
'''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			      =      16
A			      =      32





def    UpperCAmelCase							(  UpperCAmelCase__							:							Accelerator       ,							UpperCAmelCase__							:							int = 16       ,							UpperCAmelCase__							:							str = "bert-base-cased"):
				lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= load_dataset('glue'       ,							'mrpc')

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				lowerCamelCase					:			List[str]									= datasets.map(
				    UpperCAmelCase__       ,							batched=UpperCAmelCase__       ,							remove_columns=['idx', 'sentence1', 'sentence2']       ,							load_from_cache_file=UpperCAmelCase__)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCamelCase					:			List[str]									= tokenized_datasets.rename_column('label'       ,							'labels')

				def collate_fn(UpperCAmelCase__							:							List[Any]):
								# 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(UpperCAmelCase__       ,							padding='max_length'       ,							max_length=1_28       ,							return_tensors='pt')
								return tokenizer.pad(UpperCAmelCase__       ,							padding='longest'       ,							return_tensors='pt')

				# Instantiate dataloaders.
				lowerCamelCase					:			int									= DataLoader(
				    tokenized_datasets['train']       ,							shuffle=UpperCAmelCase__       ,							collate_fn=UpperCAmelCase__       ,							batch_size=UpperCAmelCase__)
				lowerCamelCase					:			Optional[int]									= DataLoader(
				    tokenized_datasets['validation']       ,							shuffle=UpperCAmelCase__       ,							collate_fn=UpperCAmelCase__       ,							batch_size=UpperCAmelCase__)

				return train_dataloader, eval_dataloader





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							str):
				model.eval()
				lowerCamelCase					:			str									= 0
				for step, batch in enumerate(UpperCAmelCase__):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device)
								with torch.no_grad():
												lowerCamelCase					:			Optional[int]									= model(**UpperCAmelCase__)
								lowerCamelCase					:			Optional[Any]									= outputs.logits.argmax(dim=-1)
								# It is slightly faster to call this once, than multiple times
								lowerCamelCase			, lowerCamelCase					:			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(UpperCAmelCase__) - 1:
																lowerCamelCase					:			Union[str, Any]									= 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=UpperCAmelCase__       ,							references=UpperCAmelCase__       ,							)

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							int):
				# Initialize accelerator
				lowerCamelCase					:			Any									= Accelerator()

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

				set_seed(UpperCAmelCase__)
				lowerCamelCase			, lowerCamelCase					:			str									= get_dataloaders(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)

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

				# 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[str]									= optimizer_cls(params=model.parameters()       ,							lr=UpperCAmelCase__)

				if accelerator.state.deepspeed_plugin is not None:
								lowerCamelCase					:			int									= accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								lowerCamelCase					:			Any									= 1
				lowerCamelCase					:			Optional[int]									= (len(UpperCAmelCase__) * 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					:			Optional[Any]									= get_linear_schedule_with_warmup(
								    optimizer=UpperCAmelCase__       ,							num_warmup_steps=0       ,							num_training_steps=UpperCAmelCase__       ,							)
				else:
								lowerCamelCase					:			str									= DummyScheduler(UpperCAmelCase__       ,							total_num_steps=UpperCAmelCase__       ,							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					:			Tuple									= accelerator.prepare(
				    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)

				# We need to keep track of how many total steps we have iterated over
				lowerCamelCase					:			Any									= 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					:			Union[str, Any]									= num_epochs

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

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

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

								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(UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			int									= argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.')
				parser.add_argument(
				    '--model_name_or_path'       ,							type=UpperCAmelCase__       ,							default='bert-base-cased'       ,							help='Path to pretrained model or model identifier from huggingface.co/models.'       ,							required=UpperCAmelCase__       ,							)
				parser.add_argument(
				    '--output_dir'       ,							type=UpperCAmelCase__       ,							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=UpperCAmelCase__       ,							default=UpperCAmelCase__       ,							help='If the training should continue from a checkpoint folder.'       ,							)
				parser.add_argument(
				    '--partial_train_epoch'       ,							type=UpperCAmelCase__       ,							default=UpperCAmelCase__       ,							help='If passed, the training will stop after this number of epochs.'       ,							)
				parser.add_argument(
				    '--num_epochs'       ,							type=UpperCAmelCase__       ,							default=2       ,							help='Number of train epochs.'       ,							)
				lowerCamelCase					:			Dict									= parser.parse_args()
				lowerCamelCase					:			List[Any]									= {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

				training_function(UpperCAmelCase__       ,							UpperCAmelCase__)


if __name__ == "__main__":
					main()


'''simple docstring'''






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A			      =      {
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]):
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
								    F''' reinstalling {pkg}.''')
				if not ops[op](version.parse(UpperCAmelCase__)       ,							version.parse(UpperCAmelCase__)):
								raise ImportError(
								    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[str] = None):
				lowerCamelCase					:			List[Any]									= F'''\n{hint}''' if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'       ,							UpperCAmelCase__):
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= requirement, None, None
				else:
								lowerCamelCase					:			Optional[Any]									= re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'       ,							UpperCAmelCase__)
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F''' got {requirement}''')
								lowerCamelCase			, lowerCamelCase					:			Dict									= match[0]
								lowerCamelCase					:			Dict									= want_full.split(',')  # there could be multiple requirements
								lowerCamelCase					:			Union[str, Any]									= {}
								for w in want_range:
												lowerCamelCase					:			int									= re.findall(R'^([\s!=<>]{1,2})(.+)'       ,							UpperCAmelCase__)
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F''' but got {requirement}''')
												lowerCamelCase			, lowerCamelCase					:			List[Any]									= match[0]
												lowerCamelCase					:			Optional[int]									= want_ver
												if op not in ops:
																raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')

    # special case
				if pkg == "python":
								lowerCamelCase					:			Optional[int]									= '.'.join([str(UpperCAmelCase__) for x in sys.version_info[:3]])
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								return

				# check if any version is installed
				try:
								lowerCamelCase					:			Any									= importlib.metadata.version(UpperCAmelCase__)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			List[str]									= 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCAmelCase__       ,							UpperCAmelCase__)
