from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				list[float]	,       lowercase:				Optional[Any]		)     ->					str:







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




def    lowerCAmelCase_  (		lowercase:				list[dict[str, int]]	,       lowercase:				list[float]	,       lowercase:				int		)     ->					int:







 '''simple docstring'''
 for j in range(lowercase		):
  _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Dict    						=			(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_  (		lowercase:				list[dict[str, int]]	,       lowercase:				int	,       lowercase:				int	,       lowercase:				int		)     ->					list[float]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			[float('''inf'''		)] * vertex_count
 _UpperCamelCase:  str    						=			0.0

 for _ in range(vertex_count - 1		):
  for j in range(lowercase		):
   _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

   if distance[u] != float('''inf'''		) and distance[u] + w < distance[v]:
    _UpperCamelCase:  List[Any]    						=			distance[u] + w

 _UpperCamelCase:  List[str]    						=			check_negative_cycle(lowercase	,       lowercase	,       lowercase		)
 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)
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			fname.split(os.path.sep		)[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''	,       lowercase		).groups()[0]




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	Any=None		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			file_names
    _UpperCamelCase:  List[Any]    						=			image_transform
    _UpperCamelCase:  Tuple    						=			label_to_id



   def __len__(						self       :	List[str]				):



    """simple docstring"""



    return len(self.file_names				)



   def __getitem__(						self       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.file_names[idx]
    _UpperCamelCase:  Optional[int]    						=			PIL.Image.open(_lowercase				)
    _UpperCamelCase:  List[str]    						=			raw_image.convert('''RGB'''				)
    if self.image_transform is not None:
     _UpperCamelCase:  Optional[Any]    						=			self.image_transform(_lowercase				)
    _UpperCamelCase:  Tuple    						=			extract_label(_lowercase				)
    if self.label_to_id is not None:
     _UpperCamelCase:  Any    						=			self.label_to_id[label]
    return {"image": image, "label": label}




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Any		)     ->					str:







 '''simple docstring'''
 # Initialize accelerator
 if args.with_tracking:
  _UpperCamelCase:  Tuple    						=			Accelerator(
      cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       log_with='''all'''	,       project_dir=args.project_dir		)
 else:
  _UpperCamelCase:  Dict    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  List[str]    						=			config['''lr''']
 _UpperCamelCase:  int    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''batch_size''']		)
 _UpperCamelCase:  List[str]    						=			config['''image_size''']
 if not isinstance(lowercase	,       (list, tuple)		):
  _UpperCamelCase:  Optional[int]    						=			(image_size, image_size)

 # 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":
   _UpperCamelCase:  Any    						=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _UpperCamelCase:  List[str]    						=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
 else:
  _UpperCamelCase:  Any    						=			None

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

 # Grab all the image filenames
 _UpperCamelCase:  List[str]    						=			[os.path.join(args.data_dir	,       lowercase		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

 # Build the label correspondences
 _UpperCamelCase:  Optional[Any]    						=			[extract_label(lowercase		) for fname in file_names]
 _UpperCamelCase:  int    						=			list(set(lowercase		)		)
 id_to_label.sort()
 _UpperCamelCase:  Tuple    						=			{lbl: i for i, lbl in enumerate(lowercase		)}

 # Set the seed before splitting the data.
 np.random.seed(lowercase		)
 torch.manual_seed(lowercase		)
 torch.cuda.manual_seed_all(lowercase		)

 # Split our filenames between train and validation
 _UpperCamelCase:  List[str]    						=			np.random.permutation(len(lowercase		)		)
 _UpperCamelCase:  Dict    						=			int(0.8 * len(lowercase		)		)
 _UpperCamelCase:  Optional[int]    						=			random_perm[:cut]
 _UpperCamelCase:  Optional[int]    						=			random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _UpperCamelCase:  List[Any]    						=			Compose([RandomResizedCrop(lowercase	,       scale=(0.5, 1.0)		), ToTensor()]		)
 _UpperCamelCase:  int    						=			PetsDataset(
     [file_names[i] for i in train_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # For evaluation, we use a deterministic Resize
 _UpperCamelCase:  Dict    						=			Compose([Resize(lowercase		), ToTensor()]		)
 _UpperCamelCase:  str    						=			PetsDataset([file_names[i] for i in eval_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # Instantiate dataloaders.
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  str    						=			create_model('''resnet50d'''	,       pretrained=lowercase	,       num_classes=len(lowercase		)		)

 # 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[int]    						=			model.to(accelerator.device		)

 # Freezing the base model
 for param in model.parameters():
  _UpperCamelCase:  Optional[Any]    						=			False
 for param in model.get_classifier().parameters():
  _UpperCamelCase:  Optional[int]    						=			True

 # We normalize the batches of images to be a bit faster.
 _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
 _UpperCamelCase:  List[Any]    						=			torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  List[str]    						=			torch.optim.Adam(params=model.parameters()	,       lr=lr / 25		)

 # Instantiate learning rate scheduler
 _UpperCamelCase:  Any    						=			OneCycleLR(optimizer=lowercase	,       max_lr=lowercase	,       epochs=lowercase	,       steps_per_epoch=len(lowercase		)		)

 # 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:  List[str]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)
 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:  Tuple    						=			0
 # We also need to keep track of the starting epoch so files are named properly
 _UpperCamelCase:  List[str]    						=			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:  Union[str, Any]    						=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   _UpperCamelCase:  Union[str, Any]    						=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   _UpperCamelCase:  Union[str, Any]    						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _UpperCamelCase:  List[Any]    						=			os.path.splitext(lowercase		)[0]

  if "epoch" in training_difference:
   _UpperCamelCase:  Any    						=			int(training_difference.replace('''epoch_'''	,       ''''''		)		) + 1
   _UpperCamelCase:  List[str]    						=			None
  else:
   _UpperCamelCase:  int    						=			int(training_difference.replace('''step_'''	,       ''''''		)		)
   _UpperCamelCase:  List[Any]    						=			resume_step // len(lowercase		)
   resume_step -= starting_epoch * len(lowercase		)

    # Now we train the model
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  if args.with_tracking:
   _UpperCamelCase:  Optional[int]    						=			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:  Union[str, Any]    						=			accelerator.skip_first_batches(lowercase	,       lowercase		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _UpperCamelCase:  Optional[int]    						=			train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  int    						=			(batch['''image'''] - mean) / std
   _UpperCamelCase:  List[str]    						=			model(lowercase		)
   _UpperCamelCase:  Dict    						=			torch.nn.functional.cross_entropy(lowercase	,       batch['''label''']		)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowercase		)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
     accelerator.save_state(lowercase		)
  model.eval()
  _UpperCamelCase:  List[str]    						=			0
  _UpperCamelCase:  Union[str, Any]    						=			0
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  Union[str, Any]    						=			(batch['''image'''] - mean) / std
   with torch.no_grad():
    _UpperCamelCase:  List[Any]    						=			model(lowercase		)
   _UpperCamelCase:  Optional[Any]    						=			outputs.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			accelerator.gather_for_metrics((predictions, batch['''label'''])		)
   _UpperCamelCase:  List[str]    						=			predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _UpperCamelCase:  Optional[int]    						=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""		)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(lowercase		),
           '''epoch''': epoch,
       }	,       step=lowercase	,       )
  if checkpointing_steps == "epoch":
   _UpperCamelCase:  Tuple    						=			F"""epoch_{epoch}"""
   if args.output_dir is not None:
    _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       lowercase		)
   accelerator.save_state(lowercase		)

 if args.with_tracking:
  accelerator.end_training()




def    lowerCAmelCase_  (		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument('''--data_dir'''	,       required=lowercase	,       help='''The data folder on disk.'''		)
 parser.add_argument('''--fp16'''	,       action='''store_true'''	,       help='''If passed, will use FP16 training.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       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=lowercase	,       default=lowercase	,       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=lowercase	,       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=lowercase	,       default=lowercase	,       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(
     '''--project_dir'''	,       type=lowercase	,       default='''logs'''	,       help='''Location on where to store experiment tracking logs` and relevent project information'''	,       )
 _UpperCamelCase:  Any    						=			parser.parse_args()
 _UpperCamelCase:  Optional[int]    						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()