
"""simple docstring"""







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 			A__	(     _lowerCamelCase):
		A_						:	Union[str, Any]           =     (UnCLIPScheduler,)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				{
			    'num_train_timesteps': 10_00,
			    'variance_type': 'fixed_small_log',
			    'clip_sample': True,
			    'clip_sample_range': 1.0,
			    'prediction_type': 'epsilon',
			}

			config.update(**_SCREAMING_SNAKE_CASE			)
			return config

		def 							__lowerCamelCase							(				self			):
			for timesteps in [1, 5, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for variance in ["fixed_small_log", "learned_range"]:
				self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for clip_sample in [True, False]:
				self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for clip_sample_range in [1, 5, 10, 20]:
				self.check_over_configs(clip_sample_range=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for prediction_type in ["epsilon", "sample"]:
				self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for time_step in [0, 5_00, 9_99]:
				for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
					if prev_timestep is not None and prev_timestep >= time_step:
						continue

					self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	,		prev_timestep=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Any						      =				self.get_scheduler_config(variance_type='fixed_small_log'			)
			__lowerCAmelCase      :						List[Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.00_00E-10			)			) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.054_9625			)			) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.999_4987			)			) < 1E-5

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config(variance_type='learned_range'			)
			__lowerCAmelCase      :						Dict						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Tuple						      =				0.5

			assert scheduler._get_variance(1	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -10.171_2790 < 1E-5
			assert scheduler._get_variance(4_87	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -5.799_8052 < 1E-5
			assert scheduler._get_variance(9_99	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -0.001_0011 < 1E-5

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						List[str]						      =				self.get_scheduler_config()
			__lowerCAmelCase      :						Any						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.timesteps

			__lowerCAmelCase      :						List[str]						      =				self.dummy_model()
			__lowerCAmelCase      :						Tuple						      =				self.dummy_sample_deter
			__lowerCAmelCase      :						int						      =				torch.manual_seed(0			)

			for i, t in enumerate(_SCREAMING_SNAKE_CASE			):
				# 1. predict noise residual
				__lowerCAmelCase      :						Optional[int]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				# 2. predict previous mean of sample x_t-1
				__lowerCAmelCase      :						Optional[int]						      =				scheduler.step(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE			).prev_sample

				__lowerCAmelCase      :						int						      =				pred_prev_sample

			__lowerCAmelCase      :						List[str]						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						str						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 252.268_2495			) < 1E-2
			assert abs(result_mean.item() - 0.328_4743			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config()
			__lowerCAmelCase      :						Optional[Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			scheduler.set_timesteps(25			)

			__lowerCAmelCase      :						int						      =				scheduler.timesteps

			__lowerCAmelCase      :						str						      =				self.dummy_model()
			__lowerCAmelCase      :						str						      =				self.dummy_sample_deter
			__lowerCAmelCase      :						Any						      =				torch.manual_seed(0			)

			for i, t in enumerate(_SCREAMING_SNAKE_CASE			):
				# 1. predict noise residual
				__lowerCAmelCase      :						List[str]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				if i + 1 == timesteps.shape[0]:
					__lowerCAmelCase      :						Union[str, Any]						      =				None
				else:
					__lowerCAmelCase      :						Optional[Any]						      =				timesteps[i + 1]

				# 2. predict previous mean of sample x_t-1
				__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.step(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		prev_timestep=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE			).prev_sample

				__lowerCAmelCase      :						int						      =				pred_prev_sample

			__lowerCAmelCase      :						Tuple						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						int						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 258.204_4983			) < 1E-2
			assert abs(result_mean.item() - 0.336_2038			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			pass







		def 							__lowerCamelCase							(				self			):
			pass

"""simple docstring"""







lowerCamelCase__        =		"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
							from .utils import rich