





'''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				:      List[str]						      =32





def 		UpperCamelCase							(				_lowerCamelCase   :   Accelerator		,			_lowerCamelCase   :   int = 16		,			_lowerCamelCase   :   str = "bert-base-cased"			):
    A__							=     AutoTokenizer.from_pretrained(_lowerCamelCase			)
    A__							=     load_dataset("glue"		,			"mrpc"			)

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

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

    # 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(_lowerCamelCase   :   List[Any]			):
        # 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(_lowerCamelCase		,			padding="max_length"		,			max_length=1_28		,			return_tensors="pt"			)
        return tokenizer.pad(_lowerCamelCase		,			padding="longest"		,			return_tensors="pt"			)

    # Instantiate dataloaders.
    A__							=     DataLoader(
        tokenized_datasets["train"]		,			shuffle=_lowerCamelCase		,			collate_fn=_lowerCamelCase		,			batch_size=_lowerCamelCase			)
    A__							=     DataLoader(
        tokenized_datasets["validation"]		,			shuffle=_lowerCamelCase		,			collate_fn=_lowerCamelCase		,			batch_size=_lowerCamelCase			)

    return train_dataloader, eval_dataloader





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Union[str, Any]			):
    # 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(_lowerCamelCase			)
    A__,      A__							=     get_dataloaders(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

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

    # 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=_lowerCamelCase			)

    if accelerator.state.deepspeed_plugin is not None:
        A__							=     accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        A__							=     1
    A__							=     (len(_lowerCamelCase			) * 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=_lowerCamelCase		,			num_warmup_steps=0		,			num_training_steps=_lowerCamelCase		,			)
    else:
        A__							=     DummyScheduler(_lowerCamelCase		,			total_num_steps=_lowerCamelCase		,			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(
        _lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

    # 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

    # Now we train the model
    A__							=     evaluate.load("glue"		,			"mrpc"			)
    A__							=     0
    A__							=     {}
    for epoch in range(_lowerCamelCase		,			_lowerCamelCase			):
        model.train()
        for step, batch in enumerate(_lowerCamelCase			):
            A__							=     model(**_lowerCamelCase			)
            A__							=     outputs.loss
            A__							=     loss / gradient_accumulation_steps
            accelerator.backward(_lowerCamelCase			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        A__							=     0
        for step, batch in enumerate(_lowerCamelCase			):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device			)
            with torch.no_grad():
                A__							=     model(**_lowerCamelCase			)
            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(_lowerCamelCase			) - 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=_lowerCamelCase		,			references=_lowerCamelCase		,			)

        A__							=     metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"		,			_lowerCamelCase			)
        A__							=     eval_metric["accuracy"]

        if best_performance < eval_metric["accuracy"]:
            A__							=     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(_lowerCamelCase		,			_lowerCamelCase			)





def 		UpperCamelCase							(				):
    A__							=     argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."			)
    parser.add_argument(
        "--model_name_or_path"		,			type=_lowerCamelCase		,			default="bert-base-cased"		,			help="Path to pretrained model or model identifier from huggingface.co/models."		,			required=_lowerCamelCase		,			)
    parser.add_argument(
        "--output_dir"		,			type=_lowerCamelCase		,			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=_lowerCamelCase		,			default=_lowerCamelCase		,			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=_lowerCamelCase		,			default=3		,			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(_lowerCamelCase		,			_lowerCamelCase			)


if __name__ == "__main__":
  main()










'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



