





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowerCAmelCase_												=       1_6
lowerCAmelCase_												=       3_2






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Tuple:






				"""simple docstring"""


				if args.with_tracking:
								snake_case_		:					List[str]			    =						Accelerator(
								    cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with='''all'''				,       project_dir=args.project_dir				)
				else:
								snake_case_		:					Optional[Any]			    =						Accelerator(cpu=args.cpu				,       mixed_precision=args.mixed_precision				)

				if hasattr(args.checkpointing_steps				,       '''isdigit'''				):
								if args.checkpointing_steps == "epoch":
												snake_case_		:					Tuple			    =						args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												snake_case_		:					Tuple			    =						int(args.checkpointing_steps				)
								else:
												raise ValueError(
												    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
				else:
								snake_case_		:					int			    =						None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case_		:					Optional[int]			    =						config['''lr''']
				snake_case_		:					Optional[int]			    =						int(config['''num_epochs''']				)
				snake_case_		:					Any			    =						int(config['''seed''']				)
				snake_case_		:					List[str]			    =						int(config['''batch_size''']				)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								snake_case_		:					int			    =						os.path.split(_UpperCamelCase				)[-1].split('''.'''				)[0]
								accelerator.init_trackers(_UpperCamelCase				,       _UpperCamelCase				)

				snake_case_		:					Optional[Any]			    =						AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				snake_case_		:					Union[str, Any]			    =						load_dataset('''glue'''				,       '''mrpc'''				)
				snake_case_		:					List[Any]			    =						evaluate.load('''glue'''				,       '''mrpc'''				)

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

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

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

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

				# Instantiate dataloaders.
				snake_case_		:					Union[str, Any]			    =						DataLoader(
				    tokenized_datasets['''train''']				,       shuffle=_UpperCamelCase				,       collate_fn=_UpperCamelCase				,       batch_size=_UpperCamelCase				)
				snake_case_		:					Dict			    =						DataLoader(
				    tokenized_datasets['''validation''']				,       shuffle=_UpperCamelCase				,       collate_fn=_UpperCamelCase				,       batch_size=_UpperCamelCase				)

				set_seed(_UpperCamelCase				)

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

				# 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).
				snake_case_		:					Union[str, Any]			    =						model.to(accelerator.device				)

				# Instantiate optimizer
				snake_case_		:					str			    =						AdamW(params=model.parameters()				,       lr=_UpperCamelCase				)

				# Instantiate scheduler
				snake_case_		:					int			    =						get_linear_schedule_with_warmup(
				    optimizer=_UpperCamelCase				,       num_warmup_steps=100				,       num_training_steps=(len(_UpperCamelCase				) * 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.
				snake_case_    ,							snake_case_    ,							snake_case_    ,							snake_case_    ,							snake_case_		:					List[str]			    =						accelerator.prepare(
				    _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)

				# We need to keep track of how many total steps we have iterated over
				snake_case_		:					int			    =						0
				# We also need to keep track of the stating epoch so files are named properly
				snake_case_		:					Optional[Any]			    =						0

				# Potentially load in the weights and states from a previous save
				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				)
												snake_case_		:					Any			    =						os.path.basename(args.resume_from_checkpoint				)
								else:
												# Get the most recent checkpoint
												snake_case_		:					Any			    =						[f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
												dirs.sort(key=os.path.getctime				)
												snake_case_		:					str			    =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								snake_case_		:					Tuple			    =						os.path.splitext(_UpperCamelCase				)[0]

								if "epoch" in training_difference:
												snake_case_		:					Tuple			    =						int(training_difference.replace('''epoch_'''				,       ''''''				)				) + 1
												snake_case_		:					List[str]			    =						None
								else:
												snake_case_		:					Dict			    =						int(training_difference.replace('''step_'''				,       ''''''				)				)
												snake_case_		:					str			    =						resume_step // len(_UpperCamelCase				)
												resume_step -= starting_epoch * len(_UpperCamelCase				)

    # Now we train the model
				for epoch in range(_UpperCamelCase				,       _UpperCamelCase				):
								model.train()
								if args.with_tracking:
												snake_case_		:					Any			    =						0
								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
												snake_case_		:					List[str]			    =						accelerator.skip_first_batches(_UpperCamelCase				,       _UpperCamelCase				)
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												snake_case_		:					Optional[int]			    =						train_dataloader
								for step, batch in enumerate(_UpperCamelCase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												snake_case_		:					str			    =						model(**_UpperCamelCase				)
												snake_case_		:					Union[str, Any]			    =						outputs.loss
												snake_case_		:					int			    =						loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(_UpperCamelCase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(_UpperCamelCase				,       _UpperCamelCase				):
																snake_case_		:					Any			    =						f'''step_{overall_step}'''
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								snake_case_		:					Dict			    =						os.path.join(args.output_dir				,       _UpperCamelCase				)
																				accelerator.save_state(_UpperCamelCase				)

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

								snake_case_		:					Optional[Any]			    =						metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''				,       _UpperCamelCase				)
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(_UpperCamelCase				),
												        '''epoch''': epoch,
												    }				,       step=_UpperCamelCase				,       )

								if checkpointing_steps == "epoch":
												snake_case_		:					Optional[Any]			    =						f'''epoch_{epoch}'''
												if args.output_dir is not None:
																snake_case_		:					Tuple			    =						os.path.join(args.output_dir				,       _UpperCamelCase				)
												accelerator.save_state(_UpperCamelCase				)

				if args.with_tracking:
								accelerator.end_training()






def   lowerCamelCase_		(       )	->		str:






				"""simple docstring"""


				snake_case_		:					Union[str, Any]			    =						argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,       type=_UpperCamelCase				,       default=_UpperCamelCase				,       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=_UpperCamelCase				,       default=_UpperCamelCase				,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,       )
				parser.add_argument(
				    '''--resume_from_checkpoint'''				,       type=_UpperCamelCase				,       default=_UpperCamelCase				,       help='''If the training should continue from a checkpoint folder.'''				,       )
				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(
				    '''--output_dir'''				,       type=_UpperCamelCase				,       default='''.'''				,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,       )
				parser.add_argument(
				    '''--project_dir'''				,       type=_UpperCamelCase				,       default='''logs'''				,       help='''Location on where to store experiment tracking logs` and relevent project information'''				,       )
				snake_case_		:					List[str]			    =						parser.parse_args()
				snake_case_		:					Dict			    =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(_UpperCamelCase				,       _UpperCamelCase				)


if __name__ == "__main__":
	main()







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
