



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 	_A	(   _UpperCamelCase      ,					**_UpperCamelCase			):
				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 							lowerCAmelCase_		(		lowercase_     ):







		@staticmethod
		def 					a_  (    UpperCAmelCase_  :   ArgumentParser					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	parser.add_parser('''run_beam'''       ,		help='''Run a Beam dataset processing pipeline'''					)
						run_beam_parser.add_argument('''dataset'''       ,		type=UpperCAmelCase_       ,		help='''Name of the dataset to download'''					)
						run_beam_parser.add_argument('''--name'''       ,		type=UpperCAmelCase_       ,		default=UpperCAmelCase_       ,		help='''Dataset config name'''					)
						run_beam_parser.add_argument(
						    '''--cache_dir'''       ,		type=UpperCAmelCase_       ,		default=UpperCAmelCase_       ,		help='''Cache directory where the datasets are stored'''       ,		)
						run_beam_parser.add_argument(
						    '''--beam_pipeline_options'''       ,		type=UpperCAmelCase_       ,		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=UpperCAmelCase_       ,		default=UpperCAmelCase_       ,		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=UpperCAmelCase_					)







		def __init__(    self  :   Any       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   bool       ,		UpperCAmelCase_  :   bool       ,		UpperCAmelCase_  :   bool       ,		UpperCAmelCase_  :   bool       ,		**UpperCAmelCase_  :   List[Any]       ,		)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	dataset
						_UpperCAmelCase						:  List[Any]     					=	name
						_UpperCAmelCase						:  List[str]     					=	cache_dir
						_UpperCAmelCase						:  Optional[int]     					=	beam_pipeline_options
						_UpperCAmelCase						:  Optional[Any]     					=	data_dir
						_UpperCAmelCase						:  Tuple     					=	all_configs
						_UpperCAmelCase						:  List[Any]     					=	save_infos
						_UpperCAmelCase						:  Optional[int]     					=	ignore_verifications
						_UpperCAmelCase						:  Union[str, Any]     					=	force_redownload
						_UpperCAmelCase						:  Tuple     					=	config_kwargs







		def 					a_  (    self  :   List[str]					)       ->		Dict:


						'''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					)
						_UpperCAmelCase					, _UpperCAmelCase						:  Any     					=	self._dataset, self._name
						_UpperCAmelCase						:  Optional[int]     					=	dataset_module_factory(UpperCAmelCase_					)
						_UpperCAmelCase						:  Any     					=	import_main_class(dataset_module.module_path					)
						_UpperCAmelCase						:  List[DatasetBuilder]     					=	[]
						if self._beam_pipeline_options:
										_UpperCAmelCase						:  Tuple     					=	beam.options.pipeline_options.PipelineOptions(
										    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''					) if opt]					)
						else:
										_UpperCAmelCase						:  Union[str, Any]     					=	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=UpperCAmelCase_       ,		cache_dir=self._cache_dir       ,		base_path=dataset_module.builder_kwargs.get('''base_path'''					)       ,		)					)
						else:
										builders.append(
										    builder_cls(
										        config_name=UpperCAmelCase_       ,		data_dir=self._data_dir       ,		beam_options=UpperCAmelCase_       ,		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=UpperCAmelCase_       ,		)
										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:
										_UpperCAmelCase						:  List[Any]     					=	os.path.join(builder_cls.get_imported_module_dir()       ,		config.DATASETDICT_INFOS_FILENAME					)

										_UpperCAmelCase						:  Union[str, Any]     					=	Path(UpperCAmelCase_					).name + '''.py'''

										_UpperCAmelCase						:  Optional[int]     					=	os.path.join(UpperCAmelCase_       ,		UpperCAmelCase_					)
										if os.path.isfile(UpperCAmelCase_					):
														_UpperCAmelCase						:  str     					=	os.path.dirname(UpperCAmelCase_					)
										elif os.path.isfile(UpperCAmelCase_					):
														_UpperCAmelCase						:  List[Any]     					=	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
										_UpperCAmelCase						:  Optional[Any]     					=	os.path.join(UpperCAmelCase_       ,		config.DATASETDICT_INFOS_FILENAME					)
										copyfile(UpperCAmelCase_       ,		UpperCAmelCase_					)
										print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

		# setable values
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	None

		# running values
		SCREAMING_SNAKE_CASE_       :			Optional[jnp.ndarray]					       =	None







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						if common is None:
										_UpperCAmelCase						:  int     					=	CommonSchedulerState.create(self					)

						_UpperCAmelCase						:  Any     					=	jnp.arange(0       ,		self.config.num_train_timesteps					).round()[::-1]
						_UpperCAmelCase						:  Any     					=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						def lms_derivative(UpperCAmelCase_  :   Optional[int]					):
										_UpperCAmelCase						:  Optional[Any]     					=	1.0
										for k in range(UpperCAmelCase_					):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

						_UpperCAmelCase						:  Optional[Any]     					=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						_UpperCAmelCase						:  Any     					=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						_UpperCAmelCase						:  Tuple     					=	jnp.concatenate([sigmas, jnp.array([0.0]       ,		dtype=self.dtype					)]					)

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

						return state.replace(
						    timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		num_inference_steps=UpperCAmelCase_       ,		derivatives=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''







						if state.num_inference_steps is None:
										raise ValueError(
										    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

						# 2. Convert to an ODE derivative
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  int     					=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_       ,		reversed(state.derivatives					)					)					)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase_       ,		state=UpperCAmelCase_					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
