





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 the experiment tracking capability,
# 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
#
########################################################################

UpperCAmelCase_     							= 16
UpperCAmelCase_     							= 32




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			List[Any] = 16      )      ->	Dict:
    UpperCamelCase__							:				Dict          =      AutoTokenizer.from_pretrained('''bert-base-cased'''      )
    UpperCamelCase__							:				Optional[int]          =      load_dataset('''glue'''		,   '''mrpc'''      )

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

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

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

    # Instantiate dataloaders.
    UpperCamelCase__							:				str          =      DataLoader(
        tokenized_datasets['''train''']		,   shuffle=__UpperCAmelCase		,   collate_fn=__UpperCAmelCase		,   batch_size=__UpperCAmelCase      )
    UpperCamelCase__							:				Optional[int]          =      DataLoader(
        tokenized_datasets['''validation''']		,   shuffle=__UpperCAmelCase		,   collate_fn=__UpperCAmelCase		,   batch_size=__UpperCAmelCase      )

    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

  UpperCAmelCase_     							= mocked_dataloaders  # noqa: F811




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Optional[int]      )      ->	Any:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,   __UpperCAmelCase      ) == "1":
        UpperCamelCase__							:				Optional[int]          =      2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        UpperCamelCase__							:				Union[str, Any]          =      Accelerator(
            cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   log_with='''all'''		,   project_dir=args.project_dir      )
    else:
        UpperCamelCase__							:				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__							:				int          =      config['''lr''']
    UpperCamelCase__							:				Optional[int]          =      int(config['''num_epochs''']      )
    UpperCamelCase__							:				Optional[Any]          =      int(config['''seed''']      )
    UpperCamelCase__							:				int          =      int(config['''batch_size''']      )
    set_seed(__UpperCAmelCase      )

    UpperCamelCase__							:				str          =      get_dataloaders(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      evaluate.load('''glue'''		,   '''mrpc'''      )

    # If the batch size is too big we use gradient accumulation
    UpperCamelCase__							:				Any          =      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__							:				Any          =      MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    UpperCamelCase__							:				Optional[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).
    UpperCamelCase__							:				List[str]          =      model.to(accelerator.device      )

    # Instantiate optimizer
    UpperCamelCase__							:				List[Any]          =      AdamW(params=model.parameters()		,   lr=__UpperCAmelCase      )

    # Instantiate scheduler
    UpperCamelCase__							:				List[str]          =      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.
    UpperCamelCase__							:				List[str]          =      accelerator.prepare(
        __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        UpperCamelCase__							:				Optional[Any]          =      os.path.split(__UpperCAmelCase      )[-1].split('''.'''      )[0]
        accelerator.init_trackers(__UpperCAmelCase		,   __UpperCAmelCase      )

    # Now we train the model
    for epoch in range(__UpperCAmelCase      ):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            UpperCamelCase__							:				str          =      0
        for step, batch in enumerate(__UpperCAmelCase      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            UpperCamelCase__							:				List[str]          =      model(**__UpperCAmelCase      )
            UpperCamelCase__							:				List[str]          =      outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            UpperCamelCase__							:				List[str]          =      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` (the default).
            batch.to(accelerator.device      )
            with torch.no_grad():
                UpperCamelCase__							:				List[str]          =      model(**__UpperCAmelCase      )
            UpperCamelCase__							:				Optional[int]          =      outputs.logits.argmax(dim=-1      )
            UpperCamelCase__							:				Optional[int]          =      accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
            metric.add_batch(
                predictions=__UpperCAmelCase		,   references=__UpperCAmelCase		,   )

        UpperCamelCase__							:				int          =      metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"		,   __UpperCAmelCase      )

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(__UpperCAmelCase      ),
                    '''epoch''': epoch,
                }		,   step=__UpperCAmelCase		,   )

             # New Code #
             # When a run is finished, you should call `accelerator.end_training()`
             # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()




def 		lowerCAmelCase_    (  )      ->	Optional[int]:
    UpperCamelCase__							:				Any          =      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.'''      )
    parser.add_argument(
        '''--with_tracking'''		,   action='''store_true'''		,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,   )
    parser.add_argument(
        '''--project_dir'''		,   type=__UpperCAmelCase		,   default='''logs'''		,   help='''Location on where to store experiment tracking logs` and relevent project information'''		,   )
    UpperCamelCase__							:				str          =      parser.parse_args()
    UpperCamelCase__							:				Tuple          =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__UpperCAmelCase		,   __UpperCAmelCase      )


if __name__ == "__main__":
  main()


'''simple docstring'''

import unittest

from knapsack import knapsack as k



class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Optional[Any]  =					0
        lowercase__				:    Any  =					[0]
        lowercase__				:    List[Any]  =					[0]
        lowercase__				:    Optional[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   0 )

        lowercase__				:    List[str]  =					[60]
        lowercase__				:    List[str]  =					[10]
        lowercase__				:    List[str]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   0 )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					3
        lowercase__				:    List[Any]  =					[1, 2, 3]
        lowercase__				:    Union[str, Any]  =					[3, 2, 1]
        lowercase__				:    List[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   5 )


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    int  =					50
        lowercase__				:    Any  =					[60, 100, 120]
        lowercase__				:    int  =					[10, 20, 30]
        lowercase__				:    Optional[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   220 )


if __name__ == "__main__":
  unittest.main()

