
'''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


UpperCAmelCase    					=   16
UpperCAmelCase    					=   32

def      A (       A_							:					Accelerator    ,							A_							:					int = 16    ,							A_							:					str = "bert-base-cased"       ):
						snake_case       :     Any =    AutoTokenizer.from_pretrained(A_       )
						snake_case       :     Optional[int] =    load_dataset('''glue'''    ,							'''mrpc'''       )

						def tokenize_function(A_							:					Any       ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case       :     Dict =    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       :     List[str] =    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       :     Optional[int] =    tokenized_datasets.rename_column('''label'''    ,							'''labels'''       )

						def collate_fn(A_							:					Dict       ):
												# 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       :     str =    DataLoader(
						    tokenized_datasets['''train''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )
						snake_case       :     Union[str, Any] =    DataLoader(
						    tokenized_datasets['''validation''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )

						return train_dataloader, eval_dataloader

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

						snake_case       :     Tuple =    metric.compute()
						return eval_metric["accuracy"]

def      A (       A_							:					Dict    ,							A_							:					Union[str, Any]       ):
						# Initialize accelerator
						snake_case       :     Dict =    Accelerator()

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

						set_seed(A_       )
						snake_case,					snake_case       :     Optional[Any] =    get_dataloaders(A_    ,							A_    ,							A_       )

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

						# Instantiate optimizer
						snake_case       :     Any =    (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						snake_case       :     Tuple =    optimizer_cls(params=model.parameters()    ,							lr=A_       )

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

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

						if args.partial_train_epoch is not None:
												snake_case       :     Optional[int] =    args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint       )
												snake_case       :     Any =    args.resume_from_checkpoint.split('''epoch_'''       )[1]
												snake_case       :     str =    ''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												snake_case       :     Any =    int(A_       ) + 1
												snake_case       :     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:
																		snake_case       :     List[Any] =    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       :     List[str] =    {}
						for epoch in range(A_    ,							A_       ):
												model.train()
												for step, batch in enumerate(A_       ):
																		snake_case       :     Optional[int] =    model(**A_       )
																		snake_case       :     Dict =    outputs.loss
																		snake_case       :     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
												snake_case       :     List[Any] =    F"""epoch_{epoch}"""
												snake_case       :     Union[str, Any] =    os.path.join(args.output_dir    ,							A_       )
												accelerator.save_state(A_       )
												snake_case       :     Tuple =    evaluation_loop(A_    ,							A_    ,							A_    ,							A_       )
												snake_case       :     Union[str, Any] =    accuracy
												snake_case       :     int =    lr_scheduler.get_lr()[0]
												snake_case       :     int =    optimizer.param_groups[0]['''lr''']
												snake_case       :     str =    epoch
												snake_case       :     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      A (       ):
						snake_case       :     Tuple =    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       :     Optional[int] =    parser.parse_args()
						snake_case       :     str =    {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(A_    ,							A_       )


if __name__ == "__main__":
						main()



'''simple docstring'''





import warnings

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


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										warnings.warn(
										    '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
										    ''' Please use LayoutLMv2ImageProcessor instead.''',    SCREAMING_SNAKE_CASE_,    )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )

