






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

_UpperCamelCase							:       Optional[int]       					=		16
_UpperCamelCase							:       Union[str, Any]       					=		32




def      snake_case    (A_ :Accelerator      ,     A_ :int = 1_6 ):







  '''simple docstring'''

  a	:     int							=  AutoTokenizer.from_pretrained('bert-base-cased' )
  a	:     Union[str, Any]							=  load_dataset('glue'      ,     'mrpc' )

  def tokenize_function(A_ :Union[str, Any] ):
    # max_length=None => use the model max length (it's actually the default)
    a	:     Optional[int]							=  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	:     List[str]							=  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	:     List[str]							=  tokenized_datasets.rename_column('label'      ,     'labels' )

  def collate_fn(A_ :List[Any] ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a	:     Optional[Any]							=  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	:     Dict							=  1_6
    elif accelerator.mixed_precision != "no":
      a	:     Optional[Any]							=  8
    else:
      a	:     int							=  None

    return tokenizer.pad(
        A_      ,     padding='longest'      ,     max_length=A_      ,     pad_to_multiple_of=A_      ,     return_tensors='pt'      ,     )

  # Instantiate dataloaders.
  a	:     str							=  DataLoader(
      tokenized_datasets['train']      ,     shuffle=A_      ,     collate_fn=A_      ,     batch_size=A_ )
  a	:     Dict							=  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

   _UpperCamelCase							:       List[Any]       					=		mocked_dataloaders  # noqa: F811




def      snake_case    (A_ :Optional[int]      ,     A_ :Optional[int] ):







  '''simple docstring'''

  if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,     A_ ) == "1":
    a	:     Union[str, Any]							=  2
  # Initialize accelerator
  a	:     Optional[int]							=  Accelerator(cpu=args.cpu      ,     mixed_precision=args.mixed_precision )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a	:     str							=  config['lr']
  a	:     str							=  int(config['num_epochs'] )
  a	:     Tuple							=  int(config['seed'] )
  a	:     Optional[Any]							=  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":
      a	:     int							=  args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      a	:     Any							=  int(args.checkpointing_steps )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
  else:
    a	:     str							=  None

  set_seed(A_ )

  a,	a	:     Union[str, Any]							=  get_dataloaders(A_      ,     A_ )
  a	:     Dict							=  evaluate.load('glue'      ,     'mrpc' )

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

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a	:     List[Any]							=  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	:     Any							=  model.to(accelerator.device )

  # Instantiate optimizer
  a	:     Optional[int]							=  AdamW(params=model.parameters()      ,     lr=A_ )

  # Instantiate scheduler
  a	:     str							=  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,	a,	a,	a,	a	:     List[Any]							=  accelerator.prepare(
      A_      ,     A_      ,     A_      ,     A_      ,     A_ )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  a	:     List[str]							=  0
  # We also need to keep track of the stating epoch so files are named properly
  a	:     Optional[int]							=  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 )
      a	:     Tuple							=  os.path.basename(args.resume_from_checkpoint )
    else:
      # Get the most recent checkpoint
      a	:     Optional[Any]							=  [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
      dirs.sort(key=os.path.getctime )
      a	:     Any							=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    a	:     Dict							=  os.path.splitext(A_ )[0]

    if "epoch" in training_difference:
      a	:     List[str]							=  int(training_difference.replace('epoch_'      ,     '' ) ) + 1
      a	:     int							=  None
    else:
      a	:     List[Any]							=  int(training_difference.replace('step_'      ,     '' ) )
      a	:     Tuple							=  resume_step // len(A_ )
      resume_step -= starting_epoch * len(A_ )

    # Now we train the model
  for epoch in range(A_      ,     A_ ):
    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
      a	:     Any							=  accelerator.skip_first_batches(A_      ,     A_ )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      a	:     Union[str, Any]							=  train_dataloader
    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	:     List[Any]							=  model(**A_ )
      a	:     Any							=  outputs.loss
      a	:     List[str]							=  loss / gradient_accumulation_steps
      accelerator.backward(A_ )
      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(A_      ,     A_ ):
        a	:     Union[str, Any]							=  f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            a	:     Tuple							=  os.path.join(args.output_dir      ,     A_ )
          accelerator.save_state(A_ )

    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	:     List[str]							=  model(**A_ )
      a	:     Optional[Any]							=  outputs.logits.argmax(dim=-1 )
      a,	a	:     Tuple							=  accelerator.gather_for_metrics((predictions, batch['labels']) )
      metric.add_batch(
          predictions=A_      ,     references=A_      ,     )

    a	:     List[str]							=  metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''      ,     A_ )

    # 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":
      a	:     Tuple							=  f'''epoch_{epoch}'''
      if args.output_dir is not None:
        a	:     str							=  os.path.join(args.output_dir      ,     A_ )
      accelerator.save_state(A_ )




def      snake_case    ():







  '''simple docstring'''

  a	:     Optional[int]							=  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(
      '--checkpointing_steps'      ,     type=A_      ,     default=A_      ,     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=A_      ,     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=A_      ,     default=A_      ,     help='If the training should continue from a checkpoint folder.'      ,     )
  a	:     Optional[int]							=  parser.parse_args()
  a	:     Union[str, Any]							=  {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
  training_function(A_      ,     A_ )


if __name__ == "__main__":
   main()












"""simple docstring"""


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


_UpperCamelCase							:       int       					=		'Update an existing config file with the latest defaults while maintaining the old configuration.'




def      snake_case    (A_ :str ):







  '''simple docstring'''

  a	:     int							=  args.config_file
  if config_file is None and Path(A_ ).exists():
    a	:     List[Any]							=  default_config_file
  elif not Path(A_ ).exists():
    raise ValueError(f'''The passed config file located at {config_file} doesn\'t exist.''' )
  a	:     Dict							=  load_config_from_file(A_ )

  if config_file.endswith('.json' ):
    config.to_json_file(A_ )
  else:
    config.to_yaml_file(A_ )
  return config_file




def      snake_case    (A_ :List[str]      ,     A_ :Union[str, Any] ):







  '''simple docstring'''

  a	:     int							=  parser.add_parser('update'      ,     parents=A_      ,     help=A_      ,     formatter_class=A_ )
  parser.add_argument(
      '--config_file'      ,     default=A_      ,     help=(
          'The path to the config file to update. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )      ,     )

  parser.set_defaults(func=A_ )
  return parser






def      snake_case    (A_ :List[str] ):







  '''simple docstring'''

  a	:     List[str]							=  update_config(A_ )
  print(f'''Sucessfully updated the configuration file at {config_file}.''' )




