

'''simple docstring'''

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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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    A_  ( snake_case      ,       snake_case = 16 ):
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoTokenizer.from_pretrained("bert-base-cased" )
  SCREAMING_SNAKE_CASE:Any						      =					load_dataset("glue"      ,       "mrpc" )

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

  def collate_fn(snake_case ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    SCREAMING_SNAKE_CASE:str						      =					128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      SCREAMING_SNAKE_CASE:Tuple						      =					16
    elif accelerator.mixed_precision != "no":
      SCREAMING_SNAKE_CASE:Dict						      =					8
    else:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					None

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

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(
      tokenized_datasets["train"]      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					DataLoader(
      tokenized_datasets["validation"]      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case )

  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    A_  ( snake_case      ,       snake_case ):






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

  SCREAMING_SNAKE_CASE:Tuple						      =					evaluate.load("glue"      ,       "mrpc" )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=snake_case )
  def inner_training_loop(snake_case ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(snake_case )

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

    # 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).
    SCREAMING_SNAKE_CASE:Optional[int]						      =					model.to(accelerator.device )

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE:Optional[int]						      =					AdamW(params=model.parameters()      ,       lr=snake_case )
    SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloaders(snake_case      ,       snake_case )

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE:int						      =					get_linear_schedule_with_warmup(
        optimizer=snake_case      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case ) * 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.
    SCREAMING_SNAKE_CASE:Tuple						      =					accelerator.prepare(
        snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

    # Now we train the model
    for epoch in range(snake_case ):
      model.train()
      for step, batch in enumerate(snake_case ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device )
        SCREAMING_SNAKE_CASE:int						      =					model(**snake_case )
        SCREAMING_SNAKE_CASE:Optional[int]						      =					outputs.loss
        accelerator.backward(snake_case )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

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

    # 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    A_  ( ):
  SCREAMING_SNAKE_CASE:int						      =					argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument(
      "--mixed_precision"      ,       type=snake_case      ,       default=snake_case      ,       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." )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					parser.parse_args()
  SCREAMING_SNAKE_CASE:Optional[int]						      =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(snake_case      ,       snake_case )


if __name__ == "__main__":
    main()









'''simple docstring'''

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  for line in lines:
    SCREAMING_SNAKE_CASE:Any						      =					re.sub(r"#.*"      ,       ""      ,       snake_case )  # remove comments
    if line:
      filtered_lines.append(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					"\n".join(snake_case )

  # Make a hash from all this code
  SCREAMING_SNAKE_CASE:str						      =					full_str.encode("utf-8" )
  return shaaaa(snake_case ).hexdigest()


# get importable module names and hash for caching
A_       						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A_       						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_       						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A_       						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
    _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")






