



'''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 checkpointing 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
#
########################################################################

UpperCamelCase__:       List[Any]							     =      16
UpperCamelCase__:       Tuple							     =      32


def 			snake_case_					( _lowerCAmelCase  :						Accelerator       ,				_lowerCAmelCase  :						int = 16  )				->       Optional[int]:
							UpperCAmelCase			:	Any										=		AutoTokenizer.from_pretrained('''bert-base-cased'''  )
							UpperCAmelCase			:	Dict										=		load_dataset('''glue'''       ,				'''mrpc'''  )

							def tokenize_function(_lowerCAmelCase  :						List[Any]  ):
														# max_length=None => use the model max length (it's actually the default)
														UpperCAmelCase			:	Tuple										=		tokenizer(examples['''sentence1''']       ,				examples['''sentence2''']       ,				truncation=_lowerCamelCase       ,				max_length=_lowerCamelCase  )
														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():
														UpperCAmelCase			:	str										=		datasets.map(
														    _lowerCamelCase       ,				batched=_lowerCamelCase       ,				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
							UpperCAmelCase			:	List[str]										=		tokenized_datasets.rename_column('''label'''       ,				'''labels'''  )

							def collate_fn(_lowerCAmelCase  :						Any  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														UpperCAmelCase			:	Optional[Any]										=		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":
																					UpperCAmelCase			:	Dict										=		16
														elif accelerator.mixed_precision != "no":
																					UpperCAmelCase			:	List[str]										=		8
														else:
																					UpperCAmelCase			:	Tuple										=		None

														return tokenizer.pad(
														    _lowerCamelCase       ,				padding='''longest'''       ,				max_length=_lowerCamelCase       ,				pad_to_multiple_of=_lowerCamelCase       ,				return_tensors='''pt'''       ,				)

							# Instantiate dataloaders.
							UpperCAmelCase			:	str										=		DataLoader(
							    tokenized_datasets['''train''']       ,				shuffle=_lowerCamelCase       ,				collate_fn=_lowerCamelCase       ,				batch_size=_lowerCamelCase  )
							UpperCAmelCase			:	int										=		DataLoader(
							    tokenized_datasets['''validation''']       ,				shuffle=_lowerCamelCase       ,				collate_fn=_lowerCamelCase       ,				batch_size=_lowerCamelCase  )

							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

						UpperCamelCase__:       Dict							     =      mocked_dataloaders  # noqa: F811


def 			snake_case_					( _lowerCAmelCase  :						Optional[int]       ,				_lowerCAmelCase  :						Optional[Any]  )				->       Any:





							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,				_lowerCamelCase  ) == "1":
														UpperCAmelCase			:	Optional[int]										=		2
							# Initialize accelerator
							UpperCAmelCase			:	Any										=		Accelerator(cpu=args.cpu       ,				mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCAmelCase			:	Optional[int]										=		config['''lr''']
							UpperCAmelCase			:	Any										=		int(config['''num_epochs''']  )
							UpperCAmelCase			:	Any										=		int(config['''seed''']  )
							UpperCAmelCase			:	Dict										=		int(config['''batch_size''']  )

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps       ,				'''isdigit'''  ):
														if args.checkpointing_steps == "epoch":
																					UpperCAmelCase			:	Dict										=		args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					UpperCAmelCase			:	int										=		int(args.checkpointing_steps  )
														else:
																					raise ValueError(
																					    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""  )
							else:
														UpperCAmelCase			:	Optional[Any]										=		None

							set_seed(_lowerCamelCase  )

							UpperCAmelCase							,		UpperCAmelCase			:	Dict										=		get_dataloaders(_lowerCamelCase       ,				_lowerCamelCase  )
							UpperCAmelCase			:	Dict										=		evaluate.load('''glue'''       ,				'''mrpc'''  )

							# If the batch size is too big we use gradient accumulation
							UpperCAmelCase			:	int										=		1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														UpperCAmelCase			:	Tuple										=		batch_size // MAX_GPU_BATCH_SIZE
														UpperCAmelCase			:	List[Any]										=		MAX_GPU_BATCH_SIZE

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							UpperCAmelCase			:	Dict										=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''       ,				return_dict=_lowerCamelCase  )

							# 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).
							UpperCAmelCase			:	int										=		model.to(accelerator.device  )

							# Instantiate optimizer
							UpperCAmelCase			:	str										=		AdamW(params=model.parameters()       ,				lr=_lowerCamelCase  )

							# Instantiate scheduler
							UpperCAmelCase			:	Union[str, Any]										=		get_linear_schedule_with_warmup(
							    optimizer=_lowerCamelCase       ,				num_warmup_steps=100       ,				num_training_steps=(len(_lowerCamelCase  ) * 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.
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Optional[Any]										=		accelerator.prepare(
							    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase  )

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							UpperCAmelCase			:	Tuple										=		0
							# We also need to keep track of the stating epoch so files are named properly
							UpperCAmelCase			:	Any										=		0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""  )
																					accelerator.load_state(args.resume_from_checkpoint  )
																					UpperCAmelCase			:	List[Any]										=		os.path.basename(args.resume_from_checkpoint  )
														else:
																					# Get the most recent checkpoint
																					UpperCAmelCase			:	Dict										=		[f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime  )
																					UpperCAmelCase			:	str										=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														UpperCAmelCase			:	str										=		os.path.splitext(_lowerCamelCase  )[0]

														if "epoch" in training_difference:
																					UpperCAmelCase			:	Union[str, Any]										=		int(training_difference.replace('''epoch_'''       ,				''''''  )  ) + 1
																					UpperCAmelCase			:	List[Any]										=		None
														else:
																					UpperCAmelCase			:	Union[str, Any]										=		int(training_difference.replace('''step_'''       ,				''''''  )  )
																					UpperCAmelCase			:	Optional[Any]										=		resume_step // len(_lowerCamelCase  )
																					resume_step -= starting_epoch * len(_lowerCamelCase  )

    # Now we train the model
							for epoch in range(_lowerCamelCase       ,				_lowerCamelCase  ):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					UpperCAmelCase			:	str										=		accelerator.skip_first_batches(_lowerCamelCase       ,				_lowerCamelCase  )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					UpperCAmelCase			:	Optional[int]										=		train_dataloader
														for step, batch in enumerate(_lowerCamelCase  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					UpperCAmelCase			:	Dict										=		model(**_lowerCamelCase  )
																					UpperCAmelCase			:	List[str]										=		outputs.loss
																					UpperCAmelCase			:	List[str]										=		loss / gradient_accumulation_steps
																					accelerator.backward(_lowerCamelCase  )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(_lowerCamelCase       ,				_lowerCamelCase  ):
																												UpperCAmelCase			:	List[str]										=		f"""step_{overall_step}"""
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										UpperCAmelCase			:	str										=		os.path.join(args.output_dir       ,				_lowerCamelCase  )
																																			accelerator.save_state(_lowerCamelCase  )

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

														UpperCAmelCase			:	Any										=		metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""       ,				_lowerCamelCase  )

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					UpperCAmelCase			:	str										=		f"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												UpperCAmelCase			:	str										=		os.path.join(args.output_dir       ,				_lowerCamelCase  )
																					accelerator.save_state(_lowerCamelCase  )


def 			snake_case_					( )				->       Tuple:
							UpperCAmelCase			:	List[str]										=		argparse.ArgumentParser(description='''Simple example of training script.'''  )
							parser.add_argument(
							    '''--mixed_precision'''       ,				type=_lowerCamelCase       ,				default=_lowerCamelCase       ,				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(
							    '''--checkpointing_steps'''       ,				type=_lowerCamelCase       ,				default=_lowerCamelCase       ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''       ,				)
							parser.add_argument(
							    '''--output_dir'''       ,				type=_lowerCamelCase       ,				default='''.'''       ,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,				)
							parser.add_argument(
							    '''--resume_from_checkpoint'''       ,				type=_lowerCamelCase       ,				default=_lowerCamelCase       ,				help='''If the training should continue from a checkpoint folder.'''       ,				)
							UpperCAmelCase			:	str										=		parser.parse_args()
							UpperCAmelCase			:	Any										=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(_lowerCamelCase       ,				_lowerCamelCase  )


if __name__ == "__main__":
						main()










'''simple docstring'''






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





