



import logging
import os
import threading
import time


try:
  import warnings
except ImportError:
  a_			=					None

try:
  import msvcrt
except ImportError:
  a_			=					None

try:
  import fcntl
except ImportError:
  a_			=					None


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


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

a_			=					"""3.0.12"""


a_			=					None





def 		a__			(		):
   global _logger
   __lowerCamelCase													=    _logger or logging.getLogger(__name__							)
   return _logger



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    lock_file
    return None




 def __str__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    F"""The file lock '{self.lock_file}' could not be acquired."""
    return temp



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    lock
    return None




 def __enter__(						self				):



    '''simple docstring'''
    return self.lock




 def __exit__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    self.lock.release()
    return None



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=-1	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    __lowerCamelCase													=    max_filename_length if max_filename_length is not None else 255
    # Hash the filename if it's too long
    __lowerCamelCase													=    self.hash_filename_if_too_long(__UpperCAmelCase	,				__UpperCAmelCase				)
    # The path to the lock file.
    __lowerCamelCase													=    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.
    __lowerCamelCase													=    None

    # The default timeout value.
    __lowerCamelCase													=    timeout

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




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._lock_file




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._timeout




 @timeout.setter
 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    float(__UpperCAmelCase				)
    return None




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    raise NotImplementedError()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    raise NotImplementedError()




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._lock_file_fd is not None




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=0.05				):



    '''simple docstring'''
    # Use the default timeout, if no timeout is provided.
    if timeout is None:
       __lowerCamelCase													=    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

    __lowerCamelCase													=    id(self				)
    __lowerCamelCase													=    self._lock_file
    __lowerCamelCase													=    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(__UpperCAmelCase				)
    except:  # noqa
       # Something did go wrong, so decrement the counter.
       with self._thread_lock:
          __lowerCamelCase													=    max(0	,				self._lock_counter - 1				)

       raise
    return _Acquire_ReturnProxy(lock=self				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=False				):



    '''simple docstring'''
    with self._thread_lock:
       if self.is_locked:
          self._lock_counter -= 1

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

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

    return None




 def __enter__(						self				):



    '''simple docstring'''
    self.acquire()
    return self




 def __exit__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    self.release()
    return None




 def __del__(						self				):



    '''simple docstring'''
    self.release(force=__UpperCAmelCase				)
    return None




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    os.path.basename(__UpperCAmelCase				)
    if len(__UpperCAmelCase				) > max_length and max_length > 0:
       __lowerCamelCase													=    os.path.dirname(__UpperCAmelCase				)
       __lowerCamelCase													=    str(hash(__UpperCAmelCase				)				)
       __lowerCamelCase													=    filename[: max_length - len(__UpperCAmelCase				) - 8] + '''...''' + hashed_filename + '''.lock'''
       return os.path.join(__UpperCAmelCase	,				__UpperCAmelCase				)
    else:
       return path



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=-1	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    from .file_utils import relative_to_absolute_path

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    os.O_RDWR | os.O_CREAT | os.O_TRUNC

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self._lock_file_fd
    __lowerCamelCase													=    None
    msvcrt.locking(__UpperCAmelCase	,				msvcrt.LK_UNLCK	,				1				)
    os.close(__UpperCAmelCase				)

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



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=-1	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    __lowerCamelCase													=    os.statvfs(os.path.dirname(__UpperCAmelCase				)				).f_namemax
    super().__init__(__UpperCAmelCase	,				timeout=__UpperCAmelCase	,				max_filename_length=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    os.O_RDWR | os.O_CREAT | os.O_TRUNC
    __lowerCamelCase													=    os.open(self._lock_file	,				__UpperCAmelCase				)

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # 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
    __lowerCamelCase													=    self._lock_file_fd
    __lowerCamelCase													=    None
    fcntl.flock(__UpperCAmelCase	,				fcntl.LOCK_UN				)
    os.close(__UpperCAmelCase				)
    return None



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
    try:
       __lowerCamelCase													=    os.open(self._lock_file	,				__UpperCAmelCase				)
    except OSError:
       pass
    else:
       __lowerCamelCase													=    fd
    return None




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    os.close(self._lock_file_fd				)
    __lowerCamelCase													=    None

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



a_			=					None

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

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











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
)
a_			=					logging.getLogger(__name__)





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    np.argmax(_UpperCamelCase ,axis=1							)
   return np.sum(outputs == labels							)





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





def 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: Dict ,_UpperCamelCase			: str ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any ,_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    []
   for dataset in encoded_datasets:
      __lowerCamelCase													=    len(_UpperCamelCase							)
      __lowerCamelCase													=    np.zeros((n_batch, 2, input_len) ,dtype=np.intaa							)
      __lowerCamelCase													=    np.zeros((n_batch, 2) ,dtype=np.intaa							)
      __lowerCamelCase													=    np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa							)
      __lowerCamelCase													=    np.zeros((n_batch,) ,dtype=np.intaa							)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(_UpperCamelCase							):
         __lowerCamelCase													=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __lowerCamelCase													=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    len(_UpperCamelCase							) - 1
         __lowerCamelCase													=    len(_UpperCamelCase							) - 1
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    mc_label
      __lowerCamelCase													=    (input_ids, mc_token_ids, lm_labels, mc_labels)
      tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase							) for t in all_inputs							)							)
   return tensor_datasets





def 		a__			(		):
   __lowerCamelCase													=    argparse.ArgumentParser()
   parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,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=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
   parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default=''''''							)
   parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default=''''''							)
   parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42							)
   parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3							)
   parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8							)
   parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16							)
   parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.'''							)
   parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1							)
   parser.add_argument(
       '''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
           '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
       ) ,)
   parser.add_argument(
       '''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before                        performing a backward/update pass.''' ,)
   parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5							)
   parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.'''							)
   parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear'''							)
   parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01							)
   parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9							)
   parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74							)

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

   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=_UpperCamelCase							)
      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							)

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

   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
   __lowerCamelCase													=    ['''_start_''', '''_delimiter_''', '''_classify_''']
   __lowerCamelCase													=    OpenAIGPTTokenizer.from_pretrained(args.model_name							)
   tokenizer.add_tokens(_UpperCamelCase							)
   __lowerCamelCase													=    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)
   __lowerCamelCase													=    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name							)
   model.resize_token_embeddings(len(_UpperCamelCase							)							)
   model.to(_UpperCamelCase							)

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

   logger.info('''Encoding dataset...'''							)
   __lowerCamelCase													=    load_rocstories_dataset(args.train_dataset							)
   __lowerCamelCase													=    load_rocstories_dataset(args.eval_dataset							)
   __lowerCamelCase													=    (train_dataset, eval_dataset)
   __lowerCamelCase													=    tokenize_and_encode(_UpperCamelCase							)

   # Compute the max input length for the Transformer
   __lowerCamelCase													=    model.config.n_positions // 2 - 2
   __lowerCamelCase													=    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							)
   __lowerCamelCase													=    min(_UpperCamelCase ,model.config.n_positions							)  # Max size of input for the pre-trained model

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

   __lowerCamelCase													=    TensorDataset(*_UpperCamelCase							)
   __lowerCamelCase													=    RandomSampler(_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size							)

   __lowerCamelCase													=    TensorDataset(*_UpperCamelCase							)
   __lowerCamelCase													=    SequentialSampler(_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size							)

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

      __lowerCamelCase													=    list(model.named_parameters()							)
      __lowerCamelCase													=    ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
      __lowerCamelCase													=    [
          {
              '''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},
      ]
      __lowerCamelCase													=    AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon							)
      __lowerCamelCase													=    get_linear_schedule_with_warmup(
          _UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase							)

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

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

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

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

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

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

         __lowerCamelCase													=    mc_logits.detach().cpu().numpy()
         __lowerCamelCase													=    mc_labels.to('''cpu'''							).numpy()
         __lowerCamelCase													=    accuracy(_UpperCamelCase ,_UpperCamelCase							)

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

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

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

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


if __name__ == "__main__":
  main()






