
import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase_   	=	1_6
UpperCamelCase_   	=	3_2






def 		_UpperCAmelCase   (	UpperCamelCase:     Accelerator			,							UpperCamelCase:     int = 1_6			,							UpperCamelCase:     str = "bert-base-cased"  ):






	"""simple docstring"""


	__lowerCAmelCase         =							AutoTokenizer.from_pretrained(UpperCamelCase  )
	__lowerCAmelCase         =							load_dataset("glue"			,							"mrpc"  )

	def tokenize_function(UpperCamelCase:     Optional[int]  ):
		# max_length=None => use the model max length (it's actually the default)
		__lowerCAmelCase         =							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
	__lowerCAmelCase         =							datasets.map(
	    UpperCamelCase			,							batched=UpperCamelCase			,							remove_columns=["idx", "sentence1", "sentence2"]			,							load_from_cache_file=UpperCamelCase  )

	# 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"  )

	def collate_fn(UpperCamelCase:     Any  ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(UpperCamelCase			,							padding="max_length"			,							max_length=1_2_8			,							return_tensors="pt"  )
		return tokenizer.pad(UpperCamelCase			,							padding="longest"			,							return_tensors="pt"  )

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

	return train_dataloader, eval_dataloader






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     int  ):






	"""simple docstring"""


	__lowerCAmelCase         =							Accelerator()

	# 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"]  )
	__lowerCAmelCase         =							args.model_name_or_path

	set_seed(UpperCamelCase  )
	__lowerCAmelCase   ,						__lowerCAmelCase         =							get_dataloaders(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowerCAmelCase         =							AutoModelForSequenceClassification.from_pretrained(UpperCamelCase			,							return_dict=UpperCamelCase  )

	# Instantiate optimizer
	__lowerCAmelCase         =							(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	__lowerCAmelCase         =							optimizer_cls(params=model.parameters()			,							lr=UpperCamelCase  )

	if accelerator.state.deepspeed_plugin is not None:
		__lowerCAmelCase         =							accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		__lowerCAmelCase         =							1
	__lowerCAmelCase         =							(len(UpperCamelCase  ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		__lowerCAmelCase         =							get_linear_schedule_with_warmup(
		    optimizer=UpperCamelCase			,							num_warmup_steps=0			,							num_training_steps=UpperCamelCase			,							)
	else:
		__lowerCAmelCase         =							DummyScheduler(UpperCamelCase			,							total_num_steps=UpperCamelCase			,							warmup_num_steps=0  )

	# 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(
	    UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	# 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

	# Now we train the model
	__lowerCAmelCase         =							evaluate.load("glue"			,							"mrpc"  )
	__lowerCAmelCase         =							0
	__lowerCAmelCase         =							{}
	for epoch in range(UpperCamelCase			,							UpperCamelCase  ):
		model.train()
		for step, batch in enumerate(UpperCamelCase  ):
			__lowerCAmelCase         =							model(**UpperCamelCase  )
			__lowerCAmelCase         =							outputs.loss
			__lowerCAmelCase         =							loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase  )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		__lowerCAmelCase         =							0
		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():
				__lowerCAmelCase         =							model(**UpperCamelCase  )
			__lowerCAmelCase         =							outputs.logits.argmax(dim=-1  )
			# It is slightly faster to call this once, than multiple times
			__lowerCAmelCase   ,						__lowerCAmelCase         =							accelerator.gather(
			    (predictions, batch["labels"])  )  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(UpperCamelCase  ) - 1:
					__lowerCAmelCase         =							predictions[: len(eval_dataloader.dataset  ) - samples_seen]
					__lowerCAmelCase         =							references[: len(eval_dataloader.dataset  ) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=UpperCamelCase			,							references=UpperCamelCase			,							)

		__lowerCAmelCase         =							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"			,							UpperCamelCase  )
		__lowerCAmelCase         =							eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			__lowerCAmelCase         =							eval_metric["accuracy"]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir			,							"all_results.json"  )			,							"w"  ) as f:
			json.dump(UpperCamelCase			,							UpperCamelCase  )






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."  )
	parser.add_argument(
	    "--model_name_or_path"			,							type=UpperCamelCase			,							default="bert-base-cased"			,							help="Path to pretrained model or model identifier from huggingface.co/models."			,							required=UpperCamelCase			,							)
	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(
	    "--performance_lower_bound"			,							type=UpperCamelCase			,							default=UpperCamelCase			,							help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."			,							)
	parser.add_argument(
	    "--num_epochs"			,							type=UpperCamelCase			,							default=3			,							help="Number of train epochs."			,							)
	__lowerCAmelCase         =							parser.parse_args()
	__lowerCAmelCase         =							{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
	training_function(UpperCamelCase			,							UpperCamelCase  )


if __name__ == "__main__":
					main()








from __future__ import annotations

from math import ceil, floor, sqrt






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 2_0_0_0_0_0_0  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[0]
	__lowerCAmelCase         =							42

	for idx in range(1			,							ceil(sqrt(target * 2  ) * 1.1  )  ):
		triangle_numbers.append(triangle_numbers[-1] + idx  )

	# we want this to be as close as possible to target
	__lowerCAmelCase         =							0
	# the area corresponding to the grid that gives the product closest to target
	__lowerCAmelCase         =							0
	# an estimate of b, using the quadratic formula
	__lowerCAmelCase         =							42
	# the largest integer less than b_estimate
	__lowerCAmelCase         =							42
	# the largest integer less than b_estimate
	__lowerCAmelCase         =							42
	# the triangle number corresponding to b_floor
	__lowerCAmelCase         =							42
	# the triangle number corresponding to b_ceil
	__lowerCAmelCase         =							42

	for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,							1  ):
		__lowerCAmelCase         =							(-1 + sqrt(1 + 8 * target / triangle_a  )) / 2
		__lowerCAmelCase         =							floor(UpperCamelCase  )
		__lowerCAmelCase         =							ceil(UpperCamelCase  )
		__lowerCAmelCase         =							triangle_numbers[b_floor]
		__lowerCAmelCase         =							triangle_numbers[b_ceil]

		if abs(target - triangle_b_first_guess * triangle_a  ) < abs(
		    target - best_product  ):
			__lowerCAmelCase         =							triangle_b_first_guess * triangle_a
			__lowerCAmelCase         =							idx_a * b_floor

		if abs(target - triangle_b_second_guess * triangle_a  ) < abs(
		    target - best_product  ):
			__lowerCAmelCase         =							triangle_b_second_guess * triangle_a
			__lowerCAmelCase         =							idx_a * b_ceil

	return area


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






