

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase__								=		4
lowerCAmelCase__								=		3







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		pass

def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				Union[str, Any]:




	'''simple docstring'''




	for shard in shards:
		for i in range(UpperCAmelCase_	):
			yield {"i": i, "shard": shard}
def 					lowerCamelCase_     (							)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						int(os.environ['RANK']	)
	_UpperCamelCase   :		str			=						int(os.environ['WORLD_SIZE']	)

	_UpperCamelCase   :		List[str]			=						ArgumentParser()
	parser.add_argument('--streaming'							,       type=UpperCAmelCase_	)
	parser.add_argument('--local_rank'							,       type=UpperCAmelCase_	)
	parser.add_argument('--num_workers'							,       type=UpperCAmelCase_							,       default=0	)
	_UpperCamelCase   :		Optional[Any]			=						parser.parse_args()
	_UpperCamelCase   :		Optional[int]			=						args.streaming
	_UpperCamelCase   :		int			=						args.num_workers

	_UpperCamelCase   :		List[str]			=						{'shards': [F'''shard_{shard_idx}''' for shard_idx in range(UpperCAmelCase_	)]}
	_UpperCamelCase   :		Tuple			=						IterableDataset.from_generator(UpperCAmelCase_							,       gen_kwargs=UpperCAmelCase_	)
	if not streaming:
		_UpperCamelCase   :		List[Any]			=						Dataset.from_list(list(UpperCAmelCase_	)	)

	_UpperCamelCase   :		int			=						split_dataset_by_node(UpperCAmelCase_							,       rank=UpperCAmelCase_							,       world_size=UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						torch.utils.data.DataLoader(UpperCAmelCase_							,       num_workers=UpperCAmelCase_	)

	_UpperCamelCase   :		Dict			=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
	_UpperCamelCase   :		Union[str, Any]			=						full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)	)

	_UpperCamelCase   :		int			=						sum(1 for _ in dataloader	)
	if local_size != expected_local_size:
		raise FailedTestError(F'''local_size {local_size} != expected_local_size {expected_local_size}'''	)


if __name__ == "__main__":
							main()







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__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

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

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

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

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

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




