"""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
#
########################################################################


_UpperCamelCase     :		Union[str, Any]											=					16
_UpperCamelCase     :		Optional[Any]											=					32





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				DatasetDict     ,				_lowerCAmelCase						:				List[int]     ,				_lowerCAmelCase						:				List[int]     ,				_lowerCAmelCase						:				int = 16  ):





       '''simple docstring'''

       lowercase__    :	int     					=			AutoTokenizer.from_pretrained('bert-base-cased'  )
       lowercase__    :	str     					=			DatasetDict(
           {
               'train': dataset['train'].select(_lowerCAmelCase  ),
               'validation': dataset['train'].select(_lowerCAmelCase  ),
               'test': dataset['validation'],
           }  )

       def tokenize_function(_lowerCAmelCase						:				Dict  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[Any]     					=			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
       # starting with the main process first:
       with accelerator.main_process_first():
              lowercase__    :	Union[str, Any]     					=			datasets.map(
                  _lowerCAmelCase     ,				batched=_lowerCAmelCase     ,				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__    :	Any     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

       def collate_fn(_lowerCAmelCase						:				List[str]  ):
              # 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__    :	Optional[Any]     					=			16
              elif accelerator.mixed_precision != "no":
                     lowercase__    :	Dict     					=			8
              else:
                     lowercase__    :	Optional[int]     					=			None

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

       # Instantiate dataloaders.
       lowercase__    :	List[str]     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	str     					=			DataLoader(
           tokenized_datasets['validation']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['test']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       return train_dataloader, eval_dataloader, test_dataloader





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			[]
       # Download the dataset
       lowercase__    :	Optional[Any]     					=			load_dataset('glue'     ,				'mrpc'  )
       # Create our splits
       lowercase__    :	List[str]     					=			StratifiedKFold(n_splits=int(args.num_folds  )  )
       # Initialize accelerator
       lowercase__    :	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
       lowercase__    :	int     					=			config['lr']
       lowercase__    :	Optional[int]     					=			int(config['num_epochs']  )
       lowercase__    :	Optional[Any]     					=			int(config['seed']  )
       lowercase__    :	Optional[int]     					=			int(config['batch_size']  )

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

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

       set_seed(_lowerCAmelCase  )

       # New Code #
       # Create our folds:
       lowercase__    :	Optional[Any]     					=			kfold.split(np.zeros(datasets['train'].num_rows  )     ,				datasets['train']['label']  )
       lowercase__    :	Optional[Any]     					=			[]
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(_lowerCAmelCase  ):
              lowercase__					,   lowercase__					,   lowercase__    :	Optional[int]     					=			get_fold_dataloaders(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				)
              # 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=_lowerCAmelCase  )

              # 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__    :	Any     					=			model.to(accelerator.device  )

              # Instantiate optimizer
              lowercase__    :	Tuple     					=			AdamW(params=model.parameters()     ,				lr=_lowerCAmelCase  )

              # Instantiate scheduler
              lowercase__    :	Dict     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=100     ,				num_training_steps=(len(_lowerCAmelCase  ) * 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__    :	List[str]     					=			accelerator.prepare(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

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

                     model.eval()
                     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():
                                   lowercase__    :	str     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	str     					=			outputs.logits.argmax(dim=-1  )
                            lowercase__					,   lowercase__    :	str     					=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
                            metric.add_batch(
                                predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase     ,				)

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

              # New Code #
              # We also run predictions on the test set at the very end
              lowercase__    :	Optional[int]     					=			[]
              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():
                            lowercase__    :	Any     					=			model(**_lowerCAmelCase  )
                     lowercase__    :	Optional[Any]     					=			outputs.logits
                     lowercase__					,   lowercase__    :	Optional[int]     					=			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(_lowerCAmelCase     ,				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__    :	Tuple     					=			torch.cat(_lowerCAmelCase     ,				dim=0  )
       lowercase__    :	Optional[int]     					=			torch.stack(_lowerCAmelCase     ,				dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
       lowercase__    :	Optional[int]     					=			metric.compute(predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase  )
       accelerator.print('Average test metrics from all folds:'     ,				_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

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


if __name__ == "__main__":
       main()


"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

