

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

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





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				List[Any]							,   _UpperCamelCase :				Dict							)      ->Any:
									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
															snake_case_   =	deepcopy(_UpperCamelCase							)
									elif os.path.exists(_UpperCamelCase							):
															with io.open(_UpperCamelCase							,   '''r'''							,   encoding='''utf-8'''							) as f:
																					snake_case_   =	json.load(_UpperCamelCase							)
									else:
															try:
																					snake_case_   =	baseaa.urlsafe_baadecode(_UpperCamelCase							).decode('''utf-8'''							)
																					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}'''							)

									snake_case_   =	config

									self.set_stage_and_offload()







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[Any]:
									# 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.
									snake_case_   =	self.get_value('''zero_optimization.stage'''							,   -1							)

									# offload
									snake_case_   =	False
									if self.is_zeroa() or self.is_zeroa():
															snake_case_   =	set(['''cpu''', '''nvme''']							)
															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:
																					snake_case_   =	True







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[Any]							)      ->List[Any]:
									snake_case_   =	self.config

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

									return config, ds_key







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				List[Any]=None							)      ->List[Any]:
									snake_case_,    snake_case_   =	self.find_config_node(_UpperCamelCase							)
									if config is None:
															return default
									return config.get(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				Tuple=False							)      ->List[Any]:
									snake_case_   =	self.config

									# find the config node of interest if it exists
									snake_case_   =	ds_key_long.split('''.'''							)
									for node in nodes:
															snake_case_   =	config
															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 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]							)      ->Optional[Any]:
									snake_case_   =	self.get_value(_UpperCamelCase							)
									return False if value is None else bool(_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Dict							)      ->Optional[Any]:
									snake_case_   =	self.get_value(_UpperCamelCase							)
									return False if value is None else not bool(_UpperCamelCase							)







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







			def 				snake_case__(     self :				List[str]							)      ->List[str]:
									return self._stage == 3







			def 				snake_case__(     self :				Any							)      ->Any:
									return self._offload






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				Tuple							)      ->Optional[int]:
									snake_case_   =	engine







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Tuple							,   **_UpperCamelCase :				Dict							)      ->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 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Tuple							,   _UpperCamelCase :				Any							)      ->Optional[Any]:
									super().__init__(_UpperCamelCase							,   device_placement=_UpperCamelCase							,   scaler=_UpperCamelCase							)
									snake_case_   =	hasattr(self.optimizer							,   '''overflow'''							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[str]=None							)      ->List[str]:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







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







			@property
			def 				snake_case__(     self :				Dict							)      ->List[str]:
									if self.__has_overflow__:
															return self.optimizer.overflow
									return False






class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Any							)      ->Dict:
									super().__init__(_UpperCamelCase							,   _UpperCamelCase							)







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






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Tuple							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Any=0.001							,   _UpperCamelCase :				Optional[int]=0							,   **_UpperCamelCase :				List[str]							)      ->int:
									snake_case_   =	params
									snake_case_   =	lr
									snake_case_   =	weight_decay
									snake_case_   =	kwargs






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				Optional[Any]=0							,   **_UpperCamelCase :				Dict							)      ->List[Any]:
									snake_case_   =	optimizer
									snake_case_   =	total_num_steps
									snake_case_   =	warmup_num_steps
									snake_case_   =	kwargs


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)