code: 
  
  
  
  
  
  
  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
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							True
  except ImportError:
        SCREAMING_SNAKE_CASE__		:       List[Any]   							=							False
  
  
  try:
        from torch.hub import _get_torch_home
  
        SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							_get_torch_home()
  except ImportError:
        SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							os.path.expanduser(
            os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
        )
  
  SCREAMING_SNAKE_CASE__		:       str   							=							os.path.join(torch_cache_home, """transformers""")
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							"""https://cdn.huggingface.co"""
  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							"""https://s3.amazonaws.com/models.huggingface.co/bert"""
  SCREAMING_SNAKE_CASE__		:       str   							=							"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
  SCREAMING_SNAKE_CASE__		:       str   							=							os.path.join(PATH, """config.yaml""")
  SCREAMING_SNAKE_CASE__		:       int   							=							os.path.join(PATH, """attributes.txt""")
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							os.path.join(PATH, """objects.txt""")
  SCREAMING_SNAKE_CASE__		:       Dict   							=							os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
  SCREAMING_SNAKE_CASE__		:       List[Any]   							=							os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							"""pytorch_model.bin"""
  SCREAMING_SNAKE_CASE__		:       Dict   							=							"""config.yaml"""
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase=OBJECTS   ,       __lowerCamelCase=ATTRIBUTES					)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= []
       with open(__lowerCamelCase					) as f:
            for object in f.readlines():
                 vg_classes.append(object.split(""","""					)[0].lower().strip()					)
  
       UpperCAmelCase__			:     str										= []
       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					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= OrderedDict()
       with open(__lowerCamelCase   ,       """rb"""					) as f:
            UpperCAmelCase__			:     Optional[int]										= pkl.load(__lowerCamelCase					)["""model"""]
       for k in copy.deepcopy(list(ckp.keys()					)					):
            UpperCAmelCase__			:     Any										= ckp.pop(__lowerCamelCase					)
            if isinstance(__lowerCamelCase   ,       np.ndarray					):
                 UpperCAmelCase__			:     str										= torch.tensor(__lowerCamelCase					)
            else:
                 assert isinstance(__lowerCamelCase   ,       torch.tensor					), type(__lowerCamelCase					)
            UpperCAmelCase__			:     Optional[Any]										= v
       return r
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     {}
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = "root"  ,	_lowerCAmelCase=0		):
            UpperCAmelCase__			:     Dict										= name
            UpperCAmelCase__			:     str										= level
            UpperCAmelCase__			:     List[str]										= {}
            for k, v in dictionary.items():
                 if v is None:
                      raise ValueError()
                 UpperCAmelCase__			:     Any										= copy.deepcopy(_lowerCAmelCase		)
                 UpperCAmelCase__			:     Optional[Any]										= copy.deepcopy(_lowerCAmelCase		)
                 if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      UpperCAmelCase__			:     int										= Config(_lowerCAmelCase  ,	name=_lowerCAmelCase  ,	level=level + 1		)
                 UpperCAmelCase__			:     str										= v
                 setattr(self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            UpperCAmelCase__			:     List[Any]										= d
  
  
  
  
       def __repr__(							self		):
            return str(list((self._pointer.keys())		)		)
  
  
  
  
       def __setattr__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Any										= val
            UpperCAmelCase__			:     Dict										= val
            UpperCAmelCase__			:     str										= key.split("""."""		)
            UpperCAmelCase__			:     Optional[Any]										= len(_lowerCAmelCase		) - 1
            UpperCAmelCase__			:     List[Any]										= self._pointer
            if len(_lowerCAmelCase		) > 1:
                 for i, l in enumerate(_lowerCAmelCase		):
                      if hasattr(self  ,	_lowerCAmelCase		) and isinstance(getattr(self  ,	_lowerCAmelCase		)  ,	_lowerCAmelCase		):
                           setattr(getattr(self  ,	_lowerCAmelCase		)  ,	""".""".join(levels[i:]		)  ,	_lowerCAmelCase		)
                      if l == last_level:
                           UpperCAmelCase__			:     Tuple										= val
                      else:
                           UpperCAmelCase__			:     str										= pointer[l]
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            return self._pointer
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            with open(f"{file_name}"  ,	"""w"""		) as stream:
                 dump(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            with open(f"{file_name}"  ,	"""w"""		) as stream:
                 json.dump(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       @staticmethod
       def      __UpperCAmelCase     (							_lowerCAmelCase		):
            with open(_lowerCAmelCase		) as stream:
                 UpperCAmelCase__			:     Union[str, Any]										= load(_lowerCAmelCase  ,	Loader=_lowerCAmelCase		)
            return data
  
  
  
  
       def __str__(							self		):
            UpperCAmelCase__			:     str										= """    """
            if self._name != "root":
                 UpperCAmelCase__			:     List[Any]										= f"{t * (self._level-1)}{self._name}:\n"
            else:
                 UpperCAmelCase__			:     str										= """"""
            UpperCAmelCase__			:     str										= self._level
            for i, (k, v) in enumerate(self._pointer.items()		):
                 if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      r += f"{t * (self._level)}{v}\n"
                      self._level += 1
                 else:
                      r += f"{t * (self._level)}{k}: {v} ({type(_lowerCAmelCase		).__name__})\n"
                 UpperCAmelCase__			:     Union[str, Any]										= level
            return r[:-1]
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	_lowerCAmelCase  ,	**_lowerCAmelCase		):
            UpperCAmelCase__			,		UpperCAmelCase__			:     Tuple										= cls.get_config_dict(_lowerCAmelCase  ,	**_lowerCAmelCase		)
            return cls(_lowerCAmelCase		)
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	_lowerCAmelCase  ,	**_lowerCAmelCase		):
            UpperCAmelCase__			:     List[Any]										= kwargs.pop("""cache_dir"""  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Tuple										= kwargs.pop("""force_download"""  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= kwargs.pop("""resume_download"""  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= kwargs.pop("""proxies"""  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Tuple										= kwargs.pop("""local_files_only"""  ,	_lowerCAmelCase		)
  
            if os.path.isdir(_lowerCAmelCase		):
                 UpperCAmelCase__			:     Any										= os.path.join(_lowerCAmelCase  ,	_lowerCAmelCase		)
            elif os.path.isfile(_lowerCAmelCase		) or is_remote_url(_lowerCAmelCase		):
                 UpperCAmelCase__			:     List[Any]										= pretrained_model_name_or_path
            else:
                 UpperCAmelCase__			:     List[str]										= hf_bucket_url(_lowerCAmelCase  ,	filename=_lowerCAmelCase  ,	use_cdn=_lowerCAmelCase		)
  
            try:
                 # Load from URL or cache if already cached
                 UpperCAmelCase__			:     List[Any]										= cached_path(
                     _lowerCAmelCase  ,	cache_dir=_lowerCAmelCase  ,	force_download=_lowerCAmelCase  ,	proxies=_lowerCAmelCase  ,	resume_download=_lowerCAmelCase  ,	local_files_only=_lowerCAmelCase  ,	)
                 # Load config dict
                 if resolved_config_file is None:
                      raise EnvironmentError
  
                 UpperCAmelCase__			:     Union[str, Any]										= Config.load_yaml(_lowerCAmelCase		)
  
            except EnvironmentError:
                 UpperCAmelCase__			:     Optional[Any]										= """Can't load config for"""
                 raise EnvironmentError(_lowerCAmelCase		)
  
            if resolved_config_file == config_file:
                 print("""loading configuration file from path"""		)
            else:
                 print("""loading configuration file cache"""		)
  
            return Config.load_yaml(_lowerCAmelCase		), kwargs
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= torch.load("""dump.pt"""   ,       map_location=in_tensor.device					)
       UpperCAmelCase__			:     Tuple										= in_tensor.numpy()
       UpperCAmelCase__			:     Optional[Any]										= 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					)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= urlparse(__lowerCamelCase					)
       return parsed.scheme in ("http", "https")
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=True					)   -> str:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[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   ,       __lowerCamelCase   ,       __lowerCamelCase=None   ,       __lowerCamelCase=0   ,       __lowerCamelCase=None   ,       )   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= """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__			:     Any										= {"""user-agent""": ua}
       if resume_size > 0:
            UpperCAmelCase__			:     Union[str, Any]										= """bytes=%d-""" % (resume_size,)
       UpperCAmelCase__			:     str										= requests.get(__lowerCamelCase   ,       stream=__lowerCamelCase   ,       proxies=__lowerCamelCase   ,       headers=__lowerCamelCase					)
       if response.status_code == 416:  # Range not satisfiable
            return
       UpperCAmelCase__			:     int										= response.headers.get("""Content-Length"""					)
       UpperCAmelCase__			:     List[str]										= resume_size + int(__lowerCamelCase					) if content_length is not None else None
       UpperCAmelCase__			:     Tuple										= 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   ,       )   -> str:
  
       '''simple docstring'''
       if cache_dir is None:
            UpperCAmelCase__			:     Optional[Any]										= TRANSFORMERS_CACHE
       if isinstance(__lowerCamelCase   ,       __lowerCamelCase					):
            UpperCAmelCase__			:     Union[str, Any]										= str(__lowerCamelCase					)
  
       os.makedirs(__lowerCamelCase   ,       exist_ok=__lowerCamelCase					)
  
       UpperCAmelCase__			:     Any										= None
       if not local_files_only:
            try:
                 UpperCAmelCase__			:     Optional[int]										= requests.head(__lowerCamelCase   ,       allow_redirects=__lowerCamelCase   ,       proxies=__lowerCamelCase   ,       timeout=__lowerCamelCase					)
                 if response.status_code == 200:
                      UpperCAmelCase__			:     Union[str, Any]										= response.headers.get("""ETag"""					)
            except (EnvironmentError, requests.exceptions.Timeout):
                 # etag is already None
                 pass
  
       UpperCAmelCase__			:     str										= url_to_filename(__lowerCamelCase   ,       __lowerCamelCase					)
  
       # get cache path to put the file
       UpperCAmelCase__			:     Tuple										= 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__			:     Dict										= 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__			:     Union[str, Any]										= cache_path + """.incomplete"""
  
                 @contextmanager
                 def _resumable_file_manager():
                      with open(__lowerCamelCase   ,       """a+b"""					) as f:
                           yield f
  
                 UpperCAmelCase__			:     Any										= _resumable_file_manager
                 if os.path.exists(__lowerCamelCase					):
                      UpperCAmelCase__			:     str										= os.stat(__lowerCamelCase					).st_size
                 else:
                      UpperCAmelCase__			:     Any										= 0
            else:
                 UpperCAmelCase__			:     Optional[Any]										= partial(tempfile.NamedTemporaryFile   ,       dir=__lowerCamelCase   ,       delete=__lowerCamelCase					)
                 UpperCAmelCase__			:     List[str]										= 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__			:     Optional[int]										= {"""url""": url, """etag""": etag}
            UpperCAmelCase__			:     Any										= cache_path + """.json"""
            with open(__lowerCamelCase   ,       """w"""					) as meta_file:
                 json.dump(__lowerCamelCase   ,       __lowerCamelCase					)
  
       return cache_path
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase=None					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= url.encode("""utf-8"""					)
       UpperCAmelCase__			:     List[str]										= shaaaa(__lowerCamelCase					)
       UpperCAmelCase__			:     Optional[Any]										= url_hash.hexdigest()
  
       if etag:
            UpperCAmelCase__			:     Optional[int]										= etag.encode("""utf-8"""					)
            UpperCAmelCase__			:     Dict										= 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   ,       )   -> Dict:
  
       '''simple docstring'''
       if cache_dir is None:
            UpperCAmelCase__			:     List[str]										= TRANSFORMERS_CACHE
       if isinstance(__lowerCamelCase   ,       __lowerCamelCase					):
            UpperCAmelCase__			:     Optional[Any]										= str(__lowerCamelCase					)
       if isinstance(__lowerCamelCase   ,       __lowerCamelCase					):
            UpperCAmelCase__			:     int										= 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__			:     List[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/"
            UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= os.path.split(__lowerCamelCase					)
            UpperCAmelCase__			:     Any										= output_file.replace("""."""   ,       """-"""					) + """-extracted"""
            UpperCAmelCase__			:     Dict										= 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__			:     List[Any]										= 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__			:     Optional[int]										= 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=","					)   -> Dict:
  
       '''simple docstring'''
       assert isinstance(__lowerCamelCase   ,       __lowerCamelCase					)
       if os.path.isfile(__lowerCamelCase					):
            with open(__lowerCamelCase					) as f:
                 UpperCAmelCase__			:     Any										= eval(f.read()					)
       else:
            UpperCAmelCase__			:     Any										= requests.get(__lowerCamelCase					)
            try:
                 UpperCAmelCase__			:     Union[str, Any]										= requests.json()
            except Exception:
                 UpperCAmelCase__			:     Optional[Any]										= req.content.decode()
                 assert data is not None, "could not connect"
                 try:
                      UpperCAmelCase__			:     Union[str, Any]										= eval(__lowerCamelCase					)
                 except Exception:
                      UpperCAmelCase__			:     int										= data.split("""\n"""					)
            req.close()
       return data
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[int]										= requests.get(__lowerCamelCase					)
       UpperCAmelCase__			:     Tuple										= np.array(Image.open(BytesIO(response.content					)					)					)
       return img
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= url.split("""/"""					)[-1]
       if fn not in os.listdir(os.getcwd()					):
            wget.download(__lowerCamelCase					)
       with open(__lowerCamelCase   ,       """rb"""					) as stream:
            UpperCAmelCase__			:     Union[str, Any]										= pkl.load(__lowerCamelCase					)
       UpperCAmelCase__			:     int										= weights.pop("""model"""					)
       UpperCAmelCase__			:     Any										= {}
       for k, v in model.items():
            UpperCAmelCase__			:     Tuple										= torch.from_numpy(__lowerCamelCase					)
            if "running_var" in k:
                 UpperCAmelCase__			:     Tuple										= torch.tensor([0]					)
                 UpperCAmelCase__			:     List[Any]										= k.replace("""running_var"""   ,       """num_batches_tracked"""					)
                 UpperCAmelCase__			:     Tuple										= zero
       return new
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> str:
  
       '''simple docstring'''
       print(F"{os.path.abspath(os.path.join(__lowerCamelCase   ,       os.pardir					)					)}/demo.ipynb"					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase="RGB"					)   -> Optional[int]:
  
       '''simple docstring'''
       assert isinstance(__lowerCamelCase   ,       __lowerCamelCase					)
       if os.path.isfile(__lowerCamelCase					):
            UpperCAmelCase__			:     Any										= cva.imread(__lowerCamelCase					)
       else:
            UpperCAmelCase__			:     Dict										= get_image_from_url(__lowerCamelCase					)
            assert img is not None, F"could not connect to: {im}"
       UpperCAmelCase__			:     Dict										= cva.cvtColor(__lowerCamelCase   ,       cva.COLOR_BGR2RGB					)
       if input_format == "RGB":
            UpperCAmelCase__			:     Tuple										= img[:, :, ::-1]
       return img
  
  
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase=1					)   -> Tuple:
  
       '''simple docstring'''
       return (images[i : i + batch] for i in range(0   ,       len(__lowerCamelCase					)   ,       __lowerCamelCase					))
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from ..utils import requires_backends
  
  
  SCREAMING_SNAKE_CASE__		:       int   							=							None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(self  ,	["""detectron2"""]		)
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(cls  ,	["""detectron2"""]		)
  
  
  
  
  
  
  