




"""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
#
########################################################################

a_										=       16
a_										=       32




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = 16 )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  AutoTokenizer.from_pretrained('''bert-base-cased''' )
    __UpperCamelCase											=  load_dataset('''glue'''   ,       '''mrpc''' )

    def tokenize_function(lowercase_ ):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase											=  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():
        __UpperCamelCase											=  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
    __UpperCamelCase											=  tokenized_datasets.rename_column('''label'''   ,       '''labels''' )

    def collate_fn(lowercase_ ):
        # 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(
            lowercase_   ,       padding='''longest'''   ,       max_length=lowercase_   ,       pad_to_multiple_of=lowercase_   ,       return_tensors='''pt'''   ,       )

    # Instantiate dataloaders.
    __UpperCamelCase											=  DataLoader(
        tokenized_datasets['''train''']   ,       shuffle=lowercase_   ,       collate_fn=lowercase_   ,       batch_size=lowercase_ )
    __UpperCamelCase											=  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_										=       mocked_dataloaders  # noqa: F811




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,       lowercase_ ) == "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(lowercase_ )

    __UpperCamelCase						, __UpperCamelCase											=  get_dataloaders(lowercase_   ,       lowercase_ )
    __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=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).
    __UpperCamelCase											=  model.to(accelerator.device )

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

    # Instantiate scheduler
    __UpperCamelCase											=  get_linear_schedule_with_warmup(
        optimizer=lowercase_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(lowercase_ ) * 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(
        lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )

    # 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(lowercase_ )[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(lowercase_ )
            resume_step -= starting_epoch * len(lowercase_ )

    # Now we train the model
    for epoch in range(lowercase_   ,       lowercase_ ):
        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(lowercase_   ,       lowercase_ )
            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(lowercase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            __UpperCamelCase											=  model(**lowercase_ )
            __UpperCamelCase											=  outputs.loss
            __UpperCamelCase											=  loss / gradient_accumulation_steps
            accelerator.backward(lowercase_ )
            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(lowercase_   ,       lowercase_ ):
                __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   ,       lowercase_ )
                    accelerator.save_state(lowercase_ )

        model.eval()
        for step, batch in enumerate(lowercase_ ):
            # 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(**lowercase_ )
            __UpperCamelCase											=  outputs.logits.argmax(dim=-1 )
            __UpperCamelCase						, __UpperCamelCase											=  accelerator.gather_for_metrics((predictions, batch['''labels''']) )
            metric.add_batch(
                predictions=lowercase_   ,       references=lowercase_   ,       )

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

        # 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   ,       lowercase_ )
            accelerator.save_state(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  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.''' )
    parser.add_argument(
        '''--checkpointing_steps'''   ,       type=lowercase_   ,       default=lowercase_   ,       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=lowercase_   ,       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=lowercase_   ,       default=lowercase_   ,       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(lowercase_   ,       lowercase_ )


if __name__ == "__main__":
 main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

 a_										=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






