





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


SCREAMING_SNAKE_CASE_						   =       1_6
SCREAMING_SNAKE_CASE_						   =       3_2



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE = 16	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
      SCREAMING_SNAKE_CASE		=						load_dataset("""glue""" ,						"""mrpc"""	)

      def tokenize_function(_SCREAMING_SNAKE_CASE	):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE		=						tokenizer(examples["""sentence1"""] ,						examples["""sentence2"""] ,						truncation=_SCREAMING_SNAKE_CASE ,						max_length=_SCREAMING_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():
            SCREAMING_SNAKE_CASE		=						datasets.map(
                _SCREAMING_SNAKE_CASE ,						batched=_SCREAMING_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
      SCREAMING_SNAKE_CASE		=						tokenized_datasets.rename_column("""label""" ,						"""labels"""	)

      def collate_fn(_SCREAMING_SNAKE_CASE	):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE		=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE		=						16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE		=						8
            else:
                  SCREAMING_SNAKE_CASE		=						None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE		=						DataLoader(
          tokenized_datasets["""train"""] ,						shuffle=_SCREAMING_SNAKE_CASE ,						collate_fn=_SCREAMING_SNAKE_CASE ,						batch_size=_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						DataLoader(
          tokenized_datasets["""validation"""] ,						shuffle=_SCREAMING_SNAKE_CASE ,						collate_fn=_SCREAMING_SNAKE_CASE ,						batch_size=_SCREAMING_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

      SCREAMING_SNAKE_CASE_						   =       mocked_dataloaders  # noqa: F811



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						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=_SCREAMING_SNAKE_CASE	)
      def inner_training_loop(_SCREAMING_SNAKE_CASE	):
            # 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(_SCREAMING_SNAKE_CASE	)

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            SCREAMING_SNAKE_CASE		=						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,						return_dict=_SCREAMING_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).
            SCREAMING_SNAKE_CASE		=						model.to(accelerator.device	)

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE		=						AdamW(params=model.parameters() ,						lr=_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						get_dataloaders(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE		=						get_linear_schedule_with_warmup(
                optimizer=_SCREAMING_SNAKE_CASE ,						num_warmup_steps=1_00 ,						num_training_steps=(len(_SCREAMING_SNAKE_CASE	) * 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.
            SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						accelerator.prepare(
                _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

            # Now we train the model
            for epoch in range(_SCREAMING_SNAKE_CASE	):
                  model.train()
                  for step, batch in enumerate(_SCREAMING_SNAKE_CASE	):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device	)
                        SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	)
                        SCREAMING_SNAKE_CASE		=						outputs.loss
                        accelerator.backward(_SCREAMING_SNAKE_CASE	)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

                  SCREAMING_SNAKE_CASE		=						metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F"""epoch {epoch}:""" ,						_SCREAMING_SNAKE_CASE	)

    # 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    __lowercase   (      ) ->   Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						argparse.ArgumentParser(description="""Simple example of training script."""	)
      parser.add_argument(
          """--mixed_precision""" ,						type=_SCREAMING_SNAKE_CASE ,						default=_SCREAMING_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."""	)
      SCREAMING_SNAKE_CASE		=						parser.parse_args()
      SCREAMING_SNAKE_CASE		=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      main()









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

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


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



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			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"
             )
         }							,      )

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

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

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      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."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      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						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           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."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           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						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           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


