
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__       :   Optional[int]   						=					1_6
A__       :   Tuple   						=					3_2



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  str    ):
   # Initialize accelerator
   if args.with_tracking:
      lowerCAmelCase__ :    int				      =			Accelerator(
          cpu=args.cpu	,mixed_precision=args.mixed_precision	,log_with='''all'''	,project_dir=args.project_dir    )
   else:
      lowerCAmelCase__ :    Dict				      =			Accelerator(cpu=args.cpu	,mixed_precision=args.mixed_precision    )

   if hasattr(args.checkpointing_steps	,'''isdigit'''    ):
      if args.checkpointing_steps == "epoch":
         lowerCAmelCase__ :    Any				      =			args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowerCAmelCase__ :    Dict				      =			int(args.checkpointing_steps    )
      else:
         raise ValueError(
             f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      lowerCAmelCase__ :    str				      =			None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase__ :    Union[str, Any]				      =			config['''lr''']
   lowerCAmelCase__ :    Optional[int]				      =			int(config['''num_epochs''']    )
   lowerCAmelCase__ :    Tuple				      =			int(config['''seed''']    )
   lowerCAmelCase__ :    str				      =			int(config['''batch_size''']    )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      lowerCAmelCase__ :    Dict				      =			os.path.split(snake_case__    )[-1].split('''.'''    )[0]
      accelerator.init_trackers(snake_case__	,snake_case__    )

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

   def tokenize_function(__UpperCamelCase       :  Tuple    ):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase__ :    Optional[int]				      =			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():
      lowerCAmelCase__ :    Optional[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
   lowerCAmelCase__ :    str				      =			tokenized_datasets.rename_column('''label'''	,'''labels'''    )

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

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

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

   # Instantiate dataloaders.
   lowerCAmelCase__ :    Optional[int]				      =			DataLoader(
       tokenized_datasets['''train''']	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=snake_case__    )
   lowerCAmelCase__ :    List[Any]				      =			DataLoader(
       tokenized_datasets['''validation''']	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=snake_case__    )

   set_seed(snake_case__    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCAmelCase__ :    Any				      =			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).
   lowerCAmelCase__ :    Union[str, Any]				      =			model.to(accelerator.device    )

   # Instantiate optimizer
   lowerCAmelCase__ :    List[str]				      =			AdamW(params=model.parameters()	,lr=snake_case__    )

   # Instantiate scheduler
   lowerCAmelCase__ :    Optional[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.
   lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			accelerator.prepare(
       snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__    )

   # We need to keep track of how many total steps we have iterated over
   lowerCAmelCase__ :    List[str]				      =			0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCAmelCase__ :    List[str]				      =			0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         lowerCAmelCase__ :    List[Any]				      =			os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         lowerCAmelCase__ :    List[str]				      =			[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         lowerCAmelCase__ :    Optional[Any]				      =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowerCAmelCase__ :    Optional[int]				      =			os.path.splitext(snake_case__    )[0]

      if "epoch" in training_difference:
         lowerCAmelCase__ :    int				      =			int(training_difference.replace('''epoch_'''	,''''''    )    ) + 1
         lowerCAmelCase__ :    Dict				      =			None
      else:
         lowerCAmelCase__ :    Any				      =			int(training_difference.replace('''step_'''	,''''''    )    )
         lowerCAmelCase__ :    Optional[int]				      =			resume_step // len(snake_case__    )
         resume_step -= starting_epoch * len(snake_case__    )

    # Now we train the model
   for epoch in range(snake_case__	,snake_case__    ):
      model.train()
      if args.with_tracking:
         lowerCAmelCase__ :    int				      =			0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         lowerCAmelCase__ :    Optional[int]				      =			accelerator.skip_first_batches(snake_case__	,snake_case__    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowerCAmelCase__ :    int				      =			train_dataloader
      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    )
         lowerCAmelCase__ :    List[str]				      =			model(**snake_case__    )
         lowerCAmelCase__ :    Any				      =			outputs.loss
         lowerCAmelCase__ :    List[str]				      =			loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(snake_case__    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(snake_case__	,snake_case__    ):
            lowerCAmelCase__ :    Optional[int]				      =			f'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowerCAmelCase__ :    str				      =			os.path.join(args.output_dir	,snake_case__    )
               accelerator.save_state(snake_case__    )

      model.eval()
      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    )
         with torch.no_grad():
            lowerCAmelCase__ :    List[Any]				      =			model(**snake_case__    )
         lowerCAmelCase__ :    Tuple				      =			outputs.logits.argmax(dim=-1    )
         lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
         metric.add_batch(
             predictions=snake_case__	,references=snake_case__	,)

      lowerCAmelCase__ :    int				      =			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''	,snake_case__    )
      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__	,)

      if checkpointing_steps == "epoch":
         lowerCAmelCase__ :    List[str]				      =			f'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowerCAmelCase__ :    List[Any]				      =			os.path.join(args.output_dir	,snake_case__    )
         accelerator.save_state(snake_case__    )

   if args.with_tracking:
      accelerator.end_training()



def      _a     (   ):
   lowerCAmelCase__ :    Optional[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(
       '''--checkpointing_steps'''	,type=snake_case__	,default=snake_case__	,help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,)
   parser.add_argument(
       '''--resume_from_checkpoint'''	,type=snake_case__	,default=snake_case__	,help='''If the training should continue from a checkpoint folder.'''	,)
   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(
       '''--output_dir'''	,type=snake_case__	,default='''.'''	,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,)
   parser.add_argument(
       '''--project_dir'''	,type=snake_case__	,default='''logs'''	,help='''Location on where to store experiment tracking logs` and relevent project information'''	,)
   lowerCAmelCase__ :    List[str]				      =			parser.parse_args()
   lowerCAmelCase__ :    Tuple				      =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(snake_case__	,snake_case__    )


if __name__ == "__main__":
       main()





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



