

"""simple docstring"""




import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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				,     __UpperCamelCase = 16   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       AutoTokenizer.from_pretrained("""bert-base-cased"""   )
 UpperCAmelCase__       :	int        =       load_dataset("""glue"""				,     """mrpc"""   )

 def tokenize_function(__UpperCamelCase   ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase__       :	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__       :	Optional[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__       :	Tuple        =       tokenized_datasets.rename_column("""label"""				,     """labels"""   )

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

  return tokenizer.pad(
      __UpperCamelCase				,     padding="""longest"""				,     max_length=__UpperCamelCase				,     pad_to_multiple_of=__UpperCamelCase				,     return_tensors="""pt"""				,     )

 # Instantiate dataloaders.
 UpperCAmelCase__       :	int        =       DataLoader(
     tokenized_datasets["""train"""]				,     shuffle=__UpperCamelCase				,     collate_fn=__UpperCamelCase				,     batch_size=__UpperCamelCase   )
 UpperCAmelCase__       :	Dict        =       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				,     __UpperCamelCase   ):
 '''simple docstring'''





 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     __UpperCamelCase   ) == "1":
  UpperCAmelCase__       :	Optional[Any]        =       2
 # Initialize accelerator
 UpperCAmelCase__       :	Tuple        =       Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision   )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCAmelCase__       :	Tuple        =       config["""lr"""]
 UpperCAmelCase__       :	Dict        =       int(config["""num_epochs"""]   )
 UpperCAmelCase__       :	Union[str, Any]        =       int(config["""seed"""]   )
 UpperCAmelCase__       :	Any        =       int(config["""batch_size"""]   )

 UpperCAmelCase__       :	Dict        =       evaluate.load("""glue"""				,     """mrpc"""   )

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=__UpperCamelCase   )
 def inner_training_loop(__UpperCamelCase   ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(__UpperCamelCase   )

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

  # Instantiate optimizer
  UpperCAmelCase__       :	Tuple        =       AdamW(params=model.parameters()				,     lr=__UpperCamelCase   )
  UpperCAmelCase__ ,   UpperCAmelCase__       :	Any        =       get_dataloaders(__UpperCamelCase				,     __UpperCamelCase   )

  # Instantiate scheduler
  UpperCAmelCase__       :	Optional[int]        =       get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase				,     num_warmup_steps=100				,     num_training_steps=(len(__UpperCamelCase   ) * 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__       :	Optional[Any]        =       accelerator.prepare(
      __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )

  # Now we train the model
  for epoch in range(__UpperCamelCase   ):
   model.train()
   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__       :	Dict        =       model(**__UpperCamelCase   )
    UpperCAmelCase__       :	Optional[int]        =       outputs.loss
    accelerator.backward(__UpperCamelCase   )
    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`.
    batch.to(accelerator.device   )
    with torch.no_grad():
     UpperCAmelCase__       :	Dict        =       model(**__UpperCamelCase   )
    UpperCAmelCase__       :	List[Any]        =       outputs.logits.argmax(dim=-1   )
    UpperCAmelCase__ ,   UpperCAmelCase__       :	List[Any]        =       accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
    metric.add_batch(
        predictions=__UpperCamelCase				,     references=__UpperCamelCase				,     )

   UpperCAmelCase__       :	Optional[int]        =       metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"epoch {epoch}:"				,     __UpperCamelCase   )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[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."""   )
 UpperCAmelCase__       :	Optional[Any]        =       parser.parse_args()
 UpperCAmelCase__       :	Tuple        =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(__UpperCamelCase				,     __UpperCamelCase   )


if __name__ == "__main__":
      main()








from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

