



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_						:     Tuple	     =		logging.get_logger(__name__)

a_						:     Optional[int]	     =		{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'vit_msn'







     def __init__(self				,			__a=7_68				,			__a=12				,			__a=12				,			__a=30_72				,			__a="gelu"				,			__a=0.0				,			__a=0.0				,			__a=0.02				,			__a=1E-06				,			__a=2_24				,			__a=16				,			__a=3				,			__a=True				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__a    )

           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		intermediate_size
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		hidden_dropout_prob
           lowerCamelCase								=		attention_probs_dropout_prob
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		image_size
           lowerCamelCase								=		patch_size
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		qkv_bias




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


a_						:     Any	     =		1_6
a_						:     Any	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16   ,   UpperCAmelCase__ = "bert-base-cased" ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

      def tokenize_function(UpperCAmelCase__ ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase								=		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
      lowerCamelCase								=		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
      lowerCamelCase								=		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.
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["train"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["validation"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )

      return train_dataloader, eval_dataloader

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		Accelerator()

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

      set_seed(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase								=		accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase								=		1
      lowerCamelCase								=		(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
      ):
            lowerCamelCase								=		get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase__   ,   num_warmup_steps=0   ,   num_training_steps=UpperCAmelCase__   ,   )
      else:
            lowerCamelCase								=		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.
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		accelerator.prepare(
          UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

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

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

                  overall_step += 1

            model.eval()
            lowerCamelCase								=		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():
                        lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.logits.argmax(dim=-1 )
                  # It is slightly faster to call this once, than multiple times
                  lowerCamelCase			,							lowerCamelCase								=		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:
                              lowerCamelCase								=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
                              lowerCamelCase								=		references[: len(eval_dataloader.dataset ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

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

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase								=		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(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		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(
          "--performance_lower_bound"   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   default=3   ,   help="Number of train epochs."   ,   )
      lowerCamelCase								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()