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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

a     :			Dict					    =						3_2
def 		snake_case__  (		lowercase       ,							lowercase = 16				):
   lowerCAmelCase_:       int         =  AutoTokenizer.from_pretrained("bert-base-cased"				)
   lowerCAmelCase_:       Optional[Any]         =  load_dataset("glue"       ,							"mrpc"				)

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

   def collate_fn(lowercase				):
      # 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_:       int         =  None

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

   # Instantiate dataloaders.
   lowerCAmelCase_:       Any         =  DataLoader(
       tokenized_datasets["train"]       ,							shuffle=lowercase       ,							collate_fn=lowercase       ,							batch_size=lowercase				)
   lowerCAmelCase_:       Tuple         =  DataLoader(
       tokenized_datasets["validation"]       ,							shuffle=lowercase       ,							collate_fn=lowercase       ,							batch_size=lowercase				)

   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

 a     :			Union[str, Any]					    =						mocked_dataloaders  # noqa: F811
def 		snake_case__  (		lowercase       ,							lowercase				):
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,							lowercase				) == "1":
      lowerCAmelCase_:       List[Any]         =  2
   # Initialize accelerator
   lowerCAmelCase_:       Tuple         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase_:       Dict         =  config["lr"]
   lowerCAmelCase_:       List[str]         =  int(config["num_epochs"]				)
   lowerCAmelCase_:       str         =  int(config["seed"]				)
   lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)

   lowerCAmelCase_:       Optional[int]         =  evaluate.load("glue"       ,							"mrpc"				)

   # New Code #
   # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
   # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
   # half each time. From this, we can calculate the number of gradient accumulation steps needed
   # and modify the Accelerator object as a result
   @find_executable_batch_size(starting_batch_size=int(lowercase				)				)
   def inner_training_loop(lowercase				):
      # Since we need to modify the outside accelerator object, we need to bring it
      # to the local scope
      nonlocal accelerator

      # We can calculate the number of gradient accumulation steps based on the current
      # batch size vs the starting batch size
      lowerCAmelCase_:       Optional[Any]         =  observed_batch_size // batch_size

      # And then set it in the Accelerator directly:
      lowerCAmelCase_:       List[str]         =  num_gradient_accumulation_steps

      # Next we need to free all of the stored model references in the Accelerator each time
      accelerator.free_memory()

      # And set the seed so our results are reproducable each reset
      set_seed(lowercase				)

      # 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=lowercase				)

      # 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_:       List[Any]         =  model.to(accelerator.device				)

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

      # Instantiate scheduler
      lowerCAmelCase_:       List[Any]         =  get_linear_schedule_with_warmup(
          optimizer=lowercase       ,							num_warmup_steps=100       ,							num_training_steps=(len(lowercase				) * 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_:       Any         =  accelerator.prepare(
          lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)

      # Now we train the model
      for epoch in range(lowercase				):
         model.train()
         for step, batch in enumerate(lowercase				):
            # And perform gradient accumulation
            with accelerator.accumulate(lowercase				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               lowerCAmelCase_:       Any         =  model(**lowercase				)
               lowerCAmelCase_:       List[Any]         =  outputs.loss
               accelerator.backward(lowercase				)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

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

         lowerCAmelCase_:       int         =  metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F'''epoch {epoch}:'''       ,							lowercase				)

    # 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 		snake_case__  (		):
   lowerCAmelCase_:       Tuple         =  argparse.ArgumentParser(description="Simple example of training script."				)
   parser.add_argument(
       "--mixed_precision"       ,							type=lowercase       ,							default=lowercase       ,							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_:       int         =  parser.parse_args()
   # New Code #
   # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
   lowerCAmelCase_:       Dict         =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
   training_function(lowercase       ,							lowercase				)


if __name__ == "__main__":
 main()
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase				)
   except (ValueError, OSError, KeyError):
      lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
      try:
         lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass
      try:
         lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor