code: 
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  @flax.struct.dataclass
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Optional[jnp.ndarray]       = None
  			UpperCAmelCase       :       Optional[jnp.ndarray]       = None
  			UpperCAmelCase       :       Optional[jnp.ndarray]       = None
  
  			@classmethod
  			def _lowercase (	cls     :     Dict					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return cls()
  
  @dataclass
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       ScoreSdeVeSchedulerState
  			UpperCAmelCase       :       jnp.ndarray
  			UpperCAmelCase       :       Optional[jnp.ndarray]       = None
  
  class 						snake_case			(       lowerCAmelCase__	, lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@property
  			def _lowercase (	self     :     List[str]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return True
  
  			@register_to_config
  			def __init__(	self     :     str							,							lowerCAmelCase_     :     int = 2_000							,							lowerCAmelCase_     :     float = 0.15							,							lowerCAmelCase_     :     float = 0.01							,							lowerCAmelCase_     :     float = 1_348.0							,							lowerCAmelCase_     :     float = 1e-5							,							lowerCAmelCase_     :     int = 1							,							)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								pass
  
  			def _lowercase (	self     :     Dict					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ScoreSdeVeSchedulerState.create()
  								return self.set_sigmas(
  								    lowerCAmelCase_							,							self.config.num_train_timesteps							,							self.config.sigma_min							,							self.config.sigma_max							,							self.config.sampling_eps							,							)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     ScoreSdeVeSchedulerState							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Tuple = ()							,							lowerCAmelCase_     :     float = None					)					->					ScoreSdeVeSchedulerState:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  								SCREAMING_SNAKE_CASE_	      =       jnp.linspace(1							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  								return state.replace(timesteps=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any							,							lowerCAmelCase_     :     ScoreSdeVeSchedulerState							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     float = None							,							lowerCAmelCase_     :     float = None							,							lowerCAmelCase_     :     float = None							,							)					->					ScoreSdeVeSchedulerState:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       sigma_min if sigma_min is not None else self.config.sigma_min
  								SCREAMING_SNAKE_CASE_	      =       sigma_max if sigma_max is not None else self.config.sigma_max
  								SCREAMING_SNAKE_CASE_	      =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  								if state.timesteps is None:
  													SCREAMING_SNAKE_CASE_	      =       self.set_timesteps(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase_					)							,							jnp.log(lowerCAmelCase_					)							,							lowerCAmelCase_					)					)
  								SCREAMING_SNAKE_CASE_	      =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)
  
  								return state.replace(discrete_sigmas=lowerCAmelCase_							,							sigmas=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Dict					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return jnp.where(timesteps == 0							,							jnp.zeros_like(lowerCAmelCase_					)							,							state.discrete_sigmas[timesteps - 1]					)
  
  			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     ScoreSdeVeSchedulerState							,							lowerCAmelCase_     :     jnp.ndarray							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     jnp.ndarray							,							lowerCAmelCase_     :     random.KeyArray							,							lowerCAmelCase_     :     bool = True							,							)					->					Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if state.timesteps is None:
  													raise ValueError(
  													    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)
  
  								SCREAMING_SNAKE_CASE_	      =       timestep * jnp.ones(
  								    sample.shape[0]							,							)
  								SCREAMING_SNAKE_CASE_	      =       (timestep * (len(state.timesteps					) - 1)).long()
  
  								SCREAMING_SNAKE_CASE_	      =       state.discrete_sigmas[timesteps]
  								SCREAMING_SNAKE_CASE_	      =       self.get_adjacent_sigma(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       jnp.zeros_like(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       (sigma**2 - adjacent_sigma**2) ** 0.5
  
  								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  								# also equation 47 shows the analog from SDE models to ancestral sampling methods
  								SCREAMING_SNAKE_CASE_	      =       diffusion.flatten()
  								SCREAMING_SNAKE_CASE_	      =       broadcast_to_shape_from_left(lowerCAmelCase_							,							sample.shape					)
  								SCREAMING_SNAKE_CASE_	      =       drift - diffusion**2 * model_output
  
  								#  equation 6: sample noise for the diffusion term of
  								SCREAMING_SNAKE_CASE_	      =       random.split(lowerCAmelCase_							,							num=1					)
  								SCREAMING_SNAKE_CASE_	      =       random.normal(key=lowerCAmelCase_							,							shape=sample.shape					)
  								SCREAMING_SNAKE_CASE_	      =       sample - drift  # subtract because `dt` is a small negative timestep
  								# TODO is the variable diffusion the correct scaling term for the noise?
  								SCREAMING_SNAKE_CASE_	      =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  								if not return_dict:
  													return (prev_sample, prev_sample_mean, state)
  
  								return FlaxSdeVeOutput(prev_sample=lowerCAmelCase_							,							prev_sample_mean=lowerCAmelCase_							,							state=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     ScoreSdeVeSchedulerState							,							lowerCAmelCase_     :     jnp.ndarray							,							lowerCAmelCase_     :     jnp.ndarray							,							lowerCAmelCase_     :     random.KeyArray							,							lowerCAmelCase_     :     bool = True							,							)					->					Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if state.timesteps is None:
  													raise ValueError(
  													    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)
  
  								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  								# sample noise for correction
  								SCREAMING_SNAKE_CASE_	      =       random.split(lowerCAmelCase_							,							num=1					)
  								SCREAMING_SNAKE_CASE_	      =       random.normal(key=lowerCAmelCase_							,							shape=sample.shape					)
  
  								# compute step size from the model_output, the noise, and the snr
  								SCREAMING_SNAKE_CASE_	      =       jnp.linalg.norm(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       jnp.linalg.norm(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  								SCREAMING_SNAKE_CASE_	      =       step_size * jnp.ones(sample.shape[0]					)
  
  								# compute corrected sample: model_output term and noise term
  								SCREAMING_SNAKE_CASE_	      =       step_size.flatten()
  								SCREAMING_SNAKE_CASE_	      =       broadcast_to_shape_from_left(lowerCAmelCase_							,							sample.shape					)
  								SCREAMING_SNAKE_CASE_	      =       sample + step_size * model_output
  								SCREAMING_SNAKE_CASE_	      =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  								if not return_dict:
  													return (prev_sample, state)
  
  								return FlaxSdeVeOutput(prev_sample=lowerCAmelCase_							,							state=lowerCAmelCase_					)
  
  
  
  
  			def __len__(	self     :     Union[str, Any]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  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 							UpperCAmelCase				(     UpperCAmelCase				,**UpperCAmelCase      )->		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				,**UpperCAmelCase				,)
  
  
  
  
  
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@staticmethod
  			def _lowercase (	lowerCAmelCase_     :     ArgumentParser					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       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     :     Any							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							**lowerCAmelCase_     :     Any							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       dataset
  								SCREAMING_SNAKE_CASE_	      =       name
  								SCREAMING_SNAKE_CASE_	      =       cache_dir
  								SCREAMING_SNAKE_CASE_	      =       beam_pipeline_options
  								SCREAMING_SNAKE_CASE_	      =       data_dir
  								SCREAMING_SNAKE_CASE_	      =       all_configs
  								SCREAMING_SNAKE_CASE_	      =       save_infos
  								SCREAMING_SNAKE_CASE_	      =       ignore_verifications
  								SCREAMING_SNAKE_CASE_	      =       force_redownload
  								SCREAMING_SNAKE_CASE_	      =       config_kwargs
  
  
  
  
  			def _lowercase (	self     :     List[str]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								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					)
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self._dataset, self._name
  								SCREAMING_SNAKE_CASE_	      =       dataset_module_factory(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       import_main_class(dataset_module.module_path					)
  								SCREAMING_SNAKE_CASE_	      =       []
  								if self._beam_pipeline_options:
  													SCREAMING_SNAKE_CASE_	      =       beam.options.pipeline_options.PipelineOptions(
  													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''					) if opt]					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       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:
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(builder_cls.get_imported_module_dir()							,							config.DATASETDICT_INFOS_FILENAME					)
  
  													SCREAMING_SNAKE_CASE_	      =       Path(lowerCAmelCase_					).name + '''.py'''
  
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							lowerCAmelCase_					)
  													if os.path.isfile(lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       os.path.dirname(lowerCAmelCase_					)
  													elif os.path.isfile(lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       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
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							config.DATASETDICT_INFOS_FILENAME					)
  													copyfile(lowerCAmelCase_							,							lowerCAmelCase_					)
  													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)
  
  
  