


'''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							:   List[Any]  						=	16
lowerCamelCase							:   Tuple  						=	32

def _SCREAMING_SNAKE_CASE      (A  , A = 16  , A = "bert-base-cased"       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= AutoTokenizer.from_pretrained(A       )
       lowercase__      			= load_dataset('''glue'''  , '''mrpc'''       )

       def tokenize_function(A       ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__      			= 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
       lowercase__      			= 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
       lowercase__      			= 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.
       lowercase__      			= DataLoader(
           tokenized_datasets['''train''']  , shuffle=A  , collate_fn=A  , batch_size=A       )
       lowercase__      			= DataLoader(
           tokenized_datasets['''validation''']  , shuffle=A  , collate_fn=A  , batch_size=A       )

       return train_dataloader, eval_dataloader

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A       )       ->       List[str]:






       """simple docstring"""







       model.eval()
       lowercase__      			= 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():
                     lowercase__      			= model(**A       )
              lowercase__      			= outputs.logits.argmax(dim=-1       )
              # It is slightly faster to call this once, than multiple times
              lowercase__			,lowercase__      			= 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:
                            lowercase__      			= predictions[: len(eval_dataloader.dataset       ) - samples_seen]
                            lowercase__      			= references[: len(eval_dataloader.dataset       ) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=A  , references=A  , )

       lowercase__      			= metric.compute()
       return eval_metric["accuracy"]

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       int:






       """simple docstring"""







       lowercase__      			= Accelerator()

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

       set_seed(A       )
       lowercase__			,lowercase__      			= get_dataloaders(A  , A  , A       )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__      			= AutoModelForSequenceClassification.from_pretrained(A  , return_dict=A       )

       # Instantiate optimizer
       lowercase__      			= (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__      			= optimizer_cls(params=model.parameters()  , lr=A       )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__      			= accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              lowercase__      			= 1
       lowercase__      			= (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
       ):
              lowercase__      			= get_linear_schedule_with_warmup(
                  optimizer=A  , num_warmup_steps=0  , num_training_steps=A  , )
       else:
              lowercase__      			= 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.
       lowercase__			,lowercase__			,lowercase__			,lowercase__			,lowercase__      			= accelerator.prepare(
           A  , A  , A  , A  , A       )

       # We need to keep track of how many total steps we have iterated over
       lowercase__      			= 0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__      			= 0
       lowercase__      			= evaluate.load('''glue'''  , '''mrpc'''       )
       lowercase__      			= num_epochs

       if args.partial_train_epoch is not None:
              lowercase__      			= args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint       )
              lowercase__      			= args.resume_from_checkpoint.split('''epoch_'''       )[1]
              lowercase__      			= ''''''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              lowercase__      			= int(A       ) + 1
              lowercase__      			= evaluation_loop(A  , A  , A  , A       )
              accelerator.print('''resumed checkpoint performance:'''  , A       )
              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:
                     lowercase__      			= json.load(A       )
                     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
       lowercase__      			= {}
       for epoch in range(A  , A       ):
              model.train()
              for step, batch in enumerate(A       ):
                     lowercase__      			= model(**A       )
                     lowercase__      			= outputs.loss
                     lowercase__      			= loss / gradient_accumulation_steps
                     accelerator.backward(A       )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              lowercase__      			= f"epoch_{epoch}"
              lowercase__      			= os.path.join(args.output_dir  , A       )
              accelerator.save_state(A       )
              lowercase__      			= evaluation_loop(A  , A  , A  , A       )
              lowercase__      			= accuracy
              lowercase__      			= lr_scheduler.get_lr()[0]
              lowercase__      			= optimizer.param_groups[0]['''lr''']
              lowercase__      			= epoch
              lowercase__      			= overall_step
              accelerator.print(f"epoch {epoch}:"  , A       )

              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(A  , A       )

def _SCREAMING_SNAKE_CASE      ()       ->       List[str]:






       """simple docstring"""







       lowercase__      			= 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(
           '''--resume_from_checkpoint'''  , type=A  , default=A  , help='''If the training should continue from a checkpoint folder.'''  , )
       parser.add_argument(
           '''--partial_train_epoch'''  , type=A  , default=A  , help='''If passed, the training will stop after this number of epochs.'''  , )
       parser.add_argument(
           '''--num_epochs'''  , type=A  , default=2  , help='''Number of train epochs.'''  , )
       lowercase__      			= parser.parse_args()
       lowercase__      			= {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

       training_function(A  , A       )


if __name__ == "__main__":
 main()











from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						tuple[float, list[float]]:






				"""simple docstring"""


				snake_case_						:    Dict				=  list(range(len(_UpperCamelCase ) ) )
				snake_case_						:    Dict				=  [v / w for v, w in zip(_UpperCamelCase ,				_UpperCamelCase )]
				index.sort(key=lambda _UpperCamelCase : ratio[i] ,				reverse=_UpperCamelCase )

				snake_case_						:    float				=  0
				snake_case_						:    list[float]				=  [0] * len(_UpperCamelCase )
				for i in index:
								if weight[i] <= capacity:
												snake_case_						:    Dict				=  1
												max_value += value[i]
												capacity -= weight[i]
								else:
												snake_case_						:    Union[str, Any]				=  capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()
