





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowercase__   :				Tuple      =   16
lowercase__   :				List[Any]      =   32






def 	_lowerCAmelCase      (  __snake_case  :			Accelerator			,							__snake_case  :			int = 16      )					->					Tuple:
     __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     __A						:			str								=	load_dataset('glue'			,							'mrpc'      )

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

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          __A						:			Optional[Any]								=	datasets.map(
              __snake_case			,							batched=__snake_case			,							remove_columns=['idx', 'sentence1', 'sentence2']			,							)

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

     def collate_fn(__snake_case  :			Any      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A						:			Union[str, Any]								=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __A						:			str								=	16
          elif accelerator.mixed_precision != "no":
               __A						:			Union[str, Any]								=	8
          else:
               __A						:			int								=	None

          return tokenizer.pad(
              __snake_case			,							padding='longest'			,							max_length=__snake_case			,							pad_to_multiple_of=__snake_case			,							return_tensors='pt'			,							)

     # Instantiate dataloaders.
     __A						:			Tuple								=	DataLoader(
         tokenized_datasets['train']			,							shuffle=__snake_case			,							collate_fn=__snake_case			,							batch_size=__snake_case      )
     __A						:			Any								=	DataLoader(
         tokenized_datasets['validation']			,							shuffle=__snake_case			,							collate_fn=__snake_case			,							batch_size=__snake_case      )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  lowercase__   :				str      =   mocked_dataloaders  # noqa: F811






def 	_lowerCAmelCase      (  __snake_case  :			Optional[int]			,							__snake_case  :			List[Any]      )					->					List[Any]:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'			,							__snake_case      ) == "1":
          __A						:			Union[str, Any]								=	2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __A						:			int								=	Accelerator(
              cpu=args.cpu			,							mixed_precision=args.mixed_precision			,							log_with='all'			,							project_dir=args.project_dir      )
     else:
          __A						:			List[str]								=	Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A						:			str								=	config['lr']
     __A						:			str								=	int(config['num_epochs']      )
     __A						:			str								=	int(config['seed']      )
     __A						:			Optional[int]								=	int(config['batch_size']      )
     set_seed(__snake_case      )

     __A						:			List[str]								=	get_dataloaders(__snake_case			,							__snake_case      )
     __A						:			List[Any]								=	evaluate.load('glue'			,							'mrpc'      )

     # If the batch size is too big we use gradient accumulation
     __A						:			Optional[int]								=	1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __A						:			Any								=	batch_size // MAX_GPU_BATCH_SIZE
          __A						:			Dict								=	MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A						:			Optional[int]								=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			,							return_dict=__snake_case      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     __A						:			List[Any]								=	model.to(accelerator.device      )

     # Instantiate optimizer
     __A						:			Optional[Any]								=	AdamW(params=model.parameters()			,							lr=__snake_case      )

     # Instantiate scheduler
     __A						:			Tuple								=	get_linear_schedule_with_warmup(
         optimizer=__snake_case			,							num_warmup_steps=1_00			,							num_training_steps=(len(__snake_case      ) * num_epochs) // gradient_accumulation_steps			,							)

     # 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						:			Dict								=	accelerator.prepare(
         __snake_case			,							__snake_case			,							__snake_case			,							__snake_case			,							__snake_case      )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __A						:			Tuple								=	os.path.split(__snake_case      )[-1].split('.'      )[0]
          accelerator.init_trackers(__snake_case			,							__snake_case      )

     # Now we train the model
     for epoch in range(__snake_case      ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __A						:			str								=	0
          for step, batch in enumerate(__snake_case      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               __A						:			Tuple								=	model(**__snake_case      )
               __A						:			Optional[Any]								=	outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __A						:			Dict								=	loss / gradient_accumulation_steps
               accelerator.backward(__snake_case      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          for step, batch in enumerate(__snake_case      ):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device      )
               with torch.no_grad():
                    __A						:			str								=	model(**__snake_case      )
               __A						:			List[str]								=	outputs.logits.argmax(dim=-1      )
               __A						:			Dict								=	accelerator.gather_for_metrics((predictions, batch['labels'])      )
               metric.add_batch(
                   predictions=__snake_case			,							references=__snake_case			,							)

          __A						:			int								=	metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'			,							__snake_case      )

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(__snake_case      ),
                       'epoch': epoch,
                   }			,							step=__snake_case			,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def 	_lowerCAmelCase      (  )					->					Optional[int]:
     __A						:			Optional[int]								=	argparse.ArgumentParser(description='Simple example of training script.'      )
     parser.add_argument(
         '--mixed_precision'			,							type=__snake_case			,							default=__snake_case			,							choices=['no', 'fp16', 'bf16', 'fp8']			,							help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.'			,							)
     parser.add_argument('--cpu'			,							action='store_true'			,							help='If passed, will train on the CPU.'      )
     parser.add_argument(
         '--with_tracking'			,							action='store_true'			,							help='Whether to load in all available experiment trackers from the environment and use them for logging.'			,							)
     parser.add_argument(
         '--project_dir'			,							type=__snake_case			,							default='logs'			,							help='Location on where to store experiment tracking logs` and relevent project information'			,							)
     __A						:			str								=	parser.parse_args()
     __A						:			Any								=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case			,							__snake_case      )


if __name__ == "__main__":
  main()






'''simple docstring'''




import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)






def 	_lowerCAmelCase      (  )					->					Union[str, Any]:
     __A						:			Optional[int]								=	argparse.ArgumentParser()
     parser.add_argument('--model_ckpt'			,							type=__snake_case			,							default='microsoft/unixcoder-base-nine'      )
     parser.add_argument('--num_epochs'			,							type=__snake_case			,							default=5      )
     parser.add_argument('--batch_size'			,							type=__snake_case			,							default=6      )
     parser.add_argument('--gradient_accumulation_steps'			,							type=__snake_case			,							default=1      )
     parser.add_argument('--freeze'			,							type=__snake_case			,							default=__snake_case      )
     parser.add_argument('--learning_rate'			,							type=__snake_case			,							default=5e-4      )
     parser.add_argument('--seed'			,							type=__snake_case			,							default=0      )
     parser.add_argument('--lr_scheduler_type'			,							type=__snake_case			,							default='cosine'      )
     parser.add_argument('--num_warmup_steps'			,							type=__snake_case			,							default=10      )
     parser.add_argument('--weight_decay'			,							type=__snake_case			,							default=0.01      )
     parser.add_argument('--output_dir'			,							type=__snake_case			,							default='./results'      )
     return parser.parse_args()


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
     __A			,__A						:			List[Any]								=	eval_pred
     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						:			str								=	deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix='train')
               return control_copy






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			List[Any]								=	get_args()
     set_seed(args.seed      )

     __A						:			Union[str, Any]								=	load_dataset('codeparrot/codecomplex'			,							split='train'      )
     __A						:			Optional[int]								=	dataset.train_test_split(test_size=0.2      )
     __A						:			Union[str, Any]								=	train_test['test'].train_test_split(test_size=0.5      )
     __A						:			Optional[Any]								=	DatasetDict(
         {
             'train': train_test['train'],
             'test': test_validation['train'],
             'valid': test_validation['test'],
         }      )

     print('Loading tokenizer and model'      )
     __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(args.model_ckpt      )
     __A						:			Tuple								=	tokenizer.eos_token
     __A						:			Dict								=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt			,							num_labels=7      )
     __A						:			Optional[int]								=	model.config.eos_token_id

     if args.freeze:
          for param in model.roberta.parameters():
               __A						:			Optional[Any]								=	False

     __A						:			Dict								=	ClassLabel(num_classes=7			,							names=list(set(train_test_validation['train']['complexity']      )      )      )

     def tokenize(__snake_case  :			Optional[Any]      ):
          __A						:			Optional[Any]								=	tokenizer(example['src']			,							truncation=__snake_case			,							max_length=10_24      )
          __A						:			str								=	labels.straint(example['complexity']      )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __A						:			str								=	train_test_validation.map(
         __snake_case			,							batched=__snake_case			,							remove_columns=train_test_validation['train'].column_names			,							)
     __A						:			str								=	DataCollatorWithPadding(tokenizer=__snake_case      )

     __A						:			str								=	TrainingArguments(
         output_dir=args.output_dir			,							learning_rate=args.learning_rate			,							lr_scheduler_type=args.lr_scheduler_type			,							evaluation_strategy='epoch'			,							save_strategy='epoch'			,							logging_strategy='epoch'			,							per_device_train_batch_size=args.batch_size			,							per_device_eval_batch_size=args.batch_size			,							num_train_epochs=args.num_epochs			,							gradient_accumulation_steps=args.gradient_accumulation_steps			,							weight_decay=0.01			,							metric_for_best_model='accuracy'			,							run_name='complexity-java'			,							report_to='wandb'			,							)

     __A						:			Tuple								=	Trainer(
         model=__snake_case			,							args=__snake_case			,							train_dataset=tokenized_datasets['train']			,							eval_dataset=tokenized_datasets['valid']			,							tokenizer=__snake_case			,							data_collator=__snake_case			,							compute_metrics=__snake_case			,							)

     print('Training...'      )
     trainer.add_callback(CustomCallback(__snake_case      )      )
     trainer.train()


if __name__ == "__main__":
  main()