






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

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




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




class 	lowerCamelCase__				:
      lowerCamelCase_							:   Union[str, Any]											=	{}






      def __init__(self					:    Optional[Any]		,		_snake_case					:    dict		,		_snake_case					:    str = "root"		,		_snake_case					:    Optional[int]=0		)       ->   List[str]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     name
       lowerCamelCase_							:    List[Any]							=     level
       lowerCamelCase_							:    Any							=     {}
       for k, v in dictionary.items():
        if v is None:
         raise ValueError()
        lowerCamelCase_							:    Dict							=     copy.deepcopy(__SCREAMING_SNAKE_CASE		)
        lowerCamelCase_							:    Tuple							=     copy.deepcopy(__SCREAMING_SNAKE_CASE		)
        if isinstance(__SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE		):
         lowerCamelCase_							:    Any							=     Config(__SCREAMING_SNAKE_CASE		,		name=__SCREAMING_SNAKE_CASE		,		level=level + 1		)
        lowerCamelCase_							:    Any							=     v
        setattr(self		,		__SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE		)

       lowerCamelCase_							:    str							=     d






      def __repr__(self					:    Dict		)       ->   List[str]:


       """simple docstring"""






       return str(list((self._pointer.keys())		)		)






      def __setattr__(self					:    Any		,		_snake_case					:    Optional[Any]		,		_snake_case					:    Any		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    Tuple							=     val
       lowerCamelCase_							:    int							=     val
       lowerCamelCase_							:    Optional[Any]							=     key.split('.'		)
       lowerCamelCase_							:    int							=     len(__SCREAMING_SNAKE_CASE		) - 1
       lowerCamelCase_							:    int							=     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_							:    Union[str, Any]							=     pointer[l]






      def      UpperCAmelCase_     (self					:    int		)       ->   Tuple:


       """simple docstring"""






       return self._pointer






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    List[str]		,		_snake_case					:    Any		)       ->   str:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Optional[int]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    List[str]		)       ->   Dict:


       """simple docstring"""






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






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    Tuple		)       ->   Optional[int]:


       """simple docstring"""






       with open(__SCREAMING_SNAKE_CASE		) as stream:
        lowerCamelCase_							:    Optional[Any]							=     load(__SCREAMING_SNAKE_CASE		,		Loader=__SCREAMING_SNAKE_CASE		)
       return data






      def __str__(self					:    List[str]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     '    '
       if self._name != "root":
        lowerCamelCase_							:    Tuple							=     f'{t * (self._level-1)}{self._name}:\n'
       else:
        lowerCamelCase_							:    List[str]							=     ''
       lowerCamelCase_							:    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_							:    List[Any]							=     level
       return r[:-1]






      @classmethod
      def      UpperCAmelCase_     (cls					:    List[str]		,		_snake_case					:    str		,		**_snake_case					:    Any		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_		,      lowerCamelCase_							:    str							=     cls.get_config_dict(__SCREAMING_SNAKE_CASE		,		**__SCREAMING_SNAKE_CASE		)
       return cls(__SCREAMING_SNAKE_CASE		)






      @classmethod
      def      UpperCAmelCase_     (cls					:    str		,		_snake_case					:    str		,		**_snake_case					:    Union[str, Any]		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     kwargs.pop('cache_dir'		,		__SCREAMING_SNAKE_CASE		)
       lowerCamelCase_							:    Any							=     kwargs.pop('force_download'		,		__SCREAMING_SNAKE_CASE		)
       lowerCamelCase_							:    str							=     kwargs.pop('resume_download'		,		__SCREAMING_SNAKE_CASE		)
       lowerCamelCase_							:    Optional[Any]							=     kwargs.pop('proxies'		,		__SCREAMING_SNAKE_CASE		)
       lowerCamelCase_							:    List[str]							=     kwargs.pop('local_files_only'		,		__SCREAMING_SNAKE_CASE		)

       if os.path.isdir(__SCREAMING_SNAKE_CASE		):
        lowerCamelCase_							:    Any							=     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_							:    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
        lowerCamelCase_							:    Optional[Any]							=     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_							:    List[Any]							=     Config.load_yaml(__SCREAMING_SNAKE_CASE		)

       except EnvironmentError:
        lowerCamelCase_							:    Dict							=     '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       _a   (							lowerCamelCase__				)  ->      int:
 lowerCamelCase_							:    Tuple							=     torch.load('dump.pt' ,			map_location=in_tensor.device				)
 lowerCamelCase_							:    List[Any]							=     in_tensor.numpy()
 lowerCamelCase_							:    Tuple							=     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()				)*1_00:.4f} %'
     " element-wise mismatch"
 )
 raise Exception('tensors are all good'				)

 # Hugging face functions below




def       _a   (							lowerCamelCase__				)  ->      Union[str, Any]:
 lowerCamelCase_							:    List[Any]							=     urlparse(lowerCamelCase_				)
 return parsed.scheme in ("http", "https")




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__=True				)  ->      Optional[Any]:
 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       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__=None ,			lowerCamelCase__=0 ,			lowerCamelCase__=None ,			)  ->      Optional[Any]:
 lowerCamelCase_							:    Tuple							=     '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
 lowerCamelCase_							:    str							=     {'user-agent': ua}
 if resume_size > 0:
  lowerCamelCase_							:    Dict							=     'bytes=%d-' % (resume_size,)
 lowerCamelCase_							:    List[Any]							=     requests.get(lowerCamelCase_ ,			stream=lowerCamelCase_ ,			proxies=lowerCamelCase_ ,			headers=lowerCamelCase_				)
 if response.status_code == 4_16:  # Range not satisfiable
  return
 lowerCamelCase_							:    Any							=     response.headers.get('Content-Length'				)
 lowerCamelCase_							:    Any							=     resume_size + int(lowerCamelCase_				) if content_length is not None else None
 lowerCamelCase_							:    Optional[int]							=     tqdm(
     unit='B' ,			unit_scale=lowerCamelCase_ ,			total=lowerCamelCase_ ,			initial=lowerCamelCase_ ,			desc='Downloading' ,			)
 for chunk in response.iter_content(chunk_size=10_24				):
  if chunk:  # filter out keep-alive new chunks
   progress.update(len(lowerCamelCase_				)				)
   temp_file.write(lowerCamelCase_				)
 progress.close()




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=None ,			lowerCamelCase__=False ,			lowerCamelCase__=None ,			lowerCamelCase__=10 ,			lowerCamelCase__=False ,			lowerCamelCase__=None ,			lowerCamelCase__=False ,			)  ->      Any:





 if cache_dir is None:
  lowerCamelCase_							:    Tuple							=     TRANSFORMERS_CACHE
 if isinstance(lowerCamelCase_ ,			lowerCamelCase_				):
  lowerCamelCase_							:    Dict							=     str(lowerCamelCase_				)

 os.makedirs(lowerCamelCase_ ,			exist_ok=lowerCamelCase_				)

 lowerCamelCase_							:    Tuple							=     None
 if not local_files_only:
  try:
   lowerCamelCase_							:    Optional[Any]							=     requests.head(lowerCamelCase_ ,			allow_redirects=lowerCamelCase_ ,			proxies=lowerCamelCase_ ,			timeout=lowerCamelCase_				)
   if response.status_code == 2_00:
    lowerCamelCase_							:    int							=     response.headers.get('ETag'				)
  except (EnvironmentError, requests.exceptions.Timeout):
   # etag is already None
   pass

 lowerCamelCase_							:    Tuple							=     url_to_filename(lowerCamelCase_ ,			lowerCamelCase_				)

 # get cache path to put the file
 lowerCamelCase_							:    List[Any]							=     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:
   lowerCamelCase_							:    Dict							=     [
       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.
 lowerCamelCase_							:    Any							=     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:
   lowerCamelCase_							:    List[str]							=     cache_path + '.incomplete'

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

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

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

  os.replace(temp_file.name ,			lowerCamelCase_				)

  lowerCamelCase_							:    Optional[int]							=     {'url': url, 'etag': etag}
  lowerCamelCase_							:    List[str]							=     cache_path + '.json'
  with open(lowerCamelCase_ ,			'w'				) as meta_file:
   json.dump(lowerCamelCase_ ,			lowerCamelCase_				)

 return cache_path




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=None				)  ->      str:
 lowerCamelCase_							:    List[Any]							=     url.encode('utf-8'				)
 lowerCamelCase_							:    int							=     shaaaa(lowerCamelCase_				)
 lowerCamelCase_							:    Optional[Any]							=     url_hash.hexdigest()

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

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

 return filename




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=None ,			lowerCamelCase__=False ,			lowerCamelCase__=None ,			lowerCamelCase__=False ,			lowerCamelCase__=None ,			lowerCamelCase__=False ,			lowerCamelCase__=False ,			lowerCamelCase__=False ,			)  ->      int:





 if cache_dir is None:
  lowerCamelCase_							:    Optional[Any]							=     TRANSFORMERS_CACHE
 if isinstance(lowerCamelCase_ ,			lowerCamelCase_				):
  lowerCamelCase_							:    Tuple							=     str(lowerCamelCase_				)
 if isinstance(lowerCamelCase_ ,			lowerCamelCase_				):
  lowerCamelCase_							:    Any							=     str(lowerCamelCase_				)

 if is_remote_url(lowerCamelCase_				):
  # URL, so get it from the cache (downloading if necessary)
  lowerCamelCase_							:    Dict							=     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.
  lowerCamelCase_							:    Optional[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/"
  lowerCamelCase_		,      lowerCamelCase_							:    Any							=     os.path.split(lowerCamelCase_				)
  lowerCamelCase_							:    List[str]							=     output_file.replace('.' ,			'-'				) + '-extracted'
  lowerCamelCase_							:    List[Any]							=     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
  lowerCamelCase_							:    Union[str, 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_				):
    lowerCamelCase_							:    Any							=     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       _a   (							lowerCamelCase__ ,			lowerCamelCase__=","				)  ->      Dict:
 assert isinstance(lowerCamelCase_ ,			lowerCamelCase_				)
 if os.path.isfile(lowerCamelCase_				):
  with open(lowerCamelCase_				) as f:
   lowerCamelCase_							:    Dict							=     eval(f.read()				)
 else:
  lowerCamelCase_							:    int							=     requests.get(lowerCamelCase_				)
  try:
   lowerCamelCase_							:    str							=     requests.json()
  except Exception:
   lowerCamelCase_							:    Tuple							=     req.content.decode()
   assert data is not None, "could not connect"
   try:
    lowerCamelCase_							:    Optional[int]							=     eval(lowerCamelCase_				)
   except Exception:
    lowerCamelCase_							:    Union[str, Any]							=     data.split('\n'				)
  req.close()
 return data




def       _a   (							lowerCamelCase__				)  ->      Any:
 lowerCamelCase_							:    Dict							=     requests.get(lowerCamelCase_				)
 lowerCamelCase_							:    List[Any]							=     np.array(Image.open(BytesIO(response.content				)				)				)
 return img




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




def       _a   (							)  ->      Dict:
 print(F'{os.path.abspath(os.path.join(lowerCamelCase_ ,			os.pardir				)				)}/demo.ipynb'				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__="RGB"				)  ->      int:
 assert isinstance(lowerCamelCase_ ,			lowerCamelCase_				)
 if os.path.isfile(lowerCamelCase_				):
  lowerCamelCase_							:    Dict							=     cva.imread(lowerCamelCase_				)
 else:
  lowerCamelCase_							:    int							=     get_image_from_url(lowerCamelCase_				)
  assert img is not None, F'could not connect to: {im}'
 lowerCamelCase_							:    str							=     cva.cvtColor(lowerCamelCase_ ,			cva.COLOR_BGR2RGB				)
 if input_format == "RGB":
  lowerCamelCase_							:    List[str]							=     img[:, :, ::-1]
 return img





def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=1				)  ->      Optional[int]:
 return (images[i : i + batch] for i in range(0 ,			len(lowerCamelCase_				) ,			lowerCamelCase_				))

import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

