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__			:     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__			:     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 .stack import Stack
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Stack[str]										= Stack()
       UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
       for bracket in parentheses:
            if bracket in bracket_pairs:
                 stack.push(__lowerCamelCase					)
            elif bracket in (")", "]", "}"):
                 if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                      return False
       return stack.is_empty()
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
        SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
        print("""Balanced parentheses demonstration:\n""")
        for example in examples:
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
              print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  
  