

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


a_      =     16
a_      =     32

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

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

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

	# 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 :   Union[str, Any]					  =							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.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_a				,      padding="max_length"				,      max_length=128				,      return_tensors="pt")
		return tokenizer.pad(_a				,      padding="longest"				,      return_tensors="pt")

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

	return train_dataloader, eval_dataloader

def 			lowerCamelCase__			(       _a				,      _a):
	# Initialize accelerator
	SCREAMING_SNAKE_CASE :   Dict					  =							Accelerator()

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

	set_seed(_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							get_dataloaders(_a				,      _a				,      _a)

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

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

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

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

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

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

			overall_step += 1

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

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

		if best_performance < eval_metric["accuracy"]:
			SCREAMING_SNAKE_CASE :   str					  =							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(_a				,      _a)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.")
	parser.add_argument(
	    "--model_name_or_path"				,      type=_a				,      default="bert-base-cased"				,      help="Path to pretrained model or model identifier from huggingface.co/models."				,      required=_a				,      )
	parser.add_argument(
	    "--output_dir"				,      type=_a				,      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=_a				,      default=_a				,      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=_a				,      default=3				,      help="Number of train epochs."				,      )
	SCREAMING_SNAKE_CASE :   str					  =							parser.parse_args()
	SCREAMING_SNAKE_CASE :   List[Any]					  =							{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_a				,      _a)


if __name__ == "__main__":
			main()


from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	if len(_a) == 0:
		return []
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							min(_a), max(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							int(max_value - min_value) + 1
	SCREAMING_SNAKE_CASE :   list[list]					  =							[[] for _ in range(_a)]

	for i in my_list:
		buckets[int(i - min_value)].append(_a)

	return [v for bucket in buckets for v in sorted(_a)]


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

			testmod()
			assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]