
'''simple docstring'''







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


lowercase       :				str =   16
lowercase       :				str =   32







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A = 16     ,							__A = "bert-base-cased"      )			->      Optional[int]:
	_snake_case  		=						AutoTokenizer.from_pretrained(__A      )
	_snake_case  		=						load_dataset('glue'     ,							'mrpc'      )

	def tokenize_function(__A      ):
		# max_length=None => use the model max length (it's actually the default)
		_snake_case  		=						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
	_snake_case  		=						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
	_snake_case  		=						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.
	_snake_case  		=						DataLoader(
	    tokenized_datasets['train']     ,							shuffle=__A     ,							collate_fn=__A     ,							batch_size=__A      )
	_snake_case  		=						DataLoader(
	    tokenized_datasets['validation']     ,							shuffle=__A     ,							collate_fn=__A     ,							batch_size=__A      )

	return train_dataloader, eval_dataloader







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A     ,							__A     ,							__A      )			->      str:
	model.eval()
	_snake_case  		=						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():
			_snake_case  		=						model(**__A      )
		_snake_case  		=						outputs.logits.argmax(dim=-1      )
		# It is slightly faster to call this once, than multiple times
		_snake_case		,					_snake_case  		=						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:
				_snake_case  		=						predictions[: len(eval_dataloader.dataset      ) - samples_seen]
				_snake_case  		=						references[: len(eval_dataloader.dataset      ) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=__A     ,							references=__A     ,							)

	_snake_case  		=						metric.compute()
	return eval_metric["accuracy"]







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A      )			->      str:
	_snake_case  		=						Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_snake_case  		=						config['lr']
	_snake_case  		=						int(config['num_epochs']      )
	_snake_case  		=						int(config['seed']      )
	_snake_case  		=						int(config['batch_size']      )
	_snake_case  		=						args.model_name_or_path

	set_seed(__A      )
	_snake_case		,					_snake_case  		=						get_dataloaders(__A     ,							__A     ,							__A      )

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

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

	if accelerator.state.deepspeed_plugin is not None:
		_snake_case  		=						accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		_snake_case  		=						1
	_snake_case  		=						(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
	):
		_snake_case  		=						get_linear_schedule_with_warmup(
		    optimizer=__A     ,							num_warmup_steps=0     ,							num_training_steps=__A     ,							)
	else:
		_snake_case  		=						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.
	_snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case  		=						accelerator.prepare(
	    __A     ,							__A     ,							__A     ,							__A     ,							__A      )

	# We need to keep track of how many total steps we have iterated over
	_snake_case  		=						0
	# We also need to keep track of the stating epoch so files are named properly
	_snake_case  		=						0
	_snake_case  		=						evaluate.load('glue'     ,							'mrpc'      )
	_snake_case  		=						num_epochs

	if args.partial_train_epoch is not None:
		_snake_case  		=						args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint      )
		_snake_case  		=						args.resume_from_checkpoint.split('epoch_'      )[1]
		_snake_case  		=						''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		_snake_case  		=						int(__A      ) + 1
		_snake_case  		=						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:
			_snake_case  		=						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
	_snake_case  		=						{}
	for epoch in range(__A     ,							__A      ):
		model.train()
		for step, batch in enumerate(__A      ):
			_snake_case  		=						model(**__A      )
			_snake_case  		=						outputs.loss
			_snake_case  		=						loss / gradient_accumulation_steps
			accelerator.backward(__A      )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		_snake_case  		=						F'epoch_{epoch}'
		_snake_case  		=						os.path.join(args.output_dir     ,							__A      )
		accelerator.save_state(__A      )
		_snake_case  		=						evaluation_loop(__A     ,							__A     ,							__A     ,							__A      )
		_snake_case  		=						accuracy
		_snake_case  		=						lr_scheduler.get_lr()[0]
		_snake_case  		=						optimizer.param_groups[0]['lr']
		_snake_case  		=						epoch
		_snake_case  		=						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 							SCREAMING_SNAKE_CASE__      (  )			->      Dict:
	_snake_case  		=						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.'     ,							)
	_snake_case  		=						parser.parse_args()
	_snake_case  		=						{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

	training_function(__A     ,							__A      )


if __name__ == "__main__":
					main()



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for _ in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
				return lrs

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for step in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase         =      os.path.join(__magic_name__		,    "schedule.bin"						)
																torch.save(scheduler.state_dict()		,    __magic_name__						)

																lowercase         =      torch.load(__magic_name__						)
																scheduler.load_state_dict(__magic_name__						)
				return lrs





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      AdamW(params=[w]		,    lr=2E-1		,    weight_decay=0.0     )
										for _ in range(100     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[Any]:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      Adafactor(
										    params=[w]		,    lr=1E-2		,    eps=(1E-3_0, 1E-3)		,    clip_threshold=1.0		,    decay_rate=-0.8		,    betaa=lowerCAmelCase__		,    weight_decay=0.0		,    relative_step=lowerCAmelCase__		,    scale_parameter=lowerCAmelCase__		,    warmup_init=lowerCAmelCase__		,    )
										for _ in range(1000     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					nn.Linear(50		,    50		) if is_torch_available() else None
						UpperCamelCase      :  Union[str, Any]				   =					AdamW(m.parameters()		,    lr=10.0		) if is_torch_available() else None
						UpperCamelCase      :  int				   =					10





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Any=None     )  ->Dict:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__		,    msg=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										lowercase         =      {"num_warmup_steps": 2, "num_training_steps": 10}
										# schedulers doct format
										# function: (sched_args_dict, expected_learning_rates)
										lowercase         =      {
										    get_constant_schedule: ({}, [10.0] * self.num_steps),
										    get_constant_schedule_with_warmup: (
										        {"num_warmup_steps": 4},
										        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
										    ),
										    get_linear_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
										    ),
										    get_cosine_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
										    ),
										    get_cosine_with_hard_restarts_schedule_with_warmup: (
										        {**common_kwargs, "num_cycles": 2},
										        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
										    ),
										    get_polynomial_decay_schedule_with_warmup: (
										        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
										        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
										    ),
										    get_inverse_sqrt_schedule: (
										        {"num_warmup_steps": 2},
										        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
										    ),
										}

										for scheduler_func, data in scheds.items():
														lowercase     ,	lowercase         =      data

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														self.assertEqual(len([scheduler.get_lr()[0]]     )		,    1     )
														lowercase         =      unwrap_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListAlmostEqual(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    tol=1E-2		,    msg=F'''failed for {scheduler_func} in normal scheduler'''		,    )

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														if scheduler_func.__name__ != "get_constant_schedule":
																		LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__     )  # wrap to test picklability of the schedule
														lowercase         =      unwrap_and_save_reload_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    msg=F'''failed for {scheduler_func} in save and reload'''     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[str]		,    lowerCAmelCase__							:int     )  ->Optional[int]:
										lowercase         =      fn





						def __call__(			self							:Optional[Any]		,    *lowerCAmelCase__							:int		,    **lowerCAmelCase__							:List[str]     )  ->List[str]:
										return self.fn(*lowerCAmelCase__		,    **lowerCAmelCase__     )





						@classmethod
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->str:
										lowercase         =      list(map(self		,    scheduler.lr_lambdas     )     )
