



"""simple docstring"""


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
snake_case_    					=	logging.getLogger(__name__)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			np.argmax(lowercase_		,     axis=1						)
						return np.sum(outputs == labels						)

def 			_lowerCAmelCase				(       lowercase_						):
						with open(lowercase_		,     encoding='utf_8'						) as f:
												UpperCAmelCase					=			csv.reader(lowercase_						)
												UpperCAmelCase					=			[]
												next(lowercase_						)  # skip the first line
												for line in tqdm(lowercase_						):
																		output.append((' '.join(line[1:5]						), line[5], line[6], int(line[-1]						) - 1)						)
						return output

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			[]
						for dataset in encoded_datasets:
												UpperCAmelCase					=			len(lowercase_						)
												UpperCAmelCase					=			np.zeros((n_batch, 2, input_len)		,     dtype=np.intaa						)
												UpperCAmelCase					=			np.zeros((n_batch, 2)		,     dtype=np.intaa						)
												UpperCAmelCase					=			np.full((n_batch, 2, input_len)		,     fill_value=-100		,     dtype=np.intaa						)
												UpperCAmelCase					=			np.zeros((n_batch,)		,     dtype=np.intaa						)
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(lowercase_						):
																		UpperCAmelCase					=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		UpperCAmelCase					=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		UpperCAmelCase					=			with_conta
																		UpperCAmelCase					=			with_conta
																		UpperCAmelCase					=			len(lowercase_						) - 1
																		UpperCAmelCase					=			len(lowercase_						) - 1
																		UpperCAmelCase					=			with_conta
																		UpperCAmelCase					=			with_conta
																		UpperCAmelCase					=			mc_label
												UpperCAmelCase					=			(input_ids, mc_token_ids, lm_labels, mc_labels)
												tensor_datasets.append(tuple(torch.tensor(lowercase_						) for t in all_inputs						)						)
						return tensor_datasets

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			argparse.ArgumentParser()
						parser.add_argument('--model_name'		,     type=lowercase_		,     default='openai-gpt'		,     help='pretrained model name'						)
						parser.add_argument('--do_train'		,     action='store_true'		,     help='Whether to run training.'						)
						parser.add_argument('--do_eval'		,     action='store_true'		,     help='Whether to run eval on the dev set.'						)
						parser.add_argument(
						    '--output_dir'		,     default=lowercase_		,     type=lowercase_		,     required=lowercase_		,     help='The output directory where the model predictions and checkpoints will be written.'		,     )
						parser.add_argument('--train_dataset'		,     type=lowercase_		,     default=''						)
						parser.add_argument('--eval_dataset'		,     type=lowercase_		,     default=''						)
						parser.add_argument('--seed'		,     type=lowercase_		,     default=42						)
						parser.add_argument('--num_train_epochs'		,     type=lowercase_		,     default=3						)
						parser.add_argument('--train_batch_size'		,     type=lowercase_		,     default=8						)
						parser.add_argument('--eval_batch_size'		,     type=lowercase_		,     default=16						)
						parser.add_argument('--adam_epsilon'		,     default=1e-8		,     type=lowercase_		,     help='Epsilon for Adam optimizer.'						)
						parser.add_argument('--max_grad_norm'		,     type=lowercase_		,     default=1						)
						parser.add_argument(
						    '--max_steps'		,     default=-1		,     type=lowercase_		,     help=(
						        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
						    )		,     )
						parser.add_argument(
						    '--gradient_accumulation_steps'		,     type=lowercase_		,     default=1		,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'		,     )
						parser.add_argument('--learning_rate'		,     type=lowercase_		,     default=6.25e-5						)
						parser.add_argument('--warmup_steps'		,     default=0		,     type=lowercase_		,     help='Linear warmup over warmup_steps.'						)
						parser.add_argument('--lr_schedule'		,     type=lowercase_		,     default='warmup_linear'						)
						parser.add_argument('--weight_decay'		,     type=lowercase_		,     default=0.0_1						)
						parser.add_argument('--lm_coef'		,     type=lowercase_		,     default=0.9						)
						parser.add_argument('--n_valid'		,     type=lowercase_		,     default=374						)

						parser.add_argument('--server_ip'		,     type=lowercase_		,     default=''		,     help='Can be used for distant debugging.'						)
						parser.add_argument('--server_port'		,     type=lowercase_		,     default=''		,     help='Can be used for distant debugging.'						)
						UpperCAmelCase					=			parser.parse_args()
						print(lowercase_						)

						if args.server_ip and args.server_port:
												# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
												import ptvsd

												print('Waiting for debugger attach'						)
												ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,     redirect_output=lowercase_						)
												ptvsd.wait_for_attach()

						random.seed(args.seed						)
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						torch.cuda.manual_seed_all(args.seed						)

						UpperCAmelCase					=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
						UpperCAmelCase					=			torch.cuda.device_count()
						logger.info('device: {}, n_gpu {}'.format(lowercase_		,     lowercase_						)						)

						if not args.do_train and not args.do_eval:
												raise ValueError('At least one of `do_train` or `do_eval` must be True.'						)

						if not os.path.exists(args.output_dir						):
												os.makedirs(args.output_dir						)

						# Load tokenizer and model
						# This loading functions also add new tokens and embeddings called `special tokens`
						# These new embeddings will be fine-tuned on the RocStories dataset
						UpperCAmelCase					=			['_start_', '_delimiter_', '_classify_']
						UpperCAmelCase					=			OpenAIGPTTokenizer.from_pretrained(args.model_name						)
						tokenizer.add_tokens(lowercase_						)
						UpperCAmelCase					=			tokenizer.convert_tokens_to_ids(lowercase_						)
						UpperCAmelCase					=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name						)
						model.resize_token_embeddings(len(lowercase_						)						)
						model.to(lowercase_						)

						# Load and encode the datasets
						def tokenize_and_encode(lowercase_						):
												if isinstance(lowercase_		,     lowercase_						):
																		return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowercase_						)						)
												elif isinstance(lowercase_		,     lowercase_						):
																		return obj
												return [tokenize_and_encode(lowercase_						) for o in obj]

						logger.info('Encoding dataset...'						)
						UpperCAmelCase					=			load_rocstories_dataset(args.train_dataset						)
						UpperCAmelCase					=			load_rocstories_dataset(args.eval_dataset						)
						UpperCAmelCase					=			(train_dataset, eval_dataset)
						UpperCAmelCase					=			tokenize_and_encode(lowercase_						)

						# Compute the max input length for the Transformer
						UpperCAmelCase					=			model.config.n_positions // 2 - 2
						UpperCAmelCase					=			max(
						    len(story[:max_length]						) + max(len(conta[:max_length]						)		,     len(conta[:max_length]						)						) + 3
						    for dataset in encoded_datasets
						    for story, conta, conta, _ in dataset						)
						UpperCAmelCase					=			min(lowercase_		,     model.config.n_positions						)  # Max size of input for the pre-trained model

						# Prepare inputs tensors and dataloaders
						UpperCAmelCase					=			pre_process_datasets(lowercase_		,     lowercase_		,     lowercase_		,     *lowercase_						)
						UpperCAmelCase				,   UpperCAmelCase					=			tensor_datasets[0], tensor_datasets[1]

						UpperCAmelCase					=			TensorDataset(*lowercase_						)
						UpperCAmelCase					=			RandomSampler(lowercase_						)
						UpperCAmelCase					=			DataLoader(lowercase_		,     sampler=lowercase_		,     batch_size=args.train_batch_size						)

						UpperCAmelCase					=			TensorDataset(*lowercase_						)
						UpperCAmelCase					=			SequentialSampler(lowercase_						)
						UpperCAmelCase					=			DataLoader(lowercase_		,     sampler=lowercase_		,     batch_size=args.eval_batch_size						)

						# Prepare optimizer
						if args.do_train:
												if args.max_steps > 0:
																		UpperCAmelCase					=			args.max_steps
																		UpperCAmelCase					=			args.max_steps // (len(lowercase_						) // args.gradient_accumulation_steps) + 1
												else:
																		UpperCAmelCase					=			len(lowercase_						) // args.gradient_accumulation_steps * args.num_train_epochs

												UpperCAmelCase					=			list(model.named_parameters()						)
												UpperCAmelCase					=			['bias', 'LayerNorm.bias', 'LayerNorm.weight']
												UpperCAmelCase					=			[
												    {
												        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay						)],
												        'weight_decay': args.weight_decay,
												    },
												    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay						)], 'weight_decay': 0.0},
												]
												UpperCAmelCase					=			AdamW(lowercase_		,     lr=args.learning_rate		,     eps=args.adam_epsilon						)
												UpperCAmelCase					=			get_linear_schedule_with_warmup(
												    lowercase_		,     num_warmup_steps=args.warmup_steps		,     num_training_steps=lowercase_						)

						if args.do_train:
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			0, 0, None
												model.train()
												for _ in trange(int(args.num_train_epochs						)		,     desc='Epoch'						):
																		UpperCAmelCase					=			0
																		UpperCAmelCase					=			0
																		UpperCAmelCase					=			tqdm(lowercase_		,     desc='Training'						)
																		for step, batch in enumerate(lowercase_						):
																								UpperCAmelCase					=			tuple(t.to(lowercase_						) for t in batch						)
																								UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			batch
																								UpperCAmelCase					=			model(lowercase_		,     mc_token_ids=lowercase_		,     lm_labels=lowercase_		,     mc_labels=lowercase_						)
																								UpperCAmelCase					=			args.lm_coef * losses[0] + losses[1]
																								loss.backward()
																								optimizer.step()
																								scheduler.step()
																								optimizer.zero_grad()
																								tr_loss += loss.item()
																								UpperCAmelCase					=			(
																								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																								)
																								nb_tr_steps += 1
																								UpperCAmelCase					=			'Training loss: {:.2e} lr: {:.2e}'.format(lowercase_		,     scheduler.get_lr()[0]						)

    # Save a trained model
						if args.do_train:
												# Save a trained model, configuration and tokenizer
												UpperCAmelCase					=			model.module if hasattr(lowercase_		,     'module'						) else model  # Only save the model itself

												# If we save using the predefined names, we can load using `from_pretrained`
												UpperCAmelCase					=			os.path.join(args.output_dir		,     lowercase_						)
												UpperCAmelCase					=			os.path.join(args.output_dir		,     lowercase_						)

												torch.save(model_to_save.state_dict()		,     lowercase_						)
												model_to_save.config.to_json_file(lowercase_						)
												tokenizer.save_vocabulary(args.output_dir						)

												# Load a trained model and vocabulary that you have fine-tuned
												UpperCAmelCase					=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
												UpperCAmelCase					=			OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
												model.to(lowercase_						)

						if args.do_eval:
												model.eval()
												UpperCAmelCase				,   UpperCAmelCase					=			0, 0
												UpperCAmelCase				,   UpperCAmelCase					=			0, 0
												for batch in tqdm(lowercase_		,     desc='Evaluating'						):
																		UpperCAmelCase					=			tuple(t.to(lowercase_						) for t in batch						)
																		UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			batch
																		with torch.no_grad():
																								UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			model(
																								    lowercase_		,     mc_token_ids=lowercase_		,     lm_labels=lowercase_		,     mc_labels=lowercase_						)

																		UpperCAmelCase					=			mc_logits.detach().cpu().numpy()
																		UpperCAmelCase					=			mc_labels.to('cpu'						).numpy()
																		UpperCAmelCase					=			accuracy(lowercase_		,     lowercase_						)

																		eval_loss += mc_loss.mean().item()
																		eval_accuracy += tmp_eval_accuracy

																		nb_eval_examples += input_ids.size(0						)
																		nb_eval_steps += 1

												UpperCAmelCase					=			eval_loss / nb_eval_steps
												UpperCAmelCase					=			eval_accuracy / nb_eval_examples
												UpperCAmelCase					=			tr_loss / nb_tr_steps if args.do_train else None
												UpperCAmelCase					=			{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

												UpperCAmelCase					=			os.path.join(args.output_dir		,     'eval_results.txt'						)
												with open(lowercase_		,     'w'						) as writer:
																		logger.info('***** Eval results *****'						)
																		for key in sorted(result.keys()						):
																								logger.info('  %s = %s'		,     lowercase_		,     str(result[key]						)						)
																								writer.write('%s = %s\n' % (key, str(result[key]						))						)


if __name__ == "__main__":
				main()










"""simple docstring"""


import logging
import os
import threading
import time


try:
				import warnings
except ImportError:
				snake_case_    					=	None

try:
				import msvcrt
except ImportError:
				snake_case_    					=	None

try:
				import fcntl
except ImportError:
				snake_case_    					=	None


# Backward compatibility
# ------------------------------------------------
try:
				TimeoutError
except NameError:
				snake_case_    					=	OSError


# Data
# ------------------------------------------------
snake_case_    					=	[
    """Timeout""",
    """BaseFileLock""",
    """WindowsFileLock""",
    """UnixFileLock""",
    """SoftFileLock""",
    """FileLock""",
]

snake_case_    					=	"""3.0.12"""


snake_case_    					=	None

def 			_lowerCAmelCase				(       ):
						global _logger
						UpperCAmelCase					=			_logger or logging.getLogger(__name__						)
						return _logger







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:int       ,  lowercase_			:List[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			lock_file
												return None






						def __str__( self			:Dict )    ->      Optional[int]:
												UpperCAmelCase					=			f"""The file lock '{self.lock_file}' could not be acquired."""
												return temp







class 				A_      :



						"""simple docstring"""






						def __init__( self			:List[Any]       ,  lowercase_			:Dict )    ->      Dict:
												UpperCAmelCase					=			lock
												return None






						def __enter__( self			:Optional[int] )    ->      Optional[int]:
												return self.lock






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







class 				A_      :



						"""simple docstring"""






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

												# The default timeout value.
												UpperCAmelCase					=			timeout

												# We use this lock primarily for the lock counter.
												UpperCAmelCase					=			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.
												UpperCAmelCase					=			0
												return None






						@property
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      int:
												return self._lock_file






						@property
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Any:
												return self._timeout






						@timeout.setter
						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Any )    ->      Tuple:
												UpperCAmelCase					=			float(lowercase_ )
												return None






						def      UpperCAmelCase__					( self			:Any )    ->      List[str]:
												raise NotImplementedError()






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[str]:
												raise NotImplementedError()






						@property
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Union[str, Any]:
												return self._lock_file_fd is not None






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Dict=None       ,  lowercase_			:int=0.05 )    ->      Union[str, Any]:
												# Use the default timeout, if no timeout is provided.
												if timeout is None:
																		UpperCAmelCase					=			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

												UpperCAmelCase					=			id(self )
												UpperCAmelCase					=			self._lock_file
												UpperCAmelCase					=			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:
																								UpperCAmelCase					=			max(0       ,  self._lock_counter - 1 )

																		raise
												return _Acquire_ReturnProxy(lock=self )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:List[str]=False )    ->      int:
												with self._thread_lock:
																		if self.is_locked:
																								self._lock_counter -= 1

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

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

												return None






						def __enter__( self			:Any )    ->      Tuple:
												self.acquire()
												return self






						def __exit__( self			:Dict       ,  lowercase_			:Optional[int]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Union[str, Any] )    ->      Any:
												self.release()
												return None






						def __del__( self			:Any )    ->      int:
												self.release(force=lowercase_ )
												return None






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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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

												super().__init__(lowercase_       ,  timeout=lowercase_       ,  max_filename_length=lowercase_ )
												UpperCAmelCase					=			'\\\\?\\' + relative_to_absolute_path(self.lock_file )






						def      UpperCAmelCase__					( self			:List[str] )    ->      str:
												UpperCAmelCase					=			os.O_RDWR | os.O_CREAT | os.O_TRUNC

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






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self._lock_file_fd
												UpperCAmelCase					=			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 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:str       ,  lowercase_			:List[str]=-1       ,  lowercase_			:str=None )    ->      str:
												UpperCAmelCase					=			os.statvfs(os.path.dirname(lowercase_ ) ).f_namemax
												super().__init__(lowercase_       ,  timeout=lowercase_       ,  max_filename_length=lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Dict:
												UpperCAmelCase					=			os.O_RDWR | os.O_CREAT | os.O_TRUNC
												UpperCAmelCase					=			os.open(self._lock_file       ,  lowercase_ )

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






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[Any]:
												# 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
												UpperCAmelCase					=			self._lock_file_fd
												UpperCAmelCase					=			None
												fcntl.flock(lowercase_       ,  fcntl.LOCK_UN )
												os.close(lowercase_ )
												return None







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[Any]:
												os.close(self._lock_file_fd )
												UpperCAmelCase					=			None

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


snake_case_    					=	None

if msvcrt:
				snake_case_    					=	WindowsFileLock
elif fcntl:
				snake_case_    					=	UnixFileLock
else:
				snake_case_    					=	SoftFileLock

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





