
'''simple docstring'''
from __future__ import annotations

import numpy as np

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_			, snake_case_ :    int     			=						np.shape(UpperCamelCase__       )
    if rows != columns:
        snake_case_ :    str     			=						(
            "\'table\' has to be of square shaped array but got a "
            f'{rows}x{columns} array:\n{table}'
        )
        raise ValueError(UpperCamelCase__       )

    snake_case_ :    Tuple     			=						np.zeros((rows, columns)       )
    snake_case_ :    Optional[Any]     			=						np.zeros((rows, columns)       )
    for i in range(UpperCamelCase__       ):
        for j in range(UpperCamelCase__       ):
            snake_case_ :    int     			=						sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__       )       )
            if upper[j][j] == 0:
                raise ArithmeticError("No LU decomposition exists"       )
            snake_case_ :    Any     			=						(table[i][j] - total) / upper[j][j]
        snake_case_ :    Union[str, Any]     			=						1
        for j in range(UpperCamelCase__,		UpperCamelCase__       ):
            snake_case_ :    Tuple     			=						sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__       )       )
            snake_case_ :    str     			=						table[i][j] - total
    return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()








'''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_											=						16
a_											=						32

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    int     			=						DatasetDict(
        {
            "train": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "validation": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "test": dataset["validation"],
        }       )

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

    def collate_fn(__SCREAMING_SNAKE_CASE       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case_ :    Dict     			=						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case_ :    Optional[int]     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    Tuple     			=						8
        else:
            snake_case_ :    Union[str, Any]     			=						None

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

    # Instantiate dataloaders.
    snake_case_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["test"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    return train_dataloader, eval_dataloader, test_dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    snake_case_ :    List[Any]     			=						evaluate.load("glue",		"mrpc"       )

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

    set_seed(__SCREAMING_SNAKE_CASE       )

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

        # Instantiate optimizer
        snake_case_ :    Any     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        snake_case_ :    List[str]     			=						get_linear_schedule_with_warmup(
            optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		num_training_steps=(len(__SCREAMING_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.
        snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_ :    str     			=						accelerator.prepare(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

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

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

            snake_case_ :    str     			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:',		__SCREAMING_SNAKE_CASE       )

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

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision",		type=__SCREAMING_SNAKE_CASE,		default=__SCREAMING_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."       )
    # New Code #
    parser.add_argument("--num_folds",		type=__SCREAMING_SNAKE_CASE,		default=3,		help="The number of splits to perform across the dataset"       )
    snake_case_ :    List[Any]     			=						parser.parse_args()
    snake_case_ :    Optional[Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()




