



"""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__              =							16
lowerCAmelCase__              =							32






def 			snake_case_    ( A_   :						Accelerator,							A_   :						int = 16,							A_   :						str = "bert-base-cased"		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					AutoTokenizer.from_pretrained(A_		)
       _lowerCamelCase		:							Tuple            =					load_dataset('''glue''',							'''mrpc'''		)

       def tokenize_function(A_   :						Dict		):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase		:							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
       _lowerCamelCase		:							Any            =					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
       _lowerCamelCase		:							List[Any]            =					tokenized_datasets.rename_column('''label''',							'''labels'''		)

       def collate_fn(A_   :						Tuple		):
              # 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=1_28,							return_tensors='''pt'''		)
              return tokenizer.pad(A_,							padding='''longest''',							return_tensors='''pt'''		)

       # Instantiate dataloaders.
       _lowerCamelCase		:							Optional[Any]            =					DataLoader(
           tokenized_datasets['''train'''],							shuffle=A_,							collate_fn=A_,							batch_size=A_		)
       _lowerCamelCase		:							int            =					DataLoader(
           tokenized_datasets['''validation'''],							shuffle=A_,							collate_fn=A_,							batch_size=A_		)

       return train_dataloader, eval_dataloader






def 			snake_case_    ( A_   :						List[str],							A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					Accelerator()

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

       set_seed(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					get_dataloaders(A_,							A_,							A_		)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCamelCase		:							Any            =					accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              _lowerCamelCase		:							Union[str, Any]            =					1
       _lowerCamelCase		:							Tuple            =					(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
       ):
              _lowerCamelCase		:							Union[str, Any]            =					get_linear_schedule_with_warmup(
                  optimizer=A_,							num_warmup_steps=0,							num_training_steps=A_,							)
       else:
              _lowerCamelCase		:							Tuple            =					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.
       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					accelerator.prepare(
           A_,							A_,							A_,							A_,							A_		)

       # We need to keep track of how many total steps we have iterated over
       _lowerCamelCase		:							Union[str, Any]            =					0
       # We also need to keep track of the stating epoch so files are named properly
       _lowerCamelCase		:							Tuple            =					0

       # Now we train the model
       _lowerCamelCase		:							int            =					evaluate.load('''glue''',							'''mrpc'''		)
       _lowerCamelCase		:							Union[str, Any]            =					0
       _lowerCamelCase		:							List[str]            =					{}
       for epoch in range(A_,							A_		):
              model.train()
              for step, batch in enumerate(A_		):
                     _lowerCamelCase		:							Tuple            =					model(**A_		)
                     _lowerCamelCase		:							List[str]            =					outputs.loss
                     _lowerCamelCase		:							Any            =					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()
              _lowerCamelCase		:							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():
                            _lowerCamelCase		:							str            =					model(**A_		)
                     _lowerCamelCase		:							Tuple            =					outputs.logits.argmax(dim=-1		)
                     # It is slightly faster to call this once, than multiple times
                     _lowerCamelCase     ,       _lowerCamelCase		:							Tuple            =					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:
                                   _lowerCamelCase		:							List[Any]            =					predictions[: len(eval_dataloader.dataset		) - samples_seen]
                                   _lowerCamelCase		:							Optional[Any]            =					references[: len(eval_dataloader.dataset		) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=A_,							references=A_,							)

              _lowerCamelCase		:							Optional[Any]            =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:''',							A_		)
              _lowerCamelCase		:							Optional[Any]            =					eval_metric['''accuracy''']

              if best_performance < eval_metric["accuracy"]:
                     _lowerCamelCase		:							Optional[int]            =					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 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[Any]            =					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.''',							)
       _lowerCamelCase		:							Tuple            =					parser.parse_args()
       _lowerCamelCase		:							str            =					{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(A_,							A_		)


if __name__ == "__main__":
   main()









__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
