


'''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 checkpointing 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
#
########################################################################

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



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Optional[Any]			,						_UpperCamelCase					: int = 16      )						->    int:





   '''simple docstring'''







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

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

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

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

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

   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

       __lowercase:						Any  		=   mocked_dataloaders  # noqa: F811



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Union[str, Any]			,						_UpperCamelCase					: str      )						->    List[str]:





   '''simple docstring'''







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

   # New Code #
   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps			,						"isdigit"      ):
      if args.checkpointing_steps == "epoch":
         UpperCamelCase__         =  args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         UpperCamelCase__         =  int(args.checkpointing_steps      )
      else:
         raise ValueError(
             F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'      )
   else:
      UpperCamelCase__         =  None

   set_seed(__UpperCamelCase      )

   UpperCamelCase__  ,						UpperCamelCase__         =  get_dataloaders(__UpperCamelCase			,						__UpperCamelCase      )
   UpperCamelCase__         =  evaluate.load("glue"			,						"mrpc"      )

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

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

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

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

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

   # New Code #
   # We need to keep track of how many total steps we have iterated over
   UpperCamelCase__         =  0
   # We also need to keep track of the stating epoch so files are named properly
   UpperCamelCase__         =  0

   # We need to load the checkpoint back in before training here with `load_state`
   # The total number of epochs is adjusted based on where the state is being loaded from,
   # as we assume continuation of the same training script
   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      )
         UpperCamelCase__         =  os.path.basename(args.resume_from_checkpoint      )
      else:
         # Get the most recent checkpoint
         UpperCamelCase__         =  [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
         dirs.sort(key=os.path.getctime      )
         UpperCamelCase__         =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      UpperCamelCase__         =  os.path.splitext(__UpperCamelCase      )[0]

      if "epoch" in training_difference:
         UpperCamelCase__         =  int(training_difference.replace("epoch_"			,						""      )      ) + 1
         UpperCamelCase__         =  None
      else:
         UpperCamelCase__         =  int(training_difference.replace("step_"			,						""      )      )
         UpperCamelCase__         =  resume_step // len(__UpperCamelCase      )
         resume_step -= starting_epoch * len(__UpperCamelCase      )

    # Now we train the model
   for epoch in range(__UpperCamelCase			,						__UpperCamelCase      ):
      model.train()
      # New Code #
      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
         UpperCamelCase__         =  accelerator.skip_first_batches(__UpperCamelCase			,						__UpperCamelCase      )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         UpperCamelCase__         =  train_dataloader
      for step, batch in enumerate(__UpperCamelCase      ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device      )
         UpperCamelCase__         =  model(**__UpperCamelCase      )
         UpperCamelCase__         =  outputs.loss
         UpperCamelCase__         =  loss / gradient_accumulation_steps
         accelerator.backward(__UpperCamelCase      )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
         # New Code #
         overall_step += 1

         # New Code #
         # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
         # These are saved to folders named `step_{overall_step}`
         # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
         # If mixed precision was used, will also save a "scalar.bin" file
         if isinstance(__UpperCamelCase			,						__UpperCamelCase      ):
            UpperCamelCase__         =  F'step_{overall_step}'
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  UpperCamelCase__         =  os.path.join(args.output_dir			,						__UpperCamelCase      )
               accelerator.save_state(__UpperCamelCase      )

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

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

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `epoch_{epoch}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if checkpointing_steps == "epoch":
         UpperCamelCase__         =  F'epoch_{epoch}'
         if args.output_dir is not None:
            UpperCamelCase__         =  os.path.join(args.output_dir			,						__UpperCamelCase      )
         accelerator.save_state(__UpperCamelCase      )



def 				SCREAMING_SNAKE_CASE__(						)						->    str:





   '''simple docstring'''







   UpperCamelCase__         =  argparse.ArgumentParser(description="Simple example of training script."      )
   parser.add_argument(
       "--mixed_precision"			,						type=__UpperCamelCase			,						default=__UpperCamelCase			,						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=__UpperCamelCase			,						default=__UpperCamelCase			,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."			,						)
   parser.add_argument(
       "--output_dir"			,						type=__UpperCamelCase			,						default="."			,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,						)
   parser.add_argument(
       "--resume_from_checkpoint"			,						type=__UpperCamelCase			,						default=__UpperCamelCase			,						help="If the training should continue from a checkpoint folder."			,						)
   UpperCamelCase__         =  parser.parse_args()
   UpperCamelCase__         =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(__UpperCamelCase			,						__UpperCamelCase      )


if __name__ == "__main__":
       main()





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

