
"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ) ->      bool:






    """simple docstring"""




    UpperCAmelCase									=					len(lowerCAmelCase ) + 1
    UpperCAmelCase									=					len(lowerCAmelCase ) + 1

    # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
    # length i of input_string matches with prefix string of length j of
    # given pattern.
    # "dp" stands for dynamic programming.
    UpperCAmelCase									=					[[0 for i in range(lowerCAmelCase )] for j in range(lowerCAmelCase )]

    # since string of zero length match pattern of zero length
    UpperCAmelCase									=					1

    # since pattern of zero length will never match with string of non-zero length
    for i in range(1 ,				lowerCAmelCase ):
        UpperCAmelCase									=					0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1 ,				lowerCAmelCase ):
        UpperCAmelCase									=					dp[0][j - 2] if pattern[j - 1] == '*' else 0

    # now using bottom-up approach to find for all remaining lengths
    for i in range(1 ,				lowerCAmelCase ):
        for j in range(1 ,				lowerCAmelCase ):
            if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                UpperCAmelCase									=					dp[i - 1][j - 1]

            elif pattern[j - 1] == "*":
                if dp[i][j - 2] == 1:
                    UpperCAmelCase									=					1
                elif pattern[j - 2] in (input_string[i - 1], "."):
                    UpperCAmelCase									=					dp[i - 1][j]
                else:
                    UpperCAmelCase									=					0
            else:
                UpperCAmelCase									=					0

    return bool(dp[-1][-1] )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # inputing the strings
   # input_string = input("input a string :")
   # pattern = input("input a pattern :")

   SCREAMING_SNAKE_CASE_    					=						'''aab'''
   SCREAMING_SNAKE_CASE_    					=						'''c*a*b'''

   # using function to check whether given string matches the given pattern
   if match_pattern(input_string, pattern):
      print(F'{input_string} matches the given pattern {pattern}')
   else:
      print(F'{input_string} does not match with the given pattern {pattern}')





"""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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE_    					=						16
SCREAMING_SNAKE_CASE_    					=						32




def     lowercase__						(					lowerCAmelCase				:					Accelerator ,				lowerCAmelCase				:					int = 16 ) ->      List[str]:






    """simple docstring"""




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

    def tokenize_function(lowerCAmelCase				:					int ):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase									=					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():
        UpperCAmelCase									=					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
    UpperCAmelCase									=					tokenized_datasets.rename_column('label' ,				'labels' )

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

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

    # Instantiate dataloaders.
    UpperCAmelCase									=					DataLoader(
        tokenized_datasets['train'] ,				shuffle=lowerCAmelCase ,				collate_fn=lowerCAmelCase ,				batch_size=lowerCAmelCase )
    UpperCAmelCase									=					DataLoader(
        tokenized_datasets['validation'] ,				shuffle=lowerCAmelCase ,				collate_fn=lowerCAmelCase ,				batch_size=lowerCAmelCase )

    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

   SCREAMING_SNAKE_CASE_    					=						mocked_dataloaders  # noqa: F811




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    if os.environ.get('TESTING_MOCKED_DATALOADERS' ,				lowerCAmelCase ) == "1":
        UpperCAmelCase									=					2
    # New Code #
    UpperCAmelCase									=					int(args.gradient_accumulation_steps )
    UpperCAmelCase									=					int(args.local_sgd_steps )
    # Initialize accelerator
    UpperCAmelCase									=					Accelerator(
        cpu=args.cpu ,				mixed_precision=args.mixed_precision ,				gradient_accumulation_steps=lowerCAmelCase )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)' )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCAmelCase									=					config['lr']
    UpperCAmelCase									=					int(config['num_epochs'] )
    UpperCAmelCase									=					int(config['seed'] )
    UpperCAmelCase									=					int(config['batch_size'] )

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

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

    # Instantiate optimizer
    UpperCAmelCase									=					AdamW(params=model.parameters() ,				lr=lowerCAmelCase )

    # Instantiate scheduler
    UpperCAmelCase									=					get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase ,				num_warmup_steps=100 ,				num_training_steps=(len(lowerCAmelCase ) * 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.
    UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					accelerator.prepare(
        lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )

    # Now we train the model
    for epoch in range(lowerCAmelCase ):
        model.train()
        with LocalSGD(
            accelerator=lowerCAmelCase ,				model=lowerCAmelCase ,				local_sgd_steps=lowerCAmelCase ,				enabled=local_sgd_steps is not None ) as local_sgd:
            for step, batch in enumerate(lowerCAmelCase ):
                # 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(lowerCAmelCase ):
                    UpperCAmelCase									=					model(**lowerCAmelCase )
                    UpperCAmelCase									=					output.loss
                    accelerator.backward(lowerCAmelCase )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

        UpperCAmelCase									=					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:" ,				lowerCAmelCase )




def     lowercase__						(					) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					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.' ,				)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps' ,				type=lowerCAmelCase ,				default=1 ,				help='The number of minibatches to be ran before gradients are accumulated.' ,				)
    parser.add_argument(
        '--local_sgd_steps' ,				type=lowerCAmelCase ,				default=8 ,				help='Number of local SGD steps or None to disable local SGD' )
    parser.add_argument('--cpu' ,				action='store_true' ,				help='If passed, will train on the CPU.' )
    UpperCAmelCase									=					parser.parse_args()
    UpperCAmelCase									=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCAmelCase ,				lowerCAmelCase )


if __name__ == "__main__":
   main()



