
"""simple docstring"""

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     :    int       =			1_6
UpperCamelCase     :    str       =			3_2






def 							A						(    snake_case				:Accelerator    , snake_case				:int = 1_6		)				->     Optional[int]:
 __UpperCamelCase      =	AutoTokenizer.from_pretrained('bert-base-cased'		)
 __UpperCamelCase      =	load_dataset('glue'    , 'mrpc'		)

 def tokenize_function(snake_case				:Dict		):
  # max_length=None => use the model max length (it's actually the default)
  __UpperCamelCase      =	tokenizer(examples['sentence1']    , examples['sentence2']    , truncation=snake_case    , max_length=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(
      snake_case    , batched=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'		)

 def collate_fn(snake_case				:Optional[Any]		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __UpperCamelCase      =	1_2_8 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      =	1_6
  elif accelerator.mixed_precision != "no":
   __UpperCamelCase      =	8
  else:
   __UpperCamelCase      =	None

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

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

 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     :    Optional[int]       =			mocked_dataloaders  # noqa: F811






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Dict		)				->     Any:
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'    , snake_case		) == "1":
  __UpperCamelCase      =	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      =	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		)
 # 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']		)
 set_seed(snake_case		)

 __UpperCamelCase  ,     __UpperCamelCase      =	get_dataloaders(snake_case    , snake_case		)
 __UpperCamelCase      =	evaluate.load('glue'    , 'mrpc'		)

 # 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

 # 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=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=snake_case		)

 # Instantiate scheduler
 __UpperCamelCase      =	get_linear_schedule_with_warmup(
     optimizer=snake_case    , num_warmup_steps=1_0_0    , num_training_steps=(len(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(
     snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)

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

 # Now we train the model
 for epoch in range(snake_case		):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   __UpperCamelCase      =	0
  for step, batch in enumerate(snake_case		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   __UpperCamelCase      =	model(**snake_case		)
   __UpperCamelCase      =	outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   __UpperCamelCase      =	loss / gradient_accumulation_steps
   accelerator.backward(snake_case		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  for step, batch in enumerate(snake_case		):
   # 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      =	model(**snake_case		)
   __UpperCamelCase      =	outputs.logits.argmax(dim=-1		)
   __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.gather_for_metrics((predictions, batch['labels'])		)
   metric.add_batch(
       predictions=snake_case    , references=snake_case    , )

  __UpperCamelCase      =	metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f'epoch {epoch}:'    , snake_case		)

  # 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(snake_case		),
           'epoch': epoch,
       }    , step=snake_case    , )

    # 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 							A						(    )				->     Tuple:
 __UpperCamelCase      =	argparse.ArgumentParser(description='Simple example of training script.'		)
 parser.add_argument(
     '--mixed_precision'    , type=snake_case    , default=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(
     '--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=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': 4_2, 'batch_size': 1_6}
 training_function(snake_case    , snake_case		)


if __name__ == "__main__":
      main()





"""simple docstring"""

UpperCamelCase     :    Union[str, Any]       =			[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 							A						(    snake_case				:Dict    , snake_case				:Tuple    , snake_case				:str    , snake_case				:Optional[int]		)				->     Union[str, Any]:
 # Return True if there is node that has not iterated.
 __UpperCamelCase      =	[False] * len(snake_case		)
 __UpperCamelCase      =	[s]
 __UpperCamelCase      =	True

 while queue:
  __UpperCamelCase      =	queue.pop(0		)
  for ind in range(len(graph[u]		)		):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(snake_case		)
    __UpperCamelCase      =	True
    __UpperCamelCase      =	u

 return visited[t]






def 							A						(    snake_case				:int    , snake_case				:Any    , snake_case				:Union[str, Any]		)				->     Optional[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		))
 __UpperCamelCase      =	0
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[i[:] for i in graph]  # Record original cut, copy.
 while bfs(snake_case    , snake_case    , snake_case    , snake_case		):
  __UpperCamelCase      =	float('Inf'		)
  __UpperCamelCase      =	sink

  while s != source:
   # Find the minimum value in select path
   __UpperCamelCase      =	min(snake_case    , graph[parent[s]][s]		)
   __UpperCamelCase      =	parent[s]

  max_flow += path_flow
  __UpperCamelCase      =	sink

  while v != source:
   __UpperCamelCase      =	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __UpperCamelCase      =	parent[v]

 for i in range(len(snake_case		)		):
  for j in range(len(graph[0]		)		):
   if graph[i][j] == 0 and temp[i][j] > 0:
    res.append((i, j)		)

 return res


if __name__ == "__main__":
      print(mincut(test_graph, source=0, sink=5))



