





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							:			Optional[int]         =			16
__A							:			Optional[Any]         =			32





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE = 16							,     _SCREAMING_SNAKE_CASE = "bert-base-cased"				)							-> int:




    """simple docstring"""




    _A     =							AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
    _A     =							load_dataset('glue'							,     'mrpc'				)

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

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

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _A     =							tokenized_datasets.rename_column('label'							,     'labels'				)

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

    # Instantiate dataloaders.
    _A     =							DataLoader(
        tokenized_datasets['train']							,     shuffle=_SCREAMING_SNAKE_CASE							,     collate_fn=_SCREAMING_SNAKE_CASE							,     batch_size=_SCREAMING_SNAKE_CASE				)
    _A     =							DataLoader(
        tokenized_datasets['validation']							,     shuffle=_SCREAMING_SNAKE_CASE							,     collate_fn=_SCREAMING_SNAKE_CASE							,     batch_size=_SCREAMING_SNAKE_CASE				)

    return train_dataloader, eval_dataloader





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




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

    _A     =							metric.compute()
    return eval_metric["accuracy"]





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							Accelerator()

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

    set_seed(_SCREAMING_SNAKE_CASE				)
    _A,			_A     =							get_dataloaders(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _A     =							AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE							,     return_dict=_SCREAMING_SNAKE_CASE				)

    # Instantiate optimizer
    _A     =							(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _A     =							optimizer_cls(params=model.parameters()							,     lr=_SCREAMING_SNAKE_CASE				)

    if accelerator.state.deepspeed_plugin is not None:
        _A     =							accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        _A     =							1
    _A     =							(len(_SCREAMING_SNAKE_CASE				) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _A     =							get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE							,     num_warmup_steps=0							,     num_training_steps=_SCREAMING_SNAKE_CASE							,     )
    else:
        _A     =							DummyScheduler(_SCREAMING_SNAKE_CASE							,     total_num_steps=_SCREAMING_SNAKE_CASE							,     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.
    _A,			_A,			_A,			_A,			_A     =							accelerator.prepare(
        _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

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

    if args.partial_train_epoch is not None:
        _A     =							args.partial_train_epoch

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

            overall_step += 1
        _A     =							F"epoch_{epoch}"
        _A     =							os.path.join(args.output_dir							,     _SCREAMING_SNAKE_CASE				)
        accelerator.save_state(_SCREAMING_SNAKE_CASE				)
        _A     =							evaluation_loop(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
        _A     =							accuracy
        _A     =							lr_scheduler.get_lr()[0]
        _A     =							optimizer.param_groups[0]['lr']
        _A     =							epoch
        _A     =							overall_step
        accelerator.print(F"epoch {epoch}:"							,     _SCREAMING_SNAKE_CASE				)

        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(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       )							-> Optional[Any]:




    """simple docstring"""




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

    training_function(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 main()









'''simple docstring'''






import argparse


lowercase				='docs/source/_static/js/custom.js'





def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   with open(__lowerCamelCase			,       encoding='utf-8'			,       newline='\n'				) as f:
      _UpperCAmelCase  :  Optional[Any]				       =f.readlines()
   _UpperCAmelCase  :  Dict				       =0

   # First let's put the right version
   while not lines[index].startswith('const stableVersion ='				):
      index += 1
   _UpperCAmelCase  :  Tuple				       =f"const stableVersion = \"v{version}\"\n"

   # Then update the dictionary
   while not lines[index].startswith('const versionMapping = {'				):
      index += 1

   # We go until the end
   while not lines[index].startswith('}'				):
      index += 1
   # We add the new version at the end
   lines[index - 1] += f"    \"v{version}\": \"v{version}\",\n"

   with open(__lowerCamelCase			,       'w'			,       encoding='utf-8'			,       newline='\n'				) as f:
      f.writelines(__lowerCamelCase				)


if __name__ == "__main__":
 lowercase				=argparse.ArgumentParser()
 parser.add_argument('--version', help='Release version.')
 lowercase				=parser.parse_args()
 update_custom_js(args.version)

