



"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str=None   ,       lowerCAmelCase__				:    Dict=None						)						->       Tuple:

		"""simple docstring"""




		return field(default_factory=lambda: default   ,       metadata=lowerCAmelCase__						)







@dataclass
class 				UpperCamelCase__       :




						"""simple docstring"""




						_SCREAMING_SNAKE_CASE      =					list_field(
						    default=[], metadata={
						        """help""": (
						            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
						            """ of all available models"""
						        )
						    }, )

						_SCREAMING_SNAKE_CASE      =					list_field(
						    default=[8], metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}  )

						_SCREAMING_SNAKE_CASE      =					list_field(
						    default=[8, 32, 128, 512], metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}, )

						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Use FP16 to accelerate inference."""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Benchmark training of model"""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Verbose memory tracing"""}  )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={
						        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
						    }, )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Trace memory line by line"""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Save result to a CSV file"""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Save all print statements in a log file"""}  )
						_SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Whether to print environment information"""}  )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={
						        """help""": (
						            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
						            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
						            """ for debugging / testing and on TPU."""
						        )
						    }, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''inference_time_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''inference_memory_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''train_time_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv for training."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''train_memory_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''env_info_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving environment information."""}, )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=F'''log_{round(time()  )}.csv''', metadata={"""help""": """Log filename used if print statements are saved in log."""}, )
						_SCREAMING_SNAKE_CASE      =					field(default=3, metadata={"""help""": """Times an experiment will be run."""}  )
						_SCREAMING_SNAKE_CASE      =					field(
						    default=lowercase_, metadata={
						        """help""": (
						            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
						            """ model weights."""
						        )
						    }, )



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
								warnings.warn(
								    F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
								    ' are deprecated in general and it is advised to use external Benchmarking libraries '
								    ' to benchmark Transformer models.'   ,      SCREAMING_SNAKE_CASE_   ,      )



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
								return json.dumps(dataclasses.asdict(self	)   ,      indent=2	)



						@property
						def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
								if len(self.models	) <= 0:
										raise ValueError(
										    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
										    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'	)
								return self.models





						@property
						def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
								if not self.multi_process:
										return False
								elif self.is_tpu:
										logger.info('Multiprocessing is currently not possible on TPU.'	)
										return False
								else:
										return True










"""simple docstring"""







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowercase__		:    Optional[int]				   =     1_6
lowercase__		:    List[str]				   =     3_2

def     UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset('glue'   ,       'mrpc'						)

  def tokenize_function(lowerCAmelCase__				:    Tuple						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_       :    Dict								=			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_       :    Dict								=			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_       :    int								=			tokenized_datasets.rename_column('label'   ,       'labels'						)

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

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

  # Instantiate dataloaders.
  lowerCAmelCase_       :    List[Any]								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			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

     lowercase__		:    int				   =     mocked_dataloaders  # noqa: F811

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[int]:

  """simple docstring"""




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

  lowerCAmelCase_       :    str								=			evaluate.load('glue'   ,       'mrpc'						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowerCAmelCase__						)
  def inner_training_loop(lowerCAmelCase__				:    Optional[int]						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowerCAmelCase__						)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase_       :    str								=			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_       :    int								=			model.to(accelerator.device						)

    # Instantiate optimizer
    lowerCAmelCase_       :    List[Any]								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)
    lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Instantiate scheduler
    lowerCAmelCase_       :    Dict								=			get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase__   ,       num_warmup_steps=100   ,       num_training_steps=(len(lowerCAmelCase__						) * num_epochs)   ,       )

    # 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_       :    List[Any]								=			accelerator.prepare(
        lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Now we train the model
    for epoch in range(lowerCAmelCase__						):
      model.train()
      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_       :    List[str]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    Optional[Any]								=			outputs.loss
        accelerator.backward(lowerCAmelCase__						)
        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`.
        batch.to(accelerator.device						)
        with torch.no_grad():
          lowerCAmelCase_       :    Union[str, Any]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    List[Any]								=			outputs.logits.argmax(dim=-1						)
        lowerCAmelCase_						,lowerCAmelCase_       :    Optional[Any]								=			accelerator.gather_for_metrics((predictions, batch['labels'])						)
        metric.add_batch(
            predictions=lowerCAmelCase__   ,       references=lowerCAmelCase__   ,       )

      lowerCAmelCase_       :    str								=			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"   ,       lowerCAmelCase__						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()

def     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			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.'						)
  lowerCAmelCase_       :    str								=			parser.parse_args()
  lowerCAmelCase_       :    Union[str, Any]								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__   ,       lowerCAmelCase__						)


if __name__ == "__main__":
     main()





