





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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     A__					(						SCREAMING_SNAKE_CASE_      :			List[str]							,  SCREAMING_SNAKE_CASE_      :			List[Any]	)     ->     List[str]:






   """simple docstring"""







   if args.with_tracking:
      _UpperCAmelCase											=      Accelerator(
          cpu=args.cpu							,  mixed_precision=args.mixed_precision							,  log_with='''all'''							,  project_dir=args.project_dir	)
   else:
      _UpperCAmelCase											=      Accelerator(cpu=args.cpu							,  mixed_precision=args.mixed_precision	)

   if hasattr(args.checkpointing_steps							,  '''isdigit'''	):
      if args.checkpointing_steps == "epoch":
         _UpperCAmelCase											=      args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         _UpperCAmelCase											=      int(args.checkpointing_steps	)
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
   else:
      _UpperCAmelCase											=      None
   # 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''']	)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      _UpperCAmelCase											=      os.path.split(SCREAMING_SNAKE_CASE_	)[-1].split('''.'''	)[0]
      accelerator.init_trackers(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      AutoTokenizer.from_pretrained('''bert-base-cased'''	)
   _UpperCAmelCase											=      load_dataset('''glue'''							,  '''mrpc'''	)
   _UpperCAmelCase											=      evaluate.load('''glue'''							,  '''mrpc'''	)

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

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

   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.
      _UpperCAmelCase											=      1_28 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(
          SCREAMING_SNAKE_CASE_							,  padding='''longest'''							,  max_length=SCREAMING_SNAKE_CASE_							,  pad_to_multiple_of=SCREAMING_SNAKE_CASE_							,  return_tensors='''pt'''							,  )

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

   set_seed(SCREAMING_SNAKE_CASE_	)

   # 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=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).
   _UpperCAmelCase											=      model.to(accelerator.device	)

   # Instantiate optimizer
   _UpperCAmelCase											=      AdamW(params=model.parameters()							,  lr=SCREAMING_SNAKE_CASE_	)

   # Instantiate scheduler
   _UpperCAmelCase											=      get_linear_schedule_with_warmup(
       optimizer=SCREAMING_SNAKE_CASE_							,  num_warmup_steps=1_00							,  num_training_steps=(len(SCREAMING_SNAKE_CASE_	) * 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											=      accelerator.prepare(
       SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)

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

   # Potentially load in the weights and states from a previous save
   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	)
         _UpperCAmelCase											=      os.path.basename(args.resume_from_checkpoint	)
      else:
         # Get the most recent checkpoint
         _UpperCAmelCase											=      [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
         dirs.sort(key=os.path.getctime	)
         _UpperCAmelCase											=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      _UpperCAmelCase											=      os.path.splitext(SCREAMING_SNAKE_CASE_	)[0]

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

    # Now we train the model
   for epoch in range(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      model.train()
      if args.with_tracking:
         _UpperCAmelCase											=      0
      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
         _UpperCAmelCase											=      accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         _UpperCAmelCase											=      train_dataloader
      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	)
         _UpperCAmelCase											=      model(**SCREAMING_SNAKE_CASE_	)
         _UpperCAmelCase											=      outputs.loss
         _UpperCAmelCase											=      loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(SCREAMING_SNAKE_CASE_	)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
            _UpperCAmelCase											=      F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  _UpperCAmelCase											=      os.path.join(args.output_dir							,  SCREAMING_SNAKE_CASE_	)
               accelerator.save_state(SCREAMING_SNAKE_CASE_	)

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

      _UpperCAmelCase											=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''							,  SCREAMING_SNAKE_CASE_	)
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE_	),
                 '''epoch''': epoch,
             }							,  step=SCREAMING_SNAKE_CASE_							,  )

      if checkpointing_steps == "epoch":
         _UpperCAmelCase											=      F'''epoch_{epoch}'''
         if args.output_dir is not None:
            _UpperCAmelCase											=      os.path.join(args.output_dir							,  SCREAMING_SNAKE_CASE_	)
         accelerator.save_state(SCREAMING_SNAKE_CASE_	)

   if args.with_tracking:
      accelerator.end_training()
def     A__					(						)     ->     int:






   """simple docstring"""







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


if __name__ == "__main__":
   main()






def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     List[str]:






   """simple docstring"""







   print('''\nVertex Distance'''	)
   for i in range(SCREAMING_SNAKE_CASE_	):
      if dist[i] != float('''inf'''	):
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  int(dist[i]	)							,  end='''\t'''	)
      else:
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  '''INF'''							,  end='''\t'''	)
      print()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      float('''inf'''	)
   _UpperCAmelCase											=      -1
   for i in range(SCREAMING_SNAKE_CASE_	):
      if (not vset[i]) and mdist[i] < min_val:
         _UpperCAmelCase											=      i
         _UpperCAmelCase											=      mdist[i]
   return min_ind
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      [float('''inf'''	) for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      [False for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      0.0

   for _ in range(v - 1	):
      _UpperCAmelCase											=      min_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      _UpperCAmelCase											=      True

      for i in range(SCREAMING_SNAKE_CASE_	):
         if (
             (not vset[i])
             and graph[u][i] != float('''inf'''	)
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _UpperCAmelCase											=      mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  int(input("Enter number of vertices: ").strip())
   UpperCAmelCase_											=  int(input("Enter number of edges: ").strip())

   UpperCAmelCase_											=  [[float("inf") for i in range(V)] for j in range(V)]

   for i in range(V):
      UpperCAmelCase_											=  0.0

   for i in range(E):
      print("\nEdge ", i + 1)
      UpperCAmelCase_											=  int(input("Enter source:").strip())
      UpperCAmelCase_											=  int(input("Enter destination:").strip())
      UpperCAmelCase_											=  float(input("Enter weight:").strip())
      UpperCAmelCase_											=  weight

   UpperCAmelCase_											=  int(input("\nEnter shortest path source:").strip())
   dijkstra(graph, V, gsrc)