


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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[int]     ,					**UpperCamelCase		:      Optional[int]	)     ->	Tuple:



	"""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 			lowerCamelCase_					(    _UpperCamelCase    ):





							@staticmethod
							def   __magic_name__      (							_SCREAMING_SNAKE_CASE    ):
								a_     			=   parser.add_parser("""run_beam"""			,       help="""Run a Beam dataset processing pipeline"""    )
								run_beam_parser.add_argument("""dataset"""			,       type=_SCREAMING_SNAKE_CASE			,       help="""Name of the dataset to download"""    )
								run_beam_parser.add_argument("""--name"""			,       type=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""Dataset config name"""    )
								run_beam_parser.add_argument(
								    """--cache_dir"""			,       type=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""Cache directory where the datasets are stored"""			,       )
								run_beam_parser.add_argument(
								    """--beam_pipeline_options"""			,       type=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE    )





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE			,       ):
								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   __magic_name__      (							self    ):
								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_			, a_     			=   self._dataset, self._name
								a_     			=   dataset_module_factory(_SCREAMING_SNAKE_CASE    )
								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=_SCREAMING_SNAKE_CASE			,       cache_dir=self._cache_dir			,       base_path=dataset_module.builder_kwargs.get("""base_path"""    )			,       )    )
								else:
									builders.append(
									    builder_cls(
									        config_name=_SCREAMING_SNAKE_CASE			,       data_dir=self._data_dir			,       beam_options=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE			,       )
									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(_SCREAMING_SNAKE_CASE    ).name + """.py"""

									a_     			=   os.path.join(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
									if os.path.isfile(_SCREAMING_SNAKE_CASE    ):
										a_     			=   os.path.dirname(_SCREAMING_SNAKE_CASE    )
									elif os.path.isfile(_SCREAMING_SNAKE_CASE    ):
										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(_SCREAMING_SNAKE_CASE			,       config.DATASETDICT_INFOS_FILENAME    )
									copyfile(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
									print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""    )







"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											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=_SCREAMING_SNAKE_CASE     ,    beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





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

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





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

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3