






"""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


lowerCamelCase_       			=   get_logger(__name__)

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

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

def        snake_case   (     A__			,A__			,A__			,A__			,A__=0		):
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    A__			,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(A__		) != 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
									UpperCAmelCase_  :				List[Any]      =      F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
									UpperCAmelCase_  :				Optional[int]      =      os.path.join(A__			,A__		)
									logger.info(F"""Loading model from {input_model_file}"""		)
									UpperCAmelCase_  :				List[str]      =      torch.load(A__		)
									logger.info(F"""Model loaded from {input_model_file}"""		)
						elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
									UpperCAmelCase_  :				str      =      (
									    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
									    if model_index == 0
									    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
									)
									UpperCAmelCase_  :				str      =      os.path.join(A__			,A__		)
									logger.info(F"""Loading model from {input_model_file}"""		)
									UpperCAmelCase_  :				List[str]      =      torch.load(A__		)
									logger.info(F"""Model loaded from {input_model_file}"""		)
						elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
									UpperCAmelCase_  :				Any      =      (
									    os.path.join(A__			,F"""{MODEL_NAME}_{model_index}"""		)
									    if F"""{MODEL_NAME}""" not in input_dir
									    else input_dir
									)
									logger.info(F"""Loading model from {ckpt_dir}"""		)
									UpperCAmelCase_  :				Dict      =      {"model": model.state_dict()}
									dist_cp.load_state_dict(
									    state_dict=A__			,storage_reader=dist_cp.FileSystemReader(A__		)			,planner=DefaultLoadPlanner()			,)
									UpperCAmelCase_  :				List[str]      =      state_dict["model"]
									logger.info(F"""Model loaded from {ckpt_dir}"""		)
						model.load_state_dict(A__		)

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




def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__=0		):
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    A__			,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:
									UpperCAmelCase_  :				Optional[Any]      =      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:
									UpperCAmelCase_  :				List[Any]      =      (
									    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
									)
									UpperCAmelCase_  :				int      =      os.path.join(A__			,A__		)
									logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""		)
									UpperCAmelCase_  :				Any      =      torch.load(A__		)
									logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""		)
						else:
									UpperCAmelCase_  :				Optional[int]      =      (
									    os.path.join(A__			,F"""{OPTIMIZER_NAME}_{optimizer_index}"""		)
									    if F"""{OPTIMIZER_NAME}""" not in input_dir
									    else input_dir
									)
									logger.info(F"""Loading Optimizer from {ckpt_dir}"""		)
									UpperCAmelCase_  :				str      =      load_sharded_optimizer_state_dict(
									    model_state_dict=model.state_dict()			,optimizer_key="optimizer"			,storage_reader=dist_cp.FileSystemReader(A__		)			,)
									UpperCAmelCase_  :				Optional[int]      =      optim_state["optimizer"]
									logger.info(F"""Optimizer loaded from {ckpt_dir}"""		)
						UpperCAmelCase_  :				Union[str, Any]      =      FSDP.optim_state_dict_to_load(A__			,A__			,A__		)
						optimizer.load_state_dict(A__		)











"""simple docstring"""
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCamelCase_       			=   pytest.mark.integration

@pytest.mark.parametrize("path"			,["paws", "csv"]		)
def        snake_case   (     A__			,A__		):
			inspect_dataset(A__			,A__		)
			UpperCAmelCase_  :				Tuple      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"		)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"		)
@pytest.mark.parametrize("path"			,["accuracy"]		)
def        snake_case   (     A__			,A__		):
			inspect_metric(A__			,A__		)
			UpperCAmelCase_  :				List[str]      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      get_dataset_config_info(A__			,config_name=A__		)
			assert info.config_name == config_name
			assert list(info.splits.keys()		) == expected_splits

@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_config_info(A__			,config_name=A__		)

@pytest.mark.parametrize(
    "path, expected"			,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,)
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				str      =      get_dataset_config_names(A__		)
			assert expected in config_names

@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      get_dataset_infos(A__		)
			assert list(infos.keys()		) == expected_configs
			UpperCAmelCase_  :				List[str]      =      expected_configs[0]
			assert expected_config in infos
			UpperCAmelCase_  :				int      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits_in_first_config

@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      get_dataset_infos(A__		)
			assert expected_config in infos
			UpperCAmelCase_  :				Union[str, Any]      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits




@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_split_names(A__			,config_name=A__		)



