






'''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


__lowerCamelCase   :					Optional[Any]   							=					False







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:Optional[int]=32				)				->  Optional[Any]:


										'''simple docstring'''




										set_seed(0				)
										snake_case_				:					Dict   =       UNetaDModel(sample_size=lowerCAmelCase__       ,							in_channels=3       ,							out_channels=3				)
										snake_case_				:					List[Any]   =       torch.optim.SGD(model.parameters()       ,							lr=0.0_0_0_1				)
										return model, optimizer






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


										'''simple docstring'''




										snake_case_				:					str   =       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
										snake_case_				:					Optional[Any]   =       DDPMScheduler(
										    num_train_timesteps=1_000       ,							beta_start=0.0_0_0_1       ,							beta_end=0.0_2       ,							beta_schedule="linear"       ,							clip_sample=lowerCAmelCase__       ,							)
										snake_case_				:					List[str]   =       DDIMScheduler(
										    num_train_timesteps=1_000       ,							beta_start=0.0_0_0_1       ,							beta_end=0.0_2       ,							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				)
										snake_case_				:					Any   =       [torch.randn((4, 3, 32, 32)				).clip(-1       ,							1				).to(lowerCAmelCase__				) for _ in range(4				)]
										snake_case_				:					Optional[Any]   =       [torch.randn((4, 3, 32, 32)				).to(lowerCAmelCase__				) for _ in range(4				)]
										snake_case_				:					Any   =       [torch.randint(0       ,							1_000       ,							(4,)				).long().to(lowerCAmelCase__				) for _ in range(4				)]

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

										# recreate the model and optimizer, and retry with DDIM
										snake_case_,	snake_case_				:					str   =       self.get_model_optimizer(resolution=32				)
										model.train().to(lowerCAmelCase__				)
										for i in range(4				):
													optimizer.zero_grad()
													snake_case_				:					Union[str, Any]   =       ddim_scheduler.add_noise(clean_images[i]       ,							noise[i]       ,							timesteps[i]				)
													snake_case_				:					str   =       model(lowerCAmelCase__       ,							timesteps[i]				).sample
													snake_case_				:					Dict   =       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				)				)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




