





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


lowercase :			str     		=    1_6
lowercase :			List[Any]     		=    3_2



def A_      (						A__						, A__ = 16						, A__ = "bert-base-cased"							)    ->						int:
       a__		:      List[Any]					=							AutoTokenizer.from_pretrained(A__							)
       a__		:      Any					=							load_dataset('glue'						, 'mrpc'							)

       def tokenize_function(A__							):
              # max_length=None => use the model max length (it's actually the default)
              a__		:      str					=							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__		:      List[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__		:      List[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__		:      Dict					=							DataLoader(
           tokenized_datasets['train']						, shuffle=A__						, collate_fn=A__						, batch_size=A__							)
       a__		:      List[Any]					=							DataLoader(
           tokenized_datasets['validation']						, shuffle=A__						, collate_fn=A__						, batch_size=A__							)

       return train_dataloader, eval_dataloader



def A_      (						A__						, A__							)    ->						int:
       # Initialize accelerator
       a__		:      Tuple					=							Accelerator()

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

       set_seed(A__							)
       a__		:      Optional[Any]					=							get_dataloaders(A__						, A__						, A__							)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__		:      int					=							AutoModelForSequenceClassification.from_pretrained(A__						, return_dict=A__							)

       # Instantiate optimizer
       a__		:      Tuple					=							(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       a__		:      Tuple					=							optimizer_cls(params=model.parameters()						, lr=A__							)

       if accelerator.state.deepspeed_plugin is not None:
              a__		:      int					=							accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              a__		:      List[Any]					=							1
       a__		:      int					=							(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__		:      Any					=							get_linear_schedule_with_warmup(
                  optimizer=A__						, num_warmup_steps=0						, num_training_steps=A__						, )
       else:
              a__		:      List[str]					=							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__		:      Any					=							accelerator.prepare(
           A__						, A__						, A__						, A__						, A__							)

       # We need to keep track of how many total steps we have iterated over
       a__		:      Tuple					=							0
       # We also need to keep track of the stating epoch so files are named properly
       a__		:      Dict					=							0

       # Now we train the model
       a__		:      Any					=							evaluate.load('glue'						, 'mrpc'							)
       a__		:      Dict					=							0
       a__		:      Optional[Any]					=							{}
       for epoch in range(A__						, A__							):
              model.train()
              for step, batch in enumerate(A__							):
                     a__		:      str					=							model(**A__							)
                     a__		:      List[Any]					=							outputs.loss
                     a__		:      Optional[int]					=							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__		:      Tuple					=							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__		:      str					=							model(**A__							)
                     a__		:      str					=							outputs.logits.argmax(dim=-1							)
                     # It is slightly faster to call this once, than multiple times
                     a__		:      Union[str, 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__		:      List[str]					=							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__		:      Optional[Any]					=							metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}:'						, A__							)
              a__		:      List[Any]					=							eval_metric["""accuracy"""]

              if best_performance < eval_metric["accuracy"]:
                     a__		:      str					=							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 A_      (						)    ->						str:
       a__		:      Dict					=							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__		:      str					=							parser.parse_args()
       a__		:      Optional[Any]					=							{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(A__						, A__							)


if __name__ == "__main__":
    main()






def    __lowerCamelCase   (      A__						: int						,						A__						: int					)     ->    int:
   return int((input_a, input_a).count(0					) == 0					)


def    __lowerCamelCase   (      )     ->    None:
   assert and_gate(0						,						0					) == 0
   assert and_gate(0						,						1					) == 0
   assert and_gate(1						,						0					) == 0
   assert and_gate(1						,						1					) == 1


if __name__ == "__main__":
      test_and_gate()
      print(and_gate(1, 0))
      print(and_gate(0, 0))
      print(and_gate(0, 1))
      print(and_gate(1, 1))





