






'''simple docstring'''







import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			str 						= ["""a""", """b""", """c"""]

														# Defaults to last layer if both are None
														lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= get_aligned_output_features_output_indices(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							["""c"""]  )
														self.assertEqual(UpperCamelCase__    ,							[2]  )

														# Out indices set to match out features
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= get_aligned_output_features_output_indices(["""a""", """c"""]    ,							UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							["""a""", """c"""]  )
														self.assertEqual(UpperCamelCase__    ,							[0, 2]  )

														# Out features set to match out indices
														lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= get_aligned_output_features_output_indices(UpperCamelCase__    ,							[0, 2]    ,							UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							["""a""", """c"""]  )
														self.assertEqual(UpperCamelCase__    ,							[0, 2]  )

														# Out features selected from negative indices
														lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= get_aligned_output_features_output_indices(UpperCamelCase__    ,							[-3, -1]    ,							UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							["""a""", """c"""]  )
														self.assertEqual(UpperCamelCase__    ,							[-3, -1]  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														# Stage names must be set
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(["""a""", """b"""]    ,							(0, 1)    ,							UpperCamelCase__  )

														# Out features must be a list
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(("""a""", """b""")    ,							(0, 1)    ,							["""a""", """b"""]  )

														# Out features must be a subset of stage names
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(["""a""", """b"""]    ,							(0, 1)    ,							["""a"""]  )

														# Out indices must be a list or tuple
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(UpperCamelCase__    ,							0    ,							["""a""", """b"""]  )

														# Out indices must be a subset of stage names
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(UpperCamelCase__    ,							(0, 1)    ,							["""a"""]  )

														# Out features and out indices must be the same length
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(["""a""", """b"""]    ,							(0,)    ,							["""a""", """b""", """c"""]  )

														# Out features should match out indices
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(["""a""", """b"""]    ,							(0, 2)    ,							["""a""", """b""", """c"""]  )

														# Out features and out indices should be in order
														with self.assertRaises(UpperCamelCase__  ):
																					verify_out_features_out_indices(["""b""", """a"""]    ,							(0, 1)    ,							["""a""", """b"""]  )

														# Check passes with valid inputs
														verify_out_features_out_indices(["""a""", """b""", """d"""]    ,							(0, 1, -1)    ,							["""a""", """b""", """c""", """d"""]  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= BackboneMixin()

														lowerCamelCase__      :			Optional[Any] 						= ["""a""", """b""", """c"""]
														lowerCamelCase__      :			List[str] 						= ["""a""", """c"""]
														lowerCamelCase__      :			Any 						= [0, 2]

														# Check that the output features and indices are set correctly
														self.assertEqual(backbone.out_features    ,							["""a""", """c"""]  )
														self.assertEqual(backbone.out_indices    ,							[0, 2]  )

														# Check out features and indices are updated correctly
														lowerCamelCase__      :			Optional[Any] 						= ["""a""", """b"""]
														self.assertEqual(backbone.out_features    ,							["""a""", """b"""]  )
														self.assertEqual(backbone.out_indices    ,							[0, 1]  )

														lowerCamelCase__      :			str 						= [-3, -1]
														self.assertEqual(backbone.out_features    ,							["""a""", """c"""]  )
														self.assertEqual(backbone.out_indices    ,							[-3, -1]  )









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

