





"""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


A 			=  16
A 			=  32

def     __A      (			a_						:Accelerator   ,       a_						:int = 16   ,       a_						:str = "bert-base-cased")		->		Any:
    __a :						Union[str, Any]			       =  AutoTokenizer.from_pretrained(a_)
    __a :						Optional[int]			       =  load_dataset('''glue'''   ,       '''mrpc''')

    def tokenize_function(a_						:List[Any]):
        # max_length=None => use the model max length (it's actually the default)
        __a :						Tuple			       =  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 :						Optional[int]			       =  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 :						Any			       =  tokenized_datasets.rename_column('''label'''   ,       '''labels''')

    def collate_fn(a_						:Dict):
        # 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.
    __a :						int			       =  DataLoader(
        tokenized_datasets['''train''']   ,       shuffle=a_   ,       collate_fn=a_   ,       batch_size=a_)
    __a :						int			       =  DataLoader(
        tokenized_datasets['''validation''']   ,       shuffle=a_   ,       collate_fn=a_   ,       batch_size=a_)

    return train_dataloader, eval_dataloader

def     __A      (			a_						:Optional[Any]   ,       a_						:Union[str, Any])		->		List[Any]:
    # Initialize accelerator
    __a :						str			       =  Accelerator()

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

    set_seed(a_)
    __a :						List[Any]			       =  get_dataloaders(a_   ,       a_   ,       a_)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __a :						List[str]			       =  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 :						Any			       =  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 :						Dict			       =  1
    __a :						List[Any]			       =  (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 :						Optional[int]			       =  get_linear_schedule_with_warmup(
            optimizer=a_   ,       num_warmup_steps=0   ,       num_training_steps=a_   ,       )
    else:
        __a :						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.
    __a :						Tuple			       =  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 :						Tuple			       =  0

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

        if best_performance < eval_metric["accuracy"]:
            __a :						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     __A      (			)		->		Union[str, Any]:
    __a :						str			       =  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 :						Optional[Any]			       =  parser.parse_args()
    __a :						Optional[int]			       =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(a_   ,       a_)


if __name__ == "__main__":
  main()






"""simple docstring"""






from math import isqrt, loga

def     __A      (			a_						:int)		->		list[int]:
				__a :						int			       =  [True] * max_number
				for i in range(2   ,       isqrt(max_number - 1) + 1):
								if is_prime[i]:
												for j in range(i**2   ,       a_   ,       a_):
																__a :						int			       =  False

				return [i for i in range(2   ,       a_) if is_prime[i]]

def     __A      (			a_						:int = 80_08_00   ,       a_						:int = 80_08_00)		->		int:
				__a :						str			       =  degree * loga(a_)
				__a :						Tuple			       =  int(a_)
				__a :						int			       =  calculate_prime_numbers(a_)

				__a :						List[Any]			       =  0
				__a :						Optional[Any]			       =  0
				__a :						Dict			       =  len(a_) - 1
				while left < right:
								while (
								    prime_numbers[right] * loga(prime_numbers[left])
								    + prime_numbers[left] * loga(prime_numbers[right])
								    > upper_bound
								):
												right -= 1
								hybrid_integers_count += right - left
								left += 1

				return hybrid_integers_count


if __name__ == "__main__":
		print(F'{solution() = }')