



'''simple docstring'''

import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


_A				:    int     						=1_6
_A				:    int     						=3_2







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase = 16					) ->			Any:
 _lowercase					:       Tuple       =		AutoTokenizer.from_pretrained('bert-base-cased'					)
 _lowercase					:       Any       =		DatasetDict(
     {
         'train': dataset['train'].select(_lowercase					),
         'validation': dataset['train'].select(_lowercase					),
         'test': dataset['validation'],
     }					)

 def tokenize_function(_lowercase					):
  # max_length=None => use the model max length (it's actually the default)
  _lowercase					:       str       =		tokenizer(examples['sentence1'],				examples['sentence2'],				truncation=_lowercase,				max_length=_lowercase					)
  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():
  _lowercase					:       Any       =		datasets.map(
      _lowercase,				batched=_lowercase,				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
 _lowercase					:       Optional[Any]       =		tokenized_datasets.rename_column('label',				'labels'					)

 def collate_fn(_lowercase					):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _lowercase					:       Tuple       =		128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   _lowercase					:       int       =		16
  elif accelerator.mixed_precision != "no":
   _lowercase					:       Optional[int]       =		8
  else:
   _lowercase					:       Dict       =		None

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

 # Instantiate dataloaders.
 _lowercase					:       Optional[int]       =		DataLoader(
     tokenized_datasets['train'],				shuffle=_lowercase,				collate_fn=_lowercase,				batch_size=_lowercase					)
 _lowercase					:       List[Any]       =		DataLoader(
     tokenized_datasets['validation'],				shuffle=_lowercase,				collate_fn=_lowercase,				batch_size=_lowercase					)

 _lowercase					:       List[str]       =		DataLoader(
     tokenized_datasets['test'],				shuffle=_lowercase,				collate_fn=_lowercase,				batch_size=_lowercase					)

 return train_dataloader, eval_dataloader, test_dataloader







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Tuple:
 _lowercase					:       List[Any]       =		[]
 # Download the dataset
 _lowercase					:       Dict       =		load_dataset('glue',				'mrpc'					)
 # Create our splits
 _lowercase					:       Optional[int]       =		StratifiedKFold(n_splits=int(args.num_folds					)					)
 # Initialize accelerator
 _lowercase					:       Tuple       =		Accelerator(cpu=args.cpu,				mixed_precision=args.mixed_precision					)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _lowercase					:       str       =		config['lr']
 _lowercase					:       Dict       =		int(config['num_epochs']					)
 _lowercase					:       Union[str, Any]       =		int(config['seed']					)
 _lowercase					:       int       =		int(config['batch_size']					)

 _lowercase					:       List[Any]       =		evaluate.load('glue',				'mrpc'					)

 # If the batch size is too big we use gradient accumulation
 _lowercase					:       str       =		1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  _lowercase					:       int       =		batch_size // MAX_GPU_BATCH_SIZE
  _lowercase					:       Tuple       =		MAX_GPU_BATCH_SIZE

 set_seed(_lowercase					)

 # New Code #
 # Create our folds:
 _lowercase					:       Optional[int]       =		kfold.split(np.zeros(datasets['train'].num_rows					),				datasets['train']['label']					)
 _lowercase					:       Dict       =		[]
 # Iterate over them
 for i, (train_idxs, valid_idxs) in enumerate(_lowercase					):
  _lowercase  ,    _lowercase  ,    _lowercase					:       Tuple       =		get_fold_dataloaders(
      _lowercase,				_lowercase,				_lowercase,				_lowercase,				)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowercase					:       Any       =		AutoModelForSequenceClassification.from_pretrained('bert-base-cased',				return_dict=_lowercase					)

  # 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).
  _lowercase					:       int       =		model.to(accelerator.device					)

  # Instantiate optimizer
  _lowercase					:       Dict       =		AdamW(params=model.parameters(),				lr=_lowercase					)

  # Instantiate scheduler
  _lowercase					:       Dict       =		get_linear_schedule_with_warmup(
      optimizer=_lowercase,				num_warmup_steps=100,				num_training_steps=(len(_lowercase					) * 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.
  _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Optional[int]       =		accelerator.prepare(
      _lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					)

  # Now we train the model
  for epoch in range(_lowercase					):
   model.train()
   for step, batch in enumerate(_lowercase					):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device					)
    _lowercase					:       str       =		model(**_lowercase					)
    _lowercase					:       str       =		outputs.loss
    _lowercase					:       List[Any]       =		loss / gradient_accumulation_steps
    accelerator.backward(_lowercase					)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

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

   _lowercase					:       List[Any]       =		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:''',				_lowercase					)

  # New Code #
  # We also run predictions on the test set at the very end
  _lowercase					:       int       =		[]
  for step, batch in enumerate(_lowercase					):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device					)
   with torch.no_grad():
    _lowercase					:       Optional[Any]       =		model(**_lowercase					)
   _lowercase					:       List[str]       =		outputs.logits
   _lowercase  ,    _lowercase					:       str       =		accelerator.gather_for_metrics((predictions, batch['labels'])					)
   fold_predictions.append(predictions.cpu()					)
   if i == 0:
    # We need all of the test predictions
    test_references.append(references.cpu()					)
        # Use accelerator.print to print only on the main process.
  test_predictions.append(torch.cat(_lowercase,				dim=0					)					)
  # We now need to release all our memory and get rid of the current model, optimizer, etc
  accelerator.free_memory()
 # New Code #
 # Finally we check the accuracy of our folded results:
 _lowercase					:       List[Any]       =		torch.cat(_lowercase,				dim=0					)
 _lowercase					:       Union[str, Any]       =		torch.stack(_lowercase,				dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
 _lowercase					:       List[Any]       =		metric.compute(predictions=_lowercase,				references=_lowercase					)
 accelerator.print('Average test metrics from all folds:',				_lowercase					)







def 						__UpperCamelCase				(							) ->			Optional[Any]:
 _lowercase					:       Dict       =		argparse.ArgumentParser(description='Simple example of training script.'					)
 parser.add_argument(
     '--mixed_precision',				type=_lowercase,				default=_lowercase,				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.'					)
 # New Code #
 parser.add_argument('--num_folds',				type=_lowercase,				default=3,				help='The number of splits to perform across the dataset'					)
 _lowercase					:       int       =		parser.parse_args()
 _lowercase					:       str       =		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(_lowercase,				_lowercase					)


if __name__ == "__main__":
  main()





'''simple docstring'''

import argparse
from collections import defaultdict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			int:
	_lowercase					:       Optional[int]       =		f'''{file}_{class_name}_{test_name}'''
	done_test[_id] += 1

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       Optional[int]       =		f.readlines()

	_lowercase					:       Dict       =		f'''class {class_name}('''
	_lowercase					:       List[Any]       =		f'''{4 * " "}def {test_name}('''
	_lowercase					:       List[str]       =		f'''{8 * " "}{correct_line.split()[0]}'''
	_lowercase					:       List[str]       =		f'''{16 * " "}{correct_line.split()[0]}'''
	_lowercase					:       Dict       =		False
	_lowercase					:       str       =		False
	_lowercase					:       List[Any]       =		False
	_lowercase					:       Union[str, Any]       =		False
	_lowercase					:       Any       =		0
	_lowercase					:       Tuple       =		0

	_lowercase					:       Optional[int]       =		[]
	for line in lines:
		if line.startswith(_lowercase					):
			_lowercase					:       int       =		True
		elif in_class and line.startswith(_lowercase					):
			_lowercase					:       List[Any]       =		True
		elif in_class and in_func and (line.startswith(_lowercase					) or line.startswith(_lowercase					)):
			_lowercase					:       str       =		len(line.split(correct_line.split()[0]					)[0]					)
			count += 1

			if count == done_test[_id]:
				_lowercase					:       List[Any]       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				_lowercase					:       Any       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f'''{spaces * " "}{correct_line}'''					)
			_lowercase					:       Any       =		False
		else:
			new_lines.append(_lowercase					)

	with open(_lowercase,				'w'					) as f:
		for line in new_lines:
			f.write(_lowercase					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase=None					) ->			Optional[Any]:
	if fail is not None:
		with open(_lowercase,				'r'					) as f:
			_lowercase					:       Any       =		{l.strip() for l in f.readlines()}
	else:
		_lowercase					:       str       =		None

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       str       =		f.readlines()

	_lowercase					:       Union[str, Any]       =		defaultdict(_lowercase					)
	for line in correct_lines:
		_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		line.split(';'					)
		if test_failures is None or "::".join([file, class_name, test_name]					) in test_failures:
			overwrite_file(_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					)


if __name__ == "__main__":
		_A				:    str     						=argparse.ArgumentParser()
		parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
		parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
		_A				:    Union[str, Any]     						=parser.parse_args()

		main(args.correct_filename, args.fail_filename)
