

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 checkpointing 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
#
########################################################################

_lowerCamelCase							=16
_lowerCamelCase							=32







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_ = 16     ):





     """simple docstring"""







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

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

     def collate_fn(lowerCAmelCase_     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE 		=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":
               SCREAMING_SNAKE_CASE 		=16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE 		=8
          else:
               SCREAMING_SNAKE_CASE 		=None

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

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE 		=DataLoader(
         tokenized_datasets['train'],       shuffle=lowerCAmelCase_,       collate_fn=lowerCAmelCase_,       batch_size=lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=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

 _lowerCamelCase							=mocked_dataloaders  # noqa: F811







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if os.environ.get('TESTING_MOCKED_DATALOADERS',       lowerCAmelCase_     ) == "1":
          SCREAMING_SNAKE_CASE 		=2
     # Initialize accelerator
     SCREAMING_SNAKE_CASE 		=Accelerator(cpu=args.cpu,       mixed_precision=args.mixed_precision     )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE 		=config['lr']
     SCREAMING_SNAKE_CASE 		=int(config['num_epochs']     )
     SCREAMING_SNAKE_CASE 		=int(config['seed']     )
     SCREAMING_SNAKE_CASE 		=int(config['batch_size']     )

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps,       'isdigit'     ):
          if args.checkpointing_steps == "epoch":
               SCREAMING_SNAKE_CASE 		=args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               SCREAMING_SNAKE_CASE 		=int(args.checkpointing_steps     )
          else:
               raise ValueError(
                   F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'     )
     else:
          SCREAMING_SNAKE_CASE 		=None

     set_seed(lowerCAmelCase_     )

     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=get_dataloaders(lowerCAmelCase_,       lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=evaluate.load('glue',       'mrpc'     )

     # If the batch size is too big we use gradient accumulation
     SCREAMING_SNAKE_CASE 		=1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          SCREAMING_SNAKE_CASE 		=batch_size // MAX_GPU_BATCH_SIZE
          SCREAMING_SNAKE_CASE 		=MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE 		=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).
     SCREAMING_SNAKE_CASE 		=model.to(accelerator.device     )

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE 		=AdamW(params=model.parameters(),       lr=lowerCAmelCase_     )

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

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     SCREAMING_SNAKE_CASE 		=0
     # We also need to keep track of the stating epoch so files are named properly
     SCREAMING_SNAKE_CASE 		=0

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'     )
               accelerator.load_state(args.resume_from_checkpoint     )
               SCREAMING_SNAKE_CASE 		=os.path.basename(args.resume_from_checkpoint     )
          else:
               # Get the most recent checkpoint
               SCREAMING_SNAKE_CASE 		=[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
               dirs.sort(key=os.path.getctime     )
               SCREAMING_SNAKE_CASE 		=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          SCREAMING_SNAKE_CASE 		=os.path.splitext(lowerCAmelCase_     )[0]

          if "epoch" in training_difference:
               SCREAMING_SNAKE_CASE 		=int(training_difference.replace('epoch_',       ''     )     ) + 1
               SCREAMING_SNAKE_CASE 		=None
          else:
               SCREAMING_SNAKE_CASE 		=int(training_difference.replace('step_',       ''     )     )
               SCREAMING_SNAKE_CASE 		=resume_step // len(lowerCAmelCase_     )
               resume_step -= starting_epoch * len(lowerCAmelCase_     )

    # Now we train the model
     for epoch in range(lowerCAmelCase_,       lowerCAmelCase_     ):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               SCREAMING_SNAKE_CASE 		=accelerator.skip_first_batches(lowerCAmelCase_,       lowerCAmelCase_     )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               SCREAMING_SNAKE_CASE 		=train_dataloader
          for step, batch in enumerate(lowerCAmelCase_     ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device     )
               SCREAMING_SNAKE_CASE 		=model(**lowerCAmelCase_     )
               SCREAMING_SNAKE_CASE 		=outputs.loss
               SCREAMING_SNAKE_CASE 		=loss / gradient_accumulation_steps
               accelerator.backward(lowerCAmelCase_     )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
                    SCREAMING_SNAKE_CASE 		=F'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              SCREAMING_SNAKE_CASE 		=os.path.join(args.output_dir,       lowerCAmelCase_     )
                         accelerator.save_state(lowerCAmelCase_     )

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

          SCREAMING_SNAKE_CASE 		=metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:',       lowerCAmelCase_     )

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               SCREAMING_SNAKE_CASE 		=F'epoch_{epoch}'
               if args.output_dir is not None:
                    SCREAMING_SNAKE_CASE 		=os.path.join(args.output_dir,       lowerCAmelCase_     )
               accelerator.save_state(lowerCAmelCase_     )







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=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.',       )
     parser.add_argument('--cpu',       action='store_true',       help='If passed, will train on the CPU.'     )
     parser.add_argument(
         '--checkpointing_steps',       type=lowerCAmelCase_,       default=lowerCAmelCase_,       help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',       )
     parser.add_argument(
         '--output_dir',       type=lowerCAmelCase_,       default='.',       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',       )
     parser.add_argument(
         '--resume_from_checkpoint',       type=lowerCAmelCase_,       default=lowerCAmelCase_,       help='If the training should continue from a checkpoint folder.',       )
     SCREAMING_SNAKE_CASE 		=parser.parse_args()
     SCREAMING_SNAKE_CASE 		={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowerCAmelCase_,       lowerCAmelCase_     )


if __name__ == "__main__":
 main()




import os







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=os.path.dirname(os.path.realpath(lowerCAmelCase_     )     )
     SCREAMING_SNAKE_CASE 		=os.path.join(lowerCAmelCase_,       'triangle.txt'     )

     with open(lowerCAmelCase_     ) as f:
          SCREAMING_SNAKE_CASE 		=f.readlines()

     SCREAMING_SNAKE_CASE 		=[[int(lowerCAmelCase_     ) for y in x.rstrip('\r\n'     ).split(' '     )] for x in triangle]

     for i in range(1,       len(lowerCAmelCase_     )     ):
          for j in range(len(a[i]     )     ):
               SCREAMING_SNAKE_CASE 		=a[i - 1][j] if j != len(a[i - 1]     ) else 0
               SCREAMING_SNAKE_CASE 		=a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(lowerCAmelCase_,       lowerCAmelCase_     )
     return max(a[-1]     )


if __name__ == "__main__":
 print(solution())

