




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
#
########################################################################


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
    _lowerCAmelCase    :    int										=      DatasetDict(
        {
            """train""": dataset["""train"""].select(UpperCamelCase_       ),
            """validation""": dataset["""train"""].select(UpperCamelCase_       ),
            """test""": dataset["""validation"""],
        }       )

    def tokenize_function(UpperCamelCase_ : str       ):
        # max_length=None => use the model max length (it's actually the default)
        _lowerCAmelCase    :    List[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():
        _lowerCAmelCase    :    List[str]										=      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
    _lowerCAmelCase    :    Optional[Any]										=      tokenized_datasets.rename_column("""label"""      ,    """labels"""       )

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

        return tokenizer.pad(
            UpperCamelCase_      ,    padding="""longest"""      ,    max_length=UpperCamelCase_      ,    pad_to_multiple_of=UpperCamelCase_      ,    return_tensors="""pt"""      ,    )

    # Instantiate dataloaders.
    _lowerCAmelCase    :    Optional[Any]										=      DataLoader(
        tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
    _lowerCAmelCase    :    Tuple										=      DataLoader(
        tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

    _lowerCAmelCase    :    List[str]										=      DataLoader(
        tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

    return train_dataloader, eval_dataloader, test_dataloader




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

    _lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

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

    set_seed(UpperCamelCase_       )

    # New Code #
    # Create our folds:
    _lowerCAmelCase    :    Optional[int]										=      kfold.split(np.zeros(datasets["""train"""].num_rows       )      ,    datasets["""train"""]["""label"""]       )
    _lowerCAmelCase    :    Any										=      []
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_       ):
        _lowerCAmelCase    :    List[str]										=      get_fold_dataloaders(
            UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        _lowerCAmelCase    :    int										=      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).
        _lowerCAmelCase    :    Union[str, Any]										=      model.to(accelerator.device       )

        # Instantiate optimizer
        _lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

        # Instantiate scheduler
        _lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    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.
        _lowerCAmelCase    :    List[str]										=      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       )
                _lowerCAmelCase    :    Tuple										=      model(**UpperCamelCase_       )
                _lowerCAmelCase    :    Optional[int]										=      outputs.loss
                _lowerCAmelCase    :    Optional[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():
                    _lowerCAmelCase    :    List[Any]										=      model(**UpperCamelCase_       )
                _lowerCAmelCase    :    List[Any]										=      outputs.logits.argmax(dim=-1       )
                _lowerCAmelCase    :    Any										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
                metric.add_batch(
                    predictions=UpperCamelCase_      ,    references=UpperCamelCase_      ,    )

            _lowerCAmelCase    :    List[Any]										=      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
        _lowerCAmelCase    :    Dict										=      []
        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():
                _lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )
            _lowerCAmelCase    :    List[Any]										=      outputs.logits
            _lowerCAmelCase    :    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(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:
    _lowerCAmelCase    :    Tuple										=      torch.cat(UpperCamelCase_      ,    dim=0       )
    _lowerCAmelCase    :    Tuple										=      torch.stack(UpperCamelCase_      ,    dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    _lowerCAmelCase    :    Union[str, Any]										=      metric.compute(predictions=UpperCamelCase_      ,    references=UpperCamelCase_       )
    accelerator.print("""Average test metrics from all folds:"""      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  ():
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[int]										=      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"""       )
    _lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
    _lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
 main()







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

