import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


UpperCamelCase__    							= get_logger(__name__)





class 					A							:


   def __init__(self					:						List[Any]	,  __UpperCAmelCase					:						Optional[int] = None		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=   (
             os.path.join(_SCREAMING_SNAKE_CASE	,  config.EXTRACTED_DATASETS_DIR		) if cache_dir else config.EXTRACTED_DATASETS_PATH
         )
         UpperCAmelCase__								=   Extractor


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Tuple		)						->    str:
         """simple docstring"""



         from .file_utils import hash_url_to_filename

         # Path where we extract compressed archives
         # We extract in the cache dir, and get the extracted path name by hashing the original path"
         UpperCAmelCase__								=   os.path.abspath(_SCREAMING_SNAKE_CASE		)
         return os.path.join(self.extract_dir	,  hash_url_to_filename(_SCREAMING_SNAKE_CASE		)		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Optional[Any]		)						->    bool:
         """simple docstring"""



         return force_extract or (
             not os.path.isfile(_SCREAMING_SNAKE_CASE		) and not (os.path.isdir(_SCREAMING_SNAKE_CASE		) and os.listdir(_SCREAMING_SNAKE_CASE		))
         )





   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int = False		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=   self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE		)
         if not extractor_format:
               return input_path
         UpperCAmelCase__								=   self._get_output_path(_SCREAMING_SNAKE_CASE		)
         if self._do_extract(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		):
               self.extractor.extract(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)
         return output_path





class 					A							(    lowerCAmelCase__					):


   @classmethod
   @abstractmethod
   def       lowercase_				(cls					:						Dict	,  __UpperCAmelCase					:						str	,  **__UpperCAmelCase					:						str		)						->    bool:
         """simple docstring"""



         ...





   @staticmethod
   @abstractmethod
   def       lowercase_				(__UpperCAmelCase					:						int	,  __UpperCAmelCase					:						int		)						->    None:
         """simple docstring"""



         ...





class 					A							(    lowerCAmelCase__		,    lowerCAmelCase__					):
   __UpperCAmelCase			:			List[bytes]              =      []


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						int		)						->    Dict:
         """simple docstring"""



         with open(_SCREAMING_SNAKE_CASE	,  "rb"		) as f:
               return f.read(_SCREAMING_SNAKE_CASE		)





   @classmethod
   def       lowercase_				(cls					:						Optional[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						int = b""		)						->    bool:
         """simple docstring"""



         if not magic_number:
               UpperCAmelCase__								=   max(len(_SCREAMING_SNAKE_CASE		) for cls_magic_number in cls.magic_numbers		)
               try:
                     UpperCAmelCase__								=   cls.read_magic_number(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)
               except OSError:
                     return False
         return any(magic_number.startswith(_SCREAMING_SNAKE_CASE		) for cls_magic_number in cls.magic_numbers		)





class 					A							(    lowerCAmelCase__					):


   @classmethod
   def       lowercase_				(cls					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  **__UpperCAmelCase					:						List[Any]		)						->    bool:
         """simple docstring"""



         return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE		)


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Optional[Any]		)						->    str:
         """simple docstring"""



         def resolved(__UpperCAmelCase					:						Any		) -> str:
               return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE		)		)

         def badpath(__UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Any		) -> bool:
               # joinpath will ignore base if path is absolute
               return not resolved(os.path.join(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)		).startswith(_SCREAMING_SNAKE_CASE		)

         def badlink(__UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						str		) -> bool:
               # Links are interpreted relative to the directory containing the link
               UpperCAmelCase__								=   resolved(os.path.join(_SCREAMING_SNAKE_CASE	,  os.path.dirname(info.name		)		)		)
               return badpath(info.linkname	,  base=_SCREAMING_SNAKE_CASE		)

         UpperCAmelCase__								=   resolved(_SCREAMING_SNAKE_CASE		)

         for finfo in members:
               if badpath(finfo.name	,  _SCREAMING_SNAKE_CASE		):
                     logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)"""		)
               elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		):
                     logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""		)
               elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		):
                     logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""		)
               else:
                     yield finfo





   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int		)						->    None:
         """simple docstring"""



         os.makedirs(_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE		)
         UpperCAmelCase__								=   tarfile.open(_SCREAMING_SNAKE_CASE		)
         tar_file.extractall(_SCREAMING_SNAKE_CASE	,  members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)		)
         tar_file.close()





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Optional[int]              =      [b"\x1F\x8B"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Union[str, Any]		)						->    None:
         """simple docstring"""



         with gzip.open(_SCREAMING_SNAKE_CASE	,  "rb"		) as gzip_file:
               with open(_SCREAMING_SNAKE_CASE	,  "wb"		) as extracted_file:
                     shutil.copyfileobj(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Optional[Any]              =      [
       b"PK\x03\x04",
       b"PK\x05\x06",  # empty archive
       b"PK\x07\x08",  # spanned archive
   ]


   @classmethod
   def       lowercase_				(cls					:						List[Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						str = b""		)						->    bool:
         """simple docstring"""



         if super().is_extractable(_SCREAMING_SNAKE_CASE	,  magic_number=_SCREAMING_SNAKE_CASE		):
               return True
         try:
               # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
               # From: https://github.com/python/cpython/pull/5053
               from zipfile import (
                   _CD_SIGNATURE,
                   _ECD_DISK_NUMBER,
                   _ECD_DISK_START,
                   _ECD_ENTRIES_TOTAL,
                   _ECD_OFFSET,
                   _ECD_SIZE,
                   _EndRecData,
                   sizeCentralDir,
                   stringCentralDir,
                   structCentralDir,
               )

               with open(_SCREAMING_SNAKE_CASE	,  "rb"		) as fp:
                     UpperCAmelCase__								=   _EndRecData(_SCREAMING_SNAKE_CASE		)
                     if endrec:
                           if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                                 return True  # Empty zipfiles are still zipfiles
                           elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                                 fp.seek(endrec[_ECD_OFFSET]		)  # Central directory is on the same disk
                                 if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                                       UpperCAmelCase__								=   fp.read(_SCREAMING_SNAKE_CASE		)  # CD is where we expect it to be
                                       if len(_SCREAMING_SNAKE_CASE		) == sizeCentralDir:
                                             UpperCAmelCase__								=   struct.unpack(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)  # CD is the right size
                                             if centdir[_CD_SIGNATURE] == stringCentralDir:
                                                   return True  # First central directory entry  has correct magic number
               return False
         except Exception:  # catch all errors in case future python versions change the zipfile internals
               return False





   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						List[str]		)						->    None:
         """simple docstring"""



         os.makedirs(_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE		)
         with zipfile.ZipFile(_SCREAMING_SNAKE_CASE	,  "r"		) as zip_file:
               zip_file.extractall(_SCREAMING_SNAKE_CASE		)
               zip_file.close()





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Optional[Any]              =      [b"\xFD\x37\x7A\x58\x5A\x00"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]		)						->    None:
         """simple docstring"""



         with lzma.open(_SCREAMING_SNAKE_CASE		) as compressed_file:
               with open(_SCREAMING_SNAKE_CASE	,  "wb"		) as extracted_file:
                     shutil.copyfileobj(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Dict              =      [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"]  # RAR_ID  # RAR5_ID


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int		)						->    None:
         """simple docstring"""



         if not config.RARFILE_AVAILABLE:
               raise ImportError("Please pip install rarfile"		)
         import rarfile

         os.makedirs(_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE		)
         UpperCAmelCase__								=   rarfile.RarFile(_SCREAMING_SNAKE_CASE		)
         rf.extractall(_SCREAMING_SNAKE_CASE		)
         rf.close()





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Optional[Any]              =      [b"\x28\xb5\x2F\xFD"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						int	,  __UpperCAmelCase					:						List[str]		)						->    None:
         """simple docstring"""



         if not config.ZSTANDARD_AVAILABLE:
               raise ImportError("Please pip install zstandard"		)
         import zstandard as zstd

         UpperCAmelCase__								=   zstd.ZstdDecompressor()
         with open(_SCREAMING_SNAKE_CASE	,  "rb"		) as ifh, open(_SCREAMING_SNAKE_CASE	,  "wb"		) as ofh:
               dctx.copy_stream(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Optional[Any]              =      [b"\x42\x5A\x68"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						str		)						->    None:
         """simple docstring"""



         with bza.open(_SCREAMING_SNAKE_CASE	,  "rb"		) as compressed_file:
               with open(_SCREAMING_SNAKE_CASE	,  "wb"		) as extracted_file:
                     shutil.copyfileobj(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			int              =      [b"\x37\x7A\xBC\xAF\x27\x1C"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Tuple		)						->    None:
         """simple docstring"""



         if not config.PY7ZR_AVAILABLE:
               raise ImportError("Please pip install py7zr"		)
         import pyazr

         os.makedirs(_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE		)
         with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE	,  "r"		) as archive:
               archive.extractall(_SCREAMING_SNAKE_CASE		)





class 					A							(    lowerCAmelCase__					):
   __UpperCAmelCase			:			Any              =      [b"\x04\x22\x4D\x18"]


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Union[str, Any]		)						->    None:
         """simple docstring"""



         if not config.LZ4_AVAILABLE:
               raise ImportError("Please pip install lz4"		)
         import lza.frame

         with lza.frame.open(_SCREAMING_SNAKE_CASE	,  "rb"		) as compressed_file:
               with open(_SCREAMING_SNAKE_CASE	,  "wb"		) as extracted_file:
                     shutil.copyfileobj(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)





class 					A							:
   #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
   __UpperCAmelCase			:			Dict[str, Type[BaseExtractor]]              =      {
       "tar": TarExtractor,
       "gzip": GzipExtractor,
       "zip": ZipExtractor,
       "xz": XzExtractor,
       "rar": RarExtractor,
       "zstd": ZstdExtractor,
       "bz2": BzipaExtractor,
       "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
       "lz4": LzaExtractor,  # <Added version="2.4.0"/>
   }


   @classmethod
   def       lowercase_				(cls					:						str		)						->    int:
         """simple docstring"""



         return max(
             len(_SCREAMING_SNAKE_CASE		)
             for extractor in cls.extractors.values()
             if issubclass(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)
             for extractor_magic_number in extractor.magic_numbers		)


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         try:
               return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE	,  magic_number_length=_SCREAMING_SNAKE_CASE		)
         except OSError:
               return b""


   @classmethod
   def       lowercase_				(cls					:						str	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Tuple = False		)						->    bool:
         """simple docstring"""



         warnings.warn(
             "Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
             "Use 'infer_extractor_format' instead."	,  category=_SCREAMING_SNAKE_CASE	,  )
         UpperCAmelCase__								=   cls.infer_extractor_format(_SCREAMING_SNAKE_CASE		)
         if extractor_format:
               return True if not return_extractor else (True, cls.extractors[extractor_format])
         return False if not return_extractor else (False, None)


   @classmethod
   def       lowercase_				(cls					:						Any	,  __UpperCAmelCase					:						Optional[int]		)						->    str:  # <Added version="2.4.0"/>
         """simple docstring"""



         UpperCAmelCase__								=   cls._get_magic_number_max_length()
         UpperCAmelCase__								=   cls._read_magic_number(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)
         for extractor_format, extractor in cls.extractors.items():
               if extractor.is_extractable(_SCREAMING_SNAKE_CASE	,  magic_number=_SCREAMING_SNAKE_CASE		):
                     return extractor_format





   @classmethod
   def       lowercase_				(cls					:						Dict	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						List[Any] = None	,  __UpperCAmelCase					:						str = "deprecated"	,  )						->    None:
         """simple docstring"""



         os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE		)	,  exist_ok=_SCREAMING_SNAKE_CASE		)
         # Prevent parallel extractions
         UpperCAmelCase__								=   str(Path(_SCREAMING_SNAKE_CASE		).with_suffix(".lock"		)		)
         with FileLock(_SCREAMING_SNAKE_CASE		):
               shutil.rmtree(_SCREAMING_SNAKE_CASE	,  ignore_errors=_SCREAMING_SNAKE_CASE		)
               if extractor_format or extractor != "deprecated":
                     if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		):  # passed as positional arg
                           warnings.warn(
                               "Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                               "Use 'extractor_format' instead."	,  category=_SCREAMING_SNAKE_CASE	,  )
                           UpperCAmelCase__								=   extractor if extractor != "deprecated" else extractor_format
                     else:
                           UpperCAmelCase__								=   cls.extractors[extractor_format]
                     return extractor.extract(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)
               else:
                     warnings.warn(
                         "Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
                         "exception in 3.0.0."	,  category=_SCREAMING_SNAKE_CASE	,  )
                     for extractor in cls.extractors.values():
                           if extractor.is_extractable(_SCREAMING_SNAKE_CASE		):
                                 return extractor.extract(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE		)




import argparse
import gc
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase__    							= 1_6
UpperCamelCase__    							= 3_2




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return int(x / 2**20							)





class 					A							:


   def __enter__(self					:						Dict		)						->    int:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         return self





   def __exit__(self					:						List[str]	,  *__UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         UpperCAmelCase__								=  torch.cuda.max_memory_allocated()
         UpperCAmelCase__								=  bamb(self.end - self.begin		)
         UpperCAmelCase__								=  bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def        lowerCAmelCase_			(					__A,  __A = 16,  __A = "bert-base-cased",  __A = 320,  __A = 160,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      UpperCAmelCase__								=  load_dataset(
          "glue",  "mrpc",  split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}							)

      def tokenize_function(__A							):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase__								=  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
      UpperCAmelCase__								=  datasets.map(
          __A,  batched=__A,  remove_columns=["idx", "sentence1", "sentence2"],  load_from_cache_file=__A							)

      # 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(__A							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__A,  padding="max_length",  max_length=128,  return_tensors="pt"							)
            return tokenizer.pad(__A,  padding="longest",  return_tensors="pt"							)

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

      return train_dataloader, eval_dataloader




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()

      # 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"]							)
      UpperCAmelCase__								=  args.model_name_or_path

      set_seed(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  get_dataloaders(__A,  __A,  __A,  args.n_train,  args.n_val							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase__								=  AutoModelForSequenceClassification.from_pretrained(__A,  return_dict=__A							)

      # Instantiate optimizer
      UpperCAmelCase__								=  (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCAmelCase__								=  optimizer_cls(params=model.parameters(),  lr=__A							)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase__								=  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCAmelCase__								=  1
      UpperCAmelCase__								=  (len(__A							) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCAmelCase__								=  get_linear_schedule_with_warmup(
                optimizer=__A,  num_warmup_steps=0,  num_training_steps=__A,  )
      else:
            UpperCAmelCase__								=  DummyScheduler(__A,  total_num_steps=__A,  warmup_num_steps=0							)

      # 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(
          __A,  __A,  __A,  __A,  __A							)

      # 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

      # Now we train the model
      UpperCAmelCase__								=  {}
      for epoch in range(__A,  __A							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__A							):
                        UpperCAmelCase__								=  model(**__A							)
                        UpperCAmelCase__								=  outputs.loss
                        UpperCAmelCase__								=  loss / gradient_accumulation_steps
                        accelerator.backward(__A							)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin							)							)							)
            accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used							)							)
            accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked							)							)
            accelerator.print(
                "Total Peak Memory consumed during the train (max): {}".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
            UpperCAmelCase__								=  tracemalloc.peaked + bamb(tracemalloc.begin							)
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir,  "peak_memory_utilization.json"							),  "w"							) as f:
                  json.dump(__A,  __A							)




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
      parser.add_argument(
          "--model_name_or_path",  type=__A,  default="bert-base-cased",  help="Path to pretrained model or model identifier from huggingface.co/models.",  required=__A,  )
      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(
          "--peak_memory_upper_bound",  type=__A,  default=__A,  help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.",  )
      parser.add_argument(
          "--n_train",  type=__A,  default=320,  help="Number of training examples to use.",  )
      parser.add_argument(
          "--n_val",  type=__A,  default=160,  help="Number of validation examples to use.",  )
      parser.add_argument(
          "--num_epochs",  type=__A,  default=1,  help="Number of train epochs.",  )
      UpperCAmelCase__								=  parser.parse_args()
      UpperCAmelCase__								=  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__A,  __A							)


if __name__ == "__main__":
      main()



