

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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_						     =    16
a_						     =    32

def  lowerCamelCase__ (					_a							,			_a = 16):
	SCREAMING_SNAKE_CASE   :      Any				     =       AutoTokenizer.from_pretrained("bert-base-cased")
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_dataset("glue"							,			"mrpc")

	def tokenize_function(_a):
		# max_length=None => use the model max length (it's actually the default)
		SCREAMING_SNAKE_CASE   :      Dict				     =       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():
		SCREAMING_SNAKE_CASE   :      int				     =       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
	SCREAMING_SNAKE_CASE   :      int				     =       tokenized_datasets.rename_column("label"							,			"labels")

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

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

	# Instantiate dataloaders.
	SCREAMING_SNAKE_CASE   :      Tuple				     =       DataLoader(
	    tokenized_datasets["train"]							,			shuffle=_snake_case							,			collate_fn=_snake_case							,			batch_size=_snake_case)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       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  lowerCamelCase__ (					_a							,			_a):






	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,			_snake_case) == "1":
		SCREAMING_SNAKE_CASE   :      str				     =       2
	# New Code #
	SCREAMING_SNAKE_CASE   :      int				     =       int(args.gradient_accumulation_steps)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       int(args.local_sgd_steps)
	# Initialize accelerator
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=_snake_case)
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)")
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       config["lr"]
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       int(config["num_epochs"])
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       int(config["seed"])
	SCREAMING_SNAKE_CASE   :      Any				     =       int(config["batch_size"])

	SCREAMING_SNAKE_CASE   :      Any				     =       evaluate.load("glue"							,			"mrpc")

	set_seed(_snake_case)
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       get_dataloaders(_snake_case							,			_snake_case)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       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).
	SCREAMING_SNAKE_CASE   :      Dict				     =       model.to(accelerator.device)

	# Instantiate optimizer
	SCREAMING_SNAKE_CASE   :      int				     =       AdamW(params=model.parameters()							,			lr=_snake_case)

	# Instantiate scheduler
	SCREAMING_SNAKE_CASE   :      str				     =       get_linear_schedule_with_warmup(
	    optimizer=_snake_case							,			num_warmup_steps=100							,			num_training_steps=(len(_snake_case) * num_epochs)							,			)

	# 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   :      List[str]				     =       accelerator.prepare(
	    _snake_case							,			_snake_case							,			_snake_case							,			_snake_case							,			_snake_case)

	# Now we train the model
	for epoch in range(_snake_case):
		model.train()
		with LocalSGD(
		    accelerator=_snake_case							,			model=_snake_case							,			local_sgd_steps=_snake_case							,			enabled=local_sgd_steps is not None) as local_sgd:
			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)
				# New code #
				# We use the new `accumulate` context manager to perform gradient accumulation
				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
				with accelerator.accumulate(_snake_case):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       model(**_snake_case)
					SCREAMING_SNAKE_CASE   :      Tuple				     =       output.loss
					accelerator.backward(_snake_case)
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		model.eval()
		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)
			with torch.no_grad():
				SCREAMING_SNAKE_CASE   :      int				     =       model(**_snake_case)
			SCREAMING_SNAKE_CASE   :      str				     =       outputs.logits.argmax(dim=-1)
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=_snake_case							,			references=_snake_case							,			)

		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"							,			_snake_case)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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."							,			)
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"							,			type=_snake_case							,			default=1							,			help="The number of minibatches to be ran before gradients are accumulated."							,			)
	parser.add_argument(
	    "--local_sgd_steps"							,			type=_snake_case							,			default=8							,			help="Number of local SGD steps or None to disable local SGD")
	parser.add_argument("--cpu"							,			action="store_true"							,			help="If passed, will train on the CPU.")
	SCREAMING_SNAKE_CASE   :      Tuple				     =       parser.parse_args()
	SCREAMING_SNAKE_CASE   :      int				     =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_snake_case							,			_snake_case)


if __name__ == "__main__":
		main()


def  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


if __name__ == "__main__":
			print(F'''{solution() = }''')