





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


snake_case_							=			1_6
snake_case_							=			3_2


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 1_6							,       SCREAMING_SNAKE_CASE_ = "bert-base-cased"		)   ->     Union[str, Any]:
		lowerCamelCase   : int								=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Any								=load_dataset('''glue'''							,       '''mrpc'''		)

		def tokenize_function(SCREAMING_SNAKE_CASE_		):
				# max_length=None => use the model max length (it's actually the default)
				lowerCamelCase   : int								=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
		lowerCamelCase   : Dict								=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
		lowerCamelCase   : Tuple								=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.
		lowerCamelCase   : str								=DataLoader(
		    tokenized_datasets['''train''']							,       shuffle=SCREAMING_SNAKE_CASE_							,       collate_fn=SCREAMING_SNAKE_CASE_							,       batch_size=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[int]								=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_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
		model.eval()
		lowerCamelCase   : Tuple								=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():
						lowerCamelCase   : List[Any]								=model(**SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : Any								=outputs.logits.argmax(dim=-1		)
				# It is slightly faster to call this once, than multiple times
				lowerCamelCase   : 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(SCREAMING_SNAKE_CASE_		) - 1:
								lowerCamelCase   : Dict								=predictions[: len(eval_dataloader.dataset		) - samples_seen]
								lowerCamelCase   : Tuple								=references[: len(eval_dataloader.dataset		) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=SCREAMING_SNAKE_CASE_							,       references=SCREAMING_SNAKE_CASE_							,       )

		lowerCamelCase   : List[str]								=metric.compute()
		return eval_metric["accuracy"]


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     int:
		lowerCamelCase   : str								=Accelerator()

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

		set_seed(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : str								=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)
		lowerCamelCase   : Any								=AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,       return_dict=SCREAMING_SNAKE_CASE_		)

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

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase   : Dict								=accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				lowerCamelCase   : int								=1
		lowerCamelCase   : Any								=(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
		):
				lowerCamelCase   : Tuple								=get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_							,       num_warmup_steps=0							,       num_training_steps=SCREAMING_SNAKE_CASE_							,       )
		else:
				lowerCamelCase   : 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.
		lowerCamelCase   : 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
		lowerCamelCase   : Tuple								=0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCamelCase   : Union[str, Any]								=0
		lowerCamelCase   : Tuple								=evaluate.load('''glue'''							,       '''mrpc'''		)
		lowerCamelCase   : List[str]								=num_epochs

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

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint		)
				lowerCamelCase   : Union[str, Any]								=args.resume_from_checkpoint.split('''epoch_'''		)[1]
				lowerCamelCase   : Union[str, Any]								=""""""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCamelCase   : Union[str, Any]								=int(SCREAMING_SNAKE_CASE_		) + 1
				lowerCamelCase   : Tuple								=evaluation_loop(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				accelerator.print('''resumed checkpoint performance:'''							,       SCREAMING_SNAKE_CASE_		)
				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:
						lowerCamelCase   : str								=json.load(SCREAMING_SNAKE_CASE_		)
						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
		lowerCamelCase   : Optional[Any]								={}
		for epoch in range(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
				model.train()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Tuple								=model(**SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Optional[Any]								=outputs.loss
						lowerCamelCase   : Tuple								=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
				lowerCamelCase   : Union[str, Any]								=F"epoch_{epoch}"
				lowerCamelCase   : Union[str, Any]								=os.path.join(args.output_dir							,       SCREAMING_SNAKE_CASE_		)
				accelerator.save_state(SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : List[Any]								=evaluation_loop(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : Union[str, Any]								=accuracy
				lowerCamelCase   : Tuple								=lr_scheduler.get_lr()[0]
				lowerCamelCase   : Union[str, Any]								=optimizer.param_groups[0]["""lr"""]
				lowerCamelCase   : Union[str, Any]								=epoch
				lowerCamelCase   : Tuple								=overall_step
				accelerator.print(F"epoch {epoch}:"							,       SCREAMING_SNAKE_CASE_		)

				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(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)


def 					A__	(       )   ->     Union[str, Any]:
		lowerCamelCase   : Union[str, Any]								=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(
		    '''--resume_from_checkpoint'''							,       type=SCREAMING_SNAKE_CASE_							,       default=SCREAMING_SNAKE_CASE_							,       help='''If the training should continue from a checkpoint folder.'''							,       )
		parser.add_argument(
		    '''--partial_train_epoch'''							,       type=SCREAMING_SNAKE_CASE_							,       default=SCREAMING_SNAKE_CASE_							,       help='''If passed, the training will stop after this number of epochs.'''							,       )
		parser.add_argument(
		    '''--num_epochs'''							,       type=SCREAMING_SNAKE_CASE_							,       default=2							,       help='''Number of train epochs.'''							,       )
		lowerCamelCase   : Any								=parser.parse_args()
		lowerCamelCase   : Tuple								={"""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()








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     tuple:
		return (data["data"], data["target"])


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     np.ndarray:
		lowerCamelCase   : Dict								=XGBRegressor(verbosity=0							,       random_state=4_2		)
		xgb.fit(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Predict target for test data
		lowerCamelCase   : List[str]								=xgb.predict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=predictions.reshape(len(SCREAMING_SNAKE_CASE_		)							,       1		)
		return predictions


def 					A__	(       )   ->     None:
		lowerCamelCase   : Union[str, Any]								=fetch_california_housing()
		lowerCamelCase		,   lowerCamelCase   : Optional[Any]								=data_handling(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : str								=train_test_split(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       test_size=0.2_5							,       random_state=1		)
		lowerCamelCase   : str								=xgboost(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Error printing
		print(F"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)
		print(F"Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

