

'''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   :			Any         = False







class   UpperCamelCase__    (   unittest.TestCase   ):


		"""simple docstring"""

		def       A_       ( self    ,			snake_case=3_2			):


							'''simple docstring'''

							set_seed(0			)
							UpperCAmelCase		:							Optional[int]   					=   UNetaDModel(sample_size=lowerCAmelCase__    ,			in_channels=3    ,			out_channels=3			)
							UpperCAmelCase		:							str   					=   torch.optim.SGD(model.parameters()    ,			lr=0.0001			)
							return model, optimizer







		@slow
		def       A_       ( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple   					=   "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							UpperCAmelCase		:							Dict   					=   DDPMScheduler(
							    num_train_timesteps=1_0_0_0    ,			beta_start=0.0001    ,			beta_end=0.02    ,			beta_schedule="linear"    ,			clip_sample=lowerCAmelCase__    ,			)
							UpperCAmelCase		:							Optional[Any]   					=   DDIMScheduler(
							    num_train_timesteps=1_0_0_0    ,			beta_start=0.0001    ,			beta_end=0.02    ,			beta_schedule="linear"    ,			clip_sample=lowerCAmelCase__    ,			)

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

							# shared batches for DDPM and DDIM
							set_seed(0			)
							UpperCAmelCase		:							Tuple   					=   [torch.randn((4, 3, 3_2, 3_2)			).clip(-1    ,			1			).to(lowerCAmelCase__			) for _ in range(4			)]
							UpperCAmelCase		:							List[str]   					=   [torch.randn((4, 3, 3_2, 3_2)			).to(lowerCAmelCase__			) for _ in range(4			)]
							UpperCAmelCase		:							Union[str, Any]   					=   [torch.randint(0    ,			1_0_0_0    ,			(4,)			).long().to(lowerCAmelCase__			) for _ in range(4			)]

							# train with a DDPM scheduler
							UpperCAmelCase		:							Tuple   					=   self.get_model_optimizer(resolution=3_2			)
							model.train().to(lowerCAmelCase__			)
							for i in range(4			):
												optimizer.zero_grad()
												UpperCAmelCase		:							Tuple   					=   ddpm_scheduler.add_noise(clean_images[i]    ,			noise[i]    ,			timesteps[i]			)
												UpperCAmelCase		:							Union[str, Any]   					=   model(lowerCAmelCase__    ,			timesteps[i]			).sample
												UpperCAmelCase		:							str   					=   torch.nn.functional.mse_loss(lowerCAmelCase__    ,			noise[i]			)
												loss.backward()
												optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							UpperCAmelCase		:							Union[str, Any]   					=   self.get_model_optimizer(resolution=3_2			)
							model.train().to(lowerCAmelCase__			)
							for i in range(4			):
												optimizer.zero_grad()
												UpperCAmelCase		:							List[Any]   					=   ddim_scheduler.add_noise(clean_images[i]    ,			noise[i]    ,			timesteps[i]			)
												UpperCAmelCase		:							List[str]   					=   model(lowerCAmelCase__    ,			timesteps[i]			).sample
												UpperCAmelCase		:							Optional[int]   					=   torch.nn.functional.mse_loss(lowerCAmelCase__    ,			noise[i]			)
												loss.backward()
												optimizer.step()
							del model, optimizer

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



def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


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

						testmod()






