





"""simple docstring"""
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 experiment tracking 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
#
########################################################################

__UpperCamelCase     :						Union[str, Any]						       =  16
__UpperCamelCase     :						Tuple						       =  32

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Accelerator  ,     _UpperCAmelCase					:    int = 16			):
 lowerCAmelCase    				= AutoTokenizer.from_pretrained('bert-base-cased'			)
 lowerCAmelCase    				= load_dataset('glue'  ,     'mrpc'			)

 def tokenize_function(_UpperCAmelCase					:    List[str]			):
  # max_length=None => use the model max length (it's actually the default)
  lowerCAmelCase    				= 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():
  lowerCAmelCase    				= 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
 lowerCAmelCase    				= tokenized_datasets.rename_column('label'  ,     'labels'			)

 def collate_fn(_UpperCAmelCase					:    Dict			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowerCAmelCase    				= 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":
   lowerCAmelCase    				= 16
  elif accelerator.mixed_precision != "no":
   lowerCAmelCase    				= 8
  else:
   lowerCAmelCase    				= None

  return tokenizer.pad(
      lowerCamelCase__  ,     padding='longest'  ,     max_length=lowerCamelCase__  ,     pad_to_multiple_of=lowerCamelCase__  ,     return_tensors='pt'  ,     )

 # Instantiate dataloaders.
 lowerCAmelCase    				= DataLoader(
     tokenized_datasets['train']  ,     shuffle=lowerCamelCase__  ,     collate_fn=lowerCamelCase__  ,     batch_size=lowerCamelCase__			)
 lowerCAmelCase    				= DataLoader(
     tokenized_datasets['validation']  ,     shuffle=lowerCamelCase__  ,     collate_fn=lowerCamelCase__  ,     batch_size=lowerCamelCase__			)

 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

       __UpperCamelCase     :						Optional[int]						       =  mocked_dataloaders  # noqa: F811

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    Optional[int]			):




 if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,     lowerCamelCase__			) == "1":
  lowerCAmelCase    				= 2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowerCAmelCase    				= Accelerator(
      cpu=args.cpu  ,     mixed_precision=args.mixed_precision  ,     log_with='all'  ,     project_dir=args.project_dir			)
 else:
  lowerCAmelCase    				= Accelerator(cpu=args.cpu  ,     mixed_precision=args.mixed_precision			)
 # 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']			)
 set_seed(lowerCamelCase__			)

 lowerCAmelCase				,lowerCAmelCase    				= get_dataloaders(lowerCamelCase__  ,     lowerCamelCase__			)
 lowerCAmelCase    				= evaluate.load('glue'  ,     'mrpc'			)

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCAmelCase    				= 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).
 lowerCAmelCase    				= model.to(accelerator.device			)

 # Instantiate optimizer
 lowerCAmelCase    				= AdamW(params=model.parameters()  ,     lr=lowerCamelCase__			)

 # Instantiate scheduler
 lowerCAmelCase    				= 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.
 lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= accelerator.prepare(
     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__			)

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowerCAmelCase    				= os.path.split(lowerCamelCase__			)[-1].split('.'			)[0]
  accelerator.init_trackers(lowerCamelCase__  ,     lowerCamelCase__			)

 # Now we train the model
 for epoch in range(lowerCamelCase__			):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowerCAmelCase    				= 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			)
   lowerCAmelCase    				= model(**lowerCamelCase__			)
   lowerCAmelCase    				= outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowerCAmelCase    				= loss / gradient_accumulation_steps
   accelerator.backward(lowerCamelCase__			)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

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

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  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__  ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= 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(
     '--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(
     '--project_dir'  ,     type=lowerCamelCase__  ,     default='logs'  ,     help='Location on where to store experiment tracking logs` and relevent project information'  ,     )
 lowerCAmelCase    				= parser.parse_args()
 lowerCAmelCase    				= {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(lowerCamelCase__  ,     lowerCamelCase__			)


if __name__ == "__main__":
       main()









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


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		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-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''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
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

