

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
# and perform gradient accumulation
#
# 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 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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Accelerator   ,      SCREAMING_SNAKE_CASE__      :     int = 16				)							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       AutoTokenizer.from_pretrained('bert-base-cased'				)
    A__   				=       load_dataset('glue'   ,      'mrpc'				)

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

    def collate_fn(SCREAMING_SNAKE_CASE__      :     Tuple				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__   				=       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":
            A__   				=       16
        elif accelerator.mixed_precision != "no":
            A__   				=       8
        else:
            A__   				=       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.
    A__   				=       DataLoader(
        tokenized_datasets['train']   ,      shuffle=SCREAMING_SNAKE_CASE__   ,      collate_fn=SCREAMING_SNAKE_CASE__   ,      batch_size=SCREAMING_SNAKE_CASE__				)
    A__   				=       DataLoader(
        tokenized_datasets['validation']   ,      shuffle=SCREAMING_SNAKE_CASE__   ,      collate_fn=SCREAMING_SNAKE_CASE__   ,      batch_size=SCREAMING_SNAKE_CASE__				)

    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

     lowercase_			=  mocked_dataloaders  # noqa: F811
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,      SCREAMING_SNAKE_CASE__				) == "1":
        A__   				=       2
    # New Code #
    A__   				=       int(args.gradient_accumulation_steps				)
    # Initialize accelerator
    A__   				=       Accelerator(
        cpu=args.cpu   ,      mixed_precision=args.mixed_precision   ,      gradient_accumulation_steps=SCREAMING_SNAKE_CASE__				)
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__   				=       config['lr']
    A__   				=       int(config['num_epochs']				)
    A__   				=       int(config['seed']				)
    A__   				=       int(config['batch_size']				)

    A__   				=       evaluate.load('glue'   ,      'mrpc'				)

    set_seed(SCREAMING_SNAKE_CASE__				)
    A__				,						A__   				=       get_dataloaders(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A__   				=       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).
    A__   				=       model.to(accelerator.device				)

    # Instantiate optimizer
    A__   				=       AdamW(params=model.parameters()   ,      lr=SCREAMING_SNAKE_CASE__				)

    # Instantiate scheduler
    A__   				=       get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE__   ,      num_warmup_steps=100   ,      num_training_steps=(len(SCREAMING_SNAKE_CASE__				) * num_epochs)   ,      )

    # 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.
    A__				,						A__				,						A__				,						A__				,						A__   				=       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				)
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(SCREAMING_SNAKE_CASE__				):
                A__   				=       model(**SCREAMING_SNAKE_CASE__				)
                A__   				=       output.loss
                accelerator.backward(SCREAMING_SNAKE_CASE__				)
                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():
                A__   				=       model(**SCREAMING_SNAKE_CASE__				)
            A__   				=       outputs.logits.argmax(dim=-1				)
            A__				,						A__   				=       accelerator.gather_for_metrics((predictions, batch['labels'])				)
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE__   ,      references=SCREAMING_SNAKE_CASE__   ,      )

        A__   				=       metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'   ,      SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   )							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       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.'   ,      )
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'   ,      type=SCREAMING_SNAKE_CASE__   ,      default=1   ,      help='The number of minibatches to be ran before gradients are accumulated.'   ,      )
    parser.add_argument('--cpu'   ,      action='store_true'   ,      help='If passed, will train on the CPU.'				)
    A__   				=       parser.parse_args()
    A__   				=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
     main()








def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

