






'''simple docstring'''







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase									=   {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """VanForImageClassification""",
		    """VanModel""",
		    """VanPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure)











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		:  int			       =32


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__ = 16							,lowerCAmelCase__ = "bert-base-cased"						):
		lowerCamelCase_	:     Any 						=      AutoTokenizer.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     Tuple 						=      load_dataset('glue'							,'mrpc'						)

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

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

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

		def collate_fn(lowerCAmelCase__						):
				# 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(lowerCAmelCase__							,padding='max_length'							,max_length=1_28							,return_tensors='pt'						)
				return tokenizer.pad(lowerCAmelCase__							,padding='longest'							,return_tensors='pt'						)

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

		return train_dataloader, eval_dataloader


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		model.eval()
		lowerCamelCase_	:     List[str] 						=      0
		for step, batch in enumerate(lowerCAmelCase__						):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device						)
				with torch.no_grad():
						lowerCamelCase_	:     Dict 						=      model(**lowerCAmelCase__						)
				lowerCamelCase_	:     Any 						=      outputs.logits.argmax(dim=-1						)
				# It is slightly faster to call this once, than multiple times
				lowerCamelCase_						,      lowerCamelCase_	:     Tuple 						=      accelerator.gather(
				    (predictions, batch['labels'])						)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(lowerCAmelCase__						) - 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=lowerCAmelCase__							,references=lowerCAmelCase__							,)

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		# Initialize accelerator
		lowerCamelCase_	:     List[Any] 						=      Accelerator()

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

		set_seed(lowerCAmelCase__						)
		lowerCamelCase_						,      lowerCamelCase_	:     List[Any] 						=      get_dataloaders(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase_	:     List[Any] 						=      AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__							,return_dict=lowerCAmelCase__						)

		# Instantiate optimizer
		lowerCamelCase_	:     List[Any] 						=      (
		    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=lowerCAmelCase__						)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase_	:     Tuple 						=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowerCamelCase_	:     Tuple 						=      1
		lowerCamelCase_	:     str 						=      (len(lowerCAmelCase__						) * 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=lowerCAmelCase__							,num_warmup_steps=0							,num_training_steps=lowerCAmelCase__							,)
		else:
				lowerCamelCase_	:     Optional[int] 						=      DummyScheduler(lowerCAmelCase__							,total_num_steps=lowerCAmelCase__							,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_	:     Dict 						=      accelerator.prepare(
		    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

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

		if args.partial_train_epoch is not None:
				lowerCamelCase_	:     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_	:     str 						=      ''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCamelCase_	:     List[str] 						=      int(lowerCAmelCase__						) + 1
				lowerCamelCase_	:     Optional[Any] 						=      evaluation_loop(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				accelerator.print('resumed checkpoint performance:'							,lowerCAmelCase__						)
				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_	:     Union[str, Any] 						=      json.load(lowerCAmelCase__						)
						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_	:     str 						=      {}
		for epoch in range(lowerCAmelCase__							,lowerCAmelCase__						):
				model.train()
				for step, batch in enumerate(lowerCAmelCase__						):
						lowerCamelCase_	:     List[Any] 						=      model(**lowerCAmelCase__						)
						lowerCamelCase_	:     Tuple 						=      outputs.loss
						lowerCamelCase_	:     str 						=      loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase__						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				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(lowerCAmelCase__							,lowerCAmelCase__						)


def 		_SCREAMING_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=lowerCAmelCase__							,default='bert-base-cased'							,help='Path to pretrained model or model identifier from huggingface.co/models.'							,required=lowerCAmelCase__							,)
		parser.add_argument(
		    '--output_dir'							,type=lowerCAmelCase__							,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=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If the training should continue from a checkpoint folder.'							,)
		parser.add_argument(
		    '--partial_train_epoch'							,type=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If passed, the training will stop after this number of epochs.'							,)
		parser.add_argument(
		    '--num_epochs'							,type=lowerCAmelCase__							,default=2							,help='Number of train epochs.'							,)
		lowerCamelCase_	:     Optional[Any] 						=      parser.parse_args()
		lowerCamelCase_	:     List[Any] 						=      {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(lowerCAmelCase__							,lowerCAmelCase__						)


if __name__ == "__main__":
		main()
