




"""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


a      =							get_logger(__name__)





def 							lowercase					(snake_case__   : str      ,       snake_case__   : List[Any]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : str=0 )     ->  Union[str, Any]:


				'''simple docstring'''






				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 ):
								lowerCAmelCase  =				model.state_dict()
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												lowerCAmelCase  =				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
												lowerCAmelCase  =				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:
												lowerCAmelCase  =				(
												    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
												    if model_index == 0
												    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
												)
												lowerCAmelCase  =				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:
												lowerCAmelCase  =				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}''' )
												lowerCAmelCase  =				{"""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 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[int]      ,       snake_case__   : int      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]=0 )     ->  Optional[Any]:


				'''simple docstring'''






				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
												lowerCAmelCase  =				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
												lowerCAmelCase  =				os.path.join(snake_case__      ,       snake_case__ )
												logger.info(f'''Loading model from {input_model_file}''' )
												lowerCAmelCase  =				torch.load(snake_case__ )
												logger.info(f'''Model loaded from {input_model_file}''' )
								elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
												lowerCAmelCase  =				(
												    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
												    if model_index == 0
												    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
												)
												lowerCAmelCase  =				os.path.join(snake_case__      ,       snake_case__ )
												logger.info(f'''Loading model from {input_model_file}''' )
												lowerCAmelCase  =				torch.load(snake_case__ )
												logger.info(f'''Model loaded from {input_model_file}''' )
								elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
												lowerCAmelCase  =				(
												    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}''' )
												lowerCAmelCase  =				{"""model""": model.state_dict()}
												dist_cp.load_state_dict(
												    state_dict=snake_case__      ,       storage_reader=dist_cp.FileSystemReader(snake_case__ )      ,       planner=DefaultLoadPlanner()      ,       )
												lowerCAmelCase  =				state_dict["""model"""]
												logger.info(f'''Model loaded from {ckpt_dir}''' )
								model.load_state_dict(snake_case__ )





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : Tuple      ,       snake_case__   : List[Any]=0 )     ->  str:


				'''simple docstring'''






				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 ):
								lowerCAmelCase  =				FSDP.optim_state_dict(snake_case__      ,       snake_case__ )
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												if accelerator.process_index == 0:
																lowerCAmelCase  =				(
																    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
																)
																lowerCAmelCase  =				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:
												lowerCAmelCase  =				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 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[int]      ,       snake_case__   : Optional[int]      ,       snake_case__   : List[str]      ,       snake_case__   : Optional[int]      ,       snake_case__   : int=0 )     ->  str:


				'''simple docstring'''






				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:
												lowerCAmelCase  =				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:
												lowerCAmelCase  =				(
												    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
												)
												lowerCAmelCase  =				os.path.join(snake_case__      ,       snake_case__ )
												logger.info(f'''Loading Optimizer state from {input_optimizer_file}''' )
												lowerCAmelCase  =				torch.load(snake_case__ )
												logger.info(f'''Optimizer state loaded from {input_optimizer_file}''' )
								else:
												lowerCAmelCase  =				(
												    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}''' )
												lowerCAmelCase  =				load_sharded_optimizer_state_dict(
												    model_state_dict=model.state_dict()      ,       optimizer_key="""optimizer"""      ,       storage_reader=dist_cp.FileSystemReader(snake_case__ )      ,       )
												lowerCAmelCase  =				optim_state["""optimizer"""]
												logger.info(f'''Optimizer loaded from {ckpt_dir}''' )
								lowerCAmelCase  =				FSDP.optim_state_dict_to_load(snake_case__      ,       snake_case__      ,       snake_case__ )
								optimizer.load_state_dict(snake_case__ )








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

				def tokenize_function(snake_case__   : List[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase  =				tokenizer(examples["""sentence1"""]      ,       examples["""sentence2"""]      ,       truncation=snake_case__      ,       max_length=snake_case__ )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCAmelCase  =				datasets.map(
								    snake_case__      ,       batched=snake_case__      ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase  =				tokenized_datasets.rename_column("""label"""      ,       """labels""" )

				def collate_fn(snake_case__   : Union[str, Any] ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase  =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase  =				16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase  =				8
								else:
												lowerCAmelCase  =				None

								return tokenizer.pad(
								    snake_case__      ,       padding="""longest"""      ,       max_length=snake_case__      ,       pad_to_multiple_of=snake_case__      ,       return_tensors="""pt"""      ,       )

				# Instantiate dataloaders.
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""train"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""validation"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "1":
								lowerCAmelCase  =				2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCAmelCase  =				Accelerator(
								    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="""all"""      ,       project_dir=args.project_dir )
				else:
								lowerCAmelCase  =				Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase  =				config["""lr"""]
				lowerCAmelCase  =				int(config["""num_epochs"""] )
				lowerCAmelCase  =				int(config["""seed"""] )
				lowerCAmelCase  =				int(config["""batch_size"""] )
				set_seed(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				evaluate.load("""glue"""      ,       """mrpc""" )

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase  =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase  =				batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase  =				MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase  =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,       return_dict=snake_case__ )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCAmelCase  =				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase  =				AdamW(params=model.parameters()      ,       lr=snake_case__ )

				# Instantiate scheduler
				lowerCAmelCase  =				get_linear_schedule_with_warmup(
				    optimizer=snake_case__      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case__ ) * num_epochs) // gradient_accumulation_steps      ,       )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				accelerator.prepare(
				    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCAmelCase  =				os.path.split(snake_case__ )[-1].split(""".""" )[0]
								accelerator.init_trackers(snake_case__      ,       snake_case__ )

				# Now we train the model
				for epoch in range(snake_case__ ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCAmelCase  =				0
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase  =				loss / gradient_accumulation_steps
												accelerator.backward(snake_case__ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device )
												with torch.no_grad():
																lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase ,						lowerCAmelCase  =				accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=snake_case__      ,       references=snake_case__      ,       )

								lowerCAmelCase  =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,       snake_case__ )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(snake_case__ ),
												        """epoch""": epoch,
												    }      ,       step=snake_case__      ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""      ,       type=snake_case__      ,       default=snake_case__      ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,       help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""      ,       )
				parser.add_argument("""--cpu"""      ,       action="""store_true"""      ,       help="""If passed, will train on the CPU.""" )
				parser.add_argument(
				    """--with_tracking"""      ,       action="""store_true"""      ,       help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,       )
				parser.add_argument(
				    """--project_dir"""      ,       type=snake_case__      ,       default="""logs"""      ,       help="""Location on where to store experiment tracking logs` and relevent project information"""      ,       )
				lowerCAmelCase  =				parser.parse_args()
				lowerCAmelCase  =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





