"""simple docstring"""



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

SCREAMING_SNAKE_CASE__:Tuple		       =       16
SCREAMING_SNAKE_CASE__:List[str]		       =       32



def      _lowerCamelCase(     a					,  a = 1_6							):
       __a	      =  AutoTokenizer.from_pretrained("bert-base-cased"							)
       __a	      =  load_dataset("glue"					,  "mrpc"							)

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

       def collate_fn(a							):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              __a	      =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     __a	      =  1_6
              elif accelerator.mixed_precision != "no":
                     __a	      =  8
              else:
                     __a	      =  None

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

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

       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__:int		       =       mocked_dataloaders  # noqa: F811



def      _lowerCamelCase(     a					,  a							):






       if os.environ.get("TESTING_MOCKED_DATALOADERS"					,  __a							) == "1":
              __a	      =  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:
              __a	      =  Accelerator(
                  cpu=args.cpu					,  mixed_precision=args.mixed_precision					,  log_with="all"					,  project_dir=args.project_dir							)
       else:
              __a	      =  Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision							)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __a	      =  config['''lr''']
       __a	      =  int(config["num_epochs"]							)
       __a	      =  int(config["seed"]							)
       __a	      =  int(config["batch_size"]							)
       set_seed(__a							)

       __a	      =  get_dataloaders(__a					,  __a							)
       __a	      =  evaluate.load("glue"					,  "mrpc"							)

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

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

       # 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).
       __a	      =  model.to(accelerator.device							)

       # Instantiate optimizer
       __a	      =  AdamW(params=model.parameters()					,  lr=__a							)

       # Instantiate scheduler
       __a	      =  get_linear_schedule_with_warmup(
           optimizer=__a					,  num_warmup_steps=1_0_0					,  num_training_steps=(len(__a							) * 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.
       __a	      =  accelerator.prepare(
           __a					,  __a					,  __a					,  __a					,  __a							)

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

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

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

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

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

    # 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      _lowerCamelCase(     ):
       __a	      =  argparse.ArgumentParser(description="Simple example of training script."							)
       parser.add_argument(
           "--mixed_precision"					,  type=__a					,  default=__a					,  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=__a					,  default="logs"					,  help="Location on where to store experiment tracking logs` and relevent project information"					,  )
       __a	      =  parser.parse_args()
       __a	      =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
       training_function(__a					,  __a							)


if __name__ == "__main__":
   main()








"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	*lowercase_     :  Dict						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          warnings.warn(
              '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
              ''' use DeiTImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


