

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


SCREAMING_SNAKE_CASE		:List[Any]						=     16
SCREAMING_SNAKE_CASE		:int						=     32




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = 1_6			,						SCREAMING_SNAKE_CASE_ = "bert-base-cased"			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						load_dataset("glue"			,						"mrpc"			)

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

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

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

    def collate_fn(SCREAMING_SNAKE_CASE_			):
        # 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(SCREAMING_SNAKE_CASE_			,						padding="max_length"			,						max_length=1_2_8			,						return_tensors="pt"			)
        return tokenizer.pad(SCREAMING_SNAKE_CASE_			,						padding="longest"			,						return_tensors="pt"			)

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

    return train_dataloader, eval_dataloader




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






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

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						Accelerator()

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

    set_seed(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_  ,       UpperCamelCase_			  =						get_dataloaders(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

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

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

    if accelerator.state.deepspeed_plugin is not None:
        UpperCamelCase_			  =						accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        UpperCamelCase_			  =						1
    UpperCamelCase_			  =						(len(SCREAMING_SNAKE_CASE_			) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        UpperCamelCase_			  =						get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE_			,						num_warmup_steps=0			,						num_training_steps=SCREAMING_SNAKE_CASE_			,						)
    else:
        UpperCamelCase_			  =						DummyScheduler(SCREAMING_SNAKE_CASE_			,						total_num_steps=SCREAMING_SNAKE_CASE_			,						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.
    UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
        SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

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

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

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

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

        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(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      )->						List[str]:



    """simple docstring"""






    UpperCamelCase_			  =						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."			)
    parser.add_argument(
        "--model_name_or_path"			,						type=SCREAMING_SNAKE_CASE_			,						default="bert-base-cased"			,						help="Path to pretrained model or model identifier from huggingface.co/models."			,						required=SCREAMING_SNAKE_CASE_			,						)
    parser.add_argument(
        "--output_dir"			,						type=SCREAMING_SNAKE_CASE_			,						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=SCREAMING_SNAKE_CASE_			,						default=SCREAMING_SNAKE_CASE_			,						help="If the training should continue from a checkpoint folder."			,						)
    parser.add_argument(
        "--partial_train_epoch"			,						type=SCREAMING_SNAKE_CASE_			,						default=SCREAMING_SNAKE_CASE_			,						help="If passed, the training will stop after this number of epochs."			,						)
    parser.add_argument(
        "--num_epochs"			,						type=SCREAMING_SNAKE_CASE_			,						default=2			,						help="Number of train epochs."			,						)
    UpperCamelCase_			  =						parser.parse_args()
    UpperCamelCase_			  =						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}

    training_function(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 main()



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
