





'''simple docstring'''

import logging
import os
import threading
import time


try:
  import warnings
except ImportError:
  __lowerCAmelCase							:	Any			=None

try:
  import msvcrt
except ImportError:
  __lowerCAmelCase							:	str			=None

try:
  import fcntl
except ImportError:
  __lowerCAmelCase							:	str			=None


# Backward compatibility
# ------------------------------------------------
try:
  TimeoutError
except NameError:
  __lowerCAmelCase							:	Any			=OSError


# Data
# ------------------------------------------------
__lowerCAmelCase							:	Union[str, Any]			=[
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

__lowerCAmelCase							:	Any			="3.0.12"


__lowerCAmelCase							:	Optional[Any]			=None





def 						UpperCamelCase				(							):
    global _logger
    A__				=			_logger or logging.getLogger(__name__       )
    return _logger






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :List[str]						,       lowercase_      :Any							)->							str:
     A__				=			lock_file
     return None



 def __str__(				self      :List[str]							)->							List[Any]:
     A__				=			F"The file lock '{self.lock_file}' could not be acquired."
     return temp






class    UpperCAmelCase	:
 def __init__(				self      :str						,       lowercase_      :str							)->							str:
     A__				=			lock
     return None
 def __enter__(				self      :Dict							)->							Any:
     return self.lock



 def __exit__(				self      :int						,       lowercase_      :int						,       lowercase_      :List[str]						,       lowercase_      :Any							)->							int:
     self.lock.release()
     return None






class    UpperCAmelCase	:
 def __init__(				self      :List[str]						,       lowercase_      :Dict						,       lowercase_      :Optional[int]=-1						,       lowercase_      :Optional[int]=None							)->							str:
     A__				=			max_filename_length if max_filename_length is not None else 2_55
     # Hash the filename if it's too long
     A__				=			self.hash_filename_if_too_long(lowercase_						,       lowercase_							)
     # The path to the lock file.
     A__				=			lock_file

     # The file descriptor for the *_lock_file* as it is returned by the
     # os.open() function.
     # This file lock is only NOT None, if the object currently holds the
     # lock.
     A__				=			None

     # The default timeout value.
     A__				=			timeout

     # We use this lock primarily for the lock counter.
     A__				=			threading.Lock()

     # The lock counter is used for implementing the nested locking
     # mechanism. Whenever the lock is acquired, the counter is increased and
     # the lock is only released, when this value is 0 again.
     A__				=			0
     return None
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Tuple:
     return self._lock_file
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Dict:
     return self._timeout
 @timeout.setter
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str							)->							int:
     A__				=			float(lowercase_							)
     return None
 def      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     raise NotImplementedError()
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     raise NotImplementedError()
 @property
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[int]:
     return self._lock_file_fd is not None
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :List[str]=None						,       lowercase_      :Tuple=0.0_5							)->							List[Any]:
     # Use the default timeout, if no timeout is provided.
     if timeout is None:
         A__				=			self.timeout

     # Increment the number right at the beginning.
     # We can still undo it, if something fails.
     with self._thread_lock:
         self._lock_counter += 1

     A__				=			id(self							)
     A__				=			self._lock_file
     A__				=			time.time()
     try:
         while True:
             with self._thread_lock:
                 if not self.is_locked:
                     logger().debug(F"Attempting to acquire lock {lock_id} on {lock_filename}"							)
                     self._acquire()

             if self.is_locked:
                 logger().debug(F"Lock {lock_id} acquired on {lock_filename}"							)
                 break
             elif timeout >= 0 and time.time() - start_time > timeout:
                 logger().debug(F"Timeout on acquiring lock {lock_id} on {lock_filename}"							)
                 raise Timeout(self._lock_file							)
             else:
                 logger().debug(
                     F"Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."							)
                 time.sleep(lowercase_							)
     except:  # noqa
         # Something did go wrong, so decrement the counter.
         with self._thread_lock:
             A__				=			max(0						,       self._lock_counter - 1							)

         raise
     return _Acquire_ReturnProxy(lock=self							)
 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :Optional[int]=False							)->							List[Any]:
     with self._thread_lock:
         if self.is_locked:
             self._lock_counter -= 1

             if self._lock_counter == 0 or force:
                 A__				=			id(self							)
                 A__				=			self._lock_file

                 logger().debug(F"Attempting to release lock {lock_id} on {lock_filename}"							)
                 self._release()
                 A__				=			0
                 logger().debug(F"Lock {lock_id} released on {lock_filename}"							)

     return None
 def __enter__(				self      :int							)->							Union[str, Any]:
     self.acquire()
     return self
 def __exit__(				self      :Tuple						,       lowercase_      :Tuple						,       lowercase_      :str						,       lowercase_      :Any							)->							Dict:
     self.release()
     return None
 def __del__(				self      :str							)->							Tuple:
     self.release(force=lowercase_							)
     return None



 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :str						,       lowercase_      :int							)->							str:
     A__				=			os.path.basename(lowercase_							)
     if len(lowercase_							) > max_length and max_length > 0:
         A__				=			os.path.dirname(lowercase_							)
         A__				=			str(hash(lowercase_							)							)
         A__				=			filename[: max_length - len(lowercase_							) - 8] + "..." + hashed_filename + ".lock"
         return os.path.join(lowercase_						,       lowercase_							)
     else:
         return path






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Dict						,       lowercase_      :Union[str, Any]						,       lowercase_      :Optional[Any]=-1						,       lowercase_      :Dict=None							)->							List[Any]:
     from .file_utils import relative_to_absolute_path

     super().__init__(lowercase_						,       timeout=lowercase_						,       max_filename_length=lowercase_							)
     A__				=			"\\\\?\\" + relative_to_absolute_path(self.lock_file							)
 def      UpperCAmelCase_							(				self      :Any							)->							Dict:
     A__				=			os.O_RDWR | os.O_CREAT | os.O_TRUNC

     try:
         A__				=			os.open(self._lock_file						,       lowercase_							)
     except OSError:
         pass
     else:
         try:
             msvcrt.locking(lowercase_						,       msvcrt.LK_NBLCK						,       1							)
         except OSError:
             os.close(lowercase_							)
         else:
             A__				=			fd
     return None



 def      UpperCAmelCase_							(				self      :int							)->							Optional[int]:
     A__				=			self._lock_file_fd
     A__				=			None
     msvcrt.locking(lowercase_						,       msvcrt.LK_UNLCK						,       1							)
     os.close(lowercase_							)

     try:
         os.remove(self._lock_file							)
     # Probably another instance of the application
     # that acquired the file lock.
     except OSError:
         pass
     return None






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Optional[Any]						,       lowercase_      :Tuple						,       lowercase_      :List[str]=-1						,       lowercase_      :List[Any]=None							)->							List[Any]:
     A__				=			os.statvfs(os.path.dirname(lowercase_							)							).f_namemax
     super().__init__(lowercase_						,       timeout=lowercase_						,       max_filename_length=lowercase_							)
 def      UpperCAmelCase_							(				self      :Tuple							)->							Any:
     A__				=			os.O_RDWR | os.O_CREAT | os.O_TRUNC
     A__				=			os.open(self._lock_file						,       lowercase_							)

     try:
         fcntl.flock(lowercase_						,       fcntl.LOCK_EX | fcntl.LOCK_NB							)
     except OSError:
         os.close(lowercase_							)
     else:
         A__				=			fd
     return None



 def      UpperCAmelCase_							(				self      :List[Any]							)->							str:
     # Do not remove the lockfile:
     #
     #   https://github.com/benediktschmitt/py-filelock/issues/31
     #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
     A__				=			self._lock_file_fd
     A__				=			None
     fcntl.flock(lowercase_						,       fcntl.LOCK_UN							)
     os.close(lowercase_							)
     return None






class    UpperCAmelCase	(       UpperCamelCase__				):
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     A__				=			os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
     try:
         A__				=			os.open(self._lock_file						,       lowercase_							)
     except OSError:
         pass
     else:
         A__				=			fd
     return None



 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							List[Any]:
     os.close(self._lock_file_fd							)
     A__				=			None

     try:
         os.remove(self._lock_file							)
     # The file is already deleted and that's what we want.
     except OSError:
         pass
     return None

__lowerCAmelCase							:	int			=None

if msvcrt:
  __lowerCAmelCase							:	List[str]			=WindowsFileLock
elif fcntl:
  __lowerCAmelCase							:	Union[str, Any]			=UnixFileLock
else:
  __lowerCAmelCase							:	str			=SoftFileLock

  if warnings is not None:
    warnings.warn("only soft file lock is available")










'''simple docstring'''

import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


__lowerCAmelCase							:	Optional[int]			=16
__lowerCAmelCase							:	Tuple			=32





def 						UpperCamelCase				(							_lowerCamelCase					:					Accelerator						,       _lowerCamelCase					:					DatasetDict						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					int = 16       ):
    A__				=			AutoTokenizer.from_pretrained("bert-base-cased"       )
    A__				=			DatasetDict(
        {
            "train": dataset["train"].select(_lowerCamelCase       ),
            "validation": dataset["train"].select(_lowerCamelCase       ),
            "test": dataset["validation"],
        }       )

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

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

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

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

    A__				=			DataLoader(
        tokenized_datasets["test"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )

    return train_dataloader, eval_dataloader, test_dataloader





def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					str       ):
    # New Code #
    A__				=			[]
    # Download the dataset
    A__				=			load_dataset("glue"						,       "mrpc"       )
    # Create our splits
    A__				=			StratifiedKFold(n_splits=int(args.num_folds       )       )
    # Initialize accelerator
    A__				=			Accelerator(cpu=args.cpu						,       mixed_precision=args.mixed_precision       )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__				=			config["lr"]
    A__				=			int(config["num_epochs"]       )
    A__				=			int(config["seed"]       )
    A__				=			int(config["batch_size"]       )

    A__				=			evaluate.load("glue"						,       "mrpc"       )

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

    set_seed(_lowerCamelCase       )

    # New Code #
    # Create our folds:
    A__				=			kfold.split(np.zeros(datasets["train"].num_rows       )						,       datasets["train"]["label"]       )
    A__				=			[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(_lowerCamelCase       ):
        A__,	A__,	A__				=			get_fold_dataloaders(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        A__				=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,       return_dict=_lowerCamelCase       )

        # 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__				=			model.to(accelerator.device       )

        # Instantiate optimizer
        A__				=			AdamW(params=model.parameters()						,       lr=_lowerCamelCase       )

        # Instantiate scheduler
        A__				=			get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase						,       num_warmup_steps=1_00						,       num_training_steps=(len(_lowerCamelCase       ) * 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__				=			accelerator.prepare(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

        # Now we train the model
        for epoch in range(_lowerCamelCase       ):
            model.train()
            for step, batch in enumerate(_lowerCamelCase       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                A__				=			model(**_lowerCamelCase       )
                A__				=			outputs.loss
                A__				=			loss / gradient_accumulation_steps
                accelerator.backward(_lowerCamelCase       )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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

        # New Code #
        # We also run predictions on the test set at the very end
        A__				=			[]
        for step, batch in enumerate(_lowerCamelCase       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                A__				=			model(**_lowerCamelCase       )
            A__				=			outputs.logits
            A__,	A__				=			accelerator.gather_for_metrics((predictions, batch["labels"])       )
            fold_predictions.append(predictions.cpu()       )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(_lowerCamelCase						,       dim=0       )       )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    A__				=			torch.cat(_lowerCamelCase						,       dim=0       )
    A__				=			torch.stack(_lowerCamelCase						,       dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    A__				=			metric.compute(predictions=_lowerCamelCase						,       references=_lowerCamelCase       )
    accelerator.print("Average test metrics from all folds:"						,       _lowerCamelCase       )





def 						UpperCamelCase				(							):
    A__				=			argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision"						,       type=_lowerCamelCase						,       default=_lowerCamelCase						,       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."       )
    # New Code #
    parser.add_argument("--num_folds"						,       type=_lowerCamelCase						,       default=3						,       help="The number of splits to perform across the dataset"       )
    A__				=			parser.parse_args()
    A__				=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase						,       _lowerCamelCase       )


if __name__ == "__main__":
  main()



