





'''simple docstring'''





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

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


try:
       import torch

       UpperCamelCase__      =True
except ImportError:
       UpperCamelCase__      =False


try:
       from torch.hub import _get_torch_home

       UpperCamelCase__      =_get_torch_home()
except ImportError:
       UpperCamelCase__      =os.path.expanduser(
           os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
       )

UpperCamelCase__      =os.path.join(torch_cache_home, 'transformers')

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






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

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							[]
     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	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							OrderedDict()
     with open(_lowerCamelCase,       "rb"	) as f:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							pkl.load(_lowerCamelCase	)["""model"""]
     for k in copy.deepcopy(list(ckp.keys()	)	):
          _SCREAMING_SNAKE_CASE       :	Dict						    =							ckp.pop(_lowerCamelCase	)
          if isinstance(_lowerCamelCase,       np.ndarray	):
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor(_lowerCamelCase	)
          else:
               assert isinstance(_lowerCamelCase,       torch.tensor	), type(_lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	str						    =							v
     return r





class 			lowerCAmelCase__:






       '''simple docstring'''


       __snake_case 			=    {}




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

            _SCREAMING_SNAKE_CASE       :	str						    =							d




       def __repr__(      self							)			->     int:
            return str(list((self._pointer.keys())							)							)




       def __setattr__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							val
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							val
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							key.split("."							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							len(_lowercase							) - 1
            _SCREAMING_SNAKE_CASE       :	int						    =							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:
                           _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							val
                      else:
                           _SCREAMING_SNAKE_CASE       :	List[str]						    =							pointer[l]




       def     UpperCamelCase_			(      self							)			->     List[str]:
            return self._pointer




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Dict:

            with open(F"""{file_name}"""    ,						"w"							) as stream:
                 dump(_lowercase    ,						_lowercase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     List[Any]:

            with open(F"""{file_name}"""    ,						"w"							) as stream:
                 json.dump(_lowercase    ,						_lowercase							)




       @staticmethod
       def     UpperCamelCase_			(      __lowerCamelCase							)			->     Any:

            with open(_lowercase							) as stream:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							load(_lowercase    ,						Loader=_lowercase							)
            return data




       def __str__(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							"""    """
            if self._name != "root":
                 _SCREAMING_SNAKE_CASE       :	int						    =							F"""{t * (self._level-1)}{self._name}:\n"""
            else:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							""""""
            _SCREAMING_SNAKE_CASE       :	str						    =							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"""
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							level
            return r[:-1]




       @classmethod
       def     UpperCamelCase_			(      cls    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							cls.get_config_dict(_lowercase    ,						**_lowercase							)
            return cls(_lowercase							)







       @classmethod
       def     UpperCamelCase_			(      cls    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							kwargs.pop("cache_dir"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							kwargs.pop("force_download"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							kwargs.pop("resume_download"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							kwargs.pop("proxies"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							kwargs.pop("local_files_only"    ,						_lowercase							)

            if os.path.isdir(_lowercase							):
                 _SCREAMING_SNAKE_CASE       :	str						    =							os.path.join(_lowercase    ,						_lowercase							)
            elif os.path.isfile(_lowercase							) or is_remote_url(_lowercase							):
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							pretrained_model_name_or_path
            else:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							hf_bucket_url(_lowercase    ,						filename=_lowercase    ,						use_cdn=_lowercase							)

            try:
                 # Load from URL or cache if already cached
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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

                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							Config.load_yaml(_lowercase							)

            except EnvironmentError:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							"""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	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.load("dump.pt",       map_location=in_tensor.device	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							in_tensor.numpy()
     _SCREAMING_SNAKE_CASE       :	Dict						    =							out_tensor.numpy()[0]
     print(na.shape,       na[0, 0, :5]	)
     print(na.shape,       na[0, 0, :5]	)
     assert np.allclose(_lowerCamelCase,       _lowerCamelCase,       rtol=0.01,       atol=0.1	), (
         f"""{sum([1 for x in np.isclose(_lowerCamelCase,       _lowerCamelCase,       rtol=0.01,       atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*100:.4f} %"""
         " element-wise mismatch"
     )
     raise Exception("tensors are all good"	)

     # Hugging face functions below






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							urlparse(_lowerCamelCase	)
     return parsed.scheme in ("http", "https")






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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=None,       __lowerCamelCase=0,       __lowerCamelCase=None,       ):
     _SCREAMING_SNAKE_CASE       :	str						    =							"""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
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"""user-agent""": ua}
     if resume_size > 0:
          _SCREAMING_SNAKE_CASE       :	Dict						    =							"""bytes=%d-""" % (resume_size,)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							requests.get(_lowerCamelCase,       stream=_lowerCamelCase,       proxies=_lowerCamelCase,       headers=_lowerCamelCase	)
     if response.status_code == 416:  # Range not satisfiable
          return
     _SCREAMING_SNAKE_CASE       :	str						    =							response.headers.get("Content-Length"	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							resume_size + int(_lowerCamelCase	) if content_length is not None else None
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							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,       __lowerCamelCase=None,       __lowerCamelCase=False,       __lowerCamelCase=None,       __lowerCamelCase=10,       __lowerCamelCase=False,       __lowerCamelCase=None,       __lowerCamelCase=False,       ):
     if cache_dir is None:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							TRANSFORMERS_CACHE
     if isinstance(_lowerCamelCase,       _lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	int						    =							str(_lowerCamelCase	)

     os.makedirs(_lowerCamelCase,       exist_ok=_lowerCamelCase	)

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

     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							url_to_filename(_lowerCamelCase,       _lowerCamelCase	)

     # get cache path to put the file
     _SCREAMING_SNAKE_CASE       :	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:
               _SCREAMING_SNAKE_CASE       :	Optional[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.
     _SCREAMING_SNAKE_CASE       :	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:
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							cache_path + """.incomplete"""

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

               _SCREAMING_SNAKE_CASE       :	Tuple						    =							_resumable_file_manager
               if os.path.exists(_lowerCamelCase	):
                    _SCREAMING_SNAKE_CASE       :	List[Any]						    =							os.stat(_lowerCamelCase	).st_size
               else:
                    _SCREAMING_SNAKE_CASE       :	int						    =							0
          else:
               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							partial(tempfile.NamedTemporaryFile,       dir=_lowerCamelCase,       delete=_lowerCamelCase	)
               _SCREAMING_SNAKE_CASE       :	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	)

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{"""url""": url, """etag""": etag}
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							cache_path + """.json"""
          with open(_lowerCamelCase,       "w"	) as meta_file:
               json.dump(_lowerCamelCase,       _lowerCamelCase	)

     return cache_path






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=None	):
     _SCREAMING_SNAKE_CASE       :	str						    =							url.encode("utf-8"	)
     _SCREAMING_SNAKE_CASE       :	str						    =							shaaaa(_lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	int						    =							url_hash.hexdigest()

     if etag:
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							etag.encode("utf-8"	)
          _SCREAMING_SNAKE_CASE       :	Any						    =							shaaaa(_lowerCamelCase	)
          filename += "." + etag_hash.hexdigest()

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

     return filename






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

     if is_remote_url(_lowerCamelCase	):
          # URL, so get it from the cache (downloading if necessary)
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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.
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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/"
          _SCREAMING_SNAKE_CASE       :	Dict						    =							os.path.split(_lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	int						    =							output_file.replace(".",       "-"	) + """-extracted"""
          _SCREAMING_SNAKE_CASE       :	Any						    =							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
          _SCREAMING_SNAKE_CASE       :	List[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	):
                    _SCREAMING_SNAKE_CASE       :	Union[str, 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,       __lowerCamelCase=","	):
     assert isinstance(_lowerCamelCase,       _lowerCamelCase	)
     if os.path.isfile(_lowerCamelCase	):
          with open(_lowerCamelCase	) as f:
               _SCREAMING_SNAKE_CASE       :	Dict						    =							eval(f.read()	)
     else:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							requests.get(_lowerCamelCase	)
          try:
               _SCREAMING_SNAKE_CASE       :	Tuple						    =							requests.json()
          except Exception:
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							req.content.decode()
               assert data is not None, "could not connect"
               try:
                    _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							eval(_lowerCamelCase	)
               except Exception:
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							data.split("\n"	)
          req.close()
     return data






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






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






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






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






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






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

     # Load weights from numpy
     load_tf_weights_in_gpta(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
     torch.save(model.state_dict(),       __lowerCamelCase	)
     print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
     with open(__lowerCamelCase,       "w",       encoding="utf-8"	) as f:
          f.write(config.to_json_string()	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--gpt2_config_file',
           default='',
           type=str,
           help=(
               'An optional config json file corresponding to the pre-trained OpenAI model. \n'
               'This specifies the model architecture.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)