
"""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

       lowerCamelCase__           =	True
except ImportError:
       lowerCamelCase__           =	False


try:
       from torch.hub import _get_torch_home

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

lowerCamelCase__           =	os.path.join(torch_cache_home, """transformers""")

lowerCamelCase__           =	"""https://cdn.huggingface.co"""
lowerCamelCase__           =	"""https://s3.amazonaws.com/models.huggingface.co/bert"""
lowerCamelCase__           =	"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
lowerCamelCase__           =	os.path.join(PATH, """config.yaml""")
lowerCamelCase__           =	os.path.join(PATH, """attributes.txt""")
lowerCamelCase__           =	os.path.join(PATH, """objects.txt""")
lowerCamelCase__           =	os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
lowerCamelCase__           =	os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
lowerCamelCase__           =	os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
lowerCamelCase__           =	"""pytorch_model.bin"""
lowerCamelCase__           =	"""config.yaml"""
def 					__lowerCAmelCase       (_UpperCamelCase=OBJECTS ,						_UpperCamelCase=ATTRIBUTES	):
 __lowerCAmelCase    :		Dict		    =   []
 with open(_UpperCamelCase	) as f:
  for object in f.readlines():
   vg_classes.append(object.split(','	)[0].lower().strip()	)

 __lowerCAmelCase    :		str		    =   []
 with open(_UpperCamelCase	) as f:
  for object in f.readlines():
   vg_attrs.append(object.split(','	)[0].lower().strip()	)
 return vg_classes, vg_attrs
def 					__lowerCAmelCase       (_UpperCamelCase	):
 __lowerCAmelCase    :		List[str]		    =   OrderedDict()
 with open(_UpperCamelCase ,						'rb'	) as f:
  __lowerCAmelCase    :		Union[str, Any]		    =   pkl.load(_UpperCamelCase	)['model']
 for k in copy.deepcopy(list(ckp.keys()	)	):
  __lowerCAmelCase    :		str		    =   ckp.pop(_UpperCamelCase	)
  if isinstance(_UpperCamelCase ,						np.ndarray	):
   __lowerCAmelCase    :		Dict		    =   torch.tensor(_UpperCamelCase	)
  else:
   assert isinstance(_UpperCamelCase ,						torch.tensor	), type(_UpperCamelCase	)
  __lowerCAmelCase    :		Tuple		    =   v
 return r




class 				A__ :
  A_		: str            =			{}

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

   __lowerCAmelCase    :		int		    =   d

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

  def __setattr__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
   __lowerCAmelCase    :		List[str]		    =   val
   __lowerCAmelCase    :		Optional[Any]		    =   val
   __lowerCAmelCase    :		Optional[Any]		    =   key.split('.'				)
   __lowerCAmelCase    :		Tuple		    =   len(_SCREAMING_SNAKE_CASE				) - 1
   __lowerCAmelCase    :		Tuple		    =   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:
      __lowerCAmelCase    :		Tuple		    =   val
     else:
      __lowerCAmelCase    :		str		    =   pointer[l]

  def 							__lowerCamelCase							(   self				):
   return self._pointer

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

  def 							__lowerCamelCase							(   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 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE				):
   with open(_SCREAMING_SNAKE_CASE				) as stream:
    __lowerCAmelCase    :		Dict		    =   load(_SCREAMING_SNAKE_CASE ,	Loader=_SCREAMING_SNAKE_CASE				)
   return data

  def __str__(   self				):
   __lowerCAmelCase    :		Union[str, Any]		    =   '    '
   if self._name != "root":
    __lowerCAmelCase    :		List[Any]		    =   f"{t * (self._level-1)}{self._name}:\n"
   else:
    __lowerCAmelCase    :		Union[str, Any]		    =   ''
   __lowerCAmelCase    :		List[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"
    __lowerCAmelCase    :		Optional[Any]		    =   level
   return r[:-1]

  @classmethod
  def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   __lowerCAmelCase    :		Dict		    =   cls.get_config_dict(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
   return cls(_SCREAMING_SNAKE_CASE				)







  @classmethod
  def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   __lowerCAmelCase    :		Optional[Any]		    =   kwargs.pop('cache_dir' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		List[str]		    =   kwargs.pop('force_download' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		Tuple		    =   kwargs.pop('resume_download' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		Optional[Any]		    =   kwargs.pop('proxies' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		List[Any]		    =   kwargs.pop('local_files_only' ,	_SCREAMING_SNAKE_CASE				)

   if os.path.isdir(_SCREAMING_SNAKE_CASE				):
    __lowerCAmelCase    :		List[str]		    =   os.path.join(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
   elif os.path.isfile(_SCREAMING_SNAKE_CASE				) or is_remote_url(_SCREAMING_SNAKE_CASE				):
    __lowerCAmelCase    :		Any		    =   pretrained_model_name_or_path
   else:
    __lowerCAmelCase    :		Union[str, 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
    __lowerCAmelCase    :		Optional[int]		    =   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

    __lowerCAmelCase    :		Tuple		    =   Config.load_yaml(_SCREAMING_SNAKE_CASE				)

   except EnvironmentError:
    __lowerCAmelCase    :		str		    =   '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 					__lowerCAmelCase       (_UpperCamelCase	):
 __lowerCAmelCase    :		int		    =   torch.load('dump.pt' ,						map_location=in_tensor.device	)
 __lowerCAmelCase    :		Tuple		    =   in_tensor.numpy()
 __lowerCAmelCase    :		Dict		    =   out_tensor.numpy()[0]
 print(na.shape ,						na[0, 0, :5]	)
 print(na.shape ,						na[0, 0, :5]	)
 assert np.allclose(_UpperCamelCase ,						_UpperCamelCase ,						rtol=0.01 ,						atol=0.1	), (
     F"{sum([1 for x in np.isclose(_UpperCamelCase ,						_UpperCamelCase ,						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       (_UpperCamelCase	):
 __lowerCAmelCase    :		Dict		    =   urlparse(_UpperCamelCase	)
 return parsed.scheme in ("http", "https")
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=True	):
 __lowerCAmelCase    :		List[str]		    =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 __lowerCAmelCase    :		Optional[int]		    =   '/' not in model_id
 if legacy_format:
  return F"{endpoint}/{model_id}-{filename}"
 else:
  return F"{endpoint}/{model_id}/{filename}"
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=0 ,						_UpperCamelCase=None ,						):
 __lowerCAmelCase    :		Union[str, Any]		    =   'python/{}'.format(sys.version.split()[0]	)
 if _torch_available:
  ua += "; torch/{}".format(torch.__version__	)
 if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
  ua += "; " + "; ".join('{}/{}'.format(_UpperCamelCase ,						_UpperCamelCase	) for k, v in user_agent.items()	)
 elif isinstance(_UpperCamelCase ,						_UpperCamelCase	):
  ua += "; " + user_agent
 __lowerCAmelCase    :		Optional[Any]		    =   {'user-agent': ua}
 if resume_size > 0:
  __lowerCAmelCase    :		Union[str, Any]		    =   'bytes=%d-' % (resume_size,)
 __lowerCAmelCase    :		int		    =   requests.get(_UpperCamelCase ,						stream=_UpperCamelCase ,						proxies=_UpperCamelCase ,						headers=_UpperCamelCase	)
 if response.status_code == 416:  # Range not satisfiable
  return
 __lowerCAmelCase    :		Dict		    =   response.headers.get('Content-Length'	)
 __lowerCAmelCase    :		int		    =   resume_size + int(_UpperCamelCase	) if content_length is not None else None
 __lowerCAmelCase    :		Union[str, Any]		    =   tqdm(
     unit='B' ,						unit_scale=_UpperCamelCase ,						total=_UpperCamelCase ,						initial=_UpperCamelCase ,						desc='Downloading' ,						)
 for chunk in response.iter_content(chunk_size=1024	):
  if chunk:  # filter out keep-alive new chunks
   progress.update(len(_UpperCamelCase	)	)
   temp_file.write(_UpperCamelCase	)
 progress.close()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=10 ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						):
 if cache_dir is None:
  __lowerCAmelCase    :		Any		    =   TRANSFORMERS_CACHE
 if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
  __lowerCAmelCase    :		Any		    =   str(_UpperCamelCase	)

 os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase	)

 __lowerCAmelCase    :		Tuple		    =   None
 if not local_files_only:
  try:
   __lowerCAmelCase    :		Optional[Any]		    =   requests.head(_UpperCamelCase ,						allow_redirects=_UpperCamelCase ,						proxies=_UpperCamelCase ,						timeout=_UpperCamelCase	)
   if response.status_code == 200:
    __lowerCAmelCase    :		Tuple		    =   response.headers.get('ETag'	)
  except (EnvironmentError, requests.exceptions.Timeout):
   # etag is already None
   pass

 __lowerCAmelCase    :		Optional[int]		    =   url_to_filename(_UpperCamelCase ,						_UpperCamelCase	)

 # get cache path to put the file
 __lowerCAmelCase    :		List[Any]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)

 # 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(_UpperCamelCase	):
   return cache_path
  else:
   __lowerCAmelCase    :		List[Any]		    =   [
       file
       for file in fnmatch.filter(os.listdir(_UpperCamelCase	) ,						filename + '.*'	)
       if not file.endswith('.json'	) and not file.endswith('.lock'	)
   ]
   if len(_UpperCamelCase	) > 0:
    return os.path.join(_UpperCamelCase ,						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(_UpperCamelCase	) and not force_download:
  return cache_path

 # Prevent parallel downloads of the same file with a lock.
 __lowerCAmelCase    :		List[Any]		    =   cache_path + '.lock'
 with FileLock(_UpperCamelCase	):
  # If the download just completed while the lock was activated.
  if os.path.exists(_UpperCamelCase	) and not force_download:
   # Even if returning early like here, the lock will be released.
   return cache_path

  if resume_download:
   __lowerCAmelCase    :		List[str]		    =   cache_path + '.incomplete'

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

   __lowerCAmelCase    :		str		    =   _resumable_file_manager
   if os.path.exists(_UpperCamelCase	):
    __lowerCAmelCase    :		Optional[Any]		    =   os.stat(_UpperCamelCase	).st_size
   else:
    __lowerCAmelCase    :		str		    =   0
  else:
   __lowerCAmelCase    :		str		    =   partial(tempfile.NamedTemporaryFile ,						dir=_UpperCamelCase ,						delete=_UpperCamelCase	)
   __lowerCAmelCase    :		Tuple		    =   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' ,						_UpperCamelCase ,						temp_file.name ,						)

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

  os.replace(temp_file.name ,						_UpperCamelCase	)

  __lowerCAmelCase    :		List[Any]		    =   {'url': url, 'etag': etag}
  __lowerCAmelCase    :		Any		    =   cache_path + '.json'
  with open(_UpperCamelCase ,						'w'	) as meta_file:
   json.dump(_UpperCamelCase ,						_UpperCamelCase	)

 return cache_path
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None	):
 __lowerCAmelCase    :		int		    =   url.encode('utf-8'	)
 __lowerCAmelCase    :		str		    =   shaaaa(_UpperCamelCase	)
 __lowerCAmelCase    :		Dict		    =   url_hash.hexdigest()

 if etag:
  __lowerCAmelCase    :		Optional[Any]		    =   etag.encode('utf-8'	)
  __lowerCAmelCase    :		int		    =   shaaaa(_UpperCamelCase	)
  filename += "." + etag_hash.hexdigest()

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

 return filename
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=False ,						_UpperCamelCase=False ,						):
 if cache_dir is None:
  __lowerCAmelCase    :		List[Any]		    =   TRANSFORMERS_CACHE
 if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
  __lowerCAmelCase    :		int		    =   str(_UpperCamelCase	)
 if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
  __lowerCAmelCase    :		Any		    =   str(_UpperCamelCase	)

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

 if extract_compressed_file:
  if not is_zipfile(_UpperCamelCase	) and not tarfile.is_tarfile(_UpperCamelCase	):
   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/"
  __lowerCAmelCase    :		Tuple		    =   os.path.split(_UpperCamelCase	)
  __lowerCAmelCase    :		Optional[int]		    =   output_file.replace('.' ,						'-'	) + '-extracted'
  __lowerCAmelCase    :		Optional[Any]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)

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

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

  return output_path_extracted

 return output_path
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=","	):
 assert isinstance(_UpperCamelCase ,						_UpperCamelCase	)
 if os.path.isfile(_UpperCamelCase	):
  with open(_UpperCamelCase	) as f:
   __lowerCAmelCase    :		Optional[Any]		    =   eval(f.read()	)
 else:
  __lowerCAmelCase    :		Optional[Any]		    =   requests.get(_UpperCamelCase	)
  try:
   __lowerCAmelCase    :		Union[str, Any]		    =   requests.json()
  except Exception:
   __lowerCAmelCase    :		List[Any]		    =   req.content.decode()
   assert data is not None, "could not connect"
   try:
    __lowerCAmelCase    :		str		    =   eval(_UpperCamelCase	)
   except Exception:
    __lowerCAmelCase    :		int		    =   data.split('\n'	)
  req.close()
 return data
def 					__lowerCAmelCase       (_UpperCamelCase	):
 __lowerCAmelCase    :		Tuple		    =   requests.get(_UpperCamelCase	)
 __lowerCAmelCase    :		List[Any]		    =   np.array(Image.open(BytesIO(response.content	)	)	)
 return img
def 					__lowerCAmelCase       (_UpperCamelCase	):
 __lowerCAmelCase    :		List[str]		    =   url.split('/'	)[-1]
 if fn not in os.listdir(os.getcwd()	):
  wget.download(_UpperCamelCase	)
 with open(_UpperCamelCase ,						'rb'	) as stream:
  __lowerCAmelCase    :		int		    =   pkl.load(_UpperCamelCase	)
 __lowerCAmelCase    :		Dict		    =   weights.pop('model'	)
 __lowerCAmelCase    :		Dict		    =   {}
 for k, v in model.items():
  __lowerCAmelCase    :		List[str]		    =   torch.from_numpy(_UpperCamelCase	)
  if "running_var" in k:
   __lowerCAmelCase    :		List[str]		    =   torch.tensor([0]	)
   __lowerCAmelCase    :		Optional[int]		    =   k.replace('running_var' ,						'num_batches_tracked'	)
   __lowerCAmelCase    :		Dict		    =   zero
 return new
def 					__lowerCAmelCase       ():
 print(F"{os.path.abspath(os.path.join(_UpperCamelCase ,						os.pardir	)	)}/demo.ipynb"	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase="RGB"	):
 assert isinstance(_UpperCamelCase ,						_UpperCamelCase	)
 if os.path.isfile(_UpperCamelCase	):
  __lowerCAmelCase    :		Union[str, Any]		    =   cva.imread(_UpperCamelCase	)
 else:
  __lowerCAmelCase    :		Any		    =   get_image_from_url(_UpperCamelCase	)
  assert img is not None, F"could not connect to: {im}"
 __lowerCAmelCase    :		Optional[int]		    =   cva.cvtColor(_UpperCamelCase ,						cva.COLOR_BGR2RGB	)
 if input_format == "RGB":
  __lowerCAmelCase    :		Optional[int]		    =   img[:, :, ::-1]
 return img
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=1	):
 return (images[i : i + batch] for i in range(0 ,						len(_UpperCamelCase	) ,						_UpperCamelCase	))

"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)




class 				A__ (			unittest.TestCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=4 ,	):
			__lowerCAmelCase    :		List[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		List[str]		    =   seq_length
			__lowerCAmelCase    :		str		    =   is_training
			__lowerCAmelCase    :		Optional[Any]		    =   use_attention_mask
			__lowerCAmelCase    :		Union[str, Any]		    =   use_token_type_ids
			__lowerCAmelCase    :		Dict		    =   use_labels
			__lowerCAmelCase    :		Union[str, Any]		    =   vocab_size
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   num_hidden_layers
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   intermediate_size
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Union[str, Any]		    =   max_position_embeddings
			__lowerCAmelCase    :		Any		    =   type_vocab_size
			__lowerCAmelCase    :		Tuple		    =   type_sequence_label_size
			__lowerCAmelCase    :		Union[str, Any]		    =   initializer_range
			__lowerCAmelCase    :		int		    =   num_choices

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_attention_mask:
				__lowerCAmelCase    :		str		    =   random_attention_mask([self.batch_size, self.seq_length]				)

			__lowerCAmelCase    :		Dict		    =   None
			if self.use_token_type_ids:
				__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.type_vocab_size				)

			__lowerCAmelCase    :		str		    =   RobertaConfig(
			    vocab_size=self.vocab_size ,	hidden_size=self.hidden_size ,	num_hidden_layers=self.num_hidden_layers ,	num_attention_heads=self.num_attention_heads ,	intermediate_size=self.intermediate_size ,	hidden_act=self.hidden_act ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	max_position_embeddings=self.max_position_embeddings ,	type_vocab_size=self.type_vocab_size ,	is_decoder=_SCREAMING_SNAKE_CASE ,	initializer_range=self.initializer_range ,	)

			return config, input_ids, token_type_ids, attention_mask

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   config_and_inputs
			__lowerCAmelCase    :		List[Any]		    =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
			return config, inputs_dict







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs

			__lowerCAmelCase    :		int		    =   True
			__lowerCAmelCase    :		Any		    =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
			__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, self.seq_length] ,	vocab_size=2				)

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)




@require_flax
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[Any]            =			True

		A_		: Optional[Any]            =			(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   FlaxRobertaModelTester(self				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		str		    =   model_class_name.from_pretrained('roberta-base' ,	from_pt=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)