"""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

     _a				:		Optional[int]	   =       True
except ImportError:
     _a				:		Optional[int]	   =       False


try:
     from torch.hub import _get_torch_home

     _a				:		List[str]	   =       _get_torch_home()
except ImportError:
     _a				:		str	   =       os.path.expanduser(
         os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
     )

_a				:		Any	   =       os.path.join(torch_cache_home, 'transformers')

_a				:		Tuple	   =       'https://cdn.huggingface.co'
_a				:		Any	   =       'https://s3.amazonaws.com/models.huggingface.co/bert'
_a				:		Union[str, Any]	   =       '/'.join(str(Path(__file__).resolve()).split('/')[:-1])
_a				:		Any	   =       os.path.join(PATH, 'config.yaml')
_a				:		int	   =       os.path.join(PATH, 'attributes.txt')
_a				:		Tuple	   =       os.path.join(PATH, 'objects.txt')
_a				:		Tuple	   =       os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
_a				:		Union[str, Any]	   =       os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
_a				:		Optional[Any]	   =       os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
_a				:		List[str]	   =       'pytorch_model.bin'
_a				:		Dict	   =       'config.yaml'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]=OBJECTS      ,_lowerCamelCase						:	List[Any]=ATTRIBUTES     )	->				Union[str, Any]:
  _lowerCAmelCase						:	int        =					[]
  with open(_lowerCamelCase     ) as f:
    for object in f.readlines():
      vg_classes.append(object.split(""","""     )[0].lower().strip()     )

  _lowerCAmelCase						:	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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict     )	->				Any:
  _lowerCAmelCase						:	int        =					OrderedDict()
  with open(_lowerCamelCase      ,"""rb"""     ) as f:
    _lowerCAmelCase						:	List[Any]        =					pkl.load(_lowerCamelCase     )["""model"""]
  for k in copy.deepcopy(list(ckp.keys()     )     ):
    _lowerCAmelCase						:	Any        =					ckp.pop(_lowerCamelCase     )
    if isinstance(_lowerCamelCase      ,np.ndarray     ):
      _lowerCAmelCase						:	Union[str, Any]        =					torch.tensor(_lowerCamelCase     )
    else:
      assert isinstance(_lowerCamelCase      ,torch.tensor     ), type(_lowerCamelCase     )
    _lowerCAmelCase						:	int        =					v
  return r
class 						__A							:
     _UpperCamelCase							:      List[str]		 =		{}






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

       _lowerCAmelCase						:	int        =					d






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






     def __setattr__(		self    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					val
       _lowerCAmelCase						:	List[str]        =					val
       _lowerCAmelCase						:	int        =					key.split("""."""  )
       _lowerCAmelCase						:	Optional[Any]        =					len(a__  ) - 1
       _lowerCAmelCase						:	Optional[int]        =					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:
             _lowerCAmelCase						:	Optional[int]        =					val
           else:
             _lowerCAmelCase						:	Union[str, Any]        =					pointer[l]






     def    __A  (		self  ):
       return self._pointer






     def    __A  (		self    ,					a__    ,					a__  ):
       with open(F"{file_name}"    ,					"""w"""  ) as stream:
         dump(a__    ,					a__  )






     def    __A  (		self    ,					a__    ,					a__  ):
       with open(F"{file_name}"    ,					"""w"""  ) as stream:
         json.dump(a__    ,					a__  )






     @staticmethod
     def    __A  (		a__  ):
       with open(a__  ) as stream:
         _lowerCAmelCase						:	List[str]        =					load(a__    ,					Loader=a__  )
       return data






     def __str__(		self  ):
       _lowerCAmelCase						:	List[str]        =					"""    """
       if self._name != "root":
         _lowerCAmelCase						:	List[str]        =					F"{t * (self._level-1)}{self._name}:\n"
       else:
         _lowerCAmelCase						:	List[str]        =					""""""
       _lowerCAmelCase						:	Dict        =					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"
         _lowerCAmelCase						:	Union[str, Any]        =					level
       return r[:-1]






     @classmethod
     def    __A  (		cls    ,					a__    ,					**a__  ):
       _lowerCAmelCase						:	List[str]        =					cls.get_config_dict(a__    ,					**a__  )
       return cls(a__  )






     @classmethod
     def    __A  (		cls    ,					a__    ,					**a__  ):
       _lowerCAmelCase						:	Tuple        =					kwargs.pop("""cache_dir"""    ,					a__  )
       _lowerCAmelCase						:	Optional[Any]        =					kwargs.pop("""force_download"""    ,					a__  )
       _lowerCAmelCase						:	List[Any]        =					kwargs.pop("""resume_download"""    ,					a__  )
       _lowerCAmelCase						:	List[str]        =					kwargs.pop("""proxies"""    ,					a__  )
       _lowerCAmelCase						:	Tuple        =					kwargs.pop("""local_files_only"""    ,					a__  )

       if os.path.isdir(a__  ):
         _lowerCAmelCase						:	List[str]        =					os.path.join(a__    ,					a__  )
       elif os.path.isfile(a__  ) or is_remote_url(a__  ):
         _lowerCAmelCase						:	Tuple        =					pretrained_model_name_or_path
       else:
         _lowerCAmelCase						:	Optional[Any]        =					hf_bucket_url(a__    ,					filename=a__    ,					use_cdn=a__  )

       try:
         # Load from URL or cache if already cached
         _lowerCAmelCase						:	List[Any]        =					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

         _lowerCAmelCase						:	List[str]        =					Config.load_yaml(a__  )

       except EnvironmentError:
         _lowerCAmelCase						:	int        =					"""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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]     )	->				List[str]:
  _lowerCAmelCase						:	Optional[int]        =					torch.load("""dump.pt"""      ,map_location=in_tensor.device     )
  _lowerCAmelCase						:	int        =					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()     )*100:.4f} %"
      " element-wise mismatch"
  )
  raise Exception("""tensors are all good"""     )

  # Hugging face functions below





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict     )	->				Optional[Any]:
  _lowerCAmelCase						:	Union[str, Any]        =					urlparse(_lowerCamelCase     )
  return parsed.scheme in ("http", "https")





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	Dict=True     )	->				str:
  _lowerCAmelCase						:	Dict        =					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  _lowerCAmelCase						:	List[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					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Any=None      ,_lowerCamelCase						:	List[Any]=0      ,_lowerCamelCase						:	str=None      ,)	->				Dict:
  _lowerCAmelCase						:	List[Any]        =					"""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						:	Any        =					{"""user-agent""": ua}
  if resume_size > 0:
    _lowerCAmelCase						:	Tuple        =					"""bytes=%d-""" % (resume_size,)
  _lowerCAmelCase						:	List[str]        =					requests.get(_lowerCamelCase      ,stream=_lowerCamelCase      ,proxies=_lowerCamelCase      ,headers=_lowerCamelCase     )
  if response.status_code == 416:  # Range not satisfiable
    return
  _lowerCAmelCase						:	Optional[int]        =					response.headers.get("""Content-Length"""     )
  _lowerCAmelCase						:	List[str]        =					resume_size + int(_lowerCamelCase     ) if content_length is not None else None
  _lowerCAmelCase						:	Dict        =					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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	List[str]=None      ,_lowerCamelCase						:	Tuple=False      ,_lowerCamelCase						:	Tuple=None      ,_lowerCamelCase						:	int=10      ,_lowerCamelCase						:	Any=False      ,_lowerCamelCase						:	List[Any]=None      ,_lowerCamelCase						:	str=False      ,)	->				Any:
  if cache_dir is None:
    _lowerCAmelCase						:	List[Any]        =					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[int]        =					requests.head(_lowerCamelCase      ,allow_redirects=_lowerCamelCase      ,proxies=_lowerCamelCase      ,timeout=_lowerCamelCase     )
      if response.status_code == 200:
        _lowerCAmelCase						:	List[str]        =					response.headers.get("""ETag"""     )
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  _lowerCAmelCase						:	int        =					url_to_filename(_lowerCamelCase      ,_lowerCamelCase     )

  # get cache path to put the file
  _lowerCAmelCase						:	str        =					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						:	int        =					[
          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						:	int        =					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						:	int        =					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						:	List[Any]        =					os.stat(_lowerCamelCase     ).st_size
      else:
        _lowerCAmelCase						:	Optional[Any]        =					0
    else:
      _lowerCAmelCase						:	Any        =					partial(tempfile.NamedTemporaryFile      ,dir=_lowerCamelCase      ,delete=_lowerCamelCase     )
      _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"""      ,_lowerCamelCase      ,temp_file.name      ,)

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

    os.replace(temp_file.name      ,_lowerCamelCase     )

    _lowerCAmelCase						:	Union[str, Any]        =					{"""url""": url, """etag""": etag}
    _lowerCAmelCase						:	List[Any]        =					cache_path + """.json"""
    with open(_lowerCamelCase      ,"""w"""     ) as meta_file:
      json.dump(_lowerCamelCase      ,_lowerCamelCase     )

  return cache_path





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[Any]=None     )	->				Dict:
  _lowerCAmelCase						:	Optional[int]        =					url.encode("""utf-8"""     )
  _lowerCAmelCase						:	Tuple        =					shaaaa(_lowerCamelCase     )
  _lowerCAmelCase						:	Optional[int]        =					url_hash.hexdigest()

  if etag:
    _lowerCAmelCase						:	List[str]        =					etag.encode("""utf-8"""     )
    _lowerCAmelCase						:	Dict        =					shaaaa(_lowerCamelCase     )
    filename += "." + etag_hash.hexdigest()

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

  return filename





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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					requests.get(_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					np.array(Image.open(BytesIO(response.content     )     )     )
  return img





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





def 							SCREAMING_SNAKE_CASE					(      )	->				Tuple:
  print(f"{os.path.abspath(os.path.join(_lowerCamelCase      ,os.pardir     )     )}/demo.ipynb"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	str="RGB"     )	->				List[Any]:
  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						:	Tuple        =					cva.cvtColor(_lowerCamelCase      ,cva.COLOR_BGR2RGB     )
  if input_format == "RGB":
    _lowerCAmelCase						:	Union[str, Any]        =					img[:, :, ::-1]
  return img






def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	Optional[Any]=1     )	->				List[str]:
  return (images[i : i + batch] for i in range(0      ,len(_lowerCamelCase     )      ,_lowerCamelCase     ))






"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




