



"""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


_UpperCAmelCase     =			1_6
_UpperCAmelCase     =			3_2





def      __magic_name__				(							lowercase							,			lowercase = 16							,			lowercase = "bert-base-cased" ):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=AutoTokenizer.from_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""glue"""							,			"""mrpc""" )

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Tuple     			=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
   SCREAMING_SNAKE_CASE_: List[str]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""idx""", """sentence1""", """sentence2"""]							,			load_from_cache_file=lowercase )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   SCREAMING_SNAKE_CASE_: Dict     			=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.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(
       tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=lowercase )
   SCREAMING_SNAKE_CASE_: Tuple     			=DataLoader(
       tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=lowercase )

   return train_dataloader, eval_dataloader





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   model.eval()
   SCREAMING_SNAKE_CASE_: str     			=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():
         SCREAMING_SNAKE_CASE_: int     			=model(**lowercase )
      SCREAMING_SNAKE_CASE_: List[Any]     			=outputs.logits.argmax(dim=-1 )
      # It is slightly faster to call this once, than multiple times
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=accelerator.gather(
          (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(lowercase ) - 1:
            SCREAMING_SNAKE_CASE_: Optional[int]     			=predictions[: len(eval_dataloader.dataset ) - samples_seen]
            SCREAMING_SNAKE_CASE_: str     			=references[: len(eval_dataloader.dataset ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=lowercase							,			references=lowercase							,			)

   SCREAMING_SNAKE_CASE_: str     			=metric.compute()
   return eval_metric["accuracy"]





def      __magic_name__				(							lowercase							,			lowercase ):
   # Initialize accelerator
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Accelerator()

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

   set_seed(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=get_dataloaders(lowercase							,			lowercase							,			lowercase )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   SCREAMING_SNAKE_CASE_: List[Any]     			=AutoModelForSequenceClassification.from_pretrained(lowercase							,			return_dict=lowercase )

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

   if accelerator.state.deepspeed_plugin is not None:
      SCREAMING_SNAKE_CASE_: Dict     			=accelerator.state.deepspeed_plugin.deepspeed_config[
          """gradient_accumulation_steps"""
      ]
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=1
   SCREAMING_SNAKE_CASE_: str     			=(len(lowercase ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      SCREAMING_SNAKE_CASE_: Any     			=get_linear_schedule_with_warmup(
          optimizer=lowercase							,			num_warmup_steps=0							,			num_training_steps=lowercase							,			)
   else:
      SCREAMING_SNAKE_CASE_: Any     			=DummyScheduler(lowercase							,			total_num_steps=lowercase							,			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.
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=accelerator.prepare(
       lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase )

   # We need to keep track of how many total steps we have iterated over
   SCREAMING_SNAKE_CASE_: str     			=0
   # We also need to keep track of the stating epoch so files are named properly
   SCREAMING_SNAKE_CASE_: int     			=0
   SCREAMING_SNAKE_CASE_: int     			=evaluate.load("""glue"""							,			"""mrpc""" )
   SCREAMING_SNAKE_CASE_: List[Any]     			=num_epochs

   if args.partial_train_epoch is not None:
      SCREAMING_SNAKE_CASE_: int     			=args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint )
      SCREAMING_SNAKE_CASE_: int     			=args.resume_from_checkpoint.split("""epoch_""" )[1]
      SCREAMING_SNAKE_CASE_: Optional[int]     			=""""""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      SCREAMING_SNAKE_CASE_: int     			=int(lowercase ) + 1
      SCREAMING_SNAKE_CASE_: Dict     			=evaluation_loop(lowercase							,			lowercase							,			lowercase							,			lowercase )
      accelerator.print("""resumed checkpoint performance:"""							,			lowercase )
      accelerator.print("""resumed checkpoint's scheduler's lr:"""							,			lr_scheduler.get_lr()[0] )
      accelerator.print("""resumed optimizers's lr:"""							,			optimizer.param_groups[0]["""lr"""] )
      with open(os.path.join(args.output_dir							,			f'''state_{starting_epoch-1}.json''' )							,			"""r""" ) as f:
         SCREAMING_SNAKE_CASE_: List[Any]     			=json.load(lowercase )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   SCREAMING_SNAKE_CASE_: Dict     			={}
   for epoch in range(lowercase							,			lowercase ):
      model.train()
      for step, batch in enumerate(lowercase ):
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(**lowercase )
         SCREAMING_SNAKE_CASE_: str     			=outputs.loss
         SCREAMING_SNAKE_CASE_: List[str]     			=loss / gradient_accumulation_steps
         accelerator.backward(lowercase )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      SCREAMING_SNAKE_CASE_: int     			=f'''epoch_{epoch}'''
      SCREAMING_SNAKE_CASE_: Optional[int]     			=os.path.join(args.output_dir							,			lowercase )
      accelerator.save_state(lowercase )
      SCREAMING_SNAKE_CASE_: Optional[int]     			=evaluation_loop(lowercase							,			lowercase							,			lowercase							,			lowercase )
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=accuracy
      SCREAMING_SNAKE_CASE_: str     			=lr_scheduler.get_lr()[0]
      SCREAMING_SNAKE_CASE_: List[str]     			=optimizer.param_groups[0]["""lr"""]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=epoch
      SCREAMING_SNAKE_CASE_: str     			=overall_step
      accelerator.print(f'''epoch {epoch}:'''							,			lowercase )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir							,			f'''state_{epoch}.json''' )							,			"""w""" ) as f:
            json.dump(lowercase							,			lowercase )





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Tuple     			=argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
   parser.add_argument(
       """--model_name_or_path"""							,			type=lowercase							,			default="""bert-base-cased"""							,			help="""Path to pretrained model or model identifier from huggingface.co/models."""							,			required=lowercase							,			)
   parser.add_argument(
       """--output_dir"""							,			type=lowercase							,			default="""."""							,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,			)
   parser.add_argument(
       """--resume_from_checkpoint"""							,			type=lowercase							,			default=lowercase							,			help="""If the training should continue from a checkpoint folder."""							,			)
   parser.add_argument(
       """--partial_train_epoch"""							,			type=lowercase							,			default=lowercase							,			help="""If passed, the training will stop after this number of epochs."""							,			)
   parser.add_argument(
       """--num_epochs"""							,			type=lowercase							,			default=2							,			help="""Number of train epochs."""							,			)
   SCREAMING_SNAKE_CASE_: List[str]     			=parser.parse_args()
   SCREAMING_SNAKE_CASE_: List[str]     			={"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

   training_function(lowercase							,			lowercase )


if __name__ == "__main__":
   main()








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



