
"""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


SCREAMING_SNAKE_CASE_            =    16
SCREAMING_SNAKE_CASE_            =    32




def      lowercase__    (       lowerCAmelCase					:       Accelerator			,					lowerCAmelCase					:       int = 16			,					lowerCAmelCase					:       str = "bert-base-cased"			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       AutoTokenizer.from_pretrained(lowerCAmelCase			)
    UpperCAmelCase          =       load_dataset('glue'			,					'mrpc'			)

    def tokenize_function(lowerCAmelCase					:       Any			):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase          =       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
    UpperCAmelCase          =       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
    UpperCAmelCase          =       tokenized_datasets.rename_column('label'			,					'labels'			)

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

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

    return train_dataloader, eval_dataloader




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       Tuple			)			->   str:






    """simple docstring"""




    model.eval()
    UpperCAmelCase          =       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():
            UpperCAmelCase          =       model(**lowerCAmelCase			)
        UpperCAmelCase          =       outputs.logits.argmax(dim=-1			)
        # It is slightly faster to call this once, than multiple times
        UpperCAmelCase      ,				UpperCAmelCase          =       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:
                UpperCAmelCase          =       predictions[: len(eval_dataloader.dataset			) - samples_seen]
                UpperCAmelCase          =       references[: len(eval_dataloader.dataset			) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=lowerCAmelCase			,					references=lowerCAmelCase			,					)

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




def      lowercase__    (       lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       List[str]			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       Accelerator()

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

    set_seed(lowerCAmelCase			)
    UpperCAmelCase      ,				UpperCAmelCase          =       get_dataloaders(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)

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

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

    if accelerator.state.deepspeed_plugin is not None:
        UpperCAmelCase          =       accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        UpperCAmelCase          =       1
    UpperCAmelCase          =       (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
    ):
        UpperCAmelCase          =       get_linear_schedule_with_warmup(
            optimizer=lowerCAmelCase			,					num_warmup_steps=0			,					num_training_steps=lowerCAmelCase			,					)
    else:
        UpperCAmelCase          =       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.
    UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       accelerator.prepare(
        lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)

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

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

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

            overall_step += 1
        UpperCAmelCase          =       F"epoch_{epoch}"
        UpperCAmelCase          =       os.path.join(args.output_dir			,					lowerCAmelCase			)
        accelerator.save_state(lowerCAmelCase			)
        UpperCAmelCase          =       evaluation_loop(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
        UpperCAmelCase          =       accuracy
        UpperCAmelCase          =       lr_scheduler.get_lr()[0]
        UpperCAmelCase          =       optimizer.param_groups[0]['lr']
        UpperCAmelCase          =       epoch
        UpperCAmelCase          =       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      lowercase__    (       )			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       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.'			,					)
    UpperCAmelCase          =       parser.parse_args()
    UpperCAmelCase          =       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

    training_function(lowerCAmelCase			,					lowerCAmelCase			)


if __name__ == "__main__":
   main()





"""simple docstring"""

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_            =    '''scheduler_config.json'''
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						1
      __SCREAMING_SNAKE_CASE :				Dict				   =						2
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						3
      __SCREAMING_SNAKE_CASE :				Any				   =						4
      __SCREAMING_SNAKE_CASE :				Any				   =						5
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						6
      __SCREAMING_SNAKE_CASE :				str				   =						7
      __SCREAMING_SNAKE_CASE :				Any				   =						8
      __SCREAMING_SNAKE_CASE :				Tuple				   =						9
      __SCREAMING_SNAKE_CASE :				int				   =						1_0
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_1
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_2
      __SCREAMING_SNAKE_CASE :				Dict				   =						1_3
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						1_4

@dataclass
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						SCHEDULER_CONFIG_NAME
      __SCREAMING_SNAKE_CASE :				Dict				   =						[]
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						True




      @classmethod
      def      a_   (				cls ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=False ,							**lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       cls.load_config(
              pretrained_model_name_or_path=lowercase_ ,							subfolder=lowercase_ ,							return_unused_kwargs=lowercase_ ,							return_commit_hash=lowercase_ ,							**lowercase_ ,							)
          return cls.from_config(lowercase_ ,							return_unused_kwargs=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ = False ,							**lowercase_				)    ->       str:
          self.save_config(save_directory=lowercase_ ,							push_to_hub=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          return self._get_compatibles()




      @classmethod
      def      a_   (				cls				)    ->       Any:
          UpperCAmelCase          =       list(set([cls.__name__] + cls._compatibles				)				)
          UpperCAmelCase          =       importlib.import_module(__name__.split('.'				)[0]				)
          UpperCAmelCase          =       [
              getattr(lowercase_ ,							lowercase_				) for c in compatible_classes_str if hasattr(lowercase_ ,							lowercase_				)
          ]
          return compatible_classes



