






"""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__					: Tuple=	False


try:
  from torch.hub import _get_torch_home

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=OBJECTS ,	SCREAMING_SNAKE_CASE=ATTRIBUTES					)					->	str:
    """simple docstring"""

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

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    OrderedDict()
    with open(SCREAMING_SNAKE_CASE ,	'rb'					) as f:
        UpperCamelCase__         =    pkl.load(SCREAMING_SNAKE_CASE					)['model']
    for k in copy.deepcopy(list(ckp.keys()					)					):
        UpperCamelCase__         =    ckp.pop(SCREAMING_SNAKE_CASE					)
        if isinstance(SCREAMING_SNAKE_CASE ,	np.ndarray					):
            UpperCamelCase__         =    torch.tensor(SCREAMING_SNAKE_CASE					)
        else:
            assert isinstance(SCREAMING_SNAKE_CASE ,	torch.tensor					), type(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    v
    return r
class    __lowerCamelCase    :
   a      :				Optional[int] 						={}

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

       UpperCamelCase__         =    d

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

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       return self._pointer

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       with open(F'{file_name}' ,				'w'      ) as stream:
           dump(snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       with open(F'{file_name}' ,				'w'      ) as stream:
           json.dump(snake_case_ ,				snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       with open(snake_case_      ) as stream:
           UpperCamelCase__         =    load(snake_case_ ,				Loader=snake_case_      )
       return data

   def __str__(					self      )		->       str:
       UpperCamelCase__         =    '    '
       if self._name != "root":
           UpperCamelCase__         =    F'{t * (self._level-1)}{self._name}:\n'
       else:
           UpperCamelCase__         =    ''
       UpperCamelCase__         =    self._level
       for i, (k, v) in enumerate(self._pointer.items()      ):
           if isinstance(snake_case_ ,				snake_case_      ):
               r += F'{t * (self._level)}{v}\n'
               self._level += 1
           else:
               r += F'{t * (self._level)}{k}: {v} ({type(snake_case_      ).__name__})\n'
           UpperCamelCase__         =    level
       return r[:-1]

   @classmethod
   def 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       UpperCamelCase__       ,						UpperCamelCase__         =    cls.get_config_dict(snake_case_ ,				**snake_case_      )
       return cls(snake_case_      )







   @classmethod
   def 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       int:
       UpperCamelCase__         =    kwargs.pop('cache_dir' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('force_download' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('resume_download' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('proxies' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('local_files_only' ,				snake_case_      )

       if os.path.isdir(snake_case_      ):
           UpperCamelCase__         =    os.path.join(snake_case_ ,				snake_case_      )
       elif os.path.isfile(snake_case_      ) or is_remote_url(snake_case_      ):
           UpperCamelCase__         =    pretrained_model_name_or_path
       else:
           UpperCamelCase__         =    hf_bucket_url(snake_case_ ,				filename=snake_case_ ,				use_cdn=snake_case_      )

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

           UpperCamelCase__         =    Config.load_yaml(snake_case_      )

       except EnvironmentError:
           UpperCamelCase__         =    'Can\'t load config for'
           raise EnvironmentError(snake_case_      )

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

       return Config.load_yaml(snake_case_      ), kwargs







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    torch.load('dump.pt' ,	map_location=in_tensor.device					)
    UpperCamelCase__         =    in_tensor.numpy()
    UpperCamelCase__         =    out_tensor.numpy()[0]
    print(na.shape ,	na[0, 0, :5]					)
    print(na.shape ,	na[0, 0, :5]					)
    assert np.allclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	rtol=0.01 ,	atol=0.1					), (
        F'{sum([1 for x in np.isclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	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   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """simple docstring"""

    UpperCamelCase__         =    urlparse(SCREAMING_SNAKE_CASE					)
    return parsed.scheme in ("http", "https")







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=True					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    UpperCamelCase__         =    '/' not in model_id
    if legacy_format:
        return F'{endpoint}/{model_id}-{filename}'
    else:
        return F'{endpoint}/{model_id}/{filename}'







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=0 ,	SCREAMING_SNAKE_CASE=None ,	)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    'python/{}'.format(sys.version.split()[0]					)
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__					)
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        ua += "; " + "; ".join('{}/{}'.format(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					) for k, v in user_agent.items()					)
    elif isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        ua += "; " + user_agent
    UpperCamelCase__         =    {'user-agent': ua}
    if resume_size > 0:
        UpperCamelCase__         =    'bytes=%d-' % (resume_size,)
    UpperCamelCase__         =    requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE ,	proxies=SCREAMING_SNAKE_CASE ,	headers=SCREAMING_SNAKE_CASE					)
    if response.status_code == 4_16:  # Range not satisfiable
        return
    UpperCamelCase__         =    response.headers.get('Content-Length'					)
    UpperCamelCase__         =    resume_size + int(SCREAMING_SNAKE_CASE					) if content_length is not None else None
    UpperCamelCase__         =    tqdm(
        unit='B' ,	unit_scale=SCREAMING_SNAKE_CASE ,	total=SCREAMING_SNAKE_CASE ,	initial=SCREAMING_SNAKE_CASE ,	desc='Downloading' ,	)
    for chunk in response.iter_content(chunk_size=10_24					):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(SCREAMING_SNAKE_CASE					)					)
            temp_file.write(SCREAMING_SNAKE_CASE					)
    progress.close()







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=10 ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=False ,	)					->	Union[str, Any]:
    """simple docstring"""

    if cache_dir is None:
        UpperCamelCase__         =    TRANSFORMERS_CACHE
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    str(SCREAMING_SNAKE_CASE					)

    os.makedirs(SCREAMING_SNAKE_CASE ,	exist_ok=SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    None
    if not local_files_only:
        try:
            UpperCamelCase__         =    requests.head(SCREAMING_SNAKE_CASE ,	allow_redirects=SCREAMING_SNAKE_CASE ,	proxies=SCREAMING_SNAKE_CASE ,	timeout=SCREAMING_SNAKE_CASE					)
            if response.status_code == 2_00:
                UpperCamelCase__         =    response.headers.get('ETag'					)
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    UpperCamelCase__         =    url_to_filename(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

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

    # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(SCREAMING_SNAKE_CASE					):
            return cache_path
        else:
            UpperCamelCase__         =    [
                file
                for file in fnmatch.filter(os.listdir(SCREAMING_SNAKE_CASE					) ,	filename + '.*'					)
                if not file.endswith('.json'					) and not file.endswith('.lock'					)
            ]
            if len(SCREAMING_SNAKE_CASE					) > 0:
                return os.path.join(SCREAMING_SNAKE_CASE ,	matching_files[-1]					)
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise ValueError(
                        'Cannot find the requested files in the cached path and outgoing traffic has been'
                        ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
                        ' to False.'					)
                return None

    # From now on, etag is not None.
    if os.path.exists(SCREAMING_SNAKE_CASE					) and not force_download:
        return cache_path

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

        if resume_download:
            UpperCamelCase__         =    cache_path + '.incomplete'

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

            UpperCamelCase__         =    _resumable_file_manager
            if os.path.exists(SCREAMING_SNAKE_CASE					):
                UpperCamelCase__         =    os.stat(SCREAMING_SNAKE_CASE					).st_size
            else:
                UpperCamelCase__         =    0
        else:
            UpperCamelCase__         =    partial(tempfile.NamedTemporaryFile ,	dir=SCREAMING_SNAKE_CASE ,	delete=SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    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' ,	SCREAMING_SNAKE_CASE ,	temp_file.name ,	)

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

        os.replace(temp_file.name ,	SCREAMING_SNAKE_CASE					)

        UpperCamelCase__         =    {'url': url, 'etag': etag}
        UpperCamelCase__         =    cache_path + '.json'
        with open(SCREAMING_SNAKE_CASE ,	'w'					) as meta_file:
            json.dump(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    return cache_path







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None					)					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    url.encode('utf-8'					)
    UpperCamelCase__         =    shaaaa(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    url_hash.hexdigest()

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

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

    return filename







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=False ,	SCREAMING_SNAKE_CASE=False ,	)					->	Optional[Any]:
    """simple docstring"""

    if cache_dir is None:
        UpperCamelCase__         =    TRANSFORMERS_CACHE
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    str(SCREAMING_SNAKE_CASE					)
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    str(SCREAMING_SNAKE_CASE					)

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

    if extract_compressed_file:
        if not is_zipfile(SCREAMING_SNAKE_CASE					) and not tarfile.is_tarfile(SCREAMING_SNAKE_CASE					):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        UpperCamelCase__       ,						UpperCamelCase__         =    os.path.split(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    output_file.replace('.' ,	'-'					) + '-extracted'
        UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

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

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

        return output_path_extracted

    return output_path







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=","					)					->	Optional[int]:
    """simple docstring"""

    assert isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    if os.path.isfile(SCREAMING_SNAKE_CASE					):
        with open(SCREAMING_SNAKE_CASE					) as f:
            UpperCamelCase__         =    eval(f.read()					)
    else:
        UpperCamelCase__         =    requests.get(SCREAMING_SNAKE_CASE					)
        try:
            UpperCamelCase__         =    requests.json()
        except Exception:
            UpperCamelCase__         =    req.content.decode()
            assert data is not None, "could not connect"
            try:
                UpperCamelCase__         =    eval(SCREAMING_SNAKE_CASE					)
            except Exception:
                UpperCamelCase__         =    data.split('\n'					)
        req.close()
    return data







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    requests.get(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    np.array(Image.open(BytesIO(response.content					)					)					)
    return img







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

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







def   lowerCAmelCase_( )					->	Dict:
    """simple docstring"""

    print(F'{os.path.abspath(os.path.join(SCREAMING_SNAKE_CASE ,	os.pardir					)					)}/demo.ipynb'					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE="RGB"					)					->	Dict:
    """simple docstring"""

    assert isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    if os.path.isfile(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    cva.imread(SCREAMING_SNAKE_CASE					)
    else:
        UpperCamelCase__         =    get_image_from_url(SCREAMING_SNAKE_CASE					)
        assert img is not None, F'could not connect to: {im}'
    UpperCamelCase__         =    cva.cvtColor(SCREAMING_SNAKE_CASE ,	cva.COLOR_BGR2RGB					)
    if input_format == "RGB":
        UpperCamelCase__         =    img[:, :, ::-1]
    return img







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=1					)					->	Optional[int]:
    """simple docstring"""

    return (images[i : i + batch] for i in range(0 ,	len(SCREAMING_SNAKE_CASE					) ,	SCREAMING_SNAKE_CASE					))












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

       if arr is None and size is not None:
           UpperCamelCase__         =    size
           UpperCamelCase__         =    [0] * size
       elif arr is not None:
           self.init(snake_case_      )
       else:
           raise ValueError('Either arr or size must be specified'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

       UpperCamelCase__         =    1  # Largest power of 2 <= size
       while j * 2 < self.size:
           j *= 2

       UpperCamelCase__         =    0

       while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
               value -= self.tree[i + j]
               i += j
           j //= 2
       return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()




