



'''simple docstring'''

UpperCAmelCase      						=       '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

UpperCAmelCase      						=       concatenate_datasets
UpperCAmelCase      						=       DownloadConfig
UpperCAmelCase      						=       DownloadManager
UpperCAmelCase      						=       DownloadMode
UpperCAmelCase      						=       DownloadConfig
UpperCAmelCase      						=       DownloadMode
UpperCAmelCase      						=       DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

"""simple docstring"""


import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_lowercase				 =					get_logger(__name__)






def    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  List[Any]						,   snake_case__ :  int						,   snake_case__ :  int						,   snake_case__ :  str=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = model.state_dict()
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			if accelerator.process_index == 0:
				logger.info(F'Saving model to {output_model_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Saving model to {output_model_file}'  )
			torch.save(snake_case__						,   snake_case__  )
			logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving model to {ckpt_dir}'  )
			A     = {'model': state_dict}

			dist_cp.save_state_dict(
			    state_dict=snake_case__						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Model saved to {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  List[str]						,   snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  Any=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if type(snake_case__  ) != FSDP and accelerator.process_index != 0:
				if not fsdp_plugin.sync_module_states:
					raise ValueError(
					    'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
					    'initializing FSDP object'  )
				return
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = (
			    os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			    if F'{MODEL_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading model from {ckpt_dir}'  )
			A     = {'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   planner=DefaultLoadPlanner()						,   )
			A     = state_dict['model']
			logger.info(F'Model loaded from {ckpt_dir}'  )
		model.load_state_dict(snake_case__  )






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Optional[int]						,   snake_case__ :  Optional[int]						,   snake_case__ :  Dict						,   snake_case__ :  List[str]						,   snake_case__ :  Any=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = FSDP.optim_state_dict(snake_case__						,   snake_case__  )
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				A     = (
				    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
				)
				A     = os.path.join(snake_case__						,   snake_case__  )
				logger.info(F'Saving Optimizer state to {output_optimizer_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Optimizer state saved in {output_optimizer_file}'  )
		else:
			A     = os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving Optimizer state to {ckpt_dir}'  )
			dist_cp.save_state_dict(
			    state_dict={'optimizer': optim_state}						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Optimizer state saved in {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[str]						,   snake_case__ :  List[str]						,   snake_case__ :  int						,   snake_case__ :  Optional[int]=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = None
			# below check should work but currently it isn't working (mostly opytorch issue),
			# in the meantime disabling it at the cost of excess memory usage
			# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
			A     = (
			    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading Optimizer state from {input_optimizer_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Optimizer state loaded from {input_optimizer_file}'  )
		else:
			A     = (
			    os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			    if F'{OPTIMIZER_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading Optimizer from {ckpt_dir}'  )
			A     = load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()						,   optimizer_key='optimizer'						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   )
			A     = optim_state['optimizer']
			logger.info(F'Optimizer loaded from {ckpt_dir}'  )
		A     = FSDP.optim_state_dict_to_load(snake_case__						,   snake_case__						,   snake_case__  )
		optimizer.load_state_dict(snake_case__  )