code: 
  
  
  
  
  
  '''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
  
        lowercase_			       =				True
  except ImportError:
        lowercase_			       =				False
  
  
  try:
        from torch.hub import _get_torch_home
  
        lowercase_			       =				_get_torch_home()
  except ImportError:
        lowercase_			       =				os.path.expanduser(
            os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
        )
  
  lowercase_			       =				os.path.join(torch_cache_home, """transformers""")
  
  lowercase_			       =				"""https://cdn.huggingface.co"""
  lowercase_			       =				"""https://s3.amazonaws.com/models.huggingface.co/bert"""
  lowercase_			       =				"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
  lowercase_			       =				os.path.join(PATH, """config.yaml""")
  lowercase_			       =				os.path.join(PATH, """attributes.txt""")
  lowercase_			       =				os.path.join(PATH, """objects.txt""")
  lowercase_			       =				os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
  lowercase_			       =				os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
  lowercase_			       =				os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
  lowercase_			       =				"""pytorch_model.bin"""
  lowercase_			       =				"""config.yaml"""
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str=OBJECTS      , __lowerCamelCase				:					Dict=ATTRIBUTES					)		->str:
      _SCREAMING_SNAKE_CASE             =    []
      with open(__lowerCamelCase					) as f:
          for object in f.readlines():
              vg_classes.append(object.split(""","""					)[0].lower().strip()					)
  
      _SCREAMING_SNAKE_CASE             =    []
      with open(__lowerCamelCase					) as f:
          for object in f.readlines():
              vg_attrs.append(object.split(""","""					)[0].lower().strip()					)
      return vg_classes, vg_attrs
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE             =    OrderedDict()
      with open(__lowerCamelCase      , """rb"""					) as f:
          _SCREAMING_SNAKE_CASE             =    pkl.load(__lowerCamelCase					)["""model"""]
      for k in copy.deepcopy(list(ckp.keys()					)					):
          _SCREAMING_SNAKE_CASE             =    ckp.pop(__lowerCamelCase					)
          if isinstance(__lowerCamelCase      , np.ndarray					):
              _SCREAMING_SNAKE_CASE             =    torch.tensor(__lowerCamelCase					)
          else:
              assert isinstance(__lowerCamelCase      , torch.tensor					), type(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    v
      return r
  
  
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				{}
  
  
      def __init__(	self				,	A				,	A = "root"				,	A=0 )	->     int:
          _SCREAMING_SNAKE_CASE             =    name
          _SCREAMING_SNAKE_CASE             =    level
          _SCREAMING_SNAKE_CASE             =    {}
          for k, v in dictionary.items():
              if v is None:
                  raise ValueError()
              _SCREAMING_SNAKE_CASE             =    copy.deepcopy(A )
              _SCREAMING_SNAKE_CASE             =    copy.deepcopy(A )
              if isinstance(A				,	A ):
                  _SCREAMING_SNAKE_CASE             =    Config(A				,	name=A				,	level=level + 1 )
              _SCREAMING_SNAKE_CASE             =    v
              setattr(self				,	A				,	A )
  
          _SCREAMING_SNAKE_CASE             =    d
  
  
      def __repr__(	self )	->     int:
          return str(list((self._pointer.keys()) ) )
  
  
      def __setattr__(	self				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    val
          _SCREAMING_SNAKE_CASE             =    val
          _SCREAMING_SNAKE_CASE             =    key.split(""".""" )
          _SCREAMING_SNAKE_CASE             =    len(A ) - 1
          _SCREAMING_SNAKE_CASE             =    self._pointer
          if len(A ) > 1:
              for i, l in enumerate(A ):
                  if hasattr(self				,	A ) and isinstance(getattr(self				,	A )				,	A ):
                      setattr(getattr(self				,	A )				,	""".""".join(levels[i:] )				,	A )
                  if l == last_level:
                      _SCREAMING_SNAKE_CASE             =    val
                  else:
                      _SCREAMING_SNAKE_CASE             =    pointer[l]
  
  
      def     snake_case_(	self )	->     str:
          return self._pointer
  
  
      def     snake_case_(	self				,	A				,	A )	->     Any:
          with open(f'{file_name}'				,	"""w""" ) as stream:
              dump(A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A )	->     str:
          with open(f'{file_name}'				,	"""w""" ) as stream:
              json.dump(A				,	A )
  
  
      @staticmethod
      def     snake_case_(	A )	->     List[str]:
          with open(A ) as stream:
              _SCREAMING_SNAKE_CASE             =    load(A				,	Loader=A )
          return data
  
  
      def __str__(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    """    """
          if self._name != "root":
              _SCREAMING_SNAKE_CASE             =    f'{t * (self._level-1)}{self._name}:\n'
          else:
              _SCREAMING_SNAKE_CASE             =    """"""
          _SCREAMING_SNAKE_CASE             =    self._level
          for i, (k, v) in enumerate(self._pointer.items() ):
              if isinstance(A				,	A ):
                  r += f'{t * (self._level)}{v}\n'
                  self._level += 1
              else:
                  r += f'{t * (self._level)}{k}: {v} ({type(A ).__name__})\n'
              _SCREAMING_SNAKE_CASE             =    level
          return r[:-1]
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	**A )	->     str:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    cls.get_config_dict(A				,	**A )
          return cls(A )
  
  
  
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	**A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    kwargs.pop("""cache_dir"""				,	A )
          _SCREAMING_SNAKE_CASE             =    kwargs.pop("""force_download"""				,	A )
          _SCREAMING_SNAKE_CASE             =    kwargs.pop("""resume_download"""				,	A )
          _SCREAMING_SNAKE_CASE             =    kwargs.pop("""proxies"""				,	A )
          _SCREAMING_SNAKE_CASE             =    kwargs.pop("""local_files_only"""				,	A )
  
          if os.path.isdir(A ):
              _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	A )
          elif os.path.isfile(A ) or is_remote_url(A ):
              _SCREAMING_SNAKE_CASE             =    pretrained_model_name_or_path
          else:
              _SCREAMING_SNAKE_CASE             =    hf_bucket_url(A				,	filename=A				,	use_cdn=A )
  
          try:
              # Load from URL or cache if already cached
              _SCREAMING_SNAKE_CASE             =    cached_path(
                  A				,	cache_dir=A				,	force_download=A				,	proxies=A				,	resume_download=A				,	local_files_only=A				,	)
              # Load config dict
              if resolved_config_file is None:
                  raise EnvironmentError
  
              _SCREAMING_SNAKE_CASE             =    Config.load_yaml(A )
  
          except EnvironmentError:
              _SCREAMING_SNAKE_CASE             =    """Can't load config for"""
              raise EnvironmentError(A )
  
          if resolved_config_file == config_file:
              print("""loading configuration file from path""" )
          else:
              print("""loading configuration file cache""" )
  
          return Config.load_yaml(A ), kwargs
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    torch.load("""dump.pt"""      , map_location=in_tensor.device					)
      _SCREAMING_SNAKE_CASE             =    in_tensor.numpy()
      _SCREAMING_SNAKE_CASE             =    out_tensor.numpy()[0]
      print(na.shape      , na[0, 0, :5]					)
      print(na.shape      , na[0, 0, :5]					)
      assert np.allclose(__lowerCamelCase      , __lowerCamelCase      , rtol=0.01      , atol=0.1					), (
          F'{sum([1 for x in np.isclose(__lowerCamelCase      , __lowerCamelCase      , rtol=0.01      , atol=0.1					).flatten() if x is False]					)/len(na.flatten()					)*100:.4f} %'
          " element-wise mismatch"
      )
      raise Exception("""tensors are all good"""					)
  
      # Hugging face functions below
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    urlparse(__lowerCamelCase					)
      return parsed.scheme in ("http", "https")
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					Dict=True					)		->str:
      _SCREAMING_SNAKE_CASE             =    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
      _SCREAMING_SNAKE_CASE             =    """/""" not in model_id
      if legacy_format:
          return F'{endpoint}/{model_id}-{filename}'
      else:
          return F'{endpoint}/{model_id}/{filename}'
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Any=None      , __lowerCamelCase				:					List[str]=0      , __lowerCamelCase				:					List[str]=None      , )		->Any:
      _SCREAMING_SNAKE_CASE             =    """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
      _SCREAMING_SNAKE_CASE             =    {"""user-agent""": ua}
      if resume_size > 0:
          _SCREAMING_SNAKE_CASE             =    """bytes=%d-""" % (resume_size,)
      _SCREAMING_SNAKE_CASE             =    requests.get(__lowerCamelCase      , stream=__lowerCamelCase      , proxies=__lowerCamelCase      , headers=__lowerCamelCase					)
      if response.status_code == 416:  # Range not satisfiable
          return
      _SCREAMING_SNAKE_CASE             =    response.headers.get("""Content-Length"""					)
      _SCREAMING_SNAKE_CASE             =    resume_size + int(__lowerCamelCase					) if content_length is not None else None
      _SCREAMING_SNAKE_CASE             =    tqdm(
          unit="""B"""      , unit_scale=__lowerCamelCase      , total=__lowerCamelCase      , initial=__lowerCamelCase      , desc="""Downloading"""      , )
      for chunk in response.iter_content(chunk_size=1024					):
          if chunk:  # filter out keep-alive new chunks
              progress.update(len(__lowerCamelCase					)					)
              temp_file.write(__lowerCamelCase					)
      progress.close()
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					Any=None      , __lowerCamelCase				:					List[str]=False      , __lowerCamelCase				:					Tuple=None      , __lowerCamelCase				:					Dict=10      , __lowerCamelCase				:					List[Any]=False      , __lowerCamelCase				:					str=None      , __lowerCamelCase				:					List[Any]=False      , )		->List[str]:
      if cache_dir is None:
          _SCREAMING_SNAKE_CASE             =    TRANSFORMERS_CACHE
      if isinstance(__lowerCamelCase      , __lowerCamelCase					):
          _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
  
      os.makedirs(__lowerCamelCase      , exist_ok=__lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    None
      if not local_files_only:
          try:
              _SCREAMING_SNAKE_CASE             =    requests.head(__lowerCamelCase      , allow_redirects=__lowerCamelCase      , proxies=__lowerCamelCase      , timeout=__lowerCamelCase					)
              if response.status_code == 200:
                  _SCREAMING_SNAKE_CASE             =    response.headers.get("""ETag"""					)
          except (EnvironmentError, requests.exceptions.Timeout):
              # etag is already None
              pass
  
      _SCREAMING_SNAKE_CASE             =    url_to_filename(__lowerCamelCase      , __lowerCamelCase					)
  
      # get cache path to put the file
      _SCREAMING_SNAKE_CASE             =    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:
              _SCREAMING_SNAKE_CASE             =    [
                  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.
      _SCREAMING_SNAKE_CASE             =    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:
              _SCREAMING_SNAKE_CASE             =    cache_path + """.incomplete"""
  
              @contextmanager
              def _resumable_file_manager():
                  with open(__lowerCamelCase      , """a+b"""					) as f:
                      yield f
  
              _SCREAMING_SNAKE_CASE             =    _resumable_file_manager
              if os.path.exists(__lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    os.stat(__lowerCamelCase					).st_size
              else:
                  _SCREAMING_SNAKE_CASE             =    0
          else:
              _SCREAMING_SNAKE_CASE             =    partial(tempfile.NamedTemporaryFile      , dir=__lowerCamelCase      , delete=__lowerCamelCase					)
              _SCREAMING_SNAKE_CASE             =    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					)
  
          _SCREAMING_SNAKE_CASE             =    {"""url""": url, """etag""": etag}
          _SCREAMING_SNAKE_CASE             =    cache_path + """.json"""
          with open(__lowerCamelCase      , """w"""					) as meta_file:
              json.dump(__lowerCamelCase      , __lowerCamelCase					)
  
      return cache_path
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					Optional[int]=None					)		->Union[str, Any]:
      _SCREAMING_SNAKE_CASE             =    url.encode("""utf-8"""					)
      _SCREAMING_SNAKE_CASE             =    shaaaa(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    url_hash.hexdigest()
  
      if etag:
          _SCREAMING_SNAKE_CASE             =    etag.encode("""utf-8"""					)
          _SCREAMING_SNAKE_CASE             =    shaaaa(__lowerCamelCase					)
          filename += "." + etag_hash.hexdigest()
  
      if url.endswith(""".h5"""					):
          filename += ".h5"
  
      return filename
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					List[str]=None      , __lowerCamelCase				:					Dict=False      , __lowerCamelCase				:					Any=None      , __lowerCamelCase				:					List[str]=False      , __lowerCamelCase				:					Any=None      , __lowerCamelCase				:					Dict=False      , __lowerCamelCase				:					Optional[int]=False      , __lowerCamelCase				:					List[Any]=False      , )		->Any:
      if cache_dir is None:
          _SCREAMING_SNAKE_CASE             =    TRANSFORMERS_CACHE
      if isinstance(__lowerCamelCase      , __lowerCamelCase					):
          _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
      if isinstance(__lowerCamelCase      , __lowerCamelCase					):
          _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
  
      if is_remote_url(__lowerCamelCase					):
          # URL, so get it from the cache (downloading if necessary)
          _SCREAMING_SNAKE_CASE             =    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.
          _SCREAMING_SNAKE_CASE             =    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/"
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    os.path.split(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    output_file.replace("""."""      , """-"""					) + """-extracted"""
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    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					):
                  _SCREAMING_SNAKE_CASE             =    tarfile.open(__lowerCamelCase					)
                  tar_file.extractall(__lowerCamelCase					)
                  tar_file.close()
              else:
                  raise EnvironmentError("""Archive format of {} could not be identified""".format(__lowerCamelCase					)					)
  
          return output_path_extracted
  
      return output_path
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					List[str]=","					)		->Optional[int]:
      assert isinstance(__lowerCamelCase      , __lowerCamelCase					)
      if os.path.isfile(__lowerCamelCase					):
          with open(__lowerCamelCase					) as f:
              _SCREAMING_SNAKE_CASE             =    eval(f.read()					)
      else:
          _SCREAMING_SNAKE_CASE             =    requests.get(__lowerCamelCase					)
          try:
              _SCREAMING_SNAKE_CASE             =    requests.json()
          except Exception:
              _SCREAMING_SNAKE_CASE             =    req.content.decode()
              assert data is not None, "could not connect"
              try:
                  _SCREAMING_SNAKE_CASE             =    eval(__lowerCamelCase					)
              except Exception:
                  _SCREAMING_SNAKE_CASE             =    data.split("""\n"""					)
          req.close()
      return data
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Any:
      _SCREAMING_SNAKE_CASE             =    requests.get(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    np.array(Image.open(BytesIO(response.content					)					)					)
      return img
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple					)		->int:
      _SCREAMING_SNAKE_CASE             =    url.split("""/"""					)[-1]
      if fn not in os.listdir(os.getcwd()					):
          wget.download(__lowerCamelCase					)
      with open(__lowerCamelCase      , """rb"""					) as stream:
          _SCREAMING_SNAKE_CASE             =    pkl.load(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    weights.pop("""model"""					)
      _SCREAMING_SNAKE_CASE             =    {}
      for k, v in model.items():
          _SCREAMING_SNAKE_CASE             =    torch.from_numpy(__lowerCamelCase					)
          if "running_var" in k:
              _SCREAMING_SNAKE_CASE             =    torch.tensor([0]					)
              _SCREAMING_SNAKE_CASE             =    k.replace("""running_var"""      , """num_batches_tracked"""					)
              _SCREAMING_SNAKE_CASE             =    zero
      return new
  
  
  
  
  
  def lowerCamelCase		(  )		->Dict:
      print(F'{os.path.abspath(os.path.join(__lowerCamelCase      , os.pardir					)					)}/demo.ipynb'					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					Optional[int]="RGB"					)		->Optional[Any]:
      assert isinstance(__lowerCamelCase      , __lowerCamelCase					)
      if os.path.isfile(__lowerCamelCase					):
          _SCREAMING_SNAKE_CASE             =    cva.imread(__lowerCamelCase					)
      else:
          _SCREAMING_SNAKE_CASE             =    get_image_from_url(__lowerCamelCase					)
          assert img is not None, F'could not connect to: {im}'
      _SCREAMING_SNAKE_CASE             =    cva.cvtColor(__lowerCamelCase      , cva.COLOR_BGR2RGB					)
      if input_format == "RGB":
          _SCREAMING_SNAKE_CASE             =    img[:, :, ::-1]
      return img
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					int=1					)		->int:
      return (images[i : i + batch] for i in range(0      , len(__lowerCamelCase					)      , __lowerCamelCase					))
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from timeit import timeit
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
      while True:
          _SCREAMING_SNAKE_CASE             =    n // 10
          digits += 1
          if n == 0:
              break
      return digits
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return len(str(abs(__lowerCamelCase					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  )		->None:
      from collections.abc import Callable
  
      def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
          _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
          _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
          print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)
  
      for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
          for func in (num_digits, num_digits_fast, num_digits_faster):
              benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
          print()
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
        benchmark()
  
  
  
  