


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     :			str      =1_6
__lowerCAmelCase     :			Dict      =3_2


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						# Initialize accelerator
						if args.with_tracking:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		Accelerator(
												    cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	log_with='''all'''   ,	project_dir=args.project_dir				)
						else:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision				)

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

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

						__SCREAMING_SNAKE_CASE						:					Dict		      =		AutoTokenizer.from_pretrained('''bert-base-cased'''				)
						__SCREAMING_SNAKE_CASE						:					str		      =		load_dataset('''glue'''   ,	'''mrpc'''				)
						__SCREAMING_SNAKE_CASE						:					str		      =		evaluate.load('''glue'''   ,	'''mrpc'''				)

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

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

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

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

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		DataLoader(
						    tokenized_datasets['''train''']   ,	shuffle=lowercase__   ,	collate_fn=lowercase__   ,	batch_size=lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		DataLoader(
						    tokenized_datasets['''validation''']   ,	shuffle=lowercase__   ,	collate_fn=lowercase__   ,	batch_size=lowercase__				)

						set_seed(lowercase__				)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,	return_dict=lowercase__				)

						# 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).
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		model.to(accelerator.device				)

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AdamW(params=model.parameters()   ,	lr=lowercase__				)

						# Instantiate scheduler
						__SCREAMING_SNAKE_CASE						:					Dict		      =		get_linear_schedule_with_warmup(
						    optimizer=lowercase__   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase__				) * 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.
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		accelerator.prepare(
						    lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				)

						# We need to keep track of how many total steps we have iterated over
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		0
						# We also need to keep track of the stating epoch so files are named properly
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		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				)
																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		os.path.basename(args.resume_from_checkpoint				)
												else:
																		# Get the most recent checkpoint
																		__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		[f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
																		dirs.sort(key=os.path.getctime				)
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		os.path.splitext(lowercase__				)[0]

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

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

																		overall_step += 1

																		if isinstance(lowercase__   ,	lowercase__				):
																								__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		os.path.join(args.output_dir   ,	lowercase__				)
																														accelerator.save_state(lowercase__				)

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

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

												if checkpointing_steps == "epoch":
																		__SCREAMING_SNAKE_CASE						:					str		      =		F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		os.path.join(args.output_dir   ,	lowercase__				)
																		accelerator.save_state(lowercase__				)

						if args.with_tracking:
												accelerator.end_training()


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					Any		      =		argparse.ArgumentParser(description='''Simple example of training script.'''				)
						parser.add_argument(
						    '''--mixed_precision'''   ,	type=lowercase__   ,	default=lowercase__   ,	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=lowercase__   ,	default=lowercase__   ,	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=lowercase__   ,	default=lowercase__   ,	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=lowercase__   ,	default='''.'''   ,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,	)
						parser.add_argument(
						    '''--project_dir'''   ,	type=lowercase__   ,	default='''logs'''   ,	help='''Location on where to store experiment tracking logs` and relevent project information'''   ,	)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__   ,	lowercase__				)


if __name__ == "__main__":
						main()




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		(
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						__SCREAMING_SNAKE_CASE						:					list		      =		[]
						for char_count in range(lowercase__				):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]				)
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]				)

						return output_result_list


if __name__ == "__main__":
						print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')
