



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


__snake_case						:Tuple      		=     16
__snake_case						:Tuple      		=     32





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase = 16   , _UpperCAmelCase = "bert-base-cased"				):
    __a						       =				AutoTokenizer.from_pretrained(_UpperCAmelCase				)
    __a						       =				load_dataset('''glue'''   , '''mrpc'''				)

    def tokenize_function(_UpperCAmelCase				):
        # max_length=None => use the model max length (it's actually the default)
        __a						       =				tokenizer(examples['''sentence1''']   , examples['''sentence2''']   , truncation=_UpperCAmelCase   , max_length=_UpperCAmelCase				)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __a						       =				datasets.map(
        _UpperCAmelCase   , batched=_UpperCAmelCase   , remove_columns=['''idx''', '''sentence1''', '''sentence2''']   , load_from_cache_file=_UpperCAmelCase				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __a						       =				tokenized_datasets.rename_column('''label'''   , '''labels'''				)

    def collate_fn(_UpperCAmelCase				):
        # 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(_UpperCAmelCase   , padding='''max_length'''   , max_length=128   , return_tensors='''pt'''				)
        return tokenizer.pad(_UpperCAmelCase   , padding='''longest'''   , return_tensors='''pt'''				)

    # Instantiate dataloaders.
    __a						       =				DataLoader(
        tokenized_datasets['''train''']   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase				)
    __a						       =				DataLoader(
        tokenized_datasets['''validation''']   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase				)

    return train_dataloader, eval_dataloader





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    model.eval()
    __a						       =				0
    for step, batch in enumerate(_UpperCAmelCase				):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device				)
        with torch.no_grad():
            __a						       =				model(**_UpperCAmelCase				)
        __a						       =				outputs.logits.argmax(dim=-1				)
        # It is slightly faster to call this once, than multiple times
        __a       ,   __a						       =				accelerator.gather(
            (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_UpperCAmelCase				) - 1:
                __a						       =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
                __a						       =				references[: len(eval_dataloader.dataset				) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_UpperCAmelCase   , references=_UpperCAmelCase   , )

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    # Initialize accelerator
    __a						       =				Accelerator()

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

    set_seed(_UpperCAmelCase				)
    __a       ,   __a						       =				get_dataloaders(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

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

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

    if accelerator.state.deepspeed_plugin is not None:
        __a						       =				accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __a						       =				1
    __a						       =				(len(_UpperCAmelCase				) * 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						       =				get_linear_schedule_with_warmup(
            optimizer=_UpperCAmelCase   , num_warmup_steps=0   , num_training_steps=_UpperCAmelCase   , )
    else:
        __a						       =				DummyScheduler(_UpperCAmelCase   , total_num_steps=_UpperCAmelCase   , 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       ,   __a       ,   __a       ,   __a       ,   __a						       =				accelerator.prepare(
        _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

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

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

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

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

        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(_UpperCAmelCase   , _UpperCAmelCase				)





def      __snake_case	(			):
    __a						       =				argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
    parser.add_argument(
        '''--model_name_or_path'''   , type=_UpperCAmelCase   , default='''bert-base-cased'''   , help='''Path to pretrained model or model identifier from huggingface.co/models.'''   , required=_UpperCAmelCase   , )
    parser.add_argument(
        '''--output_dir'''   , type=_UpperCAmelCase   , 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=_UpperCAmelCase   , default=_UpperCAmelCase   , help='''If the training should continue from a checkpoint folder.'''   , )
    parser.add_argument(
        '''--partial_train_epoch'''   , type=_UpperCAmelCase   , default=_UpperCAmelCase   , help='''If passed, the training will stop after this number of epochs.'''   , )
    parser.add_argument(
        '''--num_epochs'''   , type=_UpperCAmelCase   , default=2   , help='''Number of train epochs.'''   , )
    __a						       =				parser.parse_args()
    __a						       =				{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

    training_function(_UpperCAmelCase   , _UpperCAmelCase				)


if __name__ == "__main__":
 main()










from __future__ import annotations

from typing import Any





def      __snake_case	(			_UpperCAmelCase				):
    if not postfix_notation:
        return 0

    __a						       =				{'''+''', '''-''', '''*''', '''/'''}
    __a						       =				[]

    for token in postfix_notation:
        if token in operations:
            __a       ,   __a						       =				stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(_UpperCAmelCase				)				)

    return stack.pop()


if __name__ == "__main__":
 import doctest

 doctest.testmod()





