

'''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
#
########################################################################


lowercase__         =      16
lowercase__         =      32




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = 16     ):
 UpperCAmelCase	:     Any						 =			AutoTokenizer.from_pretrained('bert-base-cased'     )
 UpperCAmelCase	:     int						 =			DatasetDict(
     {
         'train': dataset['train'].select(UpperCAmelCase_     ),
         'validation': dataset['train'].select(UpperCAmelCase_     ),
         'test': dataset['validation'],
     }     )

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

 def collate_fn(UpperCAmelCase_     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCAmelCase	:     Optional[int]						 =			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":
   UpperCAmelCase	:     Tuple						 =			16
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase	:     Dict						 =			8
  else:
   UpperCAmelCase	:     Dict						 =			None

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

 # Instantiate dataloaders.
 UpperCAmelCase	:     int						 =			DataLoader(
     tokenized_datasets['train']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			DataLoader(
     tokenized_datasets['validation']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_     )

 UpperCAmelCase	:     Optional[int]						 =			DataLoader(
     tokenized_datasets['test']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_     )

 return train_dataloader, eval_dataloader, test_dataloader




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

 UpperCAmelCase	:     Union[str, Any]						 =			evaluate.load('glue'  ,	'mrpc'     )

 # If the batch size is too big we use gradient accumulation
 UpperCAmelCase	:     Union[str, Any]						 =			1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  UpperCAmelCase	:     Any						 =			batch_size // MAX_GPU_BATCH_SIZE
  UpperCAmelCase	:     Union[str, Any]						 =			MAX_GPU_BATCH_SIZE

 set_seed(UpperCAmelCase_     )

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

  # 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).
  UpperCAmelCase	:     List[Any]						 =			model.to(accelerator.device     )

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

  # Instantiate scheduler
  UpperCAmelCase	:     int						 =			get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase_  ,	num_warmup_steps=1_00  ,	num_training_steps=(len(UpperCAmelCase_     ) * 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.
  UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Union[str, Any]						 =			accelerator.prepare(
      UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

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

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

   UpperCAmelCase	:     Optional[int]						 =			metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"""epoch {epoch}:"""  ,	UpperCAmelCase_     )

  # New Code #
  # We also run predictions on the test set at the very end
  UpperCAmelCase	:     int						 =			[]
  for step, batch in enumerate(UpperCAmelCase_     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   with torch.no_grad():
    UpperCAmelCase	:     Any						 =			model(**UpperCAmelCase_     )
   UpperCAmelCase	:     Dict						 =			outputs.logits
   UpperCAmelCase ,			UpperCAmelCase	:     List[Any]						 =			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(UpperCAmelCase_  ,	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:
 UpperCAmelCase	:     Dict						 =			torch.cat(UpperCAmelCase_  ,	dim=0     )
 UpperCAmelCase	:     Dict						 =			torch.stack(UpperCAmelCase_  ,	dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
 UpperCAmelCase	:     str						 =			metric.compute(predictions=UpperCAmelCase_  ,	references=UpperCAmelCase_     )
 accelerator.print('Average test metrics from all folds:'  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Any						 =			argparse.ArgumentParser(description='Simple example of training script.'     )
 parser.add_argument(
     '--mixed_precision'  ,	type=UpperCAmelCase_  ,	default=UpperCAmelCase_  ,	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=UpperCAmelCase_  ,	default=3  ,	help='The number of splits to perform across the dataset'     )
 UpperCAmelCase	:     List[str]						 =			parser.parse_args()
 UpperCAmelCase	:     str						 =			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(UpperCAmelCase_  ,	UpperCAmelCase_     )


if __name__ == "__main__":
 main()







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase	:     list						 =			[]
 for char_count in range(UpperCAmelCase_     ):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]     )
  if char_count < second_str_length:
   output_list.append(second_str[char_count]     )
 return "".join(UpperCAmelCase_     )


if __name__ == "__main__":
 print(alternative_string_arrange("AB", "XYZ"), end=" ")




