



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
#
########################################################################

a__			=		16
a__			=		32



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Accelerator ,				SCREAMING_SNAKE_CASE__				:		int = 16 )     ->   Tuple:
 _snake_case			:     Dict 		=    AutoTokenizer.from_pretrained("""bert-base-cased""" )
 _snake_case			:     Dict 		=    load_dataset("""glue""" ,				"""mrpc""" )

 def tokenize_function(SCREAMING_SNAKE_CASE__				:		List[str] ):
  # max_length=None => use the model max length (it's actually the default)
  _snake_case			:     Dict 		=    tokenizer(examples["""sentence1"""] ,				examples["""sentence2"""] ,				truncation=__snake_case ,				max_length=__snake_case )
  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			:     Any 		=    datasets.map(
      __snake_case ,				batched=__snake_case ,				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			:     Dict 		=    tokenized_datasets.rename_column("""label""" ,				"""labels""" )

 def collate_fn(SCREAMING_SNAKE_CASE__				:		Union[str, Any] ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _snake_case			:     Optional[Any] 		=    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			:     List[str] 		=    16
  elif accelerator.mixed_precision != "no":
   _snake_case			:     Dict 		=    8
  else:
   _snake_case			:     Tuple 		=    None

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

 # Instantiate dataloaders.
 _snake_case			:     Tuple 		=    DataLoader(
     tokenized_datasets["""train"""] ,				shuffle=__snake_case ,				collate_fn=__snake_case ,				batch_size=__snake_case )
 _snake_case			:     Union[str, Any] 		=    DataLoader(
     tokenized_datasets["""validation"""] ,				shuffle=__snake_case ,				collate_fn=__snake_case ,				batch_size=__snake_case )

 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				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		Optional[Any] )     ->   int:
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,				__snake_case ) == "1":
  _snake_case			:     int 		=    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:
  _snake_case			:     int 		=    Accelerator(
      cpu=args.cpu ,				mixed_precision=args.mixed_precision ,				log_with="""all""" ,				project_dir=args.project_dir )
 else:
  _snake_case			:     Optional[Any] 		=    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			:     Union[str, Any] 		=    config['lr']
 _snake_case			:     Any 		=    int(config["""num_epochs"""] )
 _snake_case			:     Dict 		=    int(config["""seed"""] )
 _snake_case			:     Union[str, Any] 		=    int(config["""batch_size"""] )
 set_seed(__snake_case )

 _snake_case			:     List[str] 		=    get_dataloaders(__snake_case ,				__snake_case )
 _snake_case			:     Optional[Any] 		=    evaluate.load("""glue""" ,				"""mrpc""" )

 # If the batch size is too big we use gradient accumulation
 _snake_case			:     List[str] 		=    1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  _snake_case			:     Union[str, Any] 		=    batch_size // MAX_GPU_BATCH_SIZE
  _snake_case			:     Union[str, Any] 		=    MAX_GPU_BATCH_SIZE

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

 # 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			:     int 		=    model.to(accelerator.device )

 # Instantiate optimizer
 _snake_case			:     str 		=    AdamW(params=model.parameters() ,				lr=__snake_case )

 # Instantiate scheduler
 _snake_case			:     Any 		=    get_linear_schedule_with_warmup(
     optimizer=__snake_case ,				num_warmup_steps=100 ,				num_training_steps=(len(__snake_case ) * 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			:     Any 		=    accelerator.prepare(
     __snake_case ,				__snake_case ,				__snake_case ,				__snake_case ,				__snake_case )

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

 # Now we train the model
 for epoch in range(__snake_case ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   _snake_case			:     int 		=    0
  for step, batch in enumerate(__snake_case ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device )
   _snake_case			:     List[Any] 		=    model(**__snake_case )
   _snake_case			:     List[str] 		=    outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   _snake_case			:     List[Any] 		=    loss / gradient_accumulation_steps
   accelerator.backward(__snake_case )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  for step, batch in enumerate(__snake_case ):
   # 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			:     int 		=    model(**__snake_case )
   _snake_case			:     Any 		=    outputs.logits.argmax(dim=-1 )
   _snake_case			:     List[Any] 		=    accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
   metric.add_batch(
       predictions=__snake_case ,				references=__snake_case ,				)

  _snake_case			:     Optional[int] 		=    metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:''' ,				__snake_case )

  # 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(__snake_case ),
           """epoch""": epoch,
       } ,				step=__snake_case ,				)

    # 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 					lowercase				(      )     ->   Tuple:
 _snake_case			:     Union[str, Any] 		=    argparse.ArgumentParser(description="""Simple example of training script.""" )
 parser.add_argument(
     """--mixed_precision""" ,				type=__snake_case ,				default=__snake_case ,				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=__snake_case ,				default="""logs""" ,				help="""Location on where to store experiment tracking logs` and relevent project information""" ,				)
 _snake_case			:     Tuple 		=    parser.parse_args()
 _snake_case			:     Union[str, Any] 		=    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(__snake_case ,				__snake_case )


if __name__ == "__main__":
  main()







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])