text: # Copyright (c) Facebook, Inc. and its affiliates.
  
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  from io import open
  import json
  import logging
  from functools import wraps
  from hashlib import sha256
  from pathlib import Path
  import os
  import shutil
  import sys
  import tempfile
  from urllib.parse import urlparse
  from functools import partial, wraps
  
  import boto3
  import requests
  from botocore.exceptions import ClientError
  from tqdm import tqdm
  from torch.utils.tensorboard import SummaryWriter
  from time import gmtime, strftime
  from bisect import bisect
  from torch import nn
  import torch
  from torch._six import inf
  
  import pdb
  
  PYTORCH_PRETRAINED_BERT_CACHE = Path(
      os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", Path.home() / ".pytorch_pretrained_bert")
  )
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  class MultiTaskStopOnPlateau(object):
      def __init__(
          self,
          mode="min",
          patience=10,
          continue_threshold=0.005,
          verbose=False,
          threshold=1e-4,
          threshold_mode="rel",
          cooldown=0,
          min_lr=0,
          eps=1e-8,
      ):
  
          self.patience = patience
          self.verbose = verbose
          self.cooldown = cooldown
          self.cooldown_counter = 0
          self.mode = mode
          self.threshold = threshold
          self.threshold_mode = threshold_mode
          self.best = None
          self.num_bad_epochs = None
          self.mode_worse = None  # the worse value for the chosen mode
          self.is_better = None
          self.in_stop = False
          self.eps = eps
          self.last_epoch = -1
          self.continue_threshold = continue_threshold
          self._init_is_better(
              mode=mode, threshold=threshold, threshold_mode=threshold_mode
          )
          self._init_continue_is_better(
              mode="min", threshold=continue_threshold, threshold_mode=threshold_mode
          )
          self._reset()
  
      def _reset(self):
          """Resets num_bad_epochs counter and cooldown counter."""
          self.best = self.mode_worse
          self.cooldown_counter = 0
          self.num_bad_epochs = 0
          self.in_stop = False
  
      def step(self, metrics, epoch=None):
          # convert `metrics` to float, in case it's a zero-dim Tensor
          current = float(metrics)
          if epoch is None:
              epoch = self.last_epoch = self.last_epoch + 1
          self.last_epoch = epoch
  
          if self.is_better(current, self.best):
              self.best = current
              self.num_bad_epochs = 0
          else:
              self.num_bad_epochs += 1
  
          if self.in_cooldown:
              self.cooldown_counter -= 1
              self.num_bad_epochs = 0  # ignore any bad epochs in cooldown
  
          if self.num_bad_epochs > self.patience:
              self.in_stop = True
              self.cooldown_counter = self.cooldown
              self.num_bad_epochs = 0
  
          # if the perforance is keep dropping, then start optimizing again.
          elif self.continue_is_better(current, self.best) and self.in_stop:
              self.in_stop = False
              self.cooldown_counter = self.cooldown
              self.num_bad_epochs = 0
  
          # if we lower the learning rate, then
          # call reset.
  
      @property
      def in_cooldown(self):
          return self.cooldown_counter > 0
  
      def _cmp(self, mode, threshold_mode, threshold, a, best):
          if mode == "min" and threshold_mode == "rel":
              rel_epsilon = 1.0 - threshold
              return a < best * rel_epsilon
  
          elif mode == "min" and threshold_mode == "abs":
              return a < best - threshold
  
          elif mode == "max" and threshold_mode == "rel":
              rel_epsilon = threshold + 1.0
              return a > best * rel_epsilon
  
          else:  # mode == 'max' and epsilon_mode == 'abs':
              return a > best + threshold
  
      def _init_is_better(self, mode, threshold, threshold_mode):
          if mode not in {"min", "max"}:
              raise ValueError("mode " + mode + " is unknown!")
          if threshold_mode not in {"rel", "abs"}:
              raise ValueError("threshold mode " + threshold_mode + " is unknown!")
  
          if mode == "min":
              self.mode_worse = inf
          else:  # mode == 'max':
              self.mode_worse = -inf
  
          self.is_better = partial(self._cmp, mode, threshold_mode, threshold)
  
      def _init_continue_is_better(self, mode, threshold, threshold_mode):
  
          self.continue_is_better = partial(self._cmp, mode, threshold_mode, threshold)
  
  
  class tbLogger(object):
      def __init__(
          self,
          log_dir,
          txt_dir,
          task_names,
          task_ids,
          task_num_iters,
          gradient_accumulation_steps,
          save_logger=True,
          txt_name="out.txt",
      ):
          logger.info("logging file at: " + log_dir)
  
          self.save_logger = save_logger
          self.log_dir = log_dir
          self.txt_dir = txt_dir
          if self.save_logger:
              self.logger = SummaryWriter(log_dir=log_dir)
  
          self.txt_f = open(txt_dir + "/" + txt_name, "w")
          self.task_id2name = {
              ids: name.replace("+", "plus") for ids, name in zip(task_ids, task_names)
          }
          self.task_ids = task_ids
          self.task_loss = {task_id: 0 for task_id in task_ids}
          self.task_loss_tmp = {task_id: 0 for task_id in task_ids}
          self.task_score_tmp = {task_id: 0 for task_id in task_ids}
          self.task_norm_tmp = {task_id: 0 for task_id in task_ids}
          self.task_step = {task_id: 0 for task_id in task_ids}
          self.task_step_tmp = {task_id: 0 for task_id in task_ids}
          self.task_num_iters = task_num_iters
          self.epochId = 0
          self.gradient_accumulation_steps = gradient_accumulation_steps
          self.task_loss_val = {task_id: 0 for task_id in task_ids}
          self.task_score_val = {task_id: 0 for task_id in task_ids}
          self.task_step_val = {task_id: 0 for task_id in task_ids}
          self.task_iter_val = {task_id: 0 for task_id in task_ids}
          self.task_datasize_val = {task_id: 0 for task_id in task_ids}
  
          self.masked_t_loss = {task_id: 0 for task_id in task_ids}
          self.masked_v_loss = {task_id: 0 for task_id in task_ids}
          self.next_sentense_loss = {task_id: 0 for task_id in task_ids}
  
          self.masked_t_loss_val = {task_id: 0 for task_id in task_ids}
          self.masked_v_loss_val = {task_id: 0 for task_id in task_ids}
          self.next_sentense_loss_val = {task_id: 0 for task_id in task_ids}
  
      def __getstate__(self):
          d = dict(self.__dict__)
          del d["logger"]
          del d["txt_f"]
          return d
  
      def __setstate__(self, d):
          self.__dict__.update(d)
          if self.save_logger:
              self.logger = SummaryWriter(log_dir=self.log_dir)
  
          self.txt_f = open(self.txt_dir + "/" + "out.txt", "a")
  
      def txt_close(self):
          self.txt_f.close()
  
      def linePlot(self, step, val, split, key, xlabel="None"):
          if self.save_logger:
              self.logger.add_scalar(split + "/" + key, val, step)
  
      def step_train(self, epochId, stepId, loss, score, norm, task_id, split):
  
          self.task_loss[task_id] += loss
          self.task_loss_tmp[task_id] += loss
          self.task_score_tmp[task_id] += score
          self.task_norm_tmp[task_id] += norm
          self.task_step[task_id] += self.gradient_accumulation_steps
          self.task_step_tmp[task_id] += self.gradient_accumulation_steps
          self.epochId = epochId
  
          # plot on tensorboard.
          self.linePlot(stepId, loss, split, self.task_id2name[task_id] + "_loss")
          self.linePlot(stepId, score, split, self.task_id2name[task_id] + "_score")
          self.linePlot(stepId, norm, split, self.task_id2name[task_id] + "_norm")
  
      def step_train_CC(
          self,
          epochId,
          stepId,
          masked_loss_t,
          masked_loss_v,
          next_sentence_loss,
          norm,
          task_id,
          split,
      ):
  
          self.masked_t_loss[task_id] += masked_loss_t
          self.masked_v_loss[task_id] += masked_loss_v
          self.next_sentense_loss[task_id] += next_sentence_loss
          self.task_norm_tmp[task_id] += norm
  
          self.task_step[task_id] += self.gradient_accumulation_steps
          self.task_step_tmp[task_id] += self.gradient_accumulation_steps
          self.epochId = epochId
  
          # plot on tensorboard.
          self.linePlot(
              stepId, masked_loss_t, split, self.task_id2name[task_id] + "_masked_loss_t"
          )
          self.linePlot(
              stepId, masked_loss_v, split, self.task_id2name[task_id] + "_masked_loss_v"
          )
          self.linePlot(
              stepId,
              next_sentence_loss,
              split,
              self.task_id2name[task_id] + "_next_sentence_loss",
          )
  
      def step_val(self, epochId, loss, score, task_id, batch_size, split):
          self.task_loss_val[task_id] += loss * batch_size
          self.task_score_val[task_id] += score
          self.task_step_val[task_id] += self.gradient_accumulation_steps
          self.task_datasize_val[task_id] += batch_size
  
      def step_val_CC(
          self,
          epochId,
          masked_loss_t,
          masked_loss_v,
          next_sentence_loss,
          task_id,
          batch_size,
          split,
      ):
  
          self.masked_t_loss_val[task_id] += masked_loss_t
          self.masked_v_loss_val[task_id] += masked_loss_v
          self.next_sentense_loss_val[task_id] += next_sentence_loss
  
          self.task_step_val[task_id] += self.gradient_accumulation_steps
          self.task_datasize_val[task_id] += batch_size
  
      def showLossValAll(self):
          progressInfo = "Eval Ep: %d " % self.epochId
          lossInfo = "Validation "
          val_scores = {}
          ave_loss = 0
          for task_id in self.task_ids:
              loss = self.task_loss_val[task_id] / float(self.task_step_val[task_id])
              score = self.task_score_val[task_id] / float(
                  self.task_datasize_val[task_id]
              )
              val_scores[task_id] = score
              ave_loss += loss
              lossInfo += "[%s]: loss %.3f score %.3f " % (
                  self.task_id2name[task_id],
                  loss,
                  score * 100.0,
              )
  
              self.linePlot(
                  self.epochId, loss, "val", self.task_id2name[task_id] + "_loss"
              )
              self.linePlot(
                  self.epochId, score, "val", self.task_id2name[task_id] + "_score"
              )
  
          self.task_loss_val = {task_id: 0 for task_id in self.task_loss_val}
          self.task_score_val = {task_id: 0 for task_id in self.task_score_val}
          self.task_datasize_val = {task_id: 0 for task_id in self.task_datasize_val}
          self.task_step_val = {task_id: 0 for task_id in self.task_ids}
  
          logger.info(progressInfo)
          logger.info(lossInfo)
          print(lossInfo, file=self.txt_f)
          return val_scores
  
      def getValScore(self, task_id):
          return self.task_score_val[task_id] / float(self.task_datasize_val[task_id])
  
      def showLossVal(self, task_id, task_stop_controller=None):
          progressInfo = "Eval task %s on iteration %d " % (
              task_id,
              self.task_step[task_id],
          )
          lossInfo = "Validation "
          ave_loss = 0
          loss = self.task_loss_val[task_id] / float(self.task_datasize_val[task_id])
          score = self.task_score_val[task_id] / float(self.task_datasize_val[task_id])
          ave_loss += loss
          lossInfo += "[%s]: loss %.3f score %.3f " % (
              self.task_id2name[task_id],
              loss,
              score * 100.0,
          )
  
          self.linePlot(
              self.task_step[task_id], loss, "val", self.task_id2name[task_id] + "_loss"
          )
          self.linePlot(
              self.task_step[task_id], score, "val", self.task_id2name[task_id] + "_score"
          )
          if task_stop_controller is not None:
              self.linePlot(
                  self.task_step[task_id],
                  task_stop_controller[task_id].in_stop,
                  "val",
                  self.task_id2name[task_id] + "_early_stop",
              )
  
          self.task_loss_val[task_id] = 0
          self.task_score_val[task_id] = 0
          self.task_datasize_val[task_id] = 0
          self.task_step_val[task_id] = 0
          logger.info(progressInfo)
          logger.info(lossInfo)
          print(lossInfo, file=self.txt_f)
          return score
  
      def showLossTrain(self):
          # show the current loss, once showed, reset the loss.
          lossInfo = ""
          for task_id in self.task_ids:
              if self.task_num_iters[task_id] > 0:
                  if self.task_step_tmp[task_id]:
                      lossInfo += (
                          "[%s]: iter %d Ep: %.2f loss %.3f score %.3f lr %.6g "
                          % (
                              self.task_id2name[task_id],
                              self.task_step[task_id],
                              self.task_step[task_id]
                              / float(self.task_num_iters[task_id]),
                              self.task_loss_tmp[task_id]
                              / float(self.task_step_tmp[task_id]),
                              self.task_score_tmp[task_id]
                              / float(self.task_step_tmp[task_id]),
                              self.task_norm_tmp[task_id]
                              / float(self.task_step_tmp[task_id]),
                          )
                      )
  
          logger.info(lossInfo)
          print(lossInfo, file=self.txt_f)
  
          self.task_step_tmp = {task_id: 0 for task_id in self.task_ids}
          self.task_loss_tmp = {task_id: 0 for task_id in self.task_ids}
          self.task_score_tmp = {task_id: 0 for task_id in self.task_ids}
          self.task_norm_tmp = {task_id: 0 for task_id in self.task_ids}
  
      def showLossValCC(self):
          progressInfo = "Eval Ep: %d " % self.epochId
          lossInfo = "Validation "
          for task_id in self.task_ids:
              masked_t_loss_val = self.masked_t_loss_val[task_id] / float(
                  self.task_step_val[task_id]
              )
              masked_v_loss_val = self.masked_v_loss_val[task_id] / float(
                  self.task_step_val[task_id]
              )
              next_sentense_loss_val = self.next_sentense_loss_val[task_id] / float(
                  self.task_step_val[task_id]
              )
  
              lossInfo += "[%s]: masked_t %.3f masked_v %.3f NSP %.3f" % (
                  self.task_id2name[task_id],
                  masked_t_loss_val,
                  masked_v_loss_val,
                  next_sentense_loss_val,
              )
  
              self.linePlot(
                  self.epochId,
                  masked_t_loss_val,
                  "val",
                  self.task_id2name[task_id] + "_mask_t",
              )
              self.linePlot(
                  self.epochId,
                  masked_v_loss_val,
                  "val",
                  self.task_id2name[task_id] + "_maks_v",
              )
              self.linePlot(
                  self.epochId,
                  next_sentense_loss_val,
                  "val",
                  self.task_id2name[task_id] + "_nsp",
              )
  
          self.masked_t_loss_val = {task_id: 0 for task_id in self.masked_t_loss_val}
          self.masked_v_loss_val = {task_id: 0 for task_id in self.masked_v_loss_val}
          self.next_sentense_loss_val = {
              task_id: 0 for task_id in self.next_sentense_loss_val
          }
          self.task_datasize_val = {task_id: 0 for task_id in self.task_datasize_val}
          self.task_step_val = {task_id: 0 for task_id in self.task_ids}
  
          logger.info(lossInfo)
          print(lossInfo, file=self.txt_f)
  
      def showLossTrainCC(self):
          # show the current loss, once showed, reset the loss.
          lossInfo = ""
          for task_id in self.task_ids:
              if self.task_num_iters[task_id] > 0:
                  if self.task_step_tmp[task_id]:
                      lossInfo += (
                          "[%s]: iter %d Ep: %.2f masked_t %.3f masked_v %.3f NSP %.3f lr %.6g"
                          % (
                              self.task_id2name[task_id],
                              self.task_step[task_id],
                              self.task_step[task_id]
                              / float(self.task_num_iters[task_id]),
                              self.masked_t_loss[task_id]
                              / float(self.task_step_tmp[task_id]),
                              self.masked_v_loss[task_id]
                              / float(self.task_step_tmp[task_id]),
                              self.next_sentense_loss[task_id]
                              / float(self.task_step_tmp[task_id]),
                              self.task_norm_tmp[task_id]
                              / float(self.task_step_tmp[task_id]),
                          )
                      )
  
          logger.info(lossInfo)
          print(lossInfo, file=self.txt_f)
  
          self.task_step_tmp = {task_id: 0 for task_id in self.task_ids}
          self.masked_t_loss = {task_id: 0 for task_id in self.task_ids}
          self.masked_v_loss = {task_id: 0 for task_id in self.task_ids}
          self.next_sentense_loss = {task_id: 0 for task_id in self.task_ids}
          self.task_norm_tmp = {task_id: 0 for task_id in self.task_ids}
  
  
  def url_to_filename(url, etag=None):
      """
      Convert `url` into a hashed filename in a repeatable way.
      If `etag` is specified, append its hash to the url's, delimited
      by a period.
      """
      url_bytes = url.encode("utf-8")
      url_hash = sha256(url_bytes)
      filename = url_hash.hexdigest()
  
      if etag:
          etag_bytes = etag.encode("utf-8")
          etag_hash = sha256(etag_bytes)
          filename += "." + etag_hash.hexdigest()
  
      return filename
  
  
  def filename_to_url(filename, cache_dir=None):
      """
      Return the url and etag (which may be ``None``) stored for `filename`.
      Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      cache_path = os.path.join(cache_dir, filename)
      if not os.path.exists(cache_path):
          raise EnvironmentError("file {} not found".format(cache_path))
  
      meta_path = cache_path + ".json"
      if not os.path.exists(meta_path):
          raise EnvironmentError("file {} not found".format(meta_path))
  
      with open(meta_path, encoding="utf-8") as meta_file:
          metadata = json.load(meta_file)
      url = metadata["url"]
      etag = metadata["etag"]
  
      return url, etag
  
  
  def cached_path(url_or_filename, cache_dir=None):
      """
      Given something that might be a URL (or might be a local path),
      determine which. If it's a URL, download the file and cache it, and
      return the path to the cached file. If it's already a local path,
      make sure the file exists and then return the path.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
      if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
          url_or_filename = str(url_or_filename)
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      parsed = urlparse(url_or_filename)
  
      if parsed.scheme in ("http", "https", "s3"):
          # URL, so get it from the cache (downloading if necessary)
          return get_from_cache(url_or_filename, cache_dir)
      elif os.path.exists(url_or_filename):
          # File, and it exists.
          return url_or_filename
      elif parsed.scheme == "":
          # File, but it doesn't exist.
          raise EnvironmentError("file {} not found".format(url_or_filename))
      else:
          # Something unknown
          raise ValueError(
              "unable to parse {} as a URL or as a local path".format(url_or_filename)
          )
  
  
  def split_s3_path(url):
      """Split a full s3 path into the bucket name and path."""
      parsed = urlparse(url)
      if not parsed.netloc or not parsed.path:
          raise ValueError("bad s3 path {}".format(url))
      bucket_name = parsed.netloc
      s3_path = parsed.path
      # Remove '/' at beginning of path.
      if s3_path.startswith("/"):
          s3_path = s3_path[1:]
      return bucket_name, s3_path
  
  
  def s3_request(func):
      """
      Wrapper function for s3 requests in order to create more helpful error
      messages.
      """
  
      @wraps(func)
      def wrapper(url, *args, **kwargs):
          try:
              return func(url, *args, **kwargs)
          except ClientError as exc:
              if int(exc.response["Error"]["Code"]) == 404:
                  raise EnvironmentError("file {} not found".format(url))
              else:
                  raise
  
      return wrapper
  
  
  @s3_request
  def s3_etag(url):
      """Check ETag on S3 object."""
      s3_resource = boto3.resource("s3")
      bucket_name, s3_path = split_s3_path(url)
      s3_object = s3_resource.Object(bucket_name, s3_path)
      return s3_object.e_tag
  
  
  @s3_request
  def s3_get(url, temp_file):
      """Pull a file directly from S3."""
      s3_resource = boto3.resource("s3")
      bucket_name, s3_path = split_s3_path(url)
      s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
  
  
  def http_get(url, temp_file):
      req = requests.get(url, stream=True)
      content_length = req.headers.get("Content-Length")
      total = int(content_length) if content_length is not None else None
      progress = tqdm(unit="B", total=total)
      for chunk in req.iter_content(chunk_size=1024):
          if chunk:  # filter out keep-alive new chunks
              progress.update(len(chunk))
              temp_file.write(chunk)
      progress.close()
  
  
  def get_from_cache(url, cache_dir=None):
      """
      Given a URL, look for the corresponding dataset in the local cache.
      If it's not there, download it. Then return the path to the cached file.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      if not os.path.exists(cache_dir):
          os.makedirs(cache_dir)
  
      # Get eTag to add to filename, if it exists.
      if url.startswith("s3://"):
          etag = s3_etag(url)
      else:
          response = requests.head(url, allow_redirects=True)
          if response.status_code != 200:
              raise IOError(
                  "HEAD request failed for url {} with status code {}".format(
                      url, response.status_code
                  )
              )
          etag = response.headers.get("ETag")
  
      filename = url_to_filename(url, etag)
  
      # get cache path to put the file
      cache_path = os.path.join(cache_dir, filename)
  
      if not os.path.exists(cache_path):
          # Download to temporary file, then copy to cache dir once finished.
          # Otherwise you get corrupt cache entries if the download gets interrupted.
          with tempfile.NamedTemporaryFile() as temp_file:
              logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
  
              # GET file object
              if url.startswith("s3://"):
                  s3_get(url, temp_file)
              else:
                  http_get(url, temp_file)
  
              # we are copying the file before closing it, so flush to avoid truncation
              temp_file.flush()
              # shutil.copyfileobj() starts at the current position, so go to the start
              temp_file.seek(0)
  
              logger.info("copying %s to cache at %s", temp_file.name, cache_path)
              with open(cache_path, "wb") as cache_file:
                  shutil.copyfileobj(temp_file, cache_file)
  
              logger.info("creating metadata file for %s", cache_path)
              meta = {"url": url, "etag": etag}
              meta_path = cache_path + ".json"
              with open(meta_path, "w", encoding="utf-8") as meta_file:
                  json.dump(meta, meta_file)
  
              logger.info("removing temp file %s", temp_file.name)
  
      return cache_path
  
  
  def read_set_from_file(filename):
      """
      Extract a de-duped collection (set) of text from a file.
      Expected file format is one item per line.
      """
      collection = set()
      with open(filename, "r", encoding="utf-8") as file_:
          for line in file_:
              collection.add(line.rstrip())
      return collection
  
  
  def get_file_extension(path, dot=True, lower=True):
      ext = os.path.splitext(path)[1]
      ext = ext if dot else ext[1:]
      return ext.lower() if lower else ext
  
  
  class PreTrainedModel(nn.Module):
      r""" Base class for all models.
          :class:`~pytorch_transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
          as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
          Class attributes (overridden by derived classes):
              - ``config_class``: a class derived from :class:`~pytorch_transformers.PretrainedConfig` to use as configuration class for this model architecture.
              - ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
              - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
                  - ``model``: an instance of the relevant subclass of :class:`~pytorch_transformers.PreTrainedModel`,
                  - ``config``: an instance of the relevant subclass of :class:`~pytorch_transformers.PretrainedConfig`,
                  - ``path``: a path (string) to the TensorFlow checkpoint.
              - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
      """
      config_class = None
      pretrained_model_archive_map = {}
      load_tf_weights = lambda model, config, path: None
      base_model_prefix = ""
  
      def __init__(self, config, *inputs, **kwargs):
          super(PreTrainedModel, self).__init__()
          # if not isinstance(config, PretrainedConfig):
          #     raise ValueError(
          #         "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
          #         "To create a model from a pretrained model use "
          #         "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
          #             self.__class__.__name__, self.__class__.__name__
          #         ))
          # Save config in model
          self.config = config
  
      def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
          """ Build a resized Embedding Module from a provided token Embedding Module.
              Increasing the size will add newly initialized vectors at the end
              Reducing the size will remove vectors from the end
          Args:
              new_num_tokens: (`optional`) int
                  New number of tokens in the embedding matrix.
                  Increasing the size will add newly initialized vectors at the end
                  Reducing the size will remove vectors from the end
                  If not provided or None: return the provided token Embedding Module.
          Return: ``torch.nn.Embeddings``
              Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
          """
          if new_num_tokens is None:
              return old_embeddings
  
          old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
          if old_num_tokens == new_num_tokens:
              return old_embeddings
  
          # Build new embeddings
          new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
          new_embeddings.to(old_embeddings.weight.device)
  
          # initialize all new embeddings (in particular added tokens)
          self.init_weights(new_embeddings)
  
          # Copy word embeddings from the previous weights
          num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
          new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[
              :num_tokens_to_copy, :
          ]
  
          return new_embeddings
  
      def _tie_or_clone_weights(self, first_module, second_module):
          """ Tie or clone module weights depending of weither we are using TorchScript or not
          """
          # TODO: igore torch script here
          first_module.weight = second_module.weight
  
      def resize_token_embeddings(self, new_num_tokens=None):
          """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
          Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
          Arguments:
              new_num_tokens: (`optional`) int:
                  New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. 
                  If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.
          Return: ``torch.nn.Embeddings``
              Pointer to the input tokens Embeddings Module of the model
          """
          base_model = getattr(
              self, self.base_model_prefix, self
          )  # get the base model if needed
          model_embeds = base_model._resize_token_embeddings(new_num_tokens)
          if new_num_tokens is None:
              return model_embeds
  
          # Update base model and current model config
          self.config.vocab_size = new_num_tokens
          base_model.vocab_size = new_num_tokens
  
          # Tie weights again if needed
          if hasattr(self, "tie_weights"):
              self.tie_weights()
  
          return model_embeds
  
      def prune_heads(self, heads_to_prune):
          """ Prunes heads of the base model.
              Arguments:
                  heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
          """
          base_model = getattr(
              self, self.base_model_prefix, self
          )  # get the base model if needed
          base_model._prune_heads(heads_to_prune)
  
      def save_pretrained(self, save_directory):
          """ Save a model and its configuration file to a directory, so that it
              can be re-loaded using the `:func:`~pytorch_transformers.PreTrainedModel.from_pretrained`` class method.
          """
          assert os.path.isdir(
              save_directory
          ), "Saving path should be a directory where the model and configuration can be saved"
  
          # Only save the model it-self if we are using distributed training
          model_to_save = self.module if hasattr(self, "module") else self
  
          # Save configuration file
          model_to_save.config.save_pretrained(save_directory)
  
          # If we save using the predefined names, we can load using `from_pretrained`
          output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
  
          torch.save(model_to_save.state_dict(), output_model_file)
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
          The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with ``model.train()``
          The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
          It is up to you to train those weights with a downstream fine-tuning task.
          The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.
          Parameters:
              pretrained_model_name_or_path: either:
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
              config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
              cache_dir: (`optional`) string:
                  Path to a directory in which a downloaded pre-trained model
                  configuration should be cached if the standard cache should not be used.
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
          Examples::
              model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
              model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
          """
  
          config = kwargs.pop("config", None)
          state_dict = kwargs.pop("state_dict", None)
          cache_dir = kwargs.pop("cache_dir", None)
          from_tf = kwargs.pop("from_tf", False)
          output_loading_info = kwargs.pop("output_loading_info", False)
          default_gpu = kwargs.pop("default_gpu", True)
  
          # Load config
          assert config is not None
          model_kwargs = kwargs
  
          # Load model
          if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
              archive_file = cls.pretrained_model_archive_map[
                  pretrained_model_name_or_path
              ]
          elif os.path.isdir(pretrained_model_name_or_path):
              if from_tf:
                  # Directly load from a TensorFlow checkpoint
                  archive_file = os.path.join(
                      pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index"
                  )
              else:
                  archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
          else:
              if from_tf:
                  # Directly load from a TensorFlow checkpoint
                  archive_file = pretrained_model_name_or_path + ".index"
              else:
                  archive_file = pretrained_model_name_or_path
          # redirect to the cache, if necessary
          try:
              resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
          except EnvironmentError:
              if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                  logger.error(
                      "Couldn't reach server at '{}' to download pretrained weights.".format(
                          archive_file
                      )
                  )
              else:
                  logger.error(
                      "Model name '{}' was not found in model name list ({}). "
                      "We assumed '{}' was a path or url but couldn't find any file "
                      "associated to this path or url.".format(
                          pretrained_model_name_or_path,
                          ", ".join(cls.pretrained_model_archive_map.keys()),
                          archive_file,
                      )
                  )
              return None
          if default_gpu:
              if resolved_archive_file == archive_file:
                  logger.info("loading weights file {}".format(archive_file))
              else:
                  logger.info(
                      "loading weights file {} from cache at {}".format(
                          archive_file, resolved_archive_file
                      )
                  )
  
          # Instantiate model.
          model = cls(config, *model_args, **model_kwargs)
  
          if state_dict is None and not from_tf:
              state_dict = torch.load(resolved_archive_file, map_location="cpu")
          if from_tf:
              # Directly load from a TensorFlow checkpoint
              return cls.load_tf_weights(
                  model, config, resolved_archive_file[:-6]
              )  # Remove the '.index'
  
          # Convert old format to new format if needed from a PyTorch state_dict
          old_keys = []
          new_keys = []
          for key in state_dict.keys():
              new_key = None
              if "gamma" in key:
                  new_key = key.replace("gamma", "weight")
              if "beta" in key:
                  new_key = key.replace("beta", "bias")
              if new_key:
                  old_keys.append(key)
                  new_keys.append(new_key)
          for old_key, new_key in zip(old_keys, new_keys):
              state_dict[new_key] = state_dict.pop(old_key)
  
          # Load from a PyTorch state_dict
          missing_keys = []
          unexpected_keys = []
          error_msgs = []
          # copy state_dict so _load_from_state_dict can modify it
          metadata = getattr(state_dict, "_metadata", None)
          state_dict = state_dict.copy()
          if metadata is not None:
              state_dict._metadata = metadata
  
          def load(module, prefix=""):
              # bert.embeddings.word_embeddings.weight
              local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
              module._load_from_state_dict(
                  state_dict,
                  prefix,
                  local_metadata,
                  True,
                  missing_keys,
                  unexpected_keys,
                  error_msgs,
              )
              for name, child in module._modules.items():
                  if child is not None:
                      load(child, prefix + name + ".")
  
          # Make sure we are able to load base models as well as derived models (with heads)
          start_prefix = ""
          model_to_load = model
          if not hasattr(model, cls.base_model_prefix) and any(
              s.startswith(cls.base_model_prefix) for s in state_dict.keys()
          ):
              start_prefix = cls.base_model_prefix + "."
          if hasattr(model, cls.base_model_prefix) and not any(
              s.startswith(cls.base_model_prefix) for s in state_dict.keys()
          ):
              model_to_load = getattr(model, cls.base_model_prefix)
  
          load(model_to_load, prefix=start_prefix)
          if len(missing_keys) > 0 and default_gpu:
              logger.info(
                  "Weights of {} not initialized from pretrained model: {}".format(
                      model.__class__.__name__, missing_keys
                  )
              )
          if len(unexpected_keys) > 0 and default_gpu:
              logger.info(
                  "Weights from pretrained model not used in {}: {}".format(
                      model.__class__.__name__, unexpected_keys
                  )
              )
          if len(error_msgs) > 0 and default_gpu:
              raise RuntimeError(
                  "Error(s) in loading state_dict for {}:\n\t{}".format(
                      model.__class__.__name__, "\n\t".join(error_msgs)
                  )
              )
  
          if hasattr(model, "tie_weights"):
              model.tie_weights()  # make sure word embedding weights are still tied
  
          # Set model in evaluation mode to desactivate DropOut modules by default
          model.eval()
  
          if output_loading_info:
              loading_info = {
                  "missing_keys": missing_keys,
                  "unexpected_keys": unexpected_keys,
                  "error_msgs": error_msgs,
              }
              return model, loading_info
  
          return model
  