
'''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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]=OBJECTS   ,     snake_case_				:	List[str]=ATTRIBUTES							)       ->	Optional[Any]:
 SCREAMING_SNAKE_CASE  :  Any								=		[]
 with open(snake_case_							) as f:
  for object in f.readlines():
   vg_classes.append(object.split(','							)[0].lower().strip()							)

 SCREAMING_SNAKE_CASE  :  List[str]								=		[]
 with open(snake_case_							) as f:
  for object in f.readlines():
   vg_attrs.append(object.split(','							)[0].lower().strip()							)
 return vg_classes, vg_attrs



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[Any]							)       ->	Dict:
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		OrderedDict()
 with open(snake_case_   ,     'rb'							) as f:
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		pkl.load(snake_case_							)['model']
 for k in copy.deepcopy(list(ckp.keys()							)							):
  SCREAMING_SNAKE_CASE  :  Dict								=		ckp.pop(snake_case_							)
  if isinstance(snake_case_   ,     np.ndarray							):
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.tensor(snake_case_							)
  else:
   assert isinstance(snake_case_   ,     torch.tensor							), type(snake_case_							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		v
 return r


class 		_a     :







   """simple docstring"""




   A		= {}






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

    SCREAMING_SNAKE_CASE  :  Any								=		d






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






   def __setattr__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		val
    SCREAMING_SNAKE_CASE  :  Dict								=		val
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		key.split('.'      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		len(__SCREAMING_SNAKE_CASE      ) - 1
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self._pointer
    if len(__SCREAMING_SNAKE_CASE      ) > 1:
     for i, l in enumerate(__SCREAMING_SNAKE_CASE      ):
      if hasattr(self    ,__SCREAMING_SNAKE_CASE      ) and isinstance(getattr(self    ,__SCREAMING_SNAKE_CASE      )    ,__SCREAMING_SNAKE_CASE      ):
       setattr(getattr(self    ,__SCREAMING_SNAKE_CASE      )    ,'.'.join(levels[i:]      )    ,__SCREAMING_SNAKE_CASE      )
      if l == last_level:
       SCREAMING_SNAKE_CASE  :  int								=		val
      else:
       SCREAMING_SNAKE_CASE  :  Tuple								=		pointer[l]






   def 					__a			(       self      ):
    return self._pointer






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    with open(f"""{file_name}"""    ,'w'      ) as stream:
     dump(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    with open(f"""{file_name}"""    ,'w'      ) as stream:
     json.dump(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE      ):
    with open(__SCREAMING_SNAKE_CASE      ) as stream:
     SCREAMING_SNAKE_CASE  :  Dict								=		load(__SCREAMING_SNAKE_CASE    ,Loader=__SCREAMING_SNAKE_CASE      )
    return data






   def __str__(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		'    '
    if self._name != "root":
     SCREAMING_SNAKE_CASE  :  List[Any]								=		f"""{t * (self._level-1)}{self._name}:\n"""
    else:
     SCREAMING_SNAKE_CASE  :  Tuple								=		''
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self._level
    for i, (k, v) in enumerate(self._pointer.items()      ):
     if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
      r += f"""{t * (self._level)}{v}\n"""
      self._level += 1
     else:
      r += f"""{t * (self._level)}{k}: {v} ({type(__SCREAMING_SNAKE_CASE      ).__name__})\n"""
     SCREAMING_SNAKE_CASE  :  int								=		level
    return r[:-1]






   @classmethod
   def 					__a			(       cls    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  int								=		cls.get_config_dict(__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )
    return cls(__SCREAMING_SNAKE_CASE      )






   @classmethod
   def 					__a			(       cls    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  int								=		kwargs.pop('cache_dir'    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		kwargs.pop('force_download'    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		kwargs.pop('resume_download'    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		kwargs.pop('proxies'    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		kwargs.pop('local_files_only'    ,__SCREAMING_SNAKE_CASE      )

    if os.path.isdir(__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  int								=		os.path.join(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    elif os.path.isfile(__SCREAMING_SNAKE_CASE      ) or is_remote_url(__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  List[str]								=		pretrained_model_name_or_path
    else:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		hf_bucket_url(__SCREAMING_SNAKE_CASE    ,filename=__SCREAMING_SNAKE_CASE    ,use_cdn=__SCREAMING_SNAKE_CASE      )

    try:
     # Load from URL or cache if already cached
     SCREAMING_SNAKE_CASE  :  Tuple								=		cached_path(
         __SCREAMING_SNAKE_CASE    ,cache_dir=__SCREAMING_SNAKE_CASE    ,force_download=__SCREAMING_SNAKE_CASE    ,proxies=__SCREAMING_SNAKE_CASE    ,resume_download=__SCREAMING_SNAKE_CASE    ,local_files_only=__SCREAMING_SNAKE_CASE    ,)
     # Load config dict
     if resolved_config_file is None:
      raise EnvironmentError

     SCREAMING_SNAKE_CASE  :  Optional[int]								=		Config.load_yaml(__SCREAMING_SNAKE_CASE      )

    except EnvironmentError:
     SCREAMING_SNAKE_CASE  :  int								=		'Can\'t load config for'
     raise EnvironmentError(__SCREAMING_SNAKE_CASE      )

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

    return Config.load_yaml(__SCREAMING_SNAKE_CASE      ), kwargs



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Tuple							)       ->	Union[str, Any]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		torch.load('dump.pt'   ,     map_location=in_tensor.device							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		in_tensor.numpy()
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		out_tensor.numpy()[0]
 print(na.shape   ,     na[0, 0, :5]							)
 print(na.shape   ,     na[0, 0, :5]							)
 assert np.allclose(snake_case_   ,     snake_case_   ,     rtol=0.01   ,     atol=0.1							), (
     f"""{sum([1 for x in np.isclose(snake_case_   ,     snake_case_   ,     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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any							)       ->	Optional[int]:
 SCREAMING_SNAKE_CASE  :  List[str]								=		urlparse(snake_case_							)
 return parsed.scheme in ("http", "https")



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	Union[str, Any]=True							)       ->	str:
 SCREAMING_SNAKE_CASE  :  List[str]								=		CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		'/' not in model_id
 if legacy_format:
  return f"""{endpoint}/{model_id}-{filename}"""
 else:
  return f"""{endpoint}/{model_id}/{filename}"""



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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict   ,     snake_case_				:	List[Any]=None   ,     snake_case_				:	Optional[Any]=False   ,     snake_case_				:	List[str]=None   ,     snake_case_				:	Any=10   ,     snake_case_				:	Union[str, Any]=False   ,     snake_case_				:	Tuple=None   ,     snake_case_				:	str=False   ,     )       ->	str:
 if cache_dir is None:
  SCREAMING_SNAKE_CASE  :  List[Any]								=		TRANSFORMERS_CACHE
 if isinstance(snake_case_   ,     snake_case_							):
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		str(snake_case_							)

 os.makedirs(snake_case_   ,     exist_ok=snake_case_							)

 SCREAMING_SNAKE_CASE  :  List[Any]								=		None
 if not local_files_only:
  try:
   SCREAMING_SNAKE_CASE  :  Dict								=		requests.head(snake_case_   ,     allow_redirects=snake_case_   ,     proxies=snake_case_   ,     timeout=snake_case_							)
   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  :  List[Any]								=		url_to_filename(snake_case_   ,     snake_case_							)

 # get cache path to put the file
 SCREAMING_SNAKE_CASE  :  Tuple								=		os.path.join(snake_case_   ,     snake_case_							)

 # 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(snake_case_							):
   return cache_path
  else:
   SCREAMING_SNAKE_CASE  :  List[Any]								=		[
       file
       for file in fnmatch.filter(os.listdir(snake_case_							)   ,     filename + '.*'							)
       if not file.endswith('.json'							) and not file.endswith('.lock'							)
   ]
   if len(snake_case_							) > 0:
    return os.path.join(snake_case_   ,     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(snake_case_							) 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(snake_case_							):
  # If the download just completed while the lock was activated.
  if os.path.exists(snake_case_							) and not force_download:
   # Even if returning early like here, the lock will be released.
   return cache_path

  if resume_download:
   SCREAMING_SNAKE_CASE  :  Any								=		cache_path + '.incomplete'

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

   SCREAMING_SNAKE_CASE  :  Tuple								=		_resumable_file_manager
   if os.path.exists(snake_case_							):
    SCREAMING_SNAKE_CASE  :  int								=		os.stat(snake_case_							).st_size
   else:
    SCREAMING_SNAKE_CASE  :  List[Any]								=		0
  else:
   SCREAMING_SNAKE_CASE  :  List[Any]								=		partial(tempfile.NamedTemporaryFile   ,     dir=snake_case_   ,     delete=snake_case_							)
   SCREAMING_SNAKE_CASE  :  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'   ,     snake_case_   ,     temp_file.name   ,     )

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

  os.replace(temp_file.name   ,     snake_case_							)

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

 return cache_path



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any   ,     snake_case_				:	List[str]=None							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		url.encode('utf-8'							)
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		shaaaa(snake_case_							)
 SCREAMING_SNAKE_CASE  :  int								=		url_hash.hexdigest()

 if etag:
  SCREAMING_SNAKE_CASE  :  Dict								=		etag.encode('utf-8'							)
  SCREAMING_SNAKE_CASE  :  Any								=		shaaaa(snake_case_							)
  filename += "." + etag_hash.hexdigest()

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

 return filename



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	List[Any]=None   ,     snake_case_				:	Optional[Any]=False   ,     snake_case_				:	Tuple=None   ,     snake_case_				:	str=False   ,     snake_case_				:	Tuple=None   ,     snake_case_				:	int=False   ,     snake_case_				:	Any=False   ,     snake_case_				:	Optional[int]=False   ,     )       ->	List[str]:
 if cache_dir is None:
  SCREAMING_SNAKE_CASE  :  Tuple								=		TRANSFORMERS_CACHE
 if isinstance(snake_case_   ,     snake_case_							):
  SCREAMING_SNAKE_CASE  :  Any								=		str(snake_case_							)
 if isinstance(snake_case_   ,     snake_case_							):
  SCREAMING_SNAKE_CASE  :  str								=		str(snake_case_							)

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

 if extract_compressed_file:
  if not is_zipfile(snake_case_							) and not tarfile.is_tarfile(snake_case_							):
   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  :  Tuple								=		os.path.split(snake_case_							)
  SCREAMING_SNAKE_CASE  :  Any								=		output_file.replace('.'   ,     '-'							) + '-extracted'
  SCREAMING_SNAKE_CASE  :  List[str]								=		os.path.join(snake_case_   ,     snake_case_							)

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

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

  return output_path_extracted

 return output_path



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	Union[str, Any]=","							)       ->	Optional[Any]:
 assert isinstance(snake_case_   ,     snake_case_							)
 if os.path.isfile(snake_case_							):
  with open(snake_case_							) as f:
   SCREAMING_SNAKE_CASE  :  List[str]								=		eval(f.read()							)
 else:
  SCREAMING_SNAKE_CASE  :  List[str]								=		requests.get(snake_case_							)
  try:
   SCREAMING_SNAKE_CASE  :  Dict								=		requests.json()
  except Exception:
   SCREAMING_SNAKE_CASE  :  int								=		req.content.decode()
   assert data is not None, "could not connect"
   try:
    SCREAMING_SNAKE_CASE  :  Tuple								=		eval(snake_case_							)
   except Exception:
    SCREAMING_SNAKE_CASE  :  List[str]								=		data.split('\n'							)
  req.close()
 return data



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]							)       ->	Dict:
 SCREAMING_SNAKE_CASE  :  Any								=		requests.get(snake_case_							)
 SCREAMING_SNAKE_CASE  :  int								=		np.array(Image.open(BytesIO(response.content							)							)							)
 return img



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



def        SCREAMING_SNAKE_CASE_  (	)       ->	Optional[Any]:
 print(f"""{os.path.abspath(os.path.join(snake_case_   ,     os.pardir							)							)}/demo.ipynb"""							)



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





def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Optional[Any]=1							)       ->	Tuple:
 return (images[i : i + batch] for i in range(0   ,     len(snake_case_							)   ,     snake_case_							))




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


