

"""simple docstring"""




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class    lowerCamelCase						:

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[Any]       )   ->    Dict:
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
									# modified it, it will not be accepted here again, since `auto` values would have been overridden
									SCREAMING_SNAKE_CASE__								=      deepcopy(__UpperCAmelCase       )
						elif os.path.exists(__UpperCAmelCase       ):
									with io.open(__UpperCAmelCase    ,				"""r"""    ,				encoding="""utf-8"""       ) as f:
												SCREAMING_SNAKE_CASE__								=      json.load(__UpperCAmelCase       )
						else:
									try:
												SCREAMING_SNAKE_CASE__								=      baseaa.urlsafe_baadecode(__UpperCAmelCase       ).decode("""utf-8"""       )
												SCREAMING_SNAKE_CASE__								=      json.loads(__UpperCAmelCase       )
									except (UnicodeDecodeError, AttributeError, ValueError):
												raise ValueError(
												    F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""       )

						SCREAMING_SNAKE_CASE__								=      config

						self.set_stage_and_offload()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						# zero stage - this is done as early as possible, before model is created, to allow
						# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
						# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
						SCREAMING_SNAKE_CASE__								=      self.get_value("""zero_optimization.stage"""    ,				-1       )

						# offload
						SCREAMING_SNAKE_CASE__								=      False
						if self.is_zeroa() or self.is_zeroa():
									SCREAMING_SNAKE_CASE__								=      set(["""cpu""", """nvme"""]       )
									SCREAMING_SNAKE_CASE__								=      set(
									    [
									        self.get_value("""zero_optimization.offload_optimizer.device"""       ),
									        self.get_value("""zero_optimization.offload_param.device"""       ),
									    ]       )
									if len(offload_devices & offload_devices_valid       ) > 0:
												SCREAMING_SNAKE_CASE__								=      True

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.config

						# find the config node of interest if it exists
						SCREAMING_SNAKE_CASE__								=      ds_key_long.split("""."""       )
						SCREAMING_SNAKE_CASE__								=      nodes.pop()
						for node in nodes:
									SCREAMING_SNAKE_CASE__								=      config.get(__UpperCAmelCase       )
									if config is None:
												return None, ds_key

						return config, ds_key

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Dict=None       )   ->    Any:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.find_config_node(__UpperCAmelCase       )
						if config is None:
									return default
						return config.get(__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=False       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.config

						# find the config node of interest if it exists
						SCREAMING_SNAKE_CASE__								=      ds_key_long.split("""."""       )
						for node in nodes:
									SCREAMING_SNAKE_CASE__								=      config
									SCREAMING_SNAKE_CASE__								=      config.get(__UpperCAmelCase       )
									if config is None:
												if must_exist:
															raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""       )
												else:
															return

        # if found remove it
						if parent_config is not None:
									parent_config.pop(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Tuple       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.get_value(__UpperCAmelCase       )
						return False if value is None else bool(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.get_value(__UpperCAmelCase       )
						return False if value is None else not bool(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						return self._stage == 2

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						return self._stage == 3





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						return self._offload





class    lowerCamelCase						:

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      engine





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[Any]    ,				**__UpperCAmelCase				:						Tuple       )   ->    Tuple:
						# runs backpropagation and handles mixed precision
						self.engine.backward(__UpperCAmelCase    ,				**__UpperCAmelCase       )

						# Deepspeed's `engine.step` performs the following operations:
						# - gradient accumulation check
						# - gradient clipping
						# - optimizer step
						# - zero grad
						# - checking overflow
						# - lr_scheduler step (only if engine.lr_scheduler is not None)
						self.engine.step()
						# and this plugin overrides the above calls with no-ops when Accelerate runs under
						# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
						# training loop that works transparently under many training regimes.





class    lowerCamelCase						(A__			):

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						int       )   ->    int:
						super().__init__(__UpperCAmelCase    ,				device_placement=__UpperCAmelCase    ,				scaler=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      hasattr(self.optimizer    ,				"""overflow"""       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Tuple=None       )   ->    str:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    List[Any]:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Dict:
						if self.__has_overflow__:
									return self.optimizer.overflow
						return False





class    lowerCamelCase						(A__			):

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Dict:
						super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class    lowerCamelCase						:

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						int=0.001    ,				__UpperCAmelCase				:						Union[str, Any]=0    ,				**__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      params
						SCREAMING_SNAKE_CASE__								=      lr
						SCREAMING_SNAKE_CASE__								=      weight_decay
						SCREAMING_SNAKE_CASE__								=      kwargs





class    lowerCamelCase						:

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						List[str]=0    ,				**__UpperCAmelCase				:						Union[str, Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      optimizer
						SCREAMING_SNAKE_CASE__								=      total_num_steps
						SCREAMING_SNAKE_CASE__								=      warmup_num_steps
						SCREAMING_SNAKE_CASE__								=      kwargs





"""simple docstring"""




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

A_							:			Tuple											= "2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
						raise ImportWarning(
						    "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
						    "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

A_							:			Tuple											= concatenate_datasets
A_							:			List[str]											= DownloadConfig
A_							:			int											= DownloadManager
A_							:			Optional[Any]											= DownloadMode
A_							:			Optional[int]											= DownloadConfig
A_							:			List[Any]											= DownloadMode
A_							:			Any											= DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


