


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


SCREAMING_SNAKE_CASE__     					=			16
SCREAMING_SNAKE_CASE__     					=			32


def     lowercase	(    a     , a = 16     , a = "bert-base-cased" ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						AutoTokenizer.from_pretrained(a )
		SCREAMING_SNAKE_CASE_				: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_				: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_				:Tuple				     =						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_				:Dict				     =						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_				:Union[str, Any]				     =						DataLoader(
		    tokenized_datasets["train"]     , shuffle=a     , collate_fn=a     , batch_size=a )
		SCREAMING_SNAKE_CASE_				:Any				     =						DataLoader(
		    tokenized_datasets["validation"]     , shuffle=a     , collate_fn=a     , batch_size=a )

		return train_dataloader, eval_dataloader


def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		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_				:Optional[int]				     =						model(**a )
				SCREAMING_SNAKE_CASE_				:Dict				     =						outputs.logits.argmax(dim=-1 )
				# It is slightly faster to call this once, than multiple times
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, 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(a ) - 1:
								SCREAMING_SNAKE_CASE_				:Any				     =						predictions[: len(eval_dataloader.dataset ) - samples_seen]
								SCREAMING_SNAKE_CASE_				:Any				     =						references[: len(eval_dataloader.dataset ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=a     , references=a     , )

		SCREAMING_SNAKE_CASE_				:Dict				     =						metric.compute()
		return eval_metric["accuracy"]


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						Accelerator()

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

		set_seed(a )
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						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_				:Dict				     =						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_				:Union[str, Any]				     =						optimizer_cls(params=model.parameters()     , lr=a )

		if accelerator.state.deepspeed_plugin is not None:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				SCREAMING_SNAKE_CASE_				:str				     =						1
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(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_				:List[Any]				     =						get_linear_schedule_with_warmup(
				    optimizer=a     , num_warmup_steps=0     , num_training_steps=a     , )
		else:
				SCREAMING_SNAKE_CASE_				:str				     =						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_				:Tuple				     =						accelerator.prepare(
		    a     , a     , a     , a     , a )

		# We need to keep track of how many total steps we have iterated over
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						0
		# We also need to keep track of the stating epoch so files are named properly
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						evaluate.load("glue"     , "mrpc" )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						num_epochs

		if args.partial_train_epoch is not None:
				SCREAMING_SNAKE_CASE_				:Dict				     =						args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint )
				SCREAMING_SNAKE_CASE_				:Dict				     =						args.resume_from_checkpoint.split("epoch_" )[1]
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				SCREAMING_SNAKE_CASE_				:Any				     =						int(a ) + 1
				SCREAMING_SNAKE_CASE_				:str				     =						evaluation_loop(a     , a     , a     , a )
				accelerator.print("resumed checkpoint performance:"     , a )
				accelerator.print("resumed checkpoint's scheduler's lr:"     , lr_scheduler.get_lr()[0] )
				accelerator.print("resumed optimizers's lr:"     , optimizer.param_groups[0]["lr"] )
				with open(os.path.join(args.output_dir     , F"state_{starting_epoch-1}.json" )     , "r" ) as f:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						json.load(a )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		SCREAMING_SNAKE_CASE_				:List[str]				     =						{}
		for epoch in range(a     , a ):
				model.train()
				for step, batch in enumerate(a ):
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**a )
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						outputs.loss
						SCREAMING_SNAKE_CASE_				:Dict				     =						loss / gradient_accumulation_steps
						accelerator.backward(a )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				SCREAMING_SNAKE_CASE_				:int				     =						F"epoch_{epoch}"
				SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(args.output_dir     , a )
				accelerator.save_state(a )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						evaluation_loop(a     , a     , a     , a )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						accuracy
				SCREAMING_SNAKE_CASE_				:Dict				     =						lr_scheduler.get_lr()[0]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						optimizer.param_groups[0]["lr"]
				SCREAMING_SNAKE_CASE_				:int				     =						epoch
				SCREAMING_SNAKE_CASE_				:int				     =						overall_step
				accelerator.print(F"epoch {epoch}:"     , a )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir     , F"state_{epoch}.json" )     , "w" ) as f:
								json.dump(a     , a )


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						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(
		    "--resume_from_checkpoint"     , type=a     , default=a     , help="If the training should continue from a checkpoint folder."     , )
		parser.add_argument(
		    "--partial_train_epoch"     , type=a     , default=a     , help="If passed, the training will stop after this number of epochs."     , )
		parser.add_argument(
		    "--num_epochs"     , type=a     , default=2     , help="Number of train epochs."     , )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						parser.parse_args()
		SCREAMING_SNAKE_CASE_				:Tuple				     =						{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

		training_function(a     , a )


if __name__ == "__main__":
		main()










from timeit import timeit


def     lowercase	(    a ):



		'''simple docstring'''




		if number < 0:
				raise ValueError("the value of input must not be negative" )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0
		while number:
				number &= number - 1
				result += 1
		return result


def     lowercase	(    a ):



		'''simple docstring'''




		if number < 0:
				raise ValueError("the value of input must not be negative" )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result


def     lowercase	(    ):



		'''simple docstring'''




		def do_benchmark(a ) -> None:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"import __main__ as z"
				print(F"Benchmark when {number = }:" )
				print(F"{get_set_bits_count_using_modulo_operator(a ) = }" )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						timeit("z.get_set_bits_count_using_modulo_operator(25)"     , setup=a )
				print(F"timeit() runs in {timing} seconds" )
				print(F"{get_set_bits_count_using_brian_kernighans_algorithm(a ) = }" )
				SCREAMING_SNAKE_CASE_				:str				     =						timeit(
				    "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"     , setup=a     , )
				print(F"timeit() runs in {timing} seconds" )

		for number in (25, 37, 58, 0):
				do_benchmark(a )
				print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()






