
import operator as op


_lowerCamelCase       :				Optional[int]       =			'''scaler.pt'''
_lowerCamelCase       :				Optional[int]       =			'''pytorch_model'''
_lowerCamelCase       :				List[Any]       =			'''random_states'''
_lowerCamelCase       :				int       =			'''optimizer'''
_lowerCamelCase       :				Dict       =			'''scheduler'''
_lowerCamelCase       :				Optional[int]       =			'''pytorch_model.bin'''
_lowerCamelCase       :				Any       =			'''pytorch_model.bin.index.json'''
_lowerCamelCase       :				Optional[Any]       =			'''model.safetensors'''
_lowerCamelCase       :				Optional[Any]       =			'''model.safetensors.index.json'''
_lowerCamelCase       :				Optional[Any]       =			'''1.10.2'''
_lowerCamelCase       :				List[str]       =			'''py38'''
_lowerCamelCase       :				str       =			'''4.17.0'''
_lowerCamelCase       :				List[str]       =			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
_lowerCamelCase       :				Optional[Any]       =			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
_lowerCamelCase       :				Dict       =			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
_lowerCamelCase       :				List[Any]       =			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
_lowerCamelCase       :				List[str]       =			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
_lowerCamelCase       :				List[str]       =			'''2.0.1'''
_lowerCamelCase       :				str       =			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
_lowerCamelCase       :				int       =			['''default''', '''reduce-overhead''', '''max-autotune''']

_lowerCamelCase       :				Optional[int]       =			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
_lowerCamelCase       :				Optional[int]       =			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

_lowerCamelCase       :				str       =			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
_lowerCamelCase       :				Dict       =			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']

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       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

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

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

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

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

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)