code: 
  
  
  import copy
  import fnmatch
  import json
  import os
  import pickle as pkl
  import shutil
  import sys
  import tarfile
  import tempfile
  from collections import OrderedDict
  from contextlib import contextmanager
  from functools import partial
  from hashlib import shaaaa
  from io import BytesIO
  from pathlib import Path
  from urllib.parse import urlparse
  from zipfile import ZipFile, is_zipfile
  
  import cva
  import numpy as np
  import requests
  import wget
  from filelock import FileLock
  from PIL import Image
  from tqdm.auto import tqdm
  from yaml import Loader, dump, load
  
  
  try:
  					import torch
  
  					UpperCAmelCase__      =							True
  except ImportError:
  					UpperCAmelCase__      =							False
  
  
  try:
  					from torch.hub import _get_torch_home
  
  					UpperCAmelCase__      =							_get_torch_home()
  except ImportError:
  					UpperCAmelCase__      =							os.path.expanduser(
  					    os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
  					)
  
  UpperCAmelCase__      =							os.path.join(torch_cache_home, '''transformers''')
  
  UpperCAmelCase__      =							'''https://cdn.huggingface.co'''
  UpperCAmelCase__      =							'''https://s3.amazonaws.com/models.huggingface.co/bert'''
  UpperCAmelCase__      =							'''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
  UpperCAmelCase__      =							os.path.join(PATH, '''config.yaml''')
  UpperCAmelCase__      =							os.path.join(PATH, '''attributes.txt''')
  UpperCAmelCase__      =							os.path.join(PATH, '''objects.txt''')
  UpperCAmelCase__      =							os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
  UpperCAmelCase__      =							os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
  UpperCAmelCase__      =							os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
  UpperCAmelCase__      =							'''pytorch_model.bin'''
  UpperCAmelCase__      =							'''config.yaml'''
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Dict=OBJECTS,_SCREAMING_SNAKE_CASE				:   str=ATTRIBUTES   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  []
  			with open(__a   ) as f:
  						for object in f.readlines():
  									vg_classes.append(object.split(','   )[0].lower().strip()   )
  
  			__A=  []
  			with open(__a   ) as f:
  						for object in f.readlines():
  									vg_attrs.append(object.split(','   )[0].lower().strip()   )
  			return vg_classes, vg_attrs
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Dict   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  OrderedDict()
  			with open(__a,'rb'   ) as f:
  						__A=  pkl.load(__a   )['''model''']
  			for k in copy.deepcopy(list(ckp.keys()   )   ):
  						__A=  ckp.pop(__a   )
  						if isinstance(__a,np.ndarray   ):
  									__A=  torch.tensor(__a   )
  						else:
  									assert isinstance(__a,torch.tensor   ), type(__a   )
  						__A=  v
  			return r
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= {}
  			def __init__(     self       : int ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Dict = "root" ,						lowerCAmelCase_       : Any=0  )			->		Optional[Any]:
  						__A=  name
  						__A=  level
  						__A=  {}
  						for k, v in dictionary.items():
  									if v is None:
  												raise ValueError()
  									__A=  copy.deepcopy(snake_case_  )
  									__A=  copy.deepcopy(snake_case_  )
  									if isinstance(snake_case_ ,						snake_case_  ):
  												__A=  Config(snake_case_ ,						name=snake_case_ ,						level=level + 1  )
  									__A=  v
  									setattr(self ,						snake_case_ ,						snake_case_  )
  
  						__A=  d
  			def __repr__(     self       : str  )			->		Dict:
  						return str(list((self._pointer.keys())  )  )
  			def __setattr__(     self       : Optional[Any] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[str]  )			->		Any:
  						__A=  val
  						__A=  val
  						__A=  key.split('.'  )
  						__A=  len(snake_case_  ) - 1
  						__A=  self._pointer
  						if len(snake_case_  ) > 1:
  									for i, l in enumerate(snake_case_  ):
  												if hasattr(self ,						snake_case_  ) and isinstance(getattr(self ,						snake_case_  ) ,						snake_case_  ):
  															setattr(getattr(self ,						snake_case_  ) ,						'.'.join(levels[i:]  ) ,						snake_case_  )
  												if l == last_level:
  															__A=  val
  												else:
  															__A=  pointer[l]
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		Tuple:
  						return self._pointer
  			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any  )			->		List[Any]:
  						with open(F"""{file_name}""" ,						'w'  ) as stream:
  									dump(snake_case_ ,						snake_case_  )
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : str  )			->		List[Any]:
  						with open(F"""{file_name}""" ,						'w'  ) as stream:
  									json.dump(snake_case_ ,						snake_case_  )
  			@staticmethod
  			def 				lowerCAmelCase    (     lowerCAmelCase_       : List[str]  )			->		Optional[Any]:
  						with open(snake_case_  ) as stream:
  									__A=  load(snake_case_ ,						Loader=snake_case_  )
  						return data
  			def __str__(     self       : List[str]  )			->		Any:
  						__A=  '''    '''
  						if self._name != "root":
  									__A=  F"""{t * (self._level-1)}{self._name}:\n"""
  						else:
  									__A=  ''''''
  						__A=  self._level
  						for i, (k, v) in enumerate(self._pointer.items()  ):
  									if isinstance(snake_case_ ,						snake_case_  ):
  												r += F"""{t * (self._level)}{v}\n"""
  												self._level += 1
  									else:
  												r += F"""{t * (self._level)}{k}: {v} ({type(snake_case_  ).__name__})\n"""
  									__A=  level
  						return r[:-1]
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : List[str] ,						lowerCAmelCase_       : Dict ,						**lowerCAmelCase_       : Dict  )			->		Optional[Any]:
  						__A=  cls.get_config_dict(snake_case_ ,						**snake_case_  )
  						return cls(snake_case_  )
  
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : Any ,						**lowerCAmelCase_       : int  )			->		Tuple:
  						__A=  kwargs.pop('cache_dir' ,						snake_case_  )
  						__A=  kwargs.pop('force_download' ,						snake_case_  )
  						__A=  kwargs.pop('resume_download' ,						snake_case_  )
  						__A=  kwargs.pop('proxies' ,						snake_case_  )
  						__A=  kwargs.pop('local_files_only' ,						snake_case_  )
  
  						if os.path.isdir(snake_case_  ):
  									__A=  os.path.join(snake_case_ ,						snake_case_  )
  						elif os.path.isfile(snake_case_  ) or is_remote_url(snake_case_  ):
  									__A=  pretrained_model_name_or_path
  						else:
  									__A=  hf_bucket_url(snake_case_ ,						filename=snake_case_ ,						use_cdn=snake_case_  )
  
  						try:
  									# Load from URL or cache if already cached
  									__A=  cached_path(
  									    snake_case_ ,						cache_dir=snake_case_ ,						force_download=snake_case_ ,						proxies=snake_case_ ,						resume_download=snake_case_ ,						local_files_only=snake_case_ ,						)
  									# Load config dict
  									if resolved_config_file is None:
  												raise EnvironmentError
  
  									__A=  Config.load_yaml(snake_case_  )
  
  						except EnvironmentError:
  									__A=  '''Can\'t load config for'''
  									raise EnvironmentError(snake_case_  )
  
  						if resolved_config_file == config_file:
  									print('loading configuration file from path'  )
  						else:
  									print('loading configuration file cache'  )
  
  						return Config.load_yaml(snake_case_  ), kwargs
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  torch.load('dump.pt',map_location=in_tensor.device   )
  			__A=  in_tensor.numpy()
  			__A=  out_tensor.numpy()[0]
  			print(na.shape,na[0, 0, :5]   )
  			print(na.shape,na[0, 0, :5]   )
  			assert np.allclose(__a,__a,rtol=0.01,atol=0.1   ), (
  			    f"""{sum([1 for x in np.isclose(__a,__a,rtol=0.01,atol=0.1   ).flatten() if x is False]   )/len(na.flatten()   )*100:.4f} %"""
  			    " element-wise mismatch"
  			)
  			raise Exception('tensors are all good'   )
  
  			# Hugging face functions below
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  urlparse(__a   )
  			return parsed.scheme in ("http", "https")
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Any=True   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  			__A=  '''/''' not in model_id
  			if legacy_format:
  						return f"""{endpoint}/{model_id}-{filename}"""
  			else:
  						return f"""{endpoint}/{model_id}/{filename}"""
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   List[str]=None,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=0,_SCREAMING_SNAKE_CASE				:   Optional[int]=None,):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  '''python/{}'''.format(sys.version.split()[0]   )
  			if _torch_available:
  						ua += "; torch/{}".format(torch.__version__   )
  			if isinstance(__a,__a   ):
  						ua += "; " + "; ".join('{}/{}'.format(__a,__a   ) for k, v in user_agent.items()   )
  			elif isinstance(__a,__a   ):
  						ua += "; " + user_agent
  			__A=  {'''user-agent''': ua}
  			if resume_size > 0:
  						__A=  '''bytes=%d-''' % (resume_size,)
  			__A=  requests.get(__a,stream=__a,proxies=__a,headers=__a   )
  			if response.status_code == 416:  # Range not satisfiable
  						return
  			__A=  response.headers.get('Content-Length'   )
  			__A=  resume_size + int(__a   ) if content_length is not None else None
  			__A=  tqdm(
  			    unit='B',unit_scale=__a,total=__a,initial=__a,desc='Downloading',)
  			for chunk in response.iter_content(chunk_size=1024   ):
  						if chunk:  # filter out keep-alive new chunks
  									progress.update(len(__a   )   )
  									temp_file.write(__a   )
  			progress.close()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   Tuple=None,_SCREAMING_SNAKE_CASE				:   List[str]=False,_SCREAMING_SNAKE_CASE				:   int=None,_SCREAMING_SNAKE_CASE				:   Dict=10,_SCREAMING_SNAKE_CASE				:   List[str]=False,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=None,_SCREAMING_SNAKE_CASE				:   Optional[Any]=False,):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if cache_dir is None:
  						__A=  TRANSFORMERS_CACHE
  			if isinstance(__a,__a   ):
  						__A=  str(__a   )
  
  			os.makedirs(__a,exist_ok=__a   )
  
  			__A=  None
  			if not local_files_only:
  						try:
  									__A=  requests.head(__a,allow_redirects=__a,proxies=__a,timeout=__a   )
  									if response.status_code == 200:
  												__A=  response.headers.get('ETag'   )
  						except (EnvironmentError, requests.exceptions.Timeout):
  									# etag is already None
  									pass
  
  			__A=  url_to_filename(__a,__a   )
  
  			# get cache path to put the file
  			__A=  os.path.join(__a,__a   )
  
  			# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
  			# try to get the last downloaded one
  			if etag is None:
  						if os.path.exists(__a   ):
  									return cache_path
  						else:
  									__A=  [
  									    file
  									    for file in fnmatch.filter(os.listdir(__a   ),filename + '.*'   )
  									    if not file.endswith('.json'   ) and not file.endswith('.lock'   )
  									]
  									if len(__a   ) > 0:
  												return os.path.join(__a,matching_files[-1]   )
  									else:
  												# If files cannot be found and local_files_only=True,
  												# the models might've been found if local_files_only=False
  												# Notify the user about that
  												if local_files_only:
  															raise ValueError(
  															    'Cannot find the requested files in the cached path and outgoing traffic has been'
  															    ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
  															    ' to False.'   )
  												return None
  
      # From now on, etag is not None.
  			if os.path.exists(__a   ) and not force_download:
  						return cache_path
  
  			# Prevent parallel downloads of the same file with a lock.
  			__A=  cache_path + '''.lock'''
  			with FileLock(__a   ):
  						# If the download just completed while the lock was activated.
  						if os.path.exists(__a   ) and not force_download:
  									# Even if returning early like here, the lock will be released.
  									return cache_path
  
  						if resume_download:
  									__A=  cache_path + '''.incomplete'''
  
  									@contextmanager
  									def _resumable_file_manager():
  												with open(__a,'a+b'   ) as f:
  															yield f
  
  									__A=  _resumable_file_manager
  									if os.path.exists(__a   ):
  												__A=  os.stat(__a   ).st_size
  									else:
  												__A=  0
  						else:
  									__A=  partial(tempfile.NamedTemporaryFile,dir=__a,delete=__a   )
  									__A=  0
  
  						# Download to temporary file, then copy to cache dir once finished.
  						# Otherwise you get corrupt cache entries if the download gets interrupted.
  						with temp_file_manager() as temp_file:
  									print(
  									    '%s not found in cache or force_download set to True, downloading to %s',__a,temp_file.name,)
  
  									http_get(
  									    __a,__a,proxies=__a,resume_size=__a,user_agent=__a,)
  
  						os.replace(temp_file.name,__a   )
  
  						__A=  {'''url''': url, '''etag''': etag}
  						__A=  cache_path + '''.json'''
  						with open(__a,'w'   ) as meta_file:
  									json.dump(__a,__a   )
  
  			return cache_path
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Union[str, Any]=None   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  url.encode('utf-8'   )
  			__A=  shaaaa(__a   )
  			__A=  url_hash.hexdigest()
  
  			if etag:
  						__A=  etag.encode('utf-8'   )
  						__A=  shaaaa(__a   )
  						filename += "." + etag_hash.hexdigest()
  
  			if url.endswith('.h5'   ):
  						filename += ".h5"
  
  			return filename
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[Any],_SCREAMING_SNAKE_CASE				:   Any=None,_SCREAMING_SNAKE_CASE				:   Tuple=False,_SCREAMING_SNAKE_CASE				:   int=None,_SCREAMING_SNAKE_CASE				:   int=False,_SCREAMING_SNAKE_CASE				:   Optional[int]=None,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=False,_SCREAMING_SNAKE_CASE				:   List[str]=False,_SCREAMING_SNAKE_CASE				:   int=False,):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if cache_dir is None:
  						__A=  TRANSFORMERS_CACHE
  			if isinstance(__a,__a   ):
  						__A=  str(__a   )
  			if isinstance(__a,__a   ):
  						__A=  str(__a   )
  
  			if is_remote_url(__a   ):
  						# URL, so get it from the cache (downloading if necessary)
  						__A=  get_from_cache(
  						    __a,cache_dir=__a,force_download=__a,proxies=__a,resume_download=__a,user_agent=__a,local_files_only=__a,)
  			elif os.path.exists(__a   ):
  						# File, and it exists.
  						__A=  url_or_filename
  			elif urlparse(__a   ).scheme == "":
  						# File, but it doesn't exist.
  						raise EnvironmentError('file {} not found'.format(__a   )   )
  			else:
  						# Something unknown
  						raise ValueError('unable to parse {} as a URL or as a local path'.format(__a   )   )
  
  			if extract_compressed_file:
  						if not is_zipfile(__a   ) and not tarfile.is_tarfile(__a   ):
  									return output_path
  
  						# Path where we extract compressed archives
  						# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
  						__A=  os.path.split(__a   )
  						__A=  output_file.replace('.','-'   ) + '''-extracted'''
  						__A=  os.path.join(__a,__a   )
  
  						if os.path.isdir(__a   ) and os.listdir(__a   ) and not force_extract:
  									return output_path_extracted
  
  						# Prevent parallel extractions
  						__A=  output_path + '''.lock'''
  						with FileLock(__a   ):
  									shutil.rmtree(__a,ignore_errors=__a   )
  									os.makedirs(__a   )
  									if is_zipfile(__a   ):
  												with ZipFile(__a,'r'   ) as zip_file:
  															zip_file.extractall(__a   )
  															zip_file.close()
  									elif tarfile.is_tarfile(__a   ):
  												__A=  tarfile.open(__a   )
  												tar_file.extractall(__a   )
  												tar_file.close()
  									else:
  												raise EnvironmentError('Archive format of {} could not be identified'.format(__a   )   )
  
  						return output_path_extracted
  
  			return output_path
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Optional[int]=","   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			assert isinstance(__a,__a   )
  			if os.path.isfile(__a   ):
  						with open(__a   ) as f:
  									__A=  eval(f.read()   )
  			else:
  						__A=  requests.get(__a   )
  						try:
  									__A=  requests.json()
  						except Exception:
  									__A=  req.content.decode()
  									assert data is not None, "could not connect"
  									try:
  												__A=  eval(__a   )
  									except Exception:
  												__A=  data.split('\n'   )
  						req.close()
  			return data
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Dict   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  requests.get(__a   )
  			__A=  np.array(Image.open(BytesIO(response.content   )   )   )
  			return img
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  url.split('/'   )[-1]
  			if fn not in os.listdir(os.getcwd()   ):
  						wget.download(__a   )
  			with open(__a,'rb'   ) as stream:
  						__A=  pkl.load(__a   )
  			__A=  weights.pop('model'   )
  			__A=  {}
  			for k, v in model.items():
  						__A=  torch.from_numpy(__a   )
  						if "running_var" in k:
  									__A=  torch.tensor([0]   )
  									__A=  k.replace('running_var','num_batches_tracked'   )
  									__A=  zero
  			return new
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			print(f"""{os.path.abspath(os.path.join(__a,os.pardir   )   )}/demo.ipynb"""   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[Any],_SCREAMING_SNAKE_CASE				:   Optional[Any]="RGB"   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			assert isinstance(__a,__a   )
  			if os.path.isfile(__a   ):
  						__A=  cva.imread(__a   )
  			else:
  						__A=  get_image_from_url(__a   )
  						assert img is not None, f"""could not connect to: {im}"""
  			__A=  cva.cvtColor(__a,cva.COLOR_BGR2RGB   )
  			if input_format == "RGB":
  						__A=  img[:, :, ::-1]
  			return img
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Dict=1   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			return (images[i : i + batch] for i in range(0,len(__a   ),__a   ))
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # 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)
  #   - FSDP
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # 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__      =							1_6
  UpperCAmelCase__      =							3_2
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			return int(x / 2**20   )
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __enter__(     self       : str  )			->		Optional[Any]:
  						gc.collect()
  						torch.cuda.empty_cache()
  						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  						__A=  torch.cuda.memory_allocated()
  						__A=  psutil.Process()
  
  						__A=  self.cpu_mem_used()
  						__A=  True
  						__A=  threading.Thread(target=self.peak_monitor_func  )
  						__A=  True
  						peak_monitor_thread.start()
  						return self
  			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
  						return self.process.memory_info().rss
  			def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
  						__A=  -1
  
  						while True:
  									__A=  max(self.cpu_mem_used() ,						self.cpu_peak  )
  
  									# can't sleep or will not catch the peak right (this comment is here on purpose)
  									# time.sleep(0.001) # 1msec
  
  									if not self.peak_monitoring:
  												break
  
  			def __exit__(     self       : List[Any] ,						*lowerCAmelCase_       : Dict  )			->		List[Any]:
  						__A=  False
  
  						gc.collect()
  						torch.cuda.empty_cache()
  						__A=  torch.cuda.memory_allocated()
  						__A=  torch.cuda.max_memory_allocated()
  						__A=  bamb(self.end - self.begin  )
  						__A=  bamb(self.peak - self.begin  )
  
  						__A=  self.cpu_mem_used()
  						__A=  bamb(self.cpu_end - self.cpu_begin  )
  						__A=  bamb(self.cpu_peak - self.cpu_begin  )
  						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  					from accelerate.test_utils.training import mocked_dataloaders
  
  					UpperCAmelCase__      =							mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if os.environ.get('TESTING_MOCKED_DATALOADERS',_SCREAMING_SNAKE_CASE   ) == "1":
  						__A=  2
  
  			# New Code #
  			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  			__A=  FullyShardedDataParallelPlugin(
  			    state_dict_config=FullStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),)
  
  			# Initialize accelerator
  			if args.with_tracking:
  						__A=  Accelerator(
  						    cpu=args.cpu,mixed_precision=args.mixed_precision,log_with='wandb',project_dir=args.logging_dir,fsdp_plugin=_SCREAMING_SNAKE_CASE,)
  			else:
  						__A=  Accelerator(fsdp_plugin=_SCREAMING_SNAKE_CASE   )
  			accelerator.print(accelerator.distributed_type   )
  
  			if hasattr(args.checkpointing_steps,'isdigit'   ):
  						if args.checkpointing_steps == "epoch":
  									__A=  args.checkpointing_steps
  						elif args.checkpointing_steps.isdigit():
  									__A=  int(args.checkpointing_steps   )
  						else:
  									raise ValueError(
  									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
  			else:
  						__A=  None
  			# 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']   )
  
  			# We need to initialize the trackers we use, and also store our configuration
  			if args.with_tracking:
  						__A=  vars(_SCREAMING_SNAKE_CASE   )
  						accelerator.init_trackers('fsdp_glue_no_trainer',_SCREAMING_SNAKE_CASE   )
  
  			__A=  AutoTokenizer.from_pretrained(args.model_name_or_path   )
  			__A=  load_dataset('glue','mrpc'   )
  			__A=  evaluate.load('glue','mrpc'   )
  
  			def tokenize_function(_SCREAMING_SNAKE_CASE				:   str   ):
  						# max_length=None => use the model max length (it's actually the default)
  						__A=  tokenizer(examples['sentence1'],examples['sentence2'],truncation=_SCREAMING_SNAKE_CASE,max_length=_SCREAMING_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():
  						__A=  datasets.map(
  						    _SCREAMING_SNAKE_CASE,batched=_SCREAMING_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
  			__A=  tokenized_datasets.rename_column('label','labels'   )
  
  			# 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
  
  			def collate_fn(_SCREAMING_SNAKE_CASE				:   int   ):
  						# On TPU it's best to pad everything to the same length or training will be very slow.
  						__A=  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":
  									__A=  16
  						elif accelerator.mixed_precision != "no":
  									__A=  8
  						else:
  									__A=  None
  
  						return tokenizer.pad(
  						    _SCREAMING_SNAKE_CASE,padding='longest',max_length=_SCREAMING_SNAKE_CASE,pad_to_multiple_of=_SCREAMING_SNAKE_CASE,return_tensors='pt',)
  
  			# Instantiate dataloaders.
  			__A=  DataLoader(
  			    tokenized_datasets['train'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  			__A=  DataLoader(
  			    tokenized_datasets['validation'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  
  			set_seed(_SCREAMING_SNAKE_CASE   )
  
  			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  			__A=  AutoModelForSequenceClassification.from_pretrained(
  			    args.model_name_or_path,return_dict=_SCREAMING_SNAKE_CASE,low_cpu_mem_usage=_SCREAMING_SNAKE_CASE   )
  
  			# New Code #
  			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  			__A=  accelerator.prepare(_SCREAMING_SNAKE_CASE   )
  			accelerator.print(_SCREAMING_SNAKE_CASE   )
  
  			# Instantiate optimizer
  			# New Code #
  			# For FSDP feature, at present it doesn't support multiple parameter groups,
  			# so we need to create a single parameter group for the whole model
  			__A=  torch.optim.AdamW(params=model.parameters(),lr=_SCREAMING_SNAKE_CASE,weight_decay=2e-4   )
  
  			# Instantiate scheduler
  			__A=  get_linear_schedule_with_warmup(
  			    optimizer=_SCREAMING_SNAKE_CASE,num_warmup_steps=10,num_training_steps=(len(_SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps,)
  
  			# New Code #
  			# For FSDP feature, prepare everything except the model as we have already prepared the model
  			# before creating the optimizer
  			# 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=  accelerator.prepare(
  			    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  			__A=  0
  
  			# Potentially load in the weights and states from a previous save
  			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=  os.path.basename(args.resume_from_checkpoint   )
  						else:
  									# Get the most recent checkpoint
  									__A=  [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
  									dirs.sort(key=os.path.getctime   )
  									__A=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  						# Extract `epoch_{i}` or `step_{i}`
  						__A=  os.path.splitext(_SCREAMING_SNAKE_CASE   )[0]
  
  						if "epoch" in training_difference:
  									num_epochs -= int(training_difference.replace('epoch_',''   )   )
  									__A=  None
  						else:
  									__A=  int(training_difference.replace('step_',''   )   )
  									num_epochs -= resume_step // len(_SCREAMING_SNAKE_CASE   )
  									# If resuming by step, we also need to know exactly how far into the DataLoader we went
  									__A=  (num_epochs * len(_SCREAMING_SNAKE_CASE   )) - resume_step
  
      # Now we train the model
  			for epoch in range(_SCREAMING_SNAKE_CASE   ):
  						# New Code #
  						# context manager to track the peak memory usage during the training epoch
  						with TorchTracemalloc() as tracemalloc:
  									model.train()
  									if args.with_tracking:
  												__A=  0
  									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
  												# We need to skip steps until we reach the resumed step
  												if args.resume_from_checkpoint and epoch == 0:
  															if resume_step is not None and step < resume_step:
  																		pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
  												batch.to(accelerator.device   )
  												__A=  model(**_SCREAMING_SNAKE_CASE   )
  												__A=  outputs.loss
  												__A=  loss / gradient_accumulation_steps
  												# We keep track of the loss at each epoch
  												if args.with_tracking:
  															total_loss += loss.detach().float()
  												accelerator.backward(_SCREAMING_SNAKE_CASE   )
  												if step % gradient_accumulation_steps == 0:
  															optimizer.step()
  															lr_scheduler.step()
  															optimizer.zero_grad()
  															# accelerator.print(lr_scheduler.get_lr())
  
  												overall_step += 1
  
  												if isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ):
  															__A=  f"""step_{overall_step}"""
  															if overall_step % checkpointing_steps == 0:
  																		if args.output_dir is not None:
  																					__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
  																		accelerator.save_state(_SCREAMING_SNAKE_CASE   )
          # New Code #
          # 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   )   )   )
  						# Logging the peak memory usage of the GPU to the tracker
  						if args.with_tracking:
  									accelerator.log(
  									    {
  									        'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin   ),
  									    },step=_SCREAMING_SNAKE_CASE,)
  
  						# New Code #
  						# context manager to track the peak memory usage during the evaluation
  						with TorchTracemalloc() as tracemalloc:
  									model.eval()
  									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												batch.to(accelerator.device   )
  												with torch.no_grad():
  															__A=  model(**_SCREAMING_SNAKE_CASE   )
  												__A=  outputs.logits.argmax(dim=-1   )
  												__A,  __A=  accelerator.gather_for_metrics((predictions, batch['labels'])   )
  												metric.add_batch(
  												    predictions=_SCREAMING_SNAKE_CASE,references=_SCREAMING_SNAKE_CASE,)
  
  									__A=  metric.compute()
  									# Use accelerator.print to print only on the main process.
  									accelerator.print(f"""epoch {epoch}:""",_SCREAMING_SNAKE_CASE   )
  									if args.with_tracking:
  												accelerator.log(
  												    {
  												        'accuracy': eval_metric['accuracy'],
  												        'f1': eval_metric['f1'],
  												        'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE   ),
  												    },step=_SCREAMING_SNAKE_CASE,)
  
  									if checkpointing_steps == "epoch":
  												__A=  f"""epoch_{epoch}"""
  												if args.output_dir is not None:
  															__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
  												accelerator.save_state(_SCREAMING_SNAKE_CASE   )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  						accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin   )   )   )
  						accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used   )   )
  						accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked   )   )
  						accelerator.print(
  						    'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
  						# Logging the peak memory usage of the GPU to the tracker
  						if args.with_tracking:
  									accelerator.log(
  									    {
  									        'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin   ),
  									    },step=_SCREAMING_SNAKE_CASE,)
  
  			if args.with_tracking:
  						accelerator.end_training()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  argparse.ArgumentParser(description='Simple example of training script.'   )
  			parser.add_argument(
  			    '--mixed_precision',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_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.'   )
  			parser.add_argument(
  			    '--checkpointing_steps',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',)
  			parser.add_argument(
  			    '--resume_from_checkpoint',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='If the training should continue from a checkpoint folder.',)
  			parser.add_argument(
  			    '--with_tracking',action='store_true',help='Whether to load in all available experiment trackers from the environment and use them for logging.',)
  			parser.add_argument(
  			    '--output_dir',type=_SCREAMING_SNAKE_CASE,default='.',help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',)
  			parser.add_argument(
  			    '--logging_dir',type=_SCREAMING_SNAKE_CASE,default='logs',help='Location on where to store experiment tracking logs`',)
  			parser.add_argument(
  			    '--model_name_or_path',type=_SCREAMING_SNAKE_CASE,help='Path to pretrained model or model identifier from huggingface.co/models.',required=_SCREAMING_SNAKE_CASE,)
  			__A=  parser.parse_args()
  			__A=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  			training_function(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  