



'''simple docstring'''






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase: Union[str, Any]							=						1_6
lowerCAmelCase: Tuple							=						3_2
def 				lowerCamelCase__	(						_A			,      _A = 16			,      _A = "bert-base-cased"    ):
  a						: List[str]					       =					AutoTokenizer.from_pretrained(_A    )
  a						: int					       =					load_dataset('glue'			,      'mrpc'    )

  def tokenize_function(_A    ):
    # max_length=None => use the model max length (it's actually the default)
    a						: Union[str, Any]					       =					tokenizer(examples['sentence1']			,      examples['sentence2']			,      truncation=_A			,      max_length=_A    )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  a						: str					       =					datasets.map(
      _A			,      batched=_A			,      remove_columns=['idx', 'sentence1', 'sentence2']			,      load_from_cache_file=_A    )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a						: Optional[Any]					       =					tokenized_datasets.rename_column('label'			,      'labels'    )

  def collate_fn(_A    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(_A			,      padding='max_length'			,      max_length=128			,      return_tensors='pt'    )
    return tokenizer.pad(_A			,      padding='longest'			,      return_tensors='pt'    )

  # Instantiate dataloaders.
  a						: List[str]					       =					DataLoader(
      tokenized_datasets['train']			,      shuffle=_A			,      collate_fn=_A			,      batch_size=_A    )
  a						: str					       =					DataLoader(
      tokenized_datasets['validation']			,      shuffle=_A			,      collate_fn=_A			,      batch_size=_A    )

  return train_dataloader, eval_dataloader
def 				lowerCamelCase__	(						_A			,      _A    ):
  # Initialize accelerator
  a						: int					       =					Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a						: List[str]					       =					config['lr']
  a						: Any					       =					int(config['num_epochs']    )
  a						: Any					       =					int(config['seed']    )
  a						: Any					       =					int(config['batch_size']    )
  a						: str					       =					args.model_name_or_path

  set_seed(_A    )
  a       ,     a						: Union[str, Any]					       =					get_dataloaders(_A			,      _A			,      _A    )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a						: Union[str, Any]					       =					AutoModelForSequenceClassification.from_pretrained(_A			,      return_dict=_A    )

  # Instantiate optimizer
  a						: Any					       =					(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  a						: str					       =					optimizer_cls(params=model.parameters()			,      lr=_A    )

  if accelerator.state.deepspeed_plugin is not None:
    a						: Tuple					       =					accelerator.state.deepspeed_plugin.deepspeed_config[
        'gradient_accumulation_steps'
    ]
  else:
    a						: Optional[int]					       =					1
  a						: Any					       =					(len(_A    ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    a						: List[str]					       =					get_linear_schedule_with_warmup(
        optimizer=_A			,      num_warmup_steps=0			,      num_training_steps=_A			,      )
  else:
    a						: Any					       =					DummyScheduler(_A			,      total_num_steps=_A			,      warmup_num_steps=0    )

  # 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.
  a       ,     a       ,     a       ,     a       ,     a						: int					       =					accelerator.prepare(
      _A			,      _A			,      _A			,      _A			,      _A    )

  # We need to keep track of how many total steps we have iterated over
  a						: int					       =					0
  # We also need to keep track of the stating epoch so files are named properly
  a						: Optional[Any]					       =					0

  # Now we train the model
  a						: Optional[int]					       =					evaluate.load('glue'			,      'mrpc'    )
  a						: List[Any]					       =					0
  a						: List[str]					       =					{}
  for epoch in range(_A			,      _A    ):
    model.train()
    for step, batch in enumerate(_A    ):
      a						: List[str]					       =					model(**_A    )
      a						: Union[str, Any]					       =					outputs.loss
      a						: Dict					       =					loss / gradient_accumulation_steps
      accelerator.backward(_A    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    a						: Union[str, Any]					       =					0
    for step, batch in enumerate(_A    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      with torch.no_grad():
        a						: Tuple					       =					model(**_A    )
      a						: int					       =					outputs.logits.argmax(dim=-1    )
      # It is slightly faster to call this once, than multiple times
      a       ,     a						: Optional[Any]					       =					accelerator.gather(
          (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(_A    ) - 1:
          a						: int					       =					predictions[: len(eval_dataloader.dataset    ) - samples_seen]
          a						: Any					       =					references[: len(eval_dataloader.dataset    ) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=_A			,      references=_A			,      )

    a						: Any					       =					metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""			,      _A    )
    a						: Union[str, Any]					       =					eval_metric['accuracy']

    if best_performance < eval_metric["accuracy"]:
      a						: List[Any]					       =					eval_metric['accuracy']

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir			,      'all_results.json'    )			,      'w'    ) as f:
      json.dump(_A			,      _A    )
def 				lowerCamelCase__	(						):
  a						: Optional[int]					       =					argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
  parser.add_argument(
      '--model_name_or_path'			,      type=_A			,      default='bert-base-cased'			,      help='Path to pretrained model or model identifier from huggingface.co/models.'			,      required=_A			,      )
  parser.add_argument(
      '--output_dir'			,      type=_A			,      default='.'			,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,      )
  parser.add_argument(
      '--performance_lower_bound'			,      type=_A			,      default=_A			,      help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'			,      )
  parser.add_argument(
      '--num_epochs'			,      type=_A			,      default=3			,      help='Number of train epochs.'			,      )
  a						: Optional[int]					       =					parser.parse_args()
  a						: str					       =					{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
  training_function(_A			,      _A    )


if __name__ == "__main__":
     main()




'''simple docstring'''






import math

class 							a__:







    def __init__(				self			:  int						,      __snake_case			:  Any=0     ):  # a graph with Node 0,1,...,N-1
      a						: List[Any]					       =					n
      a						: Optional[Any]					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # adjacency matrix for weight
      a						: Dict					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # dp[i][j] stores minimum distance from i to j







    def       lowercase_	(				self			:  int						,      __snake_case			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  int     ):
      a						: str					       =					w







    def       lowercase_	(				self			:  int     ):
      for k in range(0						,      self.n     ):
        for i in range(0						,      self.n     ):
          for j in range(0						,      self.n     ):
            a						: str					       =					min(self.dp[i][j]						,      self.dp[i][k] + self.dp[k][j]     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Any						,      __snake_case			:  List[str]     ):
      return self.dp[u][v]


if __name__ == "__main__":
     lowerCAmelCase: Any							=						Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 1_0)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 1_0)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)