
"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowerCAmelCase__				=					datasets.utils.logging.get_logger(__name__)





@dataclass
class 			snake_case  (				datasets.BuilderConfig ):
							UpperCAmelCase__      =   10_000
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   None







class 			snake_case  (				datasets.ArrowBasedBuilder ):
							UpperCAmelCase__      =   ParquetConfig

							def _lowercase       (self     ):

									"""simple docstring"""







									return datasets.DatasetInfo(features=self.config.features     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if not self.config.data_files:
											raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'     )
									SCREAMING_SNAKE_CASE_    			=			dl_manager.download_and_extract(self.config.data_files     )
									if isinstance(SCREAMING_SNAKE_CASE_							,							(str, list, tuple)     ):
											SCREAMING_SNAKE_CASE_    			=			data_files
											if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			[files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											SCREAMING_SNAKE_CASE_    			=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_     ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''files''': files}     )]
									SCREAMING_SNAKE_CASE_    			=			[]
									for split_name, files in data_files.items():
											if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			[files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											SCREAMING_SNAKE_CASE_    			=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_     ) for file in files]
											# Infer features is they are stoed in the arrow schema
											if self.info.features is None:
													for file in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_     ):
															with open(SCREAMING_SNAKE_CASE_							,							'''rb'''     ) as f:
																	SCREAMING_SNAKE_CASE_    			=			datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE_     )     )
															break
											splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_							,							gen_kwargs={'''files''': files}     )     )
									return splits

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if self.info.features is not None:
											# more expensive cast to support nested features with keys in a different order
											# allows str <-> int/float or str to Audio for example
											SCREAMING_SNAKE_CASE_    			=			table_cast(SCREAMING_SNAKE_CASE_							,							self.info.features.arrow_schema     )
									return pa_table







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.info.features.arrow_schema if self.info.features is not None else None
									if self.info.features is not None and self.config.columns is not None:
											if sorted(field.name for field in schema     ) != sorted(self.config.columns     ):
													raise ValueError(
													    f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''     )
									for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_     )     ):
											with open(SCREAMING_SNAKE_CASE_							,							'''rb'''     ) as f:
													SCREAMING_SNAKE_CASE_    			=			pq.ParquetFile(SCREAMING_SNAKE_CASE_     )
													try:
															for batch_idx, record_batch in enumerate(
															    parquet_file.iter_batches(batch_size=self.config.batch_size							,							columns=self.config.columns     )     ):
																	SCREAMING_SNAKE_CASE_    			=			pa.Table.from_batches([record_batch]     )
																	# Uncomment for debugging (will print the Arrow table size and elements)
																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																	yield f'{file_idx}_{batch_idx}', self._cast_table(SCREAMING_SNAKE_CASE_     )
													except ValueError as e:
															logger.error(f'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_     )}: {e}'     )
															raise

"""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 							_lowerCamelCase			(			__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     ):
				SCREAMING_SNAKE_CASE_    			=			model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				F'{MODEL_NAME}_{model_index}'     )
						os.makedirs(__a,				exist_ok=__a     )
						logger.info(F'Saving model to {ckpt_dir}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''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 							_lowerCamelCase			(			__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
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    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}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''model''': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=__a,				storage_reader=dist_cp.FileSystemReader(__a     ),				planner=DefaultLoadPlanner(),				)
						SCREAMING_SNAKE_CASE_    			=			state_dict['''model''']
						logger.info(F'Model loaded from {ckpt_dir}'     )
				model.load_state_dict(__a     )



def 							_lowerCamelCase			(			__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     ):
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict(__a,				__a     )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_    			=			(
								    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
								)
								SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			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 							_lowerCamelCase			(			__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:
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading Optimizer state from {input_optimizer_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Optimizer state loaded from {input_optimizer_file}'     )
				else:
						SCREAMING_SNAKE_CASE_    			=			(
						    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}'     )
						SCREAMING_SNAKE_CASE_    			=			load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict(),				optimizer_key='''optimizer''',				storage_reader=dist_cp.FileSystemReader(__a     ),				)
						SCREAMING_SNAKE_CASE_    			=			optim_state['''optimizer''']
						logger.info(F'Optimizer loaded from {ckpt_dir}'     )
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict_to_load(__a,				__a,				__a     )
				optimizer.load_state_dict(__a     )