
"""simple docstring"""




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
											# modified it, it will not be accepted here again, since `auto` values would have been overridden
											SCREAMING_SNAKE_CASE_						 = deepcopy(SCREAMING_SNAKE_CASE_    )
									elif os.path.exists(SCREAMING_SNAKE_CASE_    ):
											with io.open(SCREAMING_SNAKE_CASE_       ,       '''r'''       ,       encoding='''utf-8'''    ) as f:
													SCREAMING_SNAKE_CASE_						 = json.load(SCREAMING_SNAKE_CASE_    )
									else:
											try:
													SCREAMING_SNAKE_CASE_						 = baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE_    ).decode('''utf-8'''    )
													SCREAMING_SNAKE_CASE_						 = json.loads(SCREAMING_SNAKE_CASE_    )
											except (UnicodeDecodeError, AttributeError, ValueError):
													raise ValueError(
													    f'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'    )

									SCREAMING_SNAKE_CASE_						 = config

									self.set_stage_and_offload()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_value('''zero_optimization.stage'''       ,       -1    )

									# offload
									SCREAMING_SNAKE_CASE_						 = False
									if self.is_zeroa() or self.is_zeroa():
											SCREAMING_SNAKE_CASE_						 = set(['''cpu''', '''nvme''']    )
											SCREAMING_SNAKE_CASE_						 = set(
											    [
											        self.get_value('''zero_optimization.offload_optimizer.device'''    ),
											        self.get_value('''zero_optimization.offload_param.device'''    ),
											    ]    )
											if len(offload_devices & offload_devices_valid    ) > 0:
													SCREAMING_SNAKE_CASE_						 = True

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.config

									# find the config node of interest if it exists
									SCREAMING_SNAKE_CASE_						 = ds_key_long.split('''.'''    )
									SCREAMING_SNAKE_CASE_						 = nodes.pop()
									for node in nodes:
											SCREAMING_SNAKE_CASE_						 = config.get(SCREAMING_SNAKE_CASE_    )
											if config is None:
													return None, ds_key

									return config, ds_key

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.find_config_node(SCREAMING_SNAKE_CASE_    )
									if config is None:
											return default
									return config.get(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.config

									# find the config node of interest if it exists
									SCREAMING_SNAKE_CASE_						 = ds_key_long.split('''.'''    )
									for node in nodes:
											SCREAMING_SNAKE_CASE_						 = config
											SCREAMING_SNAKE_CASE_						 = config.get(SCREAMING_SNAKE_CASE_    )
											if config is None:
													if must_exist:
															raise ValueError(f'Can\'t find {ds_key_long} entry in the config: {self.config}'    )
													else:
															return

        # if found remove it
									if parent_config is not None:
											parent_config.pop(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_value(SCREAMING_SNAKE_CASE_    )
									return False if value is None else bool(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_value(SCREAMING_SNAKE_CASE_    )
									return False if value is None else not bool(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return self._stage == 2

							def _lowercase					(self    ):

									"""simple docstring"""







									return self._stage == 3







							def _lowercase					(self    ):

									"""simple docstring"""







									return self._offload







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = engine







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.engine.backward(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

									# Deepspeed's `engine.step` performs the following operations:
									# - gradient accumulation check
									# - gradient clipping
									# - optimizer step
									# - zero grad
									# - checking overflow
									# - lr_scheduler step (only if engine.lr_scheduler is not None)
									self.engine.step()
									# and this plugin overrides the above calls with no-ops when Accelerate runs under
									# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
									# training loop that works transparently under many training regimes.







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_       ,       device_placement=SCREAMING_SNAKE_CASE_       ,       scaler=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = hasattr(self.optimizer       ,       '''overflow'''    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

							def _lowercase					(self    ):

									"""simple docstring"""







									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									if self.__has_overflow__:
											return self.optimizer.overflow
									return False







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0.0_01       ,       SCREAMING_SNAKE_CASE_=0       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = params
									SCREAMING_SNAKE_CASE_						 = lr
									SCREAMING_SNAKE_CASE_						 = weight_decay
									SCREAMING_SNAKE_CASE_						 = kwargs







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=0       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = optimizer
									SCREAMING_SNAKE_CASE_						 = total_num_steps
									SCREAMING_SNAKE_CASE_						 = warmup_num_steps
									SCREAMING_SNAKE_CASE_						 = kwargs

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3