
import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_A       =					16
_A       =					32



def 		lowercase_     (  A__					,						A__ = 16						)				->				int:
    """simple docstring"""

    snake_case						=   AutoTokenizer.from_pretrained("bert-base-cased"						)
    snake_case						=   load_dataset("glue"					,						"mrpc"						)

    def tokenize_function(A__						):
        # max_length=None => use the model max length (it's actually the default)
        snake_case						=   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
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case						=   datasets.map(
            A__					,						batched=A__					,						remove_columns=["idx", "sentence1", "sentence2"]					,						)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case						=   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.
        snake_case						=   128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case						=   16
        elif accelerator.mixed_precision != "no":
            snake_case						=   8
        else:
            snake_case						=   None

        return tokenizer.pad(
            A__					,						padding="longest"					,						max_length=A__					,						pad_to_multiple_of=A__					,						return_tensors="pt"					,						)

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

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   _A       =					mocked_dataloaders  # noqa: F811



def 		lowercase_     (  A__					,						A__						)				->				int:
    """simple docstring"""

    if os.environ.get("TESTING_MOCKED_DATALOADERS"					,						A__						) == "1":
        snake_case						=   2
    # Initialize accelerator
    snake_case						=   Accelerator(cpu=args.cpu					,						mixed_precision=args.mixed_precision						)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case						=   config["lr"]
    snake_case						=   int(config["num_epochs"]						)
    snake_case						=   int(config["seed"]						)
    snake_case						=   int(config["batch_size"]						)

    # New Code #
    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps					,						"isdigit"						):
        if args.checkpointing_steps == "epoch":
            snake_case						=   args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            snake_case						=   int(args.checkpointing_steps						)
        else:
            raise ValueError(
                F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'						)
    else:
        snake_case						=   None

    set_seed(A__						)

    snake_case	,				snake_case						=   get_dataloaders(A__					,						A__						)
    snake_case						=   evaluate.load("glue"					,						"mrpc"						)

    # If the batch size is too big we use gradient accumulation
    snake_case						=   1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case						=   batch_size // MAX_GPU_BATCH_SIZE
        snake_case						=   MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case						=   AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,						return_dict=A__						)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    snake_case						=   model.to(accelerator.device						)

    # Instantiate optimizer
    snake_case						=   AdamW(params=model.parameters()					,						lr=A__						)

    # Instantiate scheduler
    snake_case						=   get_linear_schedule_with_warmup(
        optimizer=A__					,						num_warmup_steps=100					,						num_training_steps=(len(A__						) * num_epochs) // gradient_accumulation_steps					,						)

    # 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.
    snake_case	,				snake_case	,				snake_case	,				snake_case	,				snake_case						=   accelerator.prepare(
        A__					,						A__					,						A__					,						A__					,						A__						)

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    snake_case						=   0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case						=   0

    # We need to load the checkpoint back in before training here with `load_state`
    # The total number of epochs is adjusted based on where the state is being loaded from,
    # as we assume continuation of the same training script
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'						)
            accelerator.load_state(args.resume_from_checkpoint						)
            snake_case						=   os.path.basename(args.resume_from_checkpoint						)
        else:
            # Get the most recent checkpoint
            snake_case						=   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
            dirs.sort(key=os.path.getctime						)
            snake_case						=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        snake_case						=   os.path.splitext(A__						)[0]

        if "epoch" in training_difference:
            snake_case						=   int(training_difference.replace("epoch_"					,						""						)						) + 1
            snake_case						=   None
        else:
            snake_case						=   int(training_difference.replace("step_"					,						""						)						)
            snake_case						=   resume_step // len(A__						)
            resume_step -= starting_epoch * len(A__						)

    # Now we train the model
    for epoch in range(A__					,						A__						):
        model.train()
        # New Code #
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            snake_case						=   accelerator.skip_first_batches(A__					,						A__						)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            snake_case						=   train_dataloader
        for step, batch in enumerate(A__						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            snake_case						=   model(**A__						)
            snake_case						=   outputs.loss
            snake_case						=   loss / gradient_accumulation_steps
            accelerator.backward(A__						)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
            # New Code #
            overall_step += 1

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `step_{overall_step}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if isinstance(A__					,						A__						):
                snake_case						=   F'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        snake_case						=   os.path.join(args.output_dir					,						A__						)
                    accelerator.save_state(A__						)

        model.eval()
        for step, batch in enumerate(A__						):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device						)
            with torch.no_grad():
                snake_case						=   model(**A__						)
            snake_case						=   outputs.logits.argmax(dim=-1						)
            snake_case	,				snake_case						=   accelerator.gather_for_metrics((predictions, batch["labels"])						)
            metric.add_batch(
                predictions=A__					,						references=A__					,						)

        snake_case						=   metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'					,						A__						)

        # New Code #
        # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
        # These are saved to folders named `epoch_{epoch}`
        # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
        # If mixed precision was used, will also save a "scalar.bin" file
        if checkpointing_steps == "epoch":
            snake_case						=   F'epoch_{epoch}'
            if args.output_dir is not None:
                snake_case						=   os.path.join(args.output_dir					,						A__						)
            accelerator.save_state(A__						)



def 		lowercase_     (  )				->				List[str]:
    """simple docstring"""

    snake_case						=   argparse.ArgumentParser(description="Simple example of training script."						)
    parser.add_argument(
        "--mixed_precision"					,						type=A__					,						default=A__					,						choices=["no", "fp16", "bf16", "fp8"]					,						help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."					,						)
    parser.add_argument("--cpu"					,						action="store_true"					,						help="If passed, will train on the CPU."						)
    parser.add_argument(
        "--checkpointing_steps"					,						type=A__					,						default=A__					,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."					,						)
    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."					,						)
    snake_case						=   parser.parse_args()
    snake_case						=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(A__					,						A__						)


if __name__ == "__main__":
   main()



import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

