCase         =			floats_tensor((4, 3) + (256, 256)   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			noise
										UpperCAmelCase         =			model(**lowerCAmelCase   )

										assert image is not None, "Make sure output is not None"




				@slow
				def   a__(	self    :				Optional[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
										model.to(lowerCAmelCase   )

										UpperCAmelCase         =			4
										UpperCAmelCase         =			3
										UpperCAmelCase         =			(256, 256)

										UpperCAmelCase         =			torch.ones((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor(batch_size * [1E-4]   ).to(lowerCAmelCase   )

										with torch.no_grad():
																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   ).sample

										UpperCAmelCase         =			output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]   )
										# fmt: on

										self.assertTrue(torch_all_close(lowerCAmelCase   ,					lowerCAmelCase   ,					rtol=1E-2   )   )




				def   a__(	self    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''   )
										model.to(lowerCAmelCase   )

										UpperCAmelCase         =			4
										UpperCAmelCase         =			3
										UpperCAmelCase         =			(32, 32)

										UpperCAmelCase         =			torch.ones((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor(batch_size * [1E-4]   ).to(lowerCAmelCase   )

										with torch.no_grad():
																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   ).sample

										UpperCAmelCase         =			output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]   )
										# fmt: on

										self.assertTrue(torch_all_close(lowerCAmelCase   ,					lowerCAmelCase   ,					rtol=1E-2   )   )





				def   a__(	self    :				Union[str, Any]   )->  Optional[int]:

										"""simple docstring"""




										pass









'''simple docstring'''



def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						assert column_title.isupper()
						UpperCAmelCase         =			0
						UpperCAmelCase         =			len(A  ) - 1
						UpperCAmelCase         =			0

						while index >= 0:
												UpperCAmelCase         =			(ord(column_title[index]  ) - 64) * pow(26   ,     A  )
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





