




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase__     						=						logging.get_logger(__name__)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      *lowercase  ,      **lowercase  ):
							warnings.warn(
							    'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use LayoutLMv2ImageProcessor instead.'  ,      lowercase  ,      )
							super().__init__(*lowercase  ,      **lowercase  )





"""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__     						=						16
lowercase__     						=						32







def      _snake_case			(				lowercase__							,    lowercase__ = 16							,    lowercase__ = "bert-base-cased"		):
				_lowerCamelCase     :			List[Any]    =    AutoTokenizer.from_pretrained(lowercase__		)
				_lowerCamelCase     :			Tuple    =    load_dataset('glue'							,    'mrpc'		)

				def tokenize_function(lowercase__		):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase     :			Union[str, Any]    =    tokenizer(examples['sentence1']							,    examples['sentence2']							,    truncation=lowercase__							,    max_length=lowercase__		)
								return outputs

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

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

				def collate_fn(lowercase__		):
								# 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(lowercase__							,    padding='max_length'							,    max_length=128							,    return_tensors='pt'		)
								return tokenizer.pad(lowercase__							,    padding='longest'							,    return_tensors='pt'		)

				# Instantiate dataloaders.
				_lowerCamelCase     :			List[str]    =    DataLoader(
				    tokenized_datasets['train']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)
				_lowerCamelCase     :			int    =    DataLoader(
				    tokenized_datasets['validation']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)

				return train_dataloader, eval_dataloader







def      _snake_case			(				lowercase__							,    lowercase__		):
				# Initialize accelerator
				_lowerCamelCase     :			Optional[int]    =    Accelerator()

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

				set_seed(lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    get_dataloaders(lowercase__							,    lowercase__							,    lowercase__		)

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

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

				if accelerator.state.deepspeed_plugin is not None:
								_lowerCamelCase     :			str    =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								_lowerCamelCase     :			Tuple    =    1
				_lowerCamelCase     :			List[Any]    =    (len(lowercase__		) * 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=lowercase__							,    num_warmup_steps=0							,    num_training_steps=lowercase__							,    )
				else:
								_lowerCamelCase     :			Any    =    DummyScheduler(lowercase__							,    total_num_steps=lowercase__							,    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(
				    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)

				# 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     :			Dict    =    0

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

												overall_step += 1

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

								_lowerCamelCase     :			List[Any]    =    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,    lowercase__		)
								_lowerCamelCase     :			Tuple    =    eval_metric['accuracy']

								if best_performance < eval_metric["accuracy"]:
												_lowerCamelCase     :			str    =    eval_metric['accuracy']

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir							,    'all_results.json'		)							,    'w'		) as f:
												json.dump(lowercase__							,    lowercase__		)







def      _snake_case			(				):
				_lowerCamelCase     :			Any    =    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'		)
				parser.add_argument(
				    '--model_name_or_path'							,    type=lowercase__							,    default='bert-base-cased'							,    help='Path to pretrained model or model identifier from huggingface.co/models.'							,    required=lowercase__							,    )
				parser.add_argument(
				    '--output_dir'							,    type=lowercase__							,    default='.'							,    help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'							,    )
				parser.add_argument(
				    '--performance_lower_bound'							,    type=lowercase__							,    default=lowercase__							,    help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,    )
				parser.add_argument(
				    '--num_epochs'							,    type=lowercase__							,    default=3							,    help='Number of train epochs.'							,    )
				_lowerCamelCase     :			Optional[Any]    =    parser.parse_args()
				_lowerCamelCase     :			str    =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(lowercase__							,    lowercase__		)


if __name__ == "__main__":
			main()