


'''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    :     int					=						16
lowerCAmelCase    :     List[str]					=						32






def 			lowercase				(_A   ,						_A = 1_6   ,						_A = "bert-base-cased"					):







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       AutoTokenizer.from_pretrained(A_					)
       _lowerCAmelCase    :							Optional[Any]   			=       load_dataset('glue'   ,						'mrpc'					)

       def tokenize_function(_A					):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCAmelCase    :							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
       _lowerCAmelCase    :							Union[str, 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    :							Dict   			=       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=1_2_8   ,						return_tensors='pt'					)
              return tokenizer.pad(A_   ,						padding='longest'   ,						return_tensors='pt'					)

       # Instantiate dataloaders.
       _lowerCAmelCase    :							Dict   			=       DataLoader(
           tokenized_datasets['train']   ,						shuffle=A_   ,						collate_fn=A_   ,						batch_size=A_					)
       _lowerCAmelCase    :							Dict   			=       DataLoader(
           tokenized_datasets['validation']   ,						shuffle=A_   ,						collate_fn=A_   ,						batch_size=A_					)

       return train_dataloader, eval_dataloader






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       Accelerator()

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

       set_seed(A_					)
       _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       get_dataloaders(A_   ,						A_   ,						A_					)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCAmelCase    :							str   			=       accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              _lowerCAmelCase    :							Optional[int]   			=       1
       _lowerCAmelCase    :							str   			=       (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    :							int   			=       get_linear_schedule_with_warmup(
                  optimizer=A_   ,						num_warmup_steps=0   ,						num_training_steps=A_   ,						)
       else:
              _lowerCAmelCase    :							Optional[int]   			=       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    :							Dict   			=       accelerator.prepare(
           A_   ,						A_   ,						A_   ,						A_   ,						A_					)

       # We need to keep track of how many total steps we have iterated over
       _lowerCAmelCase    :							List[str]   			=       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    :							Dict   			=       evaluate.load('glue'   ,						'mrpc'					)
       _lowerCAmelCase    :							Any   			=       0
       _lowerCAmelCase    :							List[str]   			=       {}
       for epoch in range(A_   ,						A_					):
              model.train()
              for step, batch in enumerate(A_					):
                     _lowerCAmelCase    :							Dict   			=       model(**A_					)
                     _lowerCAmelCase    :							Union[str, Any]   			=       outputs.loss
                     _lowerCAmelCase    :							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()
              _lowerCAmelCase    :							str   			=       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    :							Any   			=       model(**A_					)
                     _lowerCAmelCase    :							List[Any]   			=       outputs.logits.argmax(dim=-1					)
                     # It is slightly faster to call this once, than multiple times
                     _lowerCAmelCase			,     _lowerCAmelCase    :							List[str]   			=       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    :							Tuple   			=       predictions[: len(eval_dataloader.dataset					) - samples_seen]
                                   _lowerCAmelCase    :							Optional[int]   			=       references[: len(eval_dataloader.dataset					) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=A_   ,						references=A_   ,						)

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

              if best_performance < eval_metric["accuracy"]:
                     _lowerCAmelCase    :							Dict   			=       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 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, 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    :							Optional[int]   			=       parser.parse_args()
       _lowerCAmelCase    :							List[Any]   			=       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
       training_function(A_   ,						A_					)


if __name__ == "__main__":
       main()





'''simple docstring'''







lowerCAmelCase    :     Optional[int]					=						[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       0
       while number:
              # Increased Speed Slightly by checking every 5 digits together.
              sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
              number //= 1_0_0_0_0_0

       return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase    :     list[bool | None]					=						[None] * 10_00_00_00
lowerCAmelCase    :     List[str]					=						True
lowerCAmelCase    :     Union[str, Any]					=						False






def 			lowercase				(_A					):







       """simple docstring"""







       if CHAINS[number - 1] is not None:
              return CHAINS[number - 1]  # type: ignore

       _lowerCAmelCase    :							Any   			=       chain(next_number(_A					)					)
       _lowerCAmelCase    :							List[str]   			=       number_chain

       while number < 1_0_0_0_0_0_0_0:
              _lowerCAmelCase    :							Tuple   			=       number_chain
              number *= 1_0

       return number_chain






def 			lowercase				(_A = 1_0_0_0_0_0_0_0					):







       """simple docstring"""







       for i in range(1   ,						_A					):
              if CHAINS[i] is None:
                     chain(i + 1					)

       return CHAINS[:number].count(_A					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(F'''{solution() = }''')

