

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


_lowerCAmelCase  :      Any			 =16
_lowerCAmelCase  :      Dict			 =32



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 1_6	,SCREAMING_SNAKE_CASE = "bert-base-cased"      ):
	UpperCAmelCase__:	Any									= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[int]									= load_dataset("glue"	,"mrpc"      )

	def tokenize_function(SCREAMING_SNAKE_CASE      ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase__:	Union[str, Any]									= tokenizer(examples["sentence1"]	,examples["sentence2"]	,truncation=SCREAMING_SNAKE_CASE	,max_length=SCREAMING_SNAKE_CASE      )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	UpperCAmelCase__:	Optional[Any]									= datasets.map(
	    SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE	,remove_columns=["idx", "sentence1", "sentence2"]	,load_from_cache_file=SCREAMING_SNAKE_CASE      )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCAmelCase__:	str									= tokenized_datasets.rename_column("label"	,"labels"      )

	def collate_fn(SCREAMING_SNAKE_CASE      ):
		# 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(SCREAMING_SNAKE_CASE	,padding="max_length"	,max_length=1_2_8	,return_tensors="pt"      )
		return tokenizer.pad(SCREAMING_SNAKE_CASE	,padding="longest"	,return_tensors="pt"      )

	# Instantiate dataloaders.
	UpperCAmelCase__:	Optional[Any]									= DataLoader(
	    tokenized_datasets["train"]	,shuffle=SCREAMING_SNAKE_CASE	,collate_fn=SCREAMING_SNAKE_CASE	,batch_size=SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	str									= DataLoader(
	    tokenized_datasets["validation"]	,shuffle=SCREAMING_SNAKE_CASE	,collate_fn=SCREAMING_SNAKE_CASE	,batch_size=SCREAMING_SNAKE_CASE      )

	return train_dataloader, eval_dataloader



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	# Initialize accelerator
	UpperCAmelCase__:	Optional[int]									= Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase__:	List[str]									= config["lr"]
	UpperCAmelCase__:	Any									= int(config["num_epochs"]      )
	UpperCAmelCase__:	Any									= int(config["seed"]      )
	UpperCAmelCase__:	int									= int(config["batch_size"]      )
	UpperCAmelCase__:	List[Any]									= args.model_name_or_path

	set_seed(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__							,						UpperCAmelCase__:	int									= get_dataloaders(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase__:	Tuple									= AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE	,return_dict=SCREAMING_SNAKE_CASE      )

	# Instantiate optimizer
	UpperCAmelCase__:	str									= (
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCAmelCase__:	Union[str, Any]									= optimizer_cls(params=model.parameters()	,lr=SCREAMING_SNAKE_CASE      )

	if accelerator.state.deepspeed_plugin is not None:
		UpperCAmelCase__:	Optional[Any]									= accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		UpperCAmelCase__:	int									= 1
	UpperCAmelCase__:	str									= (len(SCREAMING_SNAKE_CASE      ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		UpperCAmelCase__:	int									= get_linear_schedule_with_warmup(
		    optimizer=SCREAMING_SNAKE_CASE	,num_warmup_steps=0	,num_training_steps=SCREAMING_SNAKE_CASE	,)
	else:
		UpperCAmelCase__:	Dict									= DummyScheduler(SCREAMING_SNAKE_CASE	,total_num_steps=SCREAMING_SNAKE_CASE	,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.
	UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= accelerator.prepare(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	# We need to keep track of how many total steps we have iterated over
	UpperCAmelCase__:	List[str]									= 0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCAmelCase__:	Dict									= 0

	# Now we train the model
	UpperCAmelCase__:	Union[str, Any]									= evaluate.load("glue"	,"mrpc"      )
	UpperCAmelCase__:	Optional[Any]									= 0
	UpperCAmelCase__:	List[str]									= {}
	for epoch in range(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		model.train()
		for step, batch in enumerate(SCREAMING_SNAKE_CASE      ):
			UpperCAmelCase__:	Dict									= model(**SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	List[str]									= outputs.loss
			UpperCAmelCase__:	Any									= loss / gradient_accumulation_steps
			accelerator.backward(SCREAMING_SNAKE_CASE      )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		UpperCAmelCase__:	List[Any]									= 0
		for step, batch in enumerate(SCREAMING_SNAKE_CASE      ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device      )
			with torch.no_grad():
				UpperCAmelCase__:	Any									= model(**SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Any									= outputs.logits.argmax(dim=-1      )
			# It is slightly faster to call this once, than multiple times
			UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= accelerator.gather(
			    (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(SCREAMING_SNAKE_CASE      ) - 1:
					UpperCAmelCase__:	int									= predictions[: len(eval_dataloader.dataset      ) - samples_seen]
					UpperCAmelCase__:	str									= references[: len(eval_dataloader.dataset      ) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=SCREAMING_SNAKE_CASE	,references=SCREAMING_SNAKE_CASE	,)

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

		if best_performance < eval_metric["accuracy"]:
			UpperCAmelCase__:	Optional[int]									= 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(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )



def 			_A	(      ):
	UpperCAmelCase__:	str									= argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
	parser.add_argument(
	    "--model_name_or_path"	,type=SCREAMING_SNAKE_CASE	,default="bert-base-cased"	,help="Path to pretrained model or model identifier from huggingface.co/models."	,required=SCREAMING_SNAKE_CASE	,)
	parser.add_argument(
	    "--output_dir"	,type=SCREAMING_SNAKE_CASE	,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=SCREAMING_SNAKE_CASE	,default=SCREAMING_SNAKE_CASE	,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=SCREAMING_SNAKE_CASE	,default=3	,help="Number of train epochs."	,)
	UpperCAmelCase__:	int									= parser.parse_args()
	UpperCAmelCase__:	int									= {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
	training_function(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					main()


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= int(SCREAMING_SNAKE_CASE      )
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= divmod(SCREAMING_SNAKE_CASE	,2      )
	return binary_recursive(SCREAMING_SNAKE_CASE      ) + str(SCREAMING_SNAKE_CASE      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= str(SCREAMING_SNAKE_CASE      ).strip()
	if not number:
		raise ValueError("No input value was provided"      )
	UpperCAmelCase__:	Tuple									= "-" if number.startswith("-"      ) else ""
	UpperCAmelCase__:	Any									= number.lstrip("-"      )
	if not number.isnumeric():
		raise ValueError("Input value is not an integer"      )
	return f"{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE      )      )}"


if __name__ == "__main__":
					from doctest import testmod

					testmod()