




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode




def      __magic_name__							(					A						:   int,    **A						:   Optional[Any]  ):






			'''simple docstring'''







			return RunBeamCommand(
			    args.dataset,    args.name,    args.cache_dir,    args.beam_pipeline_options,    args.data_dir,    args.all_configs,    args.save_info or args.save_infos,    args.ignore_verifications,    args.force_redownload,    **A,    )






class        snake_case__				(UpperCAmelCase__  ):
				"""simple docstring"""



				@staticmethod
				def 				__UpperCAmelCase				( __lowerCamelCase							:	str				)   ->	Optional[Any]:
							a       =     parser.add_parser("run_beam"   , help="Run a Beam dataset processing pipeline"				)
							run_beam_parser.add_argument("dataset"   , type=__lowerCamelCase   , help="Name of the dataset to download"				)
							run_beam_parser.add_argument("--name"   , type=__lowerCamelCase   , default=__lowerCamelCase   , help="Dataset config name"				)
							run_beam_parser.add_argument(
							    "--cache_dir"   , type=__lowerCamelCase   , default=__lowerCamelCase   , help="Cache directory where the datasets are stored"   , )
							run_beam_parser.add_argument(
							    "--beam_pipeline_options"   , type=__lowerCamelCase   , default=""   , help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"   , )
							run_beam_parser.add_argument(
							    "--data_dir"   , type=__lowerCamelCase   , default=__lowerCamelCase   , help="Can be used to specify a manual directory to get the files from"   , )
							run_beam_parser.add_argument("--all_configs"   , action="store_true"   , help="Test all dataset configurations"				)
							run_beam_parser.add_argument("--save_info"   , action="store_true"   , help="Save the dataset infos file"				)
							run_beam_parser.add_argument(
							    "--ignore_verifications"   , action="store_true"   , help="Run the test without checksums and splits checks"				)
							run_beam_parser.add_argument("--force_redownload"   , action="store_true"   , help="Force dataset redownload"				)
							# aliases
							run_beam_parser.add_argument("--save_infos"   , action="store_true"   , help="alias for save_info"				)
							run_beam_parser.set_defaults(func=__lowerCamelCase				)





				def __init__( self							:	Tuple   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	str   , **__lowerCamelCase							:	int   , )   ->	Optional[int]:
							a       =     dataset
							a       =     name
							a       =     cache_dir
							a       =     beam_pipeline_options
							a       =     data_dir
							a       =     all_configs
							a       =     save_infos
							a       =     ignore_verifications
							a       =     force_redownload
							a       =     config_kwargs





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							import apache_beam as beam

							if self._name is not None and self._all_configs:
										print("Both parameters `name` and `all_configs` can\'t be used at once."				)
										exit(1				)
							a       =     self._dataset, self._name
							a       =     dataset_module_factory(__lowerCamelCase				)
							a       =     import_main_class(dataset_module.module_path				)
							a       =     []
							if self._beam_pipeline_options:
										a       =     beam.options.pipeline_options.PipelineOptions(
										    flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","				) if opt]				)
							else:
										a       =     None
							if self._all_configs and len(builder_cls.BUILDER_CONFIGS				) > 0:
										for builder_config in builder_cls.BUILDER_CONFIGS:
													builders.append(
													    builder_cls(
													        config_name=builder_config.name   , data_dir=self._data_dir   , hash=dataset_module.hash   , beam_options=__lowerCamelCase   , cache_dir=self._cache_dir   , base_path=dataset_module.builder_kwargs.get("base_path"				)   , )				)
							else:
										builders.append(
										    builder_cls(
										        config_name=__lowerCamelCase   , data_dir=self._data_dir   , beam_options=__lowerCamelCase   , cache_dir=self._cache_dir   , base_path=dataset_module.builder_kwargs.get("base_path"				)   , **self._config_kwargs   , )				)

							for builder in builders:
										builder.download_and_prepare(
										    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
										    if not self._force_redownload
										    else DownloadMode.FORCE_REDOWNLOAD   , download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH				)   , verification_mode=VerificationMode.NO_CHECKS
										    if self._ignore_verifications
										    else VerificationMode.ALL_CHECKS   , try_from_hf_gcs=__lowerCamelCase   , )
										if self._save_infos:
													builder._save_infos()

							print("Apache beam run successful."				)

							# If save_infos=True, the dataset infos file is created next to the loaded module file.
							# Let's move it to the original directory of the dataset script, to allow the user to
							# upload them on S3 at the same time afterwards.
							if self._save_infos:
										a       =     os.path.join(builder_cls.get_imported_module_dir()   , config.DATASETDICT_INFOS_FILENAME				)

										a       =     Path(__lowerCamelCase				).name + '.py'

										a       =     os.path.join(__lowerCamelCase   , __lowerCamelCase				)
										if os.path.isfile(__lowerCamelCase				):
													a       =     os.path.dirname(__lowerCamelCase				)
										elif os.path.isfile(__lowerCamelCase				):
													a       =     path
										else:  # in case of a remote dataset
													print(f"""Dataset Infos file saved at {dataset_infos_path}"""				)
													exit(1				)

										# Move datasetinfo back to the user
										a       =     os.path.join(__lowerCamelCase   , config.DATASETDICT_INFOS_FILENAME				)
										copyfile(__lowerCamelCase   , __lowerCamelCase				)
										print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""				)








import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'	),
									    'variance_type': None,
									}

									config.update(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    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	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

												# copy over dummy past residuals (must be after setting timesteps)
												UpperCAmelCase__		:Tuple 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:int 		=    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)
															UpperCAmelCase__		:Optional[int] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    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	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    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'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCAmelCase__		:List[Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]
												UpperCAmelCase__		:Any 		=    dummy_past_residuals[: scheduler.config.solver_order]

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[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	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
															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='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
															for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																								if order == 3:
																											continue
																					else:
																								self.check_over_configs(
																								    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    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	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


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

									assert abs(result_mean.item() - 0.3301	) < 1e-3

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

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

												scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
												assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps





