
"""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


_A						=			1_6
_A						=			3_2
def 		UpperCAmelCase (		a_,						a_ = 16,						a_ = "bert-base-cased"      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   AutoTokenizer.from_pretrained(a_      )
		lowerCamelCase					:      Tuple          =   load_dataset('glue',						'mrpc'      )

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

		return train_dataloader, eval_dataloader
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      int          =   metric.compute()
		return eval_metric["accuracy"]
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   Accelerator()

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

		set_seed(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Any          =   get_dataloaders(a_,						a_,						a_      )

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

		# Instantiate optimizer
		lowerCamelCase					:      Optional[Any]          =   (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCamelCase					:      List[Any]          =   optimizer_cls(params=model.parameters(),						lr=a_      )

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase					:      List[Any]          =   accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowerCamelCase					:      Optional[Any]          =   1
		lowerCamelCase					:      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
		):
				lowerCamelCase					:      int          =   get_linear_schedule_with_warmup(
				    optimizer=a_,						num_warmup_steps=0,						num_training_steps=a_,						)
		else:
				lowerCamelCase					:      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.
		lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Union[str, Any]          =   accelerator.prepare(
		    a_,						a_,						a_,						a_,						a_      )

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

		if args.partial_train_epoch is not None:
				lowerCamelCase					:      Optional[Any]          =   args.partial_train_epoch

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

						overall_step += 1
				lowerCamelCase					:      List[str]          =   F"""epoch_{epoch}"""
				lowerCamelCase					:      Optional[Any]          =   os.path.join(args.output_dir,						a_      )
				accelerator.save_state(a_      )
				lowerCamelCase					:      Any          =   evaluation_loop(a_,						a_,						a_,						a_      )
				lowerCamelCase					:      List[str]          =   accuracy
				lowerCamelCase					:      List[str]          =   lr_scheduler.get_lr()[0]
				lowerCamelCase					:      List[Any]          =   optimizer.param_groups[0]['lr']
				lowerCamelCase					:      List[str]          =   epoch
				lowerCamelCase					:      Optional[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 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   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.',						)
		lowerCamelCase					:      str          =   parser.parse_args()
		lowerCamelCase					:      str          =   {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(a_,						a_      )


if __name__ == "__main__":
					main()








"""simple docstring"""

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A						=			re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   _str_to_version_tuple(self.version_str )




	def __repr__(			self )		->							int:
			return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return self.major, self.minor, self.patch




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return Version(UpperCAmelCase_ )
			elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return other
			raise TypeError(F"""{other} (type {type(UpperCAmelCase_ )}) cannot be compared to version.""" )




	def __eq__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			try:
					lowerCamelCase					:      List[str]          =   self._validate_operand(UpperCAmelCase_ )
			except (TypeError, ValueError):
					return False
			else:
					return self.tuple == other.tuple




	def __lt__(			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self._validate_operand(UpperCAmelCase_ )
			return self.tuple < other.tuple




	def __hash__(			self )		->							Optional[Any]:
			return hash(_version_tuple_to_str(self.tuple ) )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {f.name for f in dataclasses.fields(cls )}
			return cls(**{k: v for k, v in dic.items() if k in field_names} )




	def       _UpperCamelCase   (			self )		->							str:
			return self.version_str


def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   _VERSION_REG.match(a_      )
		if not res:
				raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""      )
		return tuple(int(a_      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return ".".join(str(a_      ) for v in version_tuple      )






