


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		:					Union[str, Any]          =							16
_A		:					Union[str, Any]          =							32



def      __lowerCAmelCase    (				snake_case						:     Any    ,   snake_case						:     Any			)							->				List[Any]:
   # Initialize accelerator
   if args.with_tracking:
      __lowerCamelCase:					List[str]	 =						Accelerator(
          cpu=args.cpu    ,   mixed_precision=args.mixed_precision    ,   log_with="""all"""    ,   project_dir=args.project_dir			)
   else:
      __lowerCamelCase:					Union[str, Any]	 =						Accelerator(cpu=args.cpu    ,   mixed_precision=args.mixed_precision			)

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

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

   __lowerCamelCase:					Optional[int]	 =						AutoTokenizer.from_pretrained("""bert-base-cased"""			)
   __lowerCamelCase:					str	 =						load_dataset("""glue"""    ,   """mrpc"""			)
   __lowerCamelCase:					List[Any]	 =						evaluate.load("""glue"""    ,   """mrpc"""			)

   def tokenize_function(snake_case						:     List[str]			):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCamelCase:					Tuple	 =						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:					Dict	 =						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:					Dict	 =						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:					int	 =						batch_size // MAX_GPU_BATCH_SIZE
      __lowerCamelCase:					List[str]	 =						MAX_GPU_BATCH_SIZE

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

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

   # Instantiate dataloaders.
   __lowerCamelCase:					int	 =						DataLoader(
       tokenized_datasets["""train"""]    ,   shuffle=snake_case    ,   collate_fn=snake_case    ,   batch_size=snake_case			)
   __lowerCamelCase:					Optional[int]	 =						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:					List[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).
   __lowerCamelCase:					Optional[Any]	 =						model.to(accelerator.device			)

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

   # Instantiate scheduler
   __lowerCamelCase:					Union[str, 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:					Optional[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:					Dict	 =						0
   # We also need to keep track of the stating epoch so files are named properly
   __lowerCamelCase:					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:					Union[str, Any]	 =						os.path.basename(args.resume_from_checkpoint			)
      else:
         # Get the most recent checkpoint
         __lowerCamelCase:					Any	 =						[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:					Dict	 =						os.path.splitext(snake_case			)[0]

      if "epoch" in training_difference:
         __lowerCamelCase:					Dict	 =						int(training_difference.replace("""epoch_"""    ,   """"""			)			) + 1
         __lowerCamelCase:					Dict	 =						None
      else:
         __lowerCamelCase:					List[str]	 =						int(training_difference.replace("""step_"""    ,   """"""			)			)
         __lowerCamelCase:					Union[str, Any]	 =						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:					str	 =						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:					str	 =						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:					Optional[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:					str	 =						outputs.loss
         __lowerCamelCase:					List[Any]	 =						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:					Any	 =						f'step_{overall_step}'
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  __lowerCamelCase:					List[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:					Union[str, Any]	 =						model(**snake_case			)
         __lowerCamelCase:					Union[str, Any]	 =						outputs.logits.argmax(dim=-1			)
         __lowerCamelCase ,  __lowerCamelCase:					Tuple	 =						accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
         metric.add_batch(
             predictions=snake_case    ,   references=snake_case    ,   )

      __lowerCamelCase:					Union[str, Any]	 =						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:					Optional[int]	 =						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      __lowerCAmelCase    (				)							->				Union[str, Any]:
   __lowerCamelCase:					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:					Any	 =						parser.parse_args()
   __lowerCamelCase:					Union[str, Any]	 =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(snake_case    ,   snake_case			)


if __name__ == "__main__":
   main()






_A		:					Union[str, Any]          =							1_000



def      __lowerCAmelCase    (				snake_case						:     float			)							->				float:
   return x * x



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     float    ,   snake_case						:     float    ,   snake_case						:     int = 4			)							->				float:
   assert callable(
       snake_case			), f'the function(object) passed should be callable your input : {function}'
   assert isinstance(snake_case    ,   (float, int)			), f'a should be float or integer your input : {a}'
   assert isinstance(function(snake_case			)    ,   (float, int)			), (
       "the function should return integer or float return type of your function, "
       f'{type(snake_case			)}'
   )
   assert isinstance(snake_case    ,   (float, int)			), f'b should be float or integer your input : {b}'
   assert (
       isinstance(snake_case    ,   snake_case			) and precision > 0
   ), f'precision should be positive integer your input : {precision}'

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   __lowerCamelCase:					List[str]	 =						(b - a) / N_STEPS
   __lowerCamelCase:					Optional[Any]	 =						function(snake_case			) + function(snake_case			)

   for i in range(1    ,   snake_case			):
      __lowerCamelCase:					Dict	 =						a + h * i
      result += function(snake_case			) * (4 if i % 2 else 2)

   result *= h / 3
   return round(snake_case    ,   snake_case			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


