





"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowerCamelCase__				  =   16
lowerCamelCase__				  =   32




def 					lowercase__    (					lowercase_		,lowercase_ = 16			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       AutoTokenizer.from_pretrained("bert-base-cased"			)
      _UpperCamelCase      :	Union[str, Any]						=       load_dataset("glue"		,"mrpc"			)

      def tokenize_function(lowercase_			):
            # max_length=None => use the model max length (it's actually the default)
            _UpperCamelCase      :	Union[str, Any]						=       tokenizer(examples["sentence1"]		,examples["sentence2"]		,truncation=lowercase_		,max_length=lowercase_			)
            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      :	Union[str, Any]						=       datasets.map(
                lowercase_		,batched=lowercase_		,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      :	str						=       tokenized_datasets.rename_column("label"		,"labels"			)

      def collate_fn(lowercase_			):
            # 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      :	Any						=       16
            elif accelerator.mixed_precision != "no":
                  _UpperCamelCase      :	List[Any]						=       8
            else:
                  _UpperCamelCase      :	Optional[Any]						=       None

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

      # Instantiate dataloaders.
      _UpperCamelCase      :	str						=       DataLoader(
          tokenized_datasets["train"]		,shuffle=lowercase_		,collate_fn=lowercase_		,batch_size=lowercase_			)
      _UpperCamelCase      :	Optional[Any]						=       DataLoader(
          tokenized_datasets["validation"]		,shuffle=lowercase_		,collate_fn=lowercase_		,batch_size=lowercase_			)

      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

  lowerCamelCase__				  =   mocked_dataloaders  # noqa: F811




def 					lowercase__    (					lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

      if os.environ.get("TESTING_MOCKED_DATALOADERS"		,lowercase_			) == "1":
            _UpperCamelCase      :	Optional[Any]						=       2
      # Initialize accelerator
      _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      :	Tuple						=       config["lr"]
      _UpperCamelCase      :	Any						=       int(config["num_epochs"]			)
      _UpperCamelCase      :	Optional[Any]						=       int(config["seed"]			)
      _UpperCamelCase      :	Optional[Any]						=       int(config["batch_size"]			)

      _UpperCamelCase      :	Tuple						=       evaluate.load("glue"		,"mrpc"			)

      # If the batch size is too big we use gradient accumulation
      _UpperCamelCase      :	Union[str, Any]						=       1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            _UpperCamelCase      :	List[str]						=       batch_size // MAX_GPU_BATCH_SIZE
            _UpperCamelCase      :	Dict						=       MAX_GPU_BATCH_SIZE

      set_seed(lowercase_			)
      _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       get_dataloaders(lowercase_		,lowercase_			)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _UpperCamelCase      :	List[Any]						=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,return_dict=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      :	List[str]						=       model.to(accelerator.device			)

      # Instantiate optimizer
      _UpperCamelCase      :	Any						=       AdamW(params=model.parameters()		,lr=lowercase_			)

      # Instantiate scheduler
      _UpperCamelCase      :	Any						=       get_linear_schedule_with_warmup(
          optimizer=lowercase_		,num_warmup_steps=100		,num_training_steps=(len(lowercase_			) * 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      :	List[str]						=       accelerator.prepare(
          lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_			)

      # Now we train the model
      for epoch in range(lowercase_			):
            model.train()
            for step, batch in enumerate(lowercase_			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device			)
                  _UpperCamelCase      :	List[str]						=       model(**lowercase_			)
                  _UpperCamelCase      :	str						=       outputs.loss
                  _UpperCamelCase      :	Tuple						=       loss / gradient_accumulation_steps
                  accelerator.backward(lowercase_			)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            _UpperCamelCase      :	Dict						=       0
            for step, batch in enumerate(lowercase_			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device			)
                  with torch.no_grad():
                        _UpperCamelCase      :	List[str]						=       model(**lowercase_			)
                  _UpperCamelCase      :	List[Any]						=       outputs.logits.argmax(dim=-1			)
                  _UpperCamelCase,  _UpperCamelCase      :	Any						=       accelerator.gather((predictions, batch["labels"])			)
                  # New Code #
                  # First we check if it's a distributed system
                  if accelerator.use_distributed:
                        # Then see if we're on the last batch of our eval dataloader
                        if step == len(lowercase_			) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              _UpperCamelCase      :	Dict						=       predictions[: len(eval_dataloader.dataset			) - samples_seen]
                              _UpperCamelCase      :	List[str]						=       references[: len(eval_dataloader.dataset			) - samples_seen]
                        else:
                              # Otherwise we add the number of samples seen
                              samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                  metric.add_batch(
                      predictions=lowercase_		,references=lowercase_		,)

            _UpperCamelCase      :	Tuple						=       metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''		,lowercase_			)




def 					lowercase__    (					)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       argparse.ArgumentParser(description="Simple example of training script."			)
      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."			)
      _UpperCamelCase      :	Optional[Any]						=       parser.parse_args()
      _UpperCamelCase      :	Any						=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(lowercase_		,lowercase_			)


if __name__ == "__main__":
  main()







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
