



'''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
#
# 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						:		Any						      = 1_6
lowerCAmelCase						:		Optional[Any]						      = 3_2



def 	__lowerCAmelCase       (				lowerCamelCase :    Any					,		lowerCamelCase :    List[Any]       ):






	'''simple docstring'''




	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       )

	if hasattr(args.checkpointing_steps					,		"isdigit"       ):
		if args.checkpointing_steps == "epoch":
			__lowerCAmelCase       				=	args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__lowerCAmelCase       				=	int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
	else:
		__lowerCAmelCase       				=	None
	# 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"]       )

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

	__lowerCAmelCase       				=	AutoTokenizer.from_pretrained("bert-base-cased"       )
	__lowerCAmelCase       				=	load_dataset("glue"					,		"mrpc"       )
	__lowerCAmelCase       				=	evaluate.load("glue"					,		"mrpc"       )

	def tokenize_function(lowerCamelCase :    str       ):
		# max_length=None => use the model max length (it's actually the default)
		__lowerCAmelCase       				=	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():
		__lowerCAmelCase       				=	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
	__lowerCAmelCase       				=	tokenized_datasets.rename_column("label"					,		"labels"       )

	# 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

	def collate_fn(lowerCamelCase :    Any       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowerCAmelCase       				=	1_28 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(
		    lowerCamelCase					,		padding="longest"					,		max_length=lowerCamelCase					,		pad_to_multiple_of=lowerCamelCase					,		return_tensors="pt"					,		)

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

	set_seed(lowerCamelCase       )

	# 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=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).
	__lowerCAmelCase       				=	model.to(accelerator.device       )

	# Instantiate optimizer
	__lowerCAmelCase       				=	AdamW(params=model.parameters()					,		lr=lowerCamelCase       )

	# Instantiate scheduler
	__lowerCAmelCase       				=	get_linear_schedule_with_warmup(
	    optimizer=lowerCamelCase					,		num_warmup_steps=1_00					,		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.
	__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase       				=	accelerator.prepare(
	    lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )

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

		if "epoch" in training_difference:
			__lowerCAmelCase       				=	int(training_difference.replace("epoch_"					,		""       )       ) + 1
			__lowerCAmelCase       				=	None
		else:
			__lowerCAmelCase       				=	int(training_difference.replace("step_"					,		""       )       )
			__lowerCAmelCase       				=	resume_step // len(lowerCamelCase       )
			resume_step -= starting_epoch * len(lowerCamelCase       )

    # Now we train the model
	for epoch in range(lowerCamelCase					,		lowerCamelCase       ):
		model.train()
		if args.with_tracking:
			__lowerCAmelCase       				=	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
			__lowerCAmelCase       				=	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
			__lowerCAmelCase       				=	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       )
			__lowerCAmelCase       				=	model(**lowerCamelCase       )
			__lowerCAmelCase       				=	outputs.loss
			__lowerCAmelCase       				=	loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(lowerCamelCase       )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(lowerCamelCase					,		lowerCamelCase       ):
				__lowerCAmelCase       				=	f'''step_{overall_step}'''
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						__lowerCAmelCase       				=	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`.
			batch.to(accelerator.device       )
			with torch.no_grad():
				__lowerCAmelCase       				=	model(**lowerCamelCase       )
			__lowerCAmelCase       				=	outputs.logits.argmax(dim=-1       )
			__lowerCAmelCase				,							__lowerCAmelCase       				=	accelerator.gather_for_metrics((predictions, batch["labels"])       )
			metric.add_batch(
			    predictions=lowerCamelCase					,		references=lowerCamelCase					,		)

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

		if checkpointing_steps == "epoch":
			__lowerCAmelCase       				=	f'''epoch_{epoch}'''
			if args.output_dir is not None:
				__lowerCAmelCase       				=	os.path.join(args.output_dir					,		lowerCamelCase       )
			accelerator.save_state(lowerCamelCase       )

	if args.with_tracking:
		accelerator.end_training()



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	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(
	    "--resume_from_checkpoint"					,		type=lowerCamelCase					,		default=lowerCamelCase					,		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=lowerCamelCase					,		default="."					,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,		)
	parser.add_argument(
	    "--project_dir"					,		type=lowerCamelCase					,		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(lowerCamelCase					,		lowerCamelCase       )


if __name__ == "__main__":
	main()




'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    list[list[int]]					,		lowerCamelCase :    list[list[int]]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	len(lowerCamelCase       )
	for i in range(lowerCamelCase       ):
		__lowerCAmelCase       				=	[]
		for j in range(lowerCamelCase       ):
			__lowerCAmelCase       				=	0
			for k in range(lowerCamelCase       ):
				__lowerCAmelCase       				=	val + matrix_a[i][k] * matrix_b[k][j]
			list_a.append(lowerCamelCase       )
		matrix_c.append(lowerCamelCase       )
	return matrix_c



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	return [[int(row == column       ) for column in range(lowerCamelCase       )] for row in range(lowerCamelCase       )]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	identity(2       )
	__lowerCAmelCase       				=	[[1, 1], [1, 0]]
	__lowerCAmelCase       				=	n - 1
	while n > 0:
		if n % 2 == 1:
			__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	int(n / 2       )
	return res_matrix[0][0]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	0
	__lowerCAmelCase       				=	1
	for _ in range(2					,		n + 1       ):
		__lowerCAmelCase				,							__lowerCAmelCase       				=	fiba, fiba + fiba
	return fiba



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
		__lowerCAmelCase       				=	int("".join(c for c in ordinal if c in "0123456789"       )       )  # 1000th --> 1000
		print(
		    f'''{ordinal} fibonacci number using matrix exponentiation is '''
		    f'''{nth_fibonacci_matrix(lowerCamelCase       )} and using bruteforce is '''
		    f'''{nth_fibonacci_bruteforce(lowerCamelCase       )}\n'''       )
	# from timeit import timeit
	# print(timeit("nth_fibonacci_matrix(1000000)",
	#              "from main import nth_fibonacci_matrix", number=5))
	# print(timeit("nth_fibonacci_bruteforce(1000000)",
	#              "from main import nth_fibonacci_bruteforce", number=5))
	# 2.3342058970001744
	# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()