
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__magic_name__           =					16
__magic_name__           =					32






def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    # Initialize accelerator
    if args.with_tracking:
        snake_case__  =			Accelerator(
            cpu=args.cpu      ,						mixed_precision=args.mixed_precision      ,						log_with="all"      ,						project_dir=args.project_dir							)
    else:
        snake_case__  =			Accelerator(cpu=args.cpu      ,						mixed_precision=args.mixed_precision							)

    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
    # 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"]							)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        snake_case__  =			os.path.split(__lowerCAmelCase							)[-1].split("."							)[0]
        accelerator.init_trackers(__lowerCAmelCase      ,						__lowerCAmelCase							)

    snake_case__  =			AutoTokenizer.from_pretrained("bert-base-cased"							)
    snake_case__  =			load_dataset("glue"      ,						"mrpc"							)
    snake_case__  =			evaluate.load("glue"      ,						"mrpc"							)

    def tokenize_function(__lowerCAmelCase							):
        # max_length=None => use the model max length (it's actually the default)
        snake_case__  =			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
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case__  =			datasets.map(
            __lowerCAmelCase      ,						batched=__lowerCAmelCase      ,						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"							)

    # 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

    def collate_fn(__lowerCAmelCase							):
        # 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(
            __lowerCAmelCase      ,						padding="longest"      ,						max_length=__lowerCAmelCase      ,						pad_to_multiple_of=__lowerCAmelCase      ,						return_tensors="pt"      ,						)

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

    set_seed(__lowerCAmelCase							)

    # 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=__lowerCAmelCase							)

    # 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=__lowerCAmelCase							)

    # Instantiate scheduler
    snake_case__  =			get_linear_schedule_with_warmup(
        optimizer=__lowerCAmelCase      ,						num_warmup_steps=100      ,						num_training_steps=(len(__lowerCAmelCase							) * 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(
        __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)

    # 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

    # Potentially load in the weights and states from a previous save
    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(__lowerCAmelCase							)[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(__lowerCAmelCase							)
            resume_step -= starting_epoch * len(__lowerCAmelCase							)

    # Now we train the model
    for epoch in range(__lowerCAmelCase      ,						__lowerCAmelCase							):
        model.train()
        if args.with_tracking:
            snake_case__  =			0
        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(__lowerCAmelCase      ,						__lowerCAmelCase							)
            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(__lowerCAmelCase							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            snake_case__  =			model(**__lowerCAmelCase							)
            snake_case__  =			outputs.loss
            snake_case__  =			loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(__lowerCAmelCase							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(__lowerCAmelCase      ,						__lowerCAmelCase							):
                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      ,						__lowerCAmelCase							)
                    accelerator.save_state(__lowerCAmelCase							)

        model.eval()
        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():
                snake_case__  =			model(**__lowerCAmelCase							)
            snake_case__  =			outputs.logits.argmax(dim=-1							)
            snake_case__  ,   snake_case__  =			accelerator.gather_for_metrics((predictions, batch["labels"])							)
            metric.add_batch(
                predictions=__lowerCAmelCase      ,						references=__lowerCAmelCase      ,						)

        snake_case__  =			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""      ,						__lowerCAmelCase							)
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(__lowerCAmelCase							),
                    "epoch": epoch,
                }      ,						step=__lowerCAmelCase      ,						)

        if checkpointing_steps == "epoch":
            snake_case__  =			F"""epoch_{epoch}"""
            if args.output_dir is not None:
                snake_case__  =			os.path.join(args.output_dir      ,						__lowerCAmelCase							)
            accelerator.save_state(__lowerCAmelCase							)

    if args.with_tracking:
        accelerator.end_training()






def 		SCREAMING_SNAKE_CASE__      ( ):
    snake_case__  =			argparse.ArgumentParser(description="Simple example of training script."							)
    parser.add_argument(
        "--mixed_precision"      ,						type=__lowerCAmelCase      ,						default=__lowerCAmelCase      ,						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=__lowerCAmelCase      ,						default=__lowerCAmelCase      ,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."      ,						)
    parser.add_argument(
        "--resume_from_checkpoint"      ,						type=__lowerCAmelCase      ,						default=__lowerCAmelCase      ,						help="If the training should continue from a checkpoint folder."      ,						)
    parser.add_argument(
        "--with_tracking"      ,						action="store_true"      ,						help="Whether to load in all available experiment trackers from the environment and use them for logging."      ,						)
    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(
        "--project_dir"      ,						type=__lowerCAmelCase      ,						default="logs"      ,						help="Location on where to store experiment tracking logs` and relevent project information"      ,						)
    snake_case__  =			parser.parse_args()
    snake_case__  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(__lowerCAmelCase      ,						__lowerCAmelCase							)


if __name__ == "__main__":
     main()





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



      @property
      def A_     (     self	):
          return 1e-4



