'''simple docstring'''
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
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 properly calculate the metrics on the
# validation dataset when in a distributed system, 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=  1_6
a=  3_2







def  _UpperCamelCase (     _a	:	Accelerator				,     _a	:	int = 1_6			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  AutoTokenizer.from_pretrained('bert-base-cased'			)
    __UpperCamelCase	:	str		    =  load_dataset('glue'				,     'mrpc'			)

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

    def collate_fn(_a	:	Optional[int]			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase	:	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":
            __UpperCamelCase	:	Union[str, Any]		    =  1_6
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase	:	Union[str, Any]		    =  8
        else:
            __UpperCamelCase	:	int		    =  None

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

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

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  a=  mocked_dataloaders  # noqa: F811







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	Optional[int]			):




    """simple docstring"""



    if os.environ.get('TESTING_MOCKED_DATALOADERS'				,     _a			) == "1":
        __UpperCamelCase	:	int		    =  2
    # Initialize accelerator
    __UpperCamelCase	:	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
    __UpperCamelCase	:	List[Any]		    =  config['lr']
    __UpperCamelCase	:	List[str]		    =  int(config['num_epochs']			)
    __UpperCamelCase	:	Dict		    =  int(config['seed']			)
    __UpperCamelCase	:	Any		    =  int(config['batch_size']			)

    __UpperCamelCase	:	Optional[int]		    =  evaluate.load('glue'				,     'mrpc'			)

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

    set_seed(_a			)
    __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  get_dataloaders(_a				,     _a			)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase	:	Union[str, Any]		    =  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,     return_dict=_a			)

    # 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	:	List[Any]		    =  AdamW(params=model.parameters()				,     lr=_a			)

    # Instantiate scheduler
    __UpperCamelCase	:	Tuple		    =  get_linear_schedule_with_warmup(
        optimizer=_a				,     num_warmup_steps=1_0_0				,     num_training_steps=(len(_a			) * 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	:	Any		    =  accelerator.prepare(
        _a				,     _a				,     _a				,     _a				,     _a			)

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

        model.eval()
        __UpperCamelCase	:	List[Any]		    =  0
        for step, batch in enumerate(_a			):
            # 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(**_a			)
            __UpperCamelCase	:	str		    =  outputs.logits.argmax(dim=-1			)
            __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  accelerator.gather((predictions, batch['labels'])			)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(_a			) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    __UpperCamelCase	:	Any		    =  predictions[: len(eval_dataloader.dataset			) - samples_seen]
                    __UpperCamelCase	:	Tuple		    =  references[: len(eval_dataloader.dataset			) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=_a				,     references=_a				,     )

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







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  argparse.ArgumentParser(description='Simple example of training script.'			)
    parser.add_argument(
        '--mixed_precision'				,     type=_a				,     default=_a				,     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.'			)
    __UpperCamelCase	:	Optional[int]		    =  parser.parse_args()
    __UpperCamelCase	:	Any		    =  {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
    training_function(_a				,     _a			)


if __name__ == "__main__":
  main()




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

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def  _UpperCamelCase (     _a	:	NDArray[floataa]				,     _a	:	NDArray[floataa]				,     _a	:	list[int]				,     _a	:	int				,     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  coefficient_matrix.shape
    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  constant_matrix.shape

    if rowsa != colsa:
        __UpperCamelCase	:	Tuple		    =  f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if colsa != 1:
        __UpperCamelCase	:	List[Any]		    =  f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if rowsa != rowsa:
        __UpperCamelCase	:	Optional[int]		    =  (
            'Coefficient and constant matrices dimensions must be nxn and nx1 but '
            f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
        )
        raise ValueError(_a			)

    if len(_a			) != rowsa:
        __UpperCamelCase	:	Union[str, Any]		    =  (
            'Number of initial values must be equal to number of rows in coefficient '
            f"""matrix but received {len(_a			)} and {rowsa}"""
        )
        raise ValueError(_a			)

    if iterations <= 0:
        raise ValueError('Iterations must be at least 1'			)

    __UpperCamelCase	:	NDArray[floataa]		    =  np.concatenate(
        (coefficient_matrix, constant_matrix)				,     axis=1			)

    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  table.shape

    strictly_diagonally_dominant(_a			)

    # Iterates the whole matrix for given number of times
    for _ in range(_a			):
        __UpperCamelCase	:	List[Any]		    =  []
        for row in range(_a			):
            __UpperCamelCase	:	List[Any]		    =  0
            for col in range(_a			):
                if col == row:
                    __UpperCamelCase	:	Optional[int]		    =  table[row][col]
                elif col == cols - 1:
                    __UpperCamelCase	:	str		    =  table[row][col]
                else:
                    temp += (-1) * table[row][col] * init_val[col]
            __UpperCamelCase	:	Any		    =  (temp + val) / denom
            new_val.append(_a			)
        __UpperCamelCase	:	List[Any]		    =  new_val

    return [float(_a			) for i in new_val]







def  _UpperCamelCase (     _a	:	NDArray[floataa]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  table.shape

    __UpperCamelCase	:	str		    =  True

    for i in range(0				,     _a			):
        __UpperCamelCase	:	Optional[Any]		    =  0
        for j in range(0				,     cols - 1			):
            if i == j:
                continue
            else:
                total += table[i][j]

        if table[i][i] <= total:
            raise ValueError('Coefficient matrix is not strictly diagonally dominant'			)

    return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()



