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
  
  							_A							    =	True
  except ImportError:
  							_A							    =	False
  
  
  try:
  							from torch.hub import _get_torch_home
  
  							_A							    =	_get_torch_home()
  except ImportError:
  							_A							    =	os.path.expanduser(
  							    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
  							)
  
  _A							    =	os.path.join(torch_cache_home, """transformers""")
  
  _A							    =	"""https://cdn.huggingface.co"""
  _A							    =	"""https://s3.amazonaws.com/models.huggingface.co/bert"""
  _A							    =	"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
  _A							    =	os.path.join(PATH, """config.yaml""")
  _A							    =	os.path.join(PATH, """attributes.txt""")
  _A							    =	os.path.join(PATH, """objects.txt""")
  _A							    =	os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
  _A							    =	os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
  _A							    =	os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
  _A							    =	"""pytorch_model.bin"""
  _A							    =	"""config.yaml"""
  def 						lowerCAmelCase_       (	__a=OBJECTS	,	__a=ATTRIBUTES )       ->			Optional[int]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							with open(__a ) as f:
  														for object in f.readlines():
  																					vg_classes.append(object.split(''',''' )[0].lower().strip() )
  
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							with open(__a ) as f:
  														for object in f.readlines():
  																					vg_attrs.append(object.split(''',''' )[0].lower().strip() )
  							return vg_classes, vg_attrs
  def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =OrderedDict()
  							with open(__a	,	'''rb''' ) as f:
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =pkl.load(__a )['''model''']
  							for k in copy.deepcopy(list(ckp.keys() ) ):
  														SCREAMING_SNAKE_CASE			: int	     =ckp.pop(__a )
  														if isinstance(__a	,	np.ndarray ):
  																					SCREAMING_SNAKE_CASE			: Dict	     =torch.tensor(__a )
  														else:
  																					assert isinstance(__a	,	torch.tensor ), type(__a )
  														SCREAMING_SNAKE_CASE			: List[str]	     =v
  							return r
  
  
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    {}
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_ = "root"		,		snake_case_=0							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: str	     =name
  											SCREAMING_SNAKE_CASE			: List[Any]	     =level
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ={}
  											for k, v in dictionary.items():
  																		if v is None:
  																									raise ValueError()
  																		SCREAMING_SNAKE_CASE			: str	     =copy.deepcopy(snake_case_							)
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =copy.deepcopy(snake_case_							)
  																		if isinstance(snake_case_		,		snake_case_							):
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Config(snake_case_		,		name=snake_case_		,		level=level + 1							)
  																		SCREAMING_SNAKE_CASE			: str	     =v
  																		setattr(self		,		snake_case_		,		snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =d
  
  
  
  
  				def __repr__( self							)    ->      Dict:
  											return str(list((self._pointer.keys())							)							)
  
  
  
  
  				def __setattr__( self		,		snake_case_		,		snake_case_							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: str	     =val
  											SCREAMING_SNAKE_CASE			: Dict	     =val
  											SCREAMING_SNAKE_CASE			: str	     =key.split('''.'''							)
  											SCREAMING_SNAKE_CASE			: Any	     =len(snake_case_							) - 1
  											SCREAMING_SNAKE_CASE			: Dict	     =self._pointer
  											if len(snake_case_							) > 1:
  																		for i, l in enumerate(snake_case_							):
  																									if hasattr(self		,		snake_case_							) and isinstance(getattr(self		,		snake_case_							)		,		snake_case_							):
  																																setattr(getattr(self		,		snake_case_							)		,		'''.'''.join(levels[i:]							)		,		snake_case_							)
  																									if l == last_level:
  																																SCREAMING_SNAKE_CASE			: int	     =val
  																									else:
  																																SCREAMING_SNAKE_CASE			: Optional[int]	     =pointer[l]
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											return self._pointer
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  											with open(F'{file_name}'		,		'''w'''							) as stream:
  																		dump(snake_case_		,		snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  											with open(F'{file_name}'		,		'''w'''							) as stream:
  																		json.dump(snake_case_		,		snake_case_							)
  
  
  
  
  				@staticmethod
  				def     __a		( snake_case_							)    ->      Optional[Any]:
  											with open(snake_case_							) as stream:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =load(snake_case_		,		Loader=snake_case_							)
  											return data
  
  
  
  
  				def __str__( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     ='''    '''
  											if self._name != "root":
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =F'{t * (self._level-1)}{self._name}:\n'
  											else:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =''''''
  											SCREAMING_SNAKE_CASE			: int	     =self._level
  											for i, (k, v) in enumerate(self._pointer.items()							):
  																		if isinstance(snake_case_		,		snake_case_							):
  																									r += F'{t * (self._level)}{v}\n'
  																									self._level += 1
  																		else:
  																									r += F'{t * (self._level)}{k}: {v} ({type(snake_case_							).__name__})\n'
  																		SCREAMING_SNAKE_CASE			: Tuple	     =level
  											return r[:-1]
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		**snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =cls.get_config_dict(snake_case_		,		**snake_case_							)
  											return cls(snake_case_							)
  
  
  
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		**snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =kwargs.pop('''cache_dir'''		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =kwargs.pop('''force_download'''		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =kwargs.pop('''resume_download'''		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Dict	     =kwargs.pop('''proxies'''		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =kwargs.pop('''local_files_only'''		,		snake_case_							)
  
  											if os.path.isdir(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: int	     =os.path.join(snake_case_		,		snake_case_							)
  											elif os.path.isfile(snake_case_							) or is_remote_url(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =pretrained_model_name_or_path
  											else:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =hf_bucket_url(snake_case_		,		filename=snake_case_		,		use_cdn=snake_case_							)
  
  											try:
  																		# Load from URL or cache if already cached
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =cached_path(
  																		    snake_case_		,		cache_dir=snake_case_		,		force_download=snake_case_		,		proxies=snake_case_		,		resume_download=snake_case_		,		local_files_only=snake_case_		,		)
  																		# Load config dict
  																		if resolved_config_file is None:
  																									raise EnvironmentError
  
  																		SCREAMING_SNAKE_CASE			: int	     =Config.load_yaml(snake_case_							)
  
  											except EnvironmentError:
  																		SCREAMING_SNAKE_CASE			: str	     ='''Can\'t load config for'''
  																		raise EnvironmentError(snake_case_							)
  
  											if resolved_config_file == config_file:
  																		print('''loading configuration file from path'''							)
  											else:
  																		print('''loading configuration file cache'''							)
  
  											return Config.load_yaml(snake_case_							), kwargs
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	__a )       ->			Optional[int]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =torch.load('''dump.pt'''	,	map_location=in_tensor.device )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =in_tensor.numpy()
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =out_tensor.numpy()[0]
  							print(na.shape	,	na[0, 0, :5] )
  							print(na.shape	,	na[0, 0, :5] )
  							assert np.allclose(__a	,	__a	,	rtol=0.01	,	atol=0.1 ), (
  							    f'{sum([1 for x in np.isclose(__a	,	__a	,	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_       (	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =urlparse(__a )
  							return parsed.scheme in ("http", "https")
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a=True )       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  							SCREAMING_SNAKE_CASE			: List[Any]	     ='''/''' not in model_id
  							if legacy_format:
  														return f'{endpoint}/{model_id}-{filename}'
  							else:
  														return f'{endpoint}/{model_id}/{filename}'
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a=None	,	__a=0	,	__a=None	,	)       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     ='''python/{}'''.format(sys.version.split()[0] )
  							if _torch_available:
  														ua += "; torch/{}".format(torch.__version__ )
  							if isinstance(__a	,	__a ):
  														ua += "; " + "; ".join('''{}/{}'''.format(__a	,	__a ) for k, v in user_agent.items() )
  							elif isinstance(__a	,	__a ):
  														ua += "; " + user_agent
  							SCREAMING_SNAKE_CASE			: Tuple	     ={'''user-agent''': ua}
  							if resume_size > 0:
  														SCREAMING_SNAKE_CASE			: Any	     ='''bytes=%d-''' % (resume_size,)
  							SCREAMING_SNAKE_CASE			: Dict	     =requests.get(__a	,	stream=__a	,	proxies=__a	,	headers=__a )
  							if response.status_code == 416:  # Range not satisfiable
  														return
  							SCREAMING_SNAKE_CASE			: int	     =response.headers.get('''Content-Length''' )
  							SCREAMING_SNAKE_CASE			: int	     =resume_size + int(__a ) if content_length is not None else None
  							SCREAMING_SNAKE_CASE			: List[Any]	     =tqdm(
  							    unit='''B'''	,	unit_scale=__a	,	total=__a	,	initial=__a	,	desc='''Downloading'''	,	)
  							for chunk in response.iter_content(chunk_size=1024 ):
  														if chunk:  # filter out keep-alive new chunks
  																					progress.update(len(__a ) )
  																					temp_file.write(__a )
  							progress.close()
  def 						lowerCAmelCase_       (	__a	,	__a=None	,	__a=False	,	__a=None	,	__a=10	,	__a=False	,	__a=None	,	__a=False	,	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if cache_dir is None:
  														SCREAMING_SNAKE_CASE			: int	     =TRANSFORMERS_CACHE
  							if isinstance(__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: List[str]	     =str(__a )
  
  							os.makedirs(__a	,	exist_ok=__a )
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =None
  							if not local_files_only:
  														try:
  																					SCREAMING_SNAKE_CASE			: Dict	     =requests.head(__a	,	allow_redirects=__a	,	proxies=__a	,	timeout=__a )
  																					if response.status_code == 200:
  																												SCREAMING_SNAKE_CASE			: Dict	     =response.headers.get('''ETag''' )
  														except (EnvironmentError, requests.exceptions.Timeout):
  																					# etag is already None
  																					pass
  
  							SCREAMING_SNAKE_CASE			: Any	     =url_to_filename(__a	,	__a )
  
  							# get cache path to put the file
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(__a	,	__a )
  
  							# 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(__a ):
  																					return cache_path
  														else:
  																					SCREAMING_SNAKE_CASE			: Any	     =[
  																					    file
  																					    for file in fnmatch.filter(os.listdir(__a )	,	filename + '''.*''' )
  																					    if not file.endswith('''.json''' ) and not file.endswith('''.lock''' )
  																					]
  																					if len(__a ) > 0:
  																												return os.path.join(__a	,	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(__a ) and not force_download:
  														return cache_path
  
  							# Prevent parallel downloads of the same file with a lock.
  							SCREAMING_SNAKE_CASE			: Any	     =cache_path + '''.lock'''
  							with FileLock(__a ):
  														# If the download just completed while the lock was activated.
  														if os.path.exists(__a ) and not force_download:
  																					# Even if returning early like here, the lock will be released.
  																					return cache_path
  
  														if resume_download:
  																					SCREAMING_SNAKE_CASE			: int	     =cache_path + '''.incomplete'''
  
  																					@contextmanager
  																					def _resumable_file_manager():
  																												with open(__a	,	'''a+b''' ) as f:
  																																			yield f
  
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =_resumable_file_manager
  																					if os.path.exists(__a ):
  																												SCREAMING_SNAKE_CASE			: List[str]	     =os.stat(__a ).st_size
  																					else:
  																												SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  														else:
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =partial(tempfile.NamedTemporaryFile	,	dir=__a	,	delete=__a )
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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'''	,	__a	,	temp_file.name	,	)
  
  																					http_get(
  																					    __a	,	__a	,	proxies=__a	,	resume_size=__a	,	user_agent=__a	,	)
  
  														os.replace(temp_file.name	,	__a )
  
  														SCREAMING_SNAKE_CASE			: Any	     ={'''url''': url, '''etag''': etag}
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =cache_path + '''.json'''
  														with open(__a	,	'''w''' ) as meta_file:
  																					json.dump(__a	,	__a )
  
  							return cache_path
  def 						lowerCAmelCase_       (	__a	,	__a=None )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =url.encode('''utf-8''' )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =shaaaa(__a )
  							SCREAMING_SNAKE_CASE			: Tuple	     =url_hash.hexdigest()
  
  							if etag:
  														SCREAMING_SNAKE_CASE			: Any	     =etag.encode('''utf-8''' )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =shaaaa(__a )
  														filename += "." + etag_hash.hexdigest()
  
  							if url.endswith('''.h5''' ):
  														filename += ".h5"
  
  							return filename
  def 						lowerCAmelCase_       (	__a	,	__a=None	,	__a=False	,	__a=None	,	__a=False	,	__a=None	,	__a=False	,	__a=False	,	__a=False	,	)       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if cache_dir is None:
  														SCREAMING_SNAKE_CASE			: Dict	     =TRANSFORMERS_CACHE
  							if isinstance(__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: Any	     =str(__a )
  							if isinstance(__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =str(__a )
  
  							if is_remote_url(__a ):
  														# URL, so get it from the cache (downloading if necessary)
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =get_from_cache(
  														    __a	,	cache_dir=__a	,	force_download=__a	,	proxies=__a	,	resume_download=__a	,	user_agent=__a	,	local_files_only=__a	,	)
  							elif os.path.exists(__a ):
  														# File, and it exists.
  														SCREAMING_SNAKE_CASE			: Dict	     =url_or_filename
  							elif urlparse(__a ).scheme == "":
  														# File, but it doesn't exist.
  														raise EnvironmentError('''file {} not found'''.format(__a ) )
  							else:
  														# Something unknown
  														raise ValueError('''unable to parse {} as a URL or as a local path'''.format(__a ) )
  
  							if extract_compressed_file:
  														if not is_zipfile(__a ) and not tarfile.is_tarfile(__a ):
  																					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     ,							SCREAMING_SNAKE_CASE			: Dict	     =os.path.split(__a )
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =output_file.replace('''.'''	,	'''-''' ) + '''-extracted'''
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =os.path.join(__a	,	__a )
  
  														if os.path.isdir(__a ) and os.listdir(__a ) and not force_extract:
  																					return output_path_extracted
  
  														# Prevent parallel extractions
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =output_path + '''.lock'''
  														with FileLock(__a ):
  																					shutil.rmtree(__a	,	ignore_errors=__a )
  																					os.makedirs(__a )
  																					if is_zipfile(__a ):
  																												with ZipFile(__a	,	'''r''' ) as zip_file:
  																																			zip_file.extractall(__a )
  																																			zip_file.close()
  																					elif tarfile.is_tarfile(__a ):
  																												SCREAMING_SNAKE_CASE			: Dict	     =tarfile.open(__a )
  																												tar_file.extractall(__a )
  																												tar_file.close()
  																					else:
  																												raise EnvironmentError('''Archive format of {} could not be identified'''.format(__a ) )
  
  														return output_path_extracted
  
  							return output_path
  def 						lowerCAmelCase_       (	__a	,	__a="," )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							assert isinstance(__a	,	__a )
  							if os.path.isfile(__a ):
  														with open(__a ) as f:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =eval(f.read() )
  							else:
  														SCREAMING_SNAKE_CASE			: List[Any]	     =requests.get(__a )
  														try:
  																					SCREAMING_SNAKE_CASE			: int	     =requests.json()
  														except Exception:
  																					SCREAMING_SNAKE_CASE			: List[str]	     =req.content.decode()
  																					assert data is not None, "could not connect"
  																					try:
  																												SCREAMING_SNAKE_CASE			: str	     =eval(__a )
  																					except Exception:
  																												SCREAMING_SNAKE_CASE			: Optional[Any]	     =data.split('''\n''' )
  														req.close()
  							return data
  def 						lowerCAmelCase_       (	__a )       ->			Optional[int]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =requests.get(__a )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.array(Image.open(BytesIO(response.content ) ) )
  							return img
  def 						lowerCAmelCase_       (	__a )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =url.split('''/''' )[-1]
  							if fn not in os.listdir(os.getcwd() ):
  														wget.download(__a )
  							with open(__a	,	'''rb''' ) as stream:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =pkl.load(__a )
  							SCREAMING_SNAKE_CASE			: Dict	     =weights.pop('''model''' )
  							SCREAMING_SNAKE_CASE			: List[str]	     ={}
  							for k, v in model.items():
  														SCREAMING_SNAKE_CASE			: List[str]	     =torch.from_numpy(__a )
  														if "running_var" in k:
  																					SCREAMING_SNAKE_CASE			: List[str]	     =torch.tensor([0] )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =k.replace('''running_var'''	,	'''num_batches_tracked''' )
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =zero
  							return new
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							print(f'{os.path.abspath(os.path.join(__a	,	os.pardir ) )}/demo.ipynb' )
  def 						lowerCAmelCase_       (	__a	,	__a="RGB" )       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							assert isinstance(__a	,	__a )
  							if os.path.isfile(__a ):
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =cva.imread(__a )
  							else:
  														SCREAMING_SNAKE_CASE			: Any	     =get_image_from_url(__a )
  														assert img is not None, f'could not connect to: {im}'
  							SCREAMING_SNAKE_CASE			: Any	     =cva.cvtColor(__a	,	cva.COLOR_BGR2RGB )
  							if input_format == "RGB":
  														SCREAMING_SNAKE_CASE			: Tuple	     =img[:, :, ::-1]
  							return img
  def 						lowerCAmelCase_       (	__a	,	__a=1 )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							return (images[i : i + batch] for i in range(0	,	len(__a )	,	__a ))
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _A							    =	{
      """vocab_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
      },
      """tokenizer_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
      },
  }
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token
  
  											super().__init__(
  											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =3
  											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
  											SCREAMING_SNAKE_CASE			: int	     =remove_space
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
  											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True
  
  											try:
  																		import jieba
  											except ModuleNotFoundError as error:
  																		raise error.__class__(
  																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
  																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
  											SCREAMING_SNAKE_CASE			: str	     =jieba
  											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
  											if token_ids_a is None:
  																		return token_ids_a + sep + cls
  											return token_ids_a + sep + token_ids_a + sep + cls
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]
  
  											if token_ids_a is None:
  																		return len(token_ids_a + sep							) * [0] + cls_segment_id
  											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
  											if not self.can_save_slow_tokenizer:
  																		raise ValueError(
  																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
  																		    '''tokenizer.'''							)
  
  											if not os.path.isdir(snake_case_							):
  																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  																		return
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
  											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
  																		copyfile(self.vocab_file		,		snake_case_							)
  
  											return (out_vocab_file,)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =[
  											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
  											    for text in batch_text_or_text_pairs
  											]
  											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
  											return text
  
  
  
  
  
  
  