



'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		(DPMSolverSDEScheduler,)
    lowercase					=		10




    def lowerCamelCase       (    self					:     str ,     **snake_case_					:     Union[str, Any]						):
          snake_case__  :    Optional[Any]    						=      {
              """num_train_timesteps""": 1_100,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """noise_sampler_seed""": 0,
          }

          config.update(**snake_case_						)
          return config




    def lowerCamelCase       (    self					:     Any						):
          for timesteps in [10, 50, 100, 1_000]:
                self.check_over_configs(num_train_timesteps=snake_case_						)




    def lowerCamelCase       (    self					:     Tuple						):
          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,     [0.0002, 0.002, 0.02]						):
                self.check_over_configs(beta_start=snake_case_ ,     beta_end=snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    List[Any]    						=      self.scheduler_classes[0]
          snake_case__  :    List[Any]    						=      self.get_scheduler_config()
          snake_case__  :    List[str]    						=      scheduler_class(**snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps						)

          snake_case__  :    str    						=      self.dummy_model()
          snake_case__  :    Any    						=      self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__  :    List[str]    						=      sample.to(snake_case_						)

          for i, t in enumerate(scheduler.timesteps						):
                snake_case__  :    Optional[Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Union[str, Any]    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    int    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      output.prev_sample

          snake_case__  :    Any    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    Union[str, Any]    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.47821044921875						) < 1E-2
                assert abs(result_mean.item() - 0.2178705964565277						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59352111816406						) < 1E-2
                assert abs(result_mean.item() - 0.22342906892299652						) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    str    						=      self.scheduler_classes[0]
          snake_case__  :    List[Any]    						=      self.get_scheduler_config(prediction_type="""v_prediction"""						)
          snake_case__  :    List[Any]    						=      scheduler_class(**snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps						)

          snake_case__  :    Any    						=      self.dummy_model()
          snake_case__  :    Tuple    						=      self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__  :    List[str]    						=      sample.to(snake_case_						)

          for i, t in enumerate(scheduler.timesteps						):
                snake_case__  :    Union[str, Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    Union[str, Any]    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      output.prev_sample

          snake_case__  :    Tuple    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    Tuple    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 124.77149200439453						) < 1E-2
                assert abs(result_mean.item() - 0.16226289014816284						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 128.1663360595703						) < 1E-2
                assert abs(result_mean.item() - 0.16688326001167297						) < 1E-3
          else:
                assert abs(result_sum.item() - 119.8487548828125						) < 1E-2
                assert abs(result_mean.item() - 0.1560530662536621						) < 1E-3




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Dict    						=      self.scheduler_classes[0]
          snake_case__  :    List[str]    						=      self.get_scheduler_config()
          snake_case__  :    Any    						=      scheduler_class(**snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps ,     device=snake_case_						)

          snake_case__  :    Tuple    						=      self.dummy_model()
          snake_case__  :    Any    						=      self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                snake_case__  :    Union[str, Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Optional[Any]    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Any    						=      output.prev_sample

          snake_case__  :    int    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    str    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.46957397460938						) < 1E-2
                assert abs(result_mean.item() - 0.21805934607982635						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59353637695312						) < 1E-2
                assert abs(result_mean.item() - 0.22342908382415771						) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Tuple    						=      self.scheduler_classes[0]
          snake_case__  :    Dict    						=      self.get_scheduler_config()
          snake_case__  :    List[str]    						=      scheduler_class(**snake_case_ ,     use_karras_sigmas=snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps ,     device=snake_case_						)

          snake_case__  :    int    						=      self.dummy_model()
          snake_case__  :    List[str]    						=      self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma
          snake_case__  :    Tuple    						=      sample.to(snake_case_						)

          for t in scheduler.timesteps:
                snake_case__  :    Optional[int]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    int    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Any    						=      output.prev_sample

          snake_case__  :    Dict    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    List[Any]    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 176.66974135742188						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 177.63653564453125						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
          else:
                assert abs(result_sum.item() - 170.3135223388672						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2

'''simple docstring'''




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

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





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# 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
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	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}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       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:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	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(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# 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     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





