

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			(DEISMultistepScheduler,)
					UpperCAmelCase__:  Tuple    				=			(('''num_inference_steps''', 25),)






					def     __A     (					self       ,			**A__     ):
						A__				:       str	  =						{
						    """num_train_timesteps""": 1000,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """solver_order""": 2,
						}

						config.update(**A__     )
						return config






					def     __A     (					self       ,			A__=0       ,			**A__     ):
						A__				:       Optional[int]	  =						dict(self.forward_default_kwargs     )
						A__				:       Tuple	  =						kwargs.pop("""num_inference_steps"""       ,			A__     )
						A__				:       List[str]	  =						self.dummy_sample
						A__				:       Optional[int]	  =						0.1 * sample
						A__				:       int	  =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__				:       Optional[int]	  =						self.get_scheduler_config(**A__     )
							A__				:       Tuple	  =						scheduler_class(**A__     )
							scheduler.set_timesteps(A__     )
							# copy over dummy past residuals
							A__				:       Union[str, Any]	  =						dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__     )
								A__				:       Optional[Any]	  =						scheduler_class.from_pretrained(A__     )
								new_scheduler.set_timesteps(A__     )
								# copy over dummy past residuals
								A__				:       Any	  =						dummy_past_residuals[: new_scheduler.config.solver_order]

							A__  ,  A__				:       str	  =						sample, sample
							for t in range(A__       ,			time_step + scheduler.config.solver_order + 1     ):
								A__				:       List[str]	  =						scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample
								A__				:       List[str]	  =						new_scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample

								assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"






					def     __A     (					self     ):
						pass






					def     __A     (					self       ,			A__=0       ,			**A__     ):
						A__				:       Tuple	  =						dict(self.forward_default_kwargs     )
						A__				:       Optional[int]	  =						kwargs.pop("""num_inference_steps"""       ,			A__     )
						A__				:       Optional[Any]	  =						self.dummy_sample
						A__				:       Tuple	  =						0.1 * sample
						A__				:       Dict	  =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__				:       List[Any]	  =						self.get_scheduler_config()
							A__				:       int	  =						scheduler_class(**A__     )
							scheduler.set_timesteps(A__     )

							# copy over dummy past residuals (must be after setting timesteps)
							A__				:       Union[str, Any]	  =						dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__     )
								A__				:       Tuple	  =						scheduler_class.from_pretrained(A__     )
								# copy over dummy past residuals
								new_scheduler.set_timesteps(A__     )

								# copy over dummy past residual (must be after setting timesteps)
								A__				:       str	  =						dummy_past_residuals[: new_scheduler.config.solver_order]

							A__				:       Tuple	  =						scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample
							A__				:       str	  =						new_scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample

							assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"






					def     __A     (					self       ,			A__=None       ,			**A__     ):
						if scheduler is None:
							A__				:       Union[str, Any]	  =						self.scheduler_classes[0]
							A__				:       Optional[int]	  =						self.get_scheduler_config(**A__     )
							A__				:       Dict	  =						scheduler_class(**A__     )

						A__				:       List[str]	  =						self.scheduler_classes[0]
						A__				:       Any	  =						self.get_scheduler_config(**A__     )
						A__				:       str	  =						scheduler_class(**A__     )

						A__				:       Tuple	  =						10
						A__				:       Optional[Any]	  =						self.dummy_model()
						A__				:       Optional[int]	  =						self.dummy_sample_deter
						scheduler.set_timesteps(A__     )

						for i, t in enumerate(scheduler.timesteps     ):
							A__				:       List[str]	  =						model(A__       ,			A__     )
							A__				:       Dict	  =						scheduler.step(A__       ,			A__       ,			A__     ).prev_sample

						return sample






					def     __A     (					self     ):
						A__				:       List[Any]	  =						dict(self.forward_default_kwargs     )

						A__				:       Optional[int]	  =						kwargs.pop("""num_inference_steps"""       ,			A__     )

						for scheduler_class in self.scheduler_classes:
							A__				:       Tuple	  =						self.get_scheduler_config()
							A__				:       List[Any]	  =						scheduler_class(**A__     )

							A__				:       int	  =						self.dummy_sample
							A__				:       Any	  =						0.1 * sample

							if num_inference_steps is not None and hasattr(A__       ,			"""set_timesteps"""     ):
								scheduler.set_timesteps(A__     )
							elif num_inference_steps is not None and not hasattr(A__       ,			"""set_timesteps"""     ):
								A__				:       Union[str, Any]	  =						num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							A__				:       List[str]	  =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							A__				:       int	  =						dummy_past_residuals[: scheduler.config.solver_order]

							A__				:       Tuple	  =						scheduler.timesteps[5]
							A__				:       Optional[int]	  =						scheduler.timesteps[6]

							A__				:       List[str]	  =						scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample
							A__				:       Optional[Any]	  =						scheduler.step(A__       ,			A__       ,			A__       ,			**A__     ).prev_sample

							self.assertEqual(output_a.shape       ,			sample.shape     )
							self.assertEqual(output_a.shape       ,			output_a.shape     )






					def     __A     (					self     ):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						A__				:       Tuple	  =						DEISMultistepScheduler(**self.get_scheduler_config()     )
						A__				:       Optional[int]	  =						self.full_loop(scheduler=A__     )
						A__				:       List[str]	  =						torch.mean(torch.abs(A__     )     )

						assert abs(result_mean.item() - 0.2_3_9_1_6     ) < 1e-3

						A__				:       Any	  =						DPMSolverSinglestepScheduler.from_config(scheduler.config     )
						A__				:       Dict	  =						DPMSolverMultistepScheduler.from_config(scheduler.config     )
						A__				:       List[Any]	  =						UniPCMultistepScheduler.from_config(scheduler.config     )
						A__				:       List[str]	  =						DEISMultistepScheduler.from_config(scheduler.config     )

						A__				:       List[str]	  =						self.full_loop(scheduler=A__     )
						A__				:       Any	  =						torch.mean(torch.abs(A__     )     )

						assert abs(result_mean.item() - 0.2_3_9_1_6     ) < 1e-3






					def     __A     (					self     ):
						for timesteps in [25, 50, 100, 999, 1000]:
							self.check_over_configs(num_train_timesteps=A__     )






					def     __A     (					self     ):
						self.check_over_configs(thresholding=A__     )
						for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=A__       ,			prediction_type=A__       ,			sample_max_value=A__       ,			algorithm_type="""deis"""       ,			solver_order=A__       ,			solver_type=A__       ,			)






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






					def     __A     (					self     ):
						for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=A__       ,			solver_type=A__       ,			prediction_type=A__       ,			algorithm_type=A__       ,			)
										A__				:       str	  =						self.full_loop(
										    solver_order=A__       ,			solver_type=A__       ,			prediction_type=A__       ,			algorithm_type=A__       ,			)
										assert not torch.isnan(A__     ).any(), "Samples have nan numbers"






					def     __A     (					self     ):
						self.check_over_configs(lower_order_final=A__     )
						self.check_over_configs(lower_order_final=A__     )






					def     __A     (					self     ):
						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
							self.check_over_forward(num_inference_steps=A__       ,			time_step=0     )






					def     __A     (					self     ):
						A__				:       Tuple	  =						self.full_loop()
						A__				:       Any	  =						torch.mean(torch.abs(A__     )     )

						assert abs(result_mean.item() - 0.2_3_9_1_6     ) < 1e-3






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						self.full_loop(prediction_type="""v_prediction"""     )
						A__				:       List[Any]	  =						torch.mean(torch.abs(A__     )     )

						assert abs(result_mean.item() - 0.0_9_1     ) < 1e-3






					def     __A     (					self     ):
						A__				:       str	  =						self.scheduler_classes[0]
						A__				:       Union[str, Any]	  =						self.get_scheduler_config(thresholding=A__       ,			dynamic_thresholding_ratio=0     )
						A__				:       Any	  =						scheduler_class(**A__     )

						A__				:       Any	  =						10
						A__				:       List[Any]	  =						self.dummy_model()
						A__				:       str	  =						self.dummy_sample_deter.half()
						scheduler.set_timesteps(A__     )

						for i, t in enumerate(scheduler.timesteps     ):
							A__				:       Any	  =						model(A__       ,			A__     )
							A__				:       Optional[int]	  =						scheduler.step(A__       ,			A__       ,			A__     ).prev_sample

						assert sample.dtype == torch.floataa






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



@experimental
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Any       , lowercase_:					str       , lowercase_:					str       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Union[str, Any]   )	-> Union[str, Any]:
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	return _map_with_joblib(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

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

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

	logger.info(
	    f"""Spawning {num_proc} processes for {len(lowercase_   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}"""   )
	A__  ,  A__				:       Optional[int]	  =						None, None
	if not disable_tqdm:
		A__  ,  A__				:       List[str]	  =						(RLock(),), tqdm.set_lock
	with Pool(lowercase_       , initargs=lowercase_       , initializer=lowercase_   ) as pool:
		A__				:       Optional[Any]	  =						pool.map(lowercase_       , lowercase_   )
	logger.info(f"""Finished {num_proc} processes"""   )
	A__				:       Tuple	  =						[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"""Unpacked {len(lowercase_   )} objects"""   )

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, 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=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						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:
		A__				:       Optional[Any]	  =						None



