






import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
       import torch

       a						:			List[Any]     	=					True
except ImportError:
       a						:			str     	=					False


try:
       from torch.hub import _get_torch_home

       a						:			List[Any]     	=					_get_torch_home()
except ImportError:
       a						:			int     	=					os.path.expanduser(
           os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
       )

a						:			Optional[Any]     	=					os.path.join(torch_cache_home, "transformers")

a						:			Optional[Any]     	=					"https://cdn.huggingface.co"
a						:			List[str]     	=					"https://s3.amazonaws.com/models.huggingface.co/bert"
a						:			Any     	=					"/".join(str(Path(__file__).resolve()).split("/")[:-1])
a						:			Optional[int]     	=					os.path.join(PATH, "config.yaml")
a						:			Dict     	=					os.path.join(PATH, "attributes.txt")
a						:			Tuple     	=					os.path.join(PATH, "objects.txt")
a						:			Dict     	=					os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
a						:			Dict     	=					os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
a						:			Optional[int]     	=					os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
a						:			Any     	=					"pytorch_model.bin"
a						:			int     	=					"config.yaml"






def 							lowerCamelCase__ (       __lowerCamelCase     :	str=OBJECTS    ,						__lowerCamelCase     :	Union[str, Any]=ATTRIBUTES						):
  __UpperCAmelCase				:      Union[str, Any]	 =    []
  with open(__lowerCamelCase						) as f:
    for object in f.readlines():
      vg_classes.append(object.split(""","""						)[0].lower().strip()						)

  __UpperCAmelCase				:      Dict	 =    []
  with open(__lowerCamelCase						) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(""","""						)[0].lower().strip()						)
  return vg_classes, vg_attrs






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any						):
  __UpperCAmelCase				:      List[str]	 =    OrderedDict()
  with open(__lowerCamelCase    ,						"""rb"""						) as f:
    __UpperCAmelCase				:      int	 =    pkl.load(__lowerCamelCase						)["""model"""]
  for k in copy.deepcopy(list(ckp.keys()						)						):
    __UpperCAmelCase				:      List[Any]	 =    ckp.pop(__lowerCamelCase						)
    if isinstance(__lowerCamelCase    ,						np.ndarray						):
      __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor(__lowerCamelCase						)
    else:
      assert isinstance(__lowerCamelCase    ,						torch.tensor						), type(__lowerCamelCase						)
    __UpperCAmelCase				:      List[str]	 =    v
  return r
class        a       :



      """simple docstring"""


      a    :					Dict							=      {}
      def __init__( self   :     Dict						, __lowercase   :     dict						, __lowercase   :     str = "root"						, __lowercase   :     Any=0   )				-> Dict:
        __UpperCAmelCase				:      List[str]	 =    name
        __UpperCAmelCase				:      str	 =    level
        __UpperCAmelCase				:      int	 =    {}
        for k, v in dictionary.items():
          if v is None:
            raise ValueError()
          __UpperCAmelCase				:      List[str]	 =    copy.deepcopy(__lowercase   )
          __UpperCAmelCase				:      Dict	 =    copy.deepcopy(__lowercase   )
          if isinstance(__lowercase						, __lowercase   ):
            __UpperCAmelCase				:      Union[str, Any]	 =    Config(__lowercase						, name=__lowercase						, level=level + 1   )
          __UpperCAmelCase				:      Union[str, Any]	 =    v
          setattr(self						, __lowercase						, __lowercase   )

        __UpperCAmelCase				:      Any	 =    d
      def __repr__( self   :     Optional[Any]   )				-> Optional[int]:
        return str(list((self._pointer.keys())   )   )
      def __setattr__( self   :     List[str]						, __lowercase   :     List[str]						, __lowercase   :     Tuple   )				-> int:
        __UpperCAmelCase				:      int	 =    val
        __UpperCAmelCase				:      List[str]	 =    val
        __UpperCAmelCase				:      Union[str, Any]	 =    key.split("""."""   )
        __UpperCAmelCase				:      List[Any]	 =    len(__lowercase   ) - 1
        __UpperCAmelCase				:      List[Any]	 =    self._pointer
        if len(__lowercase   ) > 1:
          for i, l in enumerate(__lowercase   ):
            if hasattr(self						, __lowercase   ) and isinstance(getattr(self						, __lowercase   )						, __lowercase   ):
              setattr(getattr(self						, __lowercase   )						, """.""".join(levels[i:]   )						, __lowercase   )
            if l == last_level:
              __UpperCAmelCase				:      Union[str, Any]	 =    val
            else:
              __UpperCAmelCase				:      Union[str, Any]	 =    pointer[l]
      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[int]:
        return self._pointer
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     Optional[int]						, __lowercase   :     Any   )				-> Optional[int]:
        with open(f"""{file_name}"""						, """w"""   ) as stream:
          dump(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[str]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Union[str, Any]   )				-> Any:
        with open(f"""{file_name}"""						, """w"""   ) as stream:
          json.dump(__lowercase						, __lowercase   )
      @staticmethod
      def       UpperCAmelCase ( __lowercase   :     List[Any]   )				-> Optional[Any]:
        with open(__lowercase   ) as stream:
          __UpperCAmelCase				:      Any	 =    load(__lowercase						, Loader=__lowercase   )
        return data
      def __str__( self   :     List[str]   )				-> Tuple:
        __UpperCAmelCase				:      Any	 =    """    """
        if self._name != "root":
          __UpperCAmelCase				:      Optional[Any]	 =    f"""{t * (self._level-1)}{self._name}:\n"""
        else:
          __UpperCAmelCase				:      List[Any]	 =    """"""
        __UpperCAmelCase				:      Optional[Any]	 =    self._level
        for i, (k, v) in enumerate(self._pointer.items()   ):
          if isinstance(__lowercase						, __lowercase   ):
            r += f"""{t * (self._level)}{v}\n"""
            self._level += 1
          else:
            r += f"""{t * (self._level)}{k}: {v} ({type(__lowercase   ).__name__})\n"""
          __UpperCAmelCase				:      int	 =    level
        return r[:-1]
      @classmethod
      def       UpperCAmelCase ( cls   :     List[str]						, __lowercase   :     str						, **__lowercase   :     Any   )				-> Any:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    cls.get_config_dict(__lowercase						, **__lowercase   )
        return cls(__lowercase   )




      @classmethod
      def       UpperCAmelCase ( cls   :     Dict						, __lowercase   :     str						, **__lowercase   :     Union[str, Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      int	 =    kwargs.pop("""cache_dir"""						, __lowercase   )
        __UpperCAmelCase				:      int	 =    kwargs.pop("""force_download"""						, __lowercase   )
        __UpperCAmelCase				:      str	 =    kwargs.pop("""resume_download"""						, __lowercase   )
        __UpperCAmelCase				:      Dict	 =    kwargs.pop("""proxies"""						, __lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    kwargs.pop("""local_files_only"""						, __lowercase   )

        if os.path.isdir(__lowercase   ):
          __UpperCAmelCase				:      List[Any]	 =    os.path.join(__lowercase						, __lowercase   )
        elif os.path.isfile(__lowercase   ) or is_remote_url(__lowercase   ):
          __UpperCAmelCase				:      Tuple	 =    pretrained_model_name_or_path
        else:
          __UpperCAmelCase				:      Optional[int]	 =    hf_bucket_url(__lowercase						, filename=__lowercase						, use_cdn=__lowercase   )

        try:
          # Load from URL or cache if already cached
          __UpperCAmelCase				:      Optional[int]	 =    cached_path(
              __lowercase						, cache_dir=__lowercase						, force_download=__lowercase						, proxies=__lowercase						, resume_download=__lowercase						, local_files_only=__lowercase						, )
          # Load config dict
          if resolved_config_file is None:
            raise EnvironmentError

          __UpperCAmelCase				:      Optional[int]	 =    Config.load_yaml(__lowercase   )

        except EnvironmentError:
          __UpperCAmelCase				:      str	 =    """Can't load config for"""
          raise EnvironmentError(__lowercase   )

        if resolved_config_file == config_file:
          print("""loading configuration file from path"""   )
        else:
          print("""loading configuration file cache"""   )

        return Config.load_yaml(__lowercase   ), kwargs






def 							lowerCamelCase__ (       __lowerCamelCase     :	Dict						):
  __UpperCAmelCase				:      Optional[int]	 =    torch.load("""dump.pt"""    ,						map_location=in_tensor.device						)
  __UpperCAmelCase				:      Tuple	 =    in_tensor.numpy()
  __UpperCAmelCase				:      Optional[int]	 =    out_tensor.numpy()[0]
  print(na.shape    ,						na[0, 0, :5]						)
  print(na.shape    ,						na[0, 0, :5]						)
  assert np.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						rtol=0.0_1    ,						atol=0.1						), (
      f"""{sum([1 for x in np.isclose(__lowerCamelCase    ,						__lowerCamelCase    ,						rtol=0.0_1    ,						atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*100:.4f} %"""
      " element-wise mismatch"
  )
  raise Exception("""tensors are all good"""						)

  # Hugging face functions below






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]						):
  __UpperCAmelCase				:      Tuple	 =    urlparse(__lowerCamelCase						)
  return parsed.scheme in ("http", "https")






def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	int=True						):
  __UpperCAmelCase				:      int	 =    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  __UpperCAmelCase				:      Optional[int]	 =    """/""" not in model_id
  if legacy_format:
    return f"""{endpoint}/{model_id}-{filename}"""
  else:
    return f"""{endpoint}/{model_id}/{filename}"""






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Tuple=None    ,						__lowerCamelCase     :	Optional[Any]=0    ,						__lowerCamelCase     :	Optional[int]=None    ,						):
  __UpperCAmelCase				:      Optional[int]	 =    """python/{}""".format(sys.version.split()[0]						)
  if _torch_available:
    ua += "; torch/{}".format(torch.__version__						)
  if isinstance(__lowerCamelCase    ,						__lowerCamelCase						):
    ua += "; " + "; ".join("""{}/{}""".format(__lowerCamelCase    ,						__lowerCamelCase						) for k, v in user_agent.items()						)
  elif isinstance(__lowerCamelCase    ,						__lowerCamelCase						):
    ua += "; " + user_agent
  __UpperCAmelCase				:      List[str]	 =    {"""user-agent""": ua}
  if resume_size > 0:
    __UpperCAmelCase				:      Union[str, Any]	 =    """bytes=%d-""" % (resume_size,)
  __UpperCAmelCase				:      Union[str, Any]	 =    requests.get(__lowerCamelCase    ,						stream=__lowerCamelCase    ,						proxies=__lowerCamelCase    ,						headers=__lowerCamelCase						)
  if response.status_code == 416:  # Range not satisfiable
    return
  __UpperCAmelCase				:      List[str]	 =    response.headers.get("""Content-Length"""						)
  __UpperCAmelCase				:      str	 =    resume_size + int(__lowerCamelCase						) if content_length is not None else None
  __UpperCAmelCase				:      List[Any]	 =    tqdm(
      unit="""B"""    ,						unit_scale=__lowerCamelCase    ,						total=__lowerCamelCase    ,						initial=__lowerCamelCase    ,						desc="""Downloading"""    ,						)
  for chunk in response.iter_content(chunk_size=1024						):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(__lowerCamelCase						)						)
      temp_file.write(__lowerCamelCase						)
  progress.close()






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Optional[int]=None    ,						__lowerCamelCase     :	Optional[int]=False    ,						__lowerCamelCase     :	Union[str, Any]=None    ,						__lowerCamelCase     :	str=10    ,						__lowerCamelCase     :	Optional[int]=False    ,						__lowerCamelCase     :	Dict=None    ,						__lowerCamelCase     :	List[str]=False    ,						):
  if cache_dir is None:
    __UpperCAmelCase				:      Optional[Any]	 =    TRANSFORMERS_CACHE
  if isinstance(__lowerCamelCase    ,						__lowerCamelCase						):
    __UpperCAmelCase				:      List[str]	 =    str(__lowerCamelCase						)

  os.makedirs(__lowerCamelCase    ,						exist_ok=__lowerCamelCase						)

  __UpperCAmelCase				:      List[Any]	 =    None
  if not local_files_only:
    try:
      __UpperCAmelCase				:      Optional[Any]	 =    requests.head(__lowerCamelCase    ,						allow_redirects=__lowerCamelCase    ,						proxies=__lowerCamelCase    ,						timeout=__lowerCamelCase						)
      if response.status_code == 200:
        __UpperCAmelCase				:      Dict	 =    response.headers.get("""ETag"""						)
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  __UpperCAmelCase				:      List[str]	 =    url_to_filename(__lowerCamelCase    ,						__lowerCamelCase						)

  # get cache path to put the file
  __UpperCAmelCase				:      Optional[int]	 =    os.path.join(__lowerCamelCase    ,						__lowerCamelCase						)

  # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
  # try to get the last downloaded one
  if etag is None:
    if os.path.exists(__lowerCamelCase						):
      return cache_path
    else:
      __UpperCAmelCase				:      List[Any]	 =    [
          file
          for file in fnmatch.filter(os.listdir(__lowerCamelCase						)    ,						filename + """.*"""						)
          if not file.endswith(""".json"""						) and not file.endswith(""".lock"""						)
      ]
      if len(__lowerCamelCase						) > 0:
        return os.path.join(__lowerCamelCase    ,						matching_files[-1]						)
      else:
        # If files cannot be found and local_files_only=True,
        # the models might've been found if local_files_only=False
        # Notify the user about that
        if local_files_only:
          raise ValueError(
              """Cannot find the requested files in the cached path and outgoing traffic has been"""
              """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
              """ to False."""						)
        return None

    # From now on, etag is not None.
  if os.path.exists(__lowerCamelCase						) and not force_download:
    return cache_path

  # Prevent parallel downloads of the same file with a lock.
  __UpperCAmelCase				:      str	 =    cache_path + """.lock"""
  with FileLock(__lowerCamelCase						):
    # If the download just completed while the lock was activated.
    if os.path.exists(__lowerCamelCase						) and not force_download:
      # Even if returning early like here, the lock will be released.
      return cache_path

    if resume_download:
      __UpperCAmelCase				:      int	 =    cache_path + """.incomplete"""

      @contextmanager
      def _resumable_file_manager():
        with open(__lowerCamelCase    ,						"""a+b"""						) as f:
          yield f

      __UpperCAmelCase				:      str	 =    _resumable_file_manager
      if os.path.exists(__lowerCamelCase						):
        __UpperCAmelCase				:      List[Any]	 =    os.stat(__lowerCamelCase						).st_size
      else:
        __UpperCAmelCase				:      List[Any]	 =    0
    else:
      __UpperCAmelCase				:      str	 =    partial(tempfile.NamedTemporaryFile    ,						dir=__lowerCamelCase    ,						delete=__lowerCamelCase						)
      __UpperCAmelCase				:      Optional[int]	 =    0

    # Download to temporary file, then copy to cache dir once finished.
    # Otherwise you get corrupt cache entries if the download gets interrupted.
    with temp_file_manager() as temp_file:
      print(
          """%s not found in cache or force_download set to True, downloading to %s"""    ,						__lowerCamelCase    ,						temp_file.name    ,						)

      http_get(
          __lowerCamelCase    ,						__lowerCamelCase    ,						proxies=__lowerCamelCase    ,						resume_size=__lowerCamelCase    ,						user_agent=__lowerCamelCase    ,						)

    os.replace(temp_file.name    ,						__lowerCamelCase						)

    __UpperCAmelCase				:      Any	 =    {"""url""": url, """etag""": etag}
    __UpperCAmelCase				:      Union[str, Any]	 =    cache_path + """.json"""
    with open(__lowerCamelCase    ,						"""w"""						) as meta_file:
      json.dump(__lowerCamelCase    ,						__lowerCamelCase						)

  return cache_path






def 							lowerCamelCase__ (       __lowerCamelCase     :	Union[str, Any]    ,						__lowerCamelCase     :	List[Any]=None						):
  __UpperCAmelCase				:      Tuple	 =    url.encode("""utf-8"""						)
  __UpperCAmelCase				:      Optional[Any]	 =    shaaaa(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    url_hash.hexdigest()

  if etag:
    __UpperCAmelCase				:      int	 =    etag.encode("""utf-8"""						)
    __UpperCAmelCase				:      List[str]	 =    shaaaa(__lowerCamelCase						)
    filename += "." + etag_hash.hexdigest()

  if url.endswith(""".h5"""						):
    filename += ".h5"

  return filename






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any    ,						__lowerCamelCase     :	int=None    ,						__lowerCamelCase     :	int=False    ,						__lowerCamelCase     :	Union[str, Any]=None    ,						__lowerCamelCase     :	Tuple=False    ,						__lowerCamelCase     :	str=None    ,						__lowerCamelCase     :	List[Any]=False    ,						__lowerCamelCase     :	Tuple=False    ,						__lowerCamelCase     :	str=False    ,						):
  if cache_dir is None:
    __UpperCAmelCase				:      List[str]	 =    TRANSFORMERS_CACHE
  if isinstance(__lowerCamelCase    ,						__lowerCamelCase						):
    __UpperCAmelCase				:      Any	 =    str(__lowerCamelCase						)
  if isinstance(__lowerCamelCase    ,						__lowerCamelCase						):
    __UpperCAmelCase				:      Tuple	 =    str(__lowerCamelCase						)

  if is_remote_url(__lowerCamelCase						):
    # URL, so get it from the cache (downloading if necessary)
    __UpperCAmelCase				:      Tuple	 =    get_from_cache(
        __lowerCamelCase    ,						cache_dir=__lowerCamelCase    ,						force_download=__lowerCamelCase    ,						proxies=__lowerCamelCase    ,						resume_download=__lowerCamelCase    ,						user_agent=__lowerCamelCase    ,						local_files_only=__lowerCamelCase    ,						)
  elif os.path.exists(__lowerCamelCase						):
    # File, and it exists.
    __UpperCAmelCase				:      Tuple	 =    url_or_filename
  elif urlparse(__lowerCamelCase						).scheme == "":
    # File, but it doesn't exist.
    raise EnvironmentError("""file {} not found""".format(__lowerCamelCase						)						)
  else:
    # Something unknown
    raise ValueError("""unable to parse {} as a URL or as a local path""".format(__lowerCamelCase						)						)

  if extract_compressed_file:
    if not is_zipfile(__lowerCamelCase						) and not tarfile.is_tarfile(__lowerCamelCase						):
      return output_path

    # Path where we extract compressed archives
    # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
    __UpperCAmelCase ,					__UpperCAmelCase				:      int	 =    os.path.split(__lowerCamelCase						)
    __UpperCAmelCase				:      Any	 =    output_file.replace("""."""    ,						"""-"""						) + """-extracted"""
    __UpperCAmelCase				:      List[str]	 =    os.path.join(__lowerCamelCase    ,						__lowerCamelCase						)

    if os.path.isdir(__lowerCamelCase						) and os.listdir(__lowerCamelCase						) and not force_extract:
      return output_path_extracted

    # Prevent parallel extractions
    __UpperCAmelCase				:      str	 =    output_path + """.lock"""
    with FileLock(__lowerCamelCase						):
      shutil.rmtree(__lowerCamelCase    ,						ignore_errors=__lowerCamelCase						)
      os.makedirs(__lowerCamelCase						)
      if is_zipfile(__lowerCamelCase						):
        with ZipFile(__lowerCamelCase    ,						"""r"""						) as zip_file:
          zip_file.extractall(__lowerCamelCase						)
          zip_file.close()
      elif tarfile.is_tarfile(__lowerCamelCase						):
        __UpperCAmelCase				:      Any	 =    tarfile.open(__lowerCamelCase						)
        tar_file.extractall(__lowerCamelCase						)
        tar_file.close()
      else:
        raise EnvironmentError("""Archive format of {} could not be identified""".format(__lowerCamelCase						)						)

    return output_path_extracted

  return output_path






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	int=","						):
  assert isinstance(__lowerCamelCase    ,						__lowerCamelCase						)
  if os.path.isfile(__lowerCamelCase						):
    with open(__lowerCamelCase						) as f:
      __UpperCAmelCase				:      List[Any]	 =    eval(f.read()						)
  else:
    __UpperCAmelCase				:      List[str]	 =    requests.get(__lowerCamelCase						)
    try:
      __UpperCAmelCase				:      int	 =    requests.json()
    except Exception:
      __UpperCAmelCase				:      List[Any]	 =    req.content.decode()
      assert data is not None, "could not connect"
      try:
        __UpperCAmelCase				:      str	 =    eval(__lowerCamelCase						)
      except Exception:
        __UpperCAmelCase				:      List[Any]	 =    data.split("""\n"""						)
    req.close()
  return data






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any						):
  __UpperCAmelCase				:      Optional[int]	 =    requests.get(__lowerCamelCase						)
  __UpperCAmelCase				:      List[Any]	 =    np.array(Image.open(BytesIO(response.content						)						)						)
  return img






def 							lowerCamelCase__ (       __lowerCamelCase     :	str						):
  __UpperCAmelCase				:      int	 =    url.split("""/"""						)[-1]
  if fn not in os.listdir(os.getcwd()						):
    wget.download(__lowerCamelCase						)
  with open(__lowerCamelCase    ,						"""rb"""						) as stream:
    __UpperCAmelCase				:      List[str]	 =    pkl.load(__lowerCamelCase						)
  __UpperCAmelCase				:      Dict	 =    weights.pop("""model"""						)
  __UpperCAmelCase				:      Union[str, Any]	 =    {}
  for k, v in model.items():
    __UpperCAmelCase				:      int	 =    torch.from_numpy(__lowerCamelCase						)
    if "running_var" in k:
      __UpperCAmelCase				:      Optional[int]	 =    torch.tensor([0]						)
      __UpperCAmelCase				:      Tuple	 =    k.replace("""running_var"""    ,						"""num_batches_tracked"""						)
      __UpperCAmelCase				:      Any	 =    zero
  return new






def 							lowerCamelCase__ (       ):
  print(f"""{os.path.abspath(os.path.join(__lowerCamelCase    ,						os.pardir						)						)}/demo.ipynb"""						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any    ,						__lowerCamelCase     :	List[Any]="RGB"						):
  assert isinstance(__lowerCamelCase    ,						__lowerCamelCase						)
  if os.path.isfile(__lowerCamelCase						):
    __UpperCAmelCase				:      List[str]	 =    cva.imread(__lowerCamelCase						)
  else:
    __UpperCAmelCase				:      int	 =    get_image_from_url(__lowerCamelCase						)
    assert img is not None, f"""could not connect to: {im}"""
  __UpperCAmelCase				:      Any	 =    cva.cvtColor(__lowerCamelCase    ,						cva.COLOR_BGR2RGB						)
  if input_format == "RGB":
    __UpperCAmelCase				:      Optional[int]	 =    img[:, :, ::-1]
  return img







def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	int=1						):
  return (images[i : i + batch] for i in range(0    ,						len(__lowerCamelCase						)    ,						__lowerCamelCase						))








'''simple docstring'''


import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowercase  =			2
class 						a_				:




	def __init__(       self     :  Tuple       ,		*,  # begin keyword-only arguments
	    __lowerCAmelCase     :  Optional[Any]="<s>"       ,		__lowerCAmelCase     :  Dict="<pad>"       ,		__lowerCAmelCase     :  Union[str, Any]="</s>"       ,		__lowerCAmelCase     :  Dict="<unk>"       ,		__lowerCAmelCase     :  Optional[int]=None       ,		):
							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   bos, unk, pad, eos
							__snake_case						 =   []
							__snake_case						 =   []
							__snake_case						 =   {}
							__snake_case						 =   self.add_symbol(__lowerCAmelCase      )
							__snake_case						 =   self.add_symbol(__lowerCAmelCase      )
							__snake_case						 =   self.add_symbol(__lowerCAmelCase      )
							__snake_case						 =   self.add_symbol(__lowerCAmelCase      )
							if extra_special_symbols:
													for s in extra_special_symbols:
																			self.add_symbol(__lowerCAmelCase      )
							__snake_case						 =   len(self.symbols      )




	def __eq__(       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[Any]      ):
							return self.indices == other.indices




	def __getitem__(       self     :  Optional[int]       ,		__lowerCAmelCase     :  Any      ):
							if idx < len(self.symbols      ):
													return self.symbols[idx]
							return self.unk_word




	def __len__(       self     :  Tuple      ):
							return len(self.symbols      )




	def __contains__(       self     :  str       ,		__lowerCAmelCase     :  Dict      ):
							return sym in self.indices




	@classmethod
	def   lowercase__   (       cls     :  str       ,		__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   cls()
							d.add_from_file(__lowerCAmelCase      )
							return d




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Dict=1       ,		__lowerCAmelCase     :  List[Any]=False      ):
							if word in self.indices and not overwrite:
													__snake_case						 =   self.indices[word]
													__snake_case						 =   self.count[idx] + n
													return idx
							else:
													__snake_case						 =   len(self.symbols      )
													__snake_case						 =   idx
													self.symbols.append(__lowerCAmelCase      )
													self.count.append(__lowerCAmelCase      )
													return idx




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  int      ):
							return 0




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  List[str]      ):
							if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
													try:
																			with open(__lowerCAmelCase       ,		'r'       ,		encoding='utf-8'      ) as fd:
																									self.add_from_file(__lowerCAmelCase      )
													except FileNotFoundError as fnfe:
																			raise fnfe
													except UnicodeError:
																			raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(__lowerCAmelCase      )      )
													return

							__snake_case						 =   f.readlines()
							__snake_case						 =   self._load_meta(__lowerCAmelCase      )

							for line in lines[indices_start_line:]:
													try:
																			__snake_case ,       __snake_case						 =   line.rstrip().rsplit(' '       ,		1      )
																			if field == "#fairseq:overwrite":
																									__snake_case						 =   True
																									__snake_case ,       __snake_case						 =   line.rsplit(' '       ,		1      )
																			else:
																									__snake_case						 =   False
																			__snake_case						 =   int(__lowerCAmelCase      )
																			__snake_case						 =   line
																			if word in self and not overwrite:
																									raise RuntimeError(
																									    'Duplicate word found when loading Dictionary: \'{}\'. '
																									    'Duplicate words can overwrite earlier ones by adding the '
																									    '#fairseq:overwrite flag at the end of the corresponding row '
																									    'in the dictionary file. If using the Camembert model, please '
																									    'download an updated copy of the model file.'.format(__lowerCAmelCase      )      )
																			self.add_symbol(__lowerCAmelCase       ,		n=__lowerCAmelCase       ,		overwrite=__lowerCAmelCase      )
													except ValueError:
																			raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''      )





def 						lowerCamelCase__   (				a							):
						# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
						# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
						__snake_case						 =   dict((re.sub(r'@@$'    ,	''    ,	a							), v) if k.endswith('@@'							) else (re.sub(r'$'    ,	'</w>'    ,	a							), v) for k, v in d.items()							)
						__snake_case						 =   '<s> <pad> </s> <unk>'.split()
						# restore the special tokens
						for k in keep_keys:
												del da[f'{k}</w>']
												__snake_case						 =   d[k]  # restore
						return da
def 						lowerCamelCase__   (				a    ,	a							):
						# prep
						if not os.path.exists(a							):
												raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'							)
						os.makedirs(a    ,	exist_ok=a							)
						print(f'Writing results to {pytorch_dump_folder_path}'							)

						# handle various types of models

						__snake_case						 =   os.path.join(a    ,	'checkpoint.pt'							)
						if not os.path.isfile(a							):
												raise ValueError(f'path to the file {checkpoint_file} does not exist!'							)
						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)

						__snake_case						 =   chkpt['cfg']['model']

						# dicts
						__snake_case						 =   os.path.join(a    ,	'dict.txt'							)
						if not os.path.isfile(a							):
												raise ValueError(f'path to the file {dict_file} does not exist!'							)
						__snake_case						 =   Dictionary.load(a							)
						__snake_case						 =   rewrite_dict_keys(src_dict.indices							)
						__snake_case						 =   len(a							)
						__snake_case						 =   os.path.join(a    ,	VOCAB_FILES_NAMES['vocab_file']							)
						print(f'Generating {src_vocab_file} of {src_vocab_size} records'							)
						with open(a    ,	'w'    ,	encoding='utf-8'							) as f:
												f.write(json.dumps(a    ,	ensure_ascii=a    ,	indent=a							)							)

						# merges_file (bpecodes)
						__snake_case						 =   os.path.join(a    ,	'bpecodes'							)
						if not os.path.isfile(a							):
												raise ValueError(f'path to the file {bpecodes_file} does not exist!'							)

						__snake_case						 =   os.path.join(a    ,	VOCAB_FILES_NAMES['merges_file']							)
						shutil.copyfile(a    ,	a							)

						# model config
						__snake_case						 =   os.path.join(a    ,	'config.json'							)

						__snake_case						 =   {
						    'activation_dropout': args['activation_dropout'],
						    'architectures': ['BioGptForCausalLM'],
						    'attention_probs_dropout_prob': args['attention_dropout'],
						    'bos_token_id': 0,
						    'eos_token_id': 2,
						    'hidden_act': args['activation_fn'],
						    'hidden_dropout_prob': args['dropout'],
						    'hidden_size': args['decoder_embed_dim'],
						    'initializer_range': 0.02,
						    'intermediate_size': args['decoder_ffn_embed_dim'],
						    'layer_norm_eps': 1E-1_2,
						    'layerdrop': args['decoder_layerdrop'],
						    'max_position_embeddings': args['max_target_positions'],
						    'model_type': 'biogpt',
						    'num_attention_heads': args['decoder_attention_heads'],
						    'num_hidden_layers': args['decoder_layers'],
						    'pad_token_id': 1,
						    'scale_embedding': not args['no_scale_embedding'],
						    'tie_word_embeddings': args['share_decoder_input_output_embed'],
						    'vocab_size': src_vocab_size,
						}

						# good hparam defaults to start with

						print(f'Generating {biogpt_model_config_file}'							)
						with open(a    ,	'w'    ,	encoding='utf-8'							) as f:
												f.write(json.dumps(a    ,	ensure_ascii=a    ,	indent=a							)							)

						# tokenizer config
						__snake_case						 =   os.path.join(a    ,	a							)

						__snake_case						 =   {
						    'bos_token': '<s>',
						    'eos_token': '</s>',
						    'model_max_length': 1024,
						    'pad_token': '<pad>',
						    'special_tokens_map_file': None,
						    'tokenizer_class': 'BioGptTokenizer',
						    'unk_token': '<unk>',
						}

						print(f'Generating {biogpt_tokenizer_config_file}'							)
						with open(a    ,	'w'    ,	encoding='utf-8'							) as f:
												f.write(json.dumps(a    ,	ensure_ascii=a    ,	indent=a							)							)

						# model
						__snake_case						 =   chkpt['model']

						# remove unneeded keys
						__snake_case						 =   [
						    'decoder.version',
						]
						for k in ignore_keys:
												model_state_dict.pop(a    ,	a							)

						__snake_case						 =   list(model_state_dict.keys()							)
						for layer_name in layer_names:
												if layer_name.endswith('output_projection.weight'							):
																		__snake_case						 =   model_state_dict.pop(a							)
												else:
																		__snake_case						 =   model_state_dict.pop(a							)

						__snake_case						 =   BioGptConfig.from_pretrained(a							)
						__snake_case						 =   BioGptForCausalLM(a							)

						# check that it loads ok
						model_new.load_state_dict(a							)

						# save
						__snake_case						 =   os.path.join(a    ,	a							)
						print(f'Generating {pytorch_weights_dump_path}'							)
						torch.save(a    ,	a							)

						print('Conversion is done!'							)


if __name__ == "__main__":
	_lowercase  =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--biogpt_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
	        """ bpecodes, etc."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	_lowercase  =			parser.parse_args()
	convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

