import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a_       ):
							lowercase__  :    Optional[int]									=							(DDIMParallelScheduler,)
							lowercase__  :    int									=							(('''eta''', 0.0), ('''num_inference_steps''', 50))




							def 		__snake_case(  self				:   Dict		,							**_UpperCamelCase				:   List[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "clip_sample": True,
												}

												config.update(**lowercase_ )
												return config




							def 		__snake_case(  self				:   Tuple		,							**_UpperCamelCase				:   str )   ->			List[str]:



												'''simple docstring'''

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

												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(lowercase_ )

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

												return sample




							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												for timesteps in [100, 500, 1_000]:
																	self.check_over_configs(num_train_timesteps=lowercase_ )




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(steps_offset=1 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**lowercase_ )
												scheduler.set_timesteps(5 )
												assert torch.equal(scheduler.timesteps		,							torch.LongTensor([801, 601, 401, 201, 1] ) )




							def 		__snake_case(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		,							[0.0_0_2, 0.0_2, 0.2, 2] ):
																	self.check_over_configs(beta_start=lowercase_		,							beta_end=lowercase_ )




							def 		__snake_case(  self				:   Optional[int] )   ->			Optional[int]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   int )   ->			Optional[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   int )   ->			Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

												for t, num_inference_steps in zip([1, 10, 50]		,							[10, 50, 500] ):
																	self.check_over_forward(time_step=lowercase_		,							num_inference_steps=lowercase_ )




							def 		__snake_case(  self				:   Any )   ->			Optional[int]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

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

												assert torch.sum(torch.abs(scheduler._get_variance(0		,							0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(420		,							400 ) - 0.1_4_7_7_1 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(980		,							960 ) - 0.3_2_4_6_0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(0		,							0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487		,							486 ) - 0.0_0_9_7_9 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999		,							998 ) - 0.0_2 ) ) < 1e-5




							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    10, 0.0
												scheduler.set_timesteps(lowercase_ )

												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(lowercase_ )[0:3, None].repeat(1		,							lowercase_ )

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

												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(lowercase_ ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_sum.item() - 1_147.7_904 ) < 1e-2
												assert abs(result_mean.item() - 0.4_9_8_2 ) < 1e-3




							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()

												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(lowercase_ ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_sum.item() - 172.0_067 ) < 1e-2
												assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1e-3




							def 		__snake_case(  self				:   int )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )

												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(lowercase_ ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1e-2
												assert abs(result_mean.item() - 0.0_6_8_4 ) < 1e-3




							def 		__snake_case(  self				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(set_alpha_to_one=lowercase_		,							beta_start=0.0_1 )
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(lowercase_ ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_sum.item() - 149.8_295 ) < 1e-2
												assert abs(result_mean.item() - 0.1_9_5_1 ) < 1e-3





							def 		__snake_case(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(set_alpha_to_one=lowercase_		,							beta_start=0.0_1 )
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(lowercase_ ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_sum.item() - 149.0_784 ) < 1e-2
												assert abs(result_mean.item() - 0.1_9_4_1 ) < 1e-3





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




