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


try:
	from torch.hub import _get_torch_home

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

_a	:	Optional[int]=					os.path.join(torch_cache_home, "transformers")

_a	:	Dict=					"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	:	Tuple=					os.path.join(PATH, "config.yaml")
_a	:	Tuple=					os.path.join(PATH, "attributes.txt")
_a	:	Union[str, Any]=					os.path.join(PATH, "objects.txt")
_a	:	Optional[int]=					os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
_a	:	Union[str, Any]=					os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
_a	:	Dict=					os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
_a	:	str=					"pytorch_model.bin"
_a	:	Dict=					"config.yaml"
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]=OBJECTS     ,				UpperCAmelCase_       :							Tuple=ATTRIBUTES    )				->     Any:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					[]
	with open(UpperCAmelCase_    ) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','    )[0].lower().strip()    )

	__snake_case     :				Tuple 						=					[]
	with open(UpperCAmelCase_    ) as f:
		for object in f.readlines():
			vg_attrs.append(object.split(','    )[0].lower().strip()    )
	return vg_classes, vg_attrs
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[Any]    )				->     str:

	'''simple docstring'''





	__snake_case     :				Any 						=					OrderedDict()
	with open(UpperCAmelCase_     ,				'rb'    ) as f:
		__snake_case     :				List[Any] 						=					pkl.load(UpperCAmelCase_    )['model']
	for k in copy.deepcopy(list(ckp.keys()    )    ):
		__snake_case     :				Union[str, Any] 						=					ckp.pop(UpperCAmelCase_    )
		if isinstance(UpperCAmelCase_     ,				np.ndarray    ):
			__snake_case     :				Optional[int] 						=					torch.tensor(UpperCAmelCase_    )
		else:
			assert isinstance(UpperCAmelCase_     ,				torch.tensor    ), type(UpperCAmelCase_    )
		__snake_case     :				List[str] 						=					v
	return r

class 						UpperCamelCase     :
				UpperCAmelCase							: Tuple       =						{}



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

					__snake_case     :				List[str] 						=					d



				def __repr__(self							: Optional[Any])       ->      Optional[int]:
					return str(list((self._pointer.keys())))



				def __setattr__(self							: Optional[Any]      ,    _A							: Union[str, Any]      ,    _A							: Optional[Any])       ->      Any:
					__snake_case     :				Optional[int] 						=					val
					__snake_case     :				Tuple 						=					val
					__snake_case     :				List[str] 						=					key.split('.')
					__snake_case     :				str 						=					len(_A) - 1
					__snake_case     :				Dict 						=					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:
								__snake_case     :				List[str] 						=					val
							else:
								__snake_case     :				List[Any] 						=					pointer[l]



				def    _lowercase	(self							: Tuple)       ->      List[Any]:
					return self._pointer



				def    _lowercase	(self							: List[Any]      ,    _A							: Any      ,    _A							: str)       ->      Union[str, Any]:
					with open(f"{file_name}"      ,    'w') as stream:
						dump(_A      ,    _A)



				def    _lowercase	(self							: Tuple      ,    _A							: int      ,    _A							: Optional[Any])       ->      Optional[int]:
					with open(f"{file_name}"      ,    'w') as stream:
						json.dump(_A      ,    _A)



				@staticmethod
				def    _lowercase	(_A							: Optional[int])       ->      Tuple:
					with open(_A) as stream:
						__snake_case     :				Any 						=					load(_A      ,    Loader=_A)
					return data



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



				@classmethod
				def    _lowercase	(cls							: Dict      ,    _A							: str      ,    **_A							: Dict)       ->      Optional[Any]:
					__snake_case						,							__snake_case     :				Union[str, Any] 						=					cls.get_config_dict(_A      ,    **_A)
					return cls(_A)






				@classmethod
				def    _lowercase	(cls							: Union[str, Any]      ,    _A							: str      ,    **_A							: Union[str, Any])       ->      Union[str, Any]:
					__snake_case     :				Optional[int] 						=					kwargs.pop('cache_dir'      ,    _A)
					__snake_case     :				Union[str, Any] 						=					kwargs.pop('force_download'      ,    _A)
					__snake_case     :				Optional[Any] 						=					kwargs.pop('resume_download'      ,    _A)
					__snake_case     :				Any 						=					kwargs.pop('proxies'      ,    _A)
					__snake_case     :				Dict 						=					kwargs.pop('local_files_only'      ,    _A)

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

					try:
						# Load from URL or cache if already cached
						__snake_case     :				Union[str, 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

						__snake_case     :				List[Any] 						=					Config.load_yaml(_A)

					except EnvironmentError:
						__snake_case     :				Tuple 						=					'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     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[int]    )				->     Optional[int]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					torch.load('dump.pt'     ,				map_location=in_tensor.device    )
	__snake_case     :				Optional[Any] 						=					in_tensor.numpy()
	__snake_case     :				List[str] 						=					out_tensor.numpy()[0]
	print(na.shape     ,				na[0, 0, :5]    )
	print(na.shape     ,				na[0, 0, :5]    )
	assert np.allclose(UpperCAmelCase_     ,				UpperCAmelCase_     ,				rtol=0.01     ,				atol=0.1    ), (
	    F"{sum([1 for x in np.isclose(UpperCAmelCase_     ,				UpperCAmelCase_     ,				rtol=0.01     ,				atol=0.1    ).flatten() if x is False]    )/len(na.flatten()    )*1_00:.4f} %"
	    " element-wise mismatch"
	)
	raise Exception('tensors are all good'    )

	# Hugging face functions below
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     Optional[Any]:

	'''simple docstring'''





	__snake_case     :				Any 						=					urlparse(UpperCAmelCase_    )
	return parsed.scheme in ("http", "https")
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							Optional[Any]=True    )				->     str:

	'''simple docstring'''





	__snake_case     :				List[Any] 						=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	__snake_case     :				str 						=					'/' not in model_id
	if legacy_format:
		return F"{endpoint}/{model_id}-{filename}"
	else:
		return F"{endpoint}/{model_id}/{filename}"
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							int=None     ,				UpperCAmelCase_       :							Optional[int]=0     ,				UpperCAmelCase_       :							int=None     ,				)				->     Optional[Any]:

	'''simple docstring'''





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

	'''simple docstring'''





	if cache_dir is None:
		__snake_case     :				Union[str, Any] 						=					TRANSFORMERS_CACHE
	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		__snake_case     :				str 						=					str(UpperCAmelCase_    )

	os.makedirs(UpperCAmelCase_     ,				exist_ok=UpperCAmelCase_    )

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

	__snake_case     :				Optional[Any] 						=					url_to_filename(UpperCAmelCase_     ,				UpperCAmelCase_    )

	# get cache path to put the file
	__snake_case     :				Optional[Any] 						=					os.path.join(UpperCAmelCase_     ,				UpperCAmelCase_    )

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

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

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

		if resume_download:
			__snake_case     :				Union[str, Any] 						=					cache_path + '.incomplete'

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

			__snake_case     :				Tuple 						=					_resumable_file_manager
			if os.path.exists(UpperCAmelCase_    ):
				__snake_case     :				Optional[int] 						=					os.stat(UpperCAmelCase_    ).st_size
			else:
				__snake_case     :				Optional[Any] 						=					0
		else:
			__snake_case     :				Any 						=					partial(tempfile.NamedTemporaryFile     ,				dir=UpperCAmelCase_     ,				delete=UpperCAmelCase_    )
			__snake_case     :				List[str] 						=					0

		# Download to temporary file, then copy to cache dir once finished.
		# Otherwise you get corrupt cache entries if the download gets interrupted.
		with temp_file_manager() as temp_file:
			print(
			    '%s not found in cache or force_download set to True, downloading to %s'     ,				UpperCAmelCase_     ,				temp_file.name     ,				)

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

		os.replace(temp_file.name     ,				UpperCAmelCase_    )

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

	return cache_path
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							Any=None    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Optional[int] 						=					url.encode('utf-8'    )
	__snake_case     :				Union[str, Any] 						=					shaaaa(UpperCAmelCase_    )
	__snake_case     :				str 						=					url_hash.hexdigest()

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

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

	return filename
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							Union[str, Any]=None     ,				UpperCAmelCase_       :							List[Any]=False     ,				UpperCAmelCase_       :							str=None     ,				UpperCAmelCase_       :							Any=False     ,				UpperCAmelCase_       :							Dict=None     ,				UpperCAmelCase_       :							Union[str, Any]=False     ,				UpperCAmelCase_       :							Dict=False     ,				UpperCAmelCase_       :							Any=False     ,				)				->     int:

	'''simple docstring'''





	if cache_dir is None:
		__snake_case     :				Any 						=					TRANSFORMERS_CACHE
	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		__snake_case     :				Tuple 						=					str(UpperCAmelCase_    )
	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		__snake_case     :				List[str] 						=					str(UpperCAmelCase_    )

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

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

		# Path where we extract compressed archives
		# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
		__snake_case						,							__snake_case     :				List[Any] 						=					os.path.split(UpperCAmelCase_    )
		__snake_case     :				Tuple 						=					output_file.replace('.'     ,				'-'    ) + '-extracted'
		__snake_case     :				List[str] 						=					os.path.join(UpperCAmelCase_     ,				UpperCAmelCase_    )

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

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

		return output_path_extracted

	return output_path
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Any     ,				UpperCAmelCase_       :							str=","    )				->     Any:

	'''simple docstring'''





	assert isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    )
	if os.path.isfile(UpperCAmelCase_    ):
		with open(UpperCAmelCase_    ) as f:
			__snake_case     :				Optional[Any] 						=					eval(f.read()    )
	else:
		__snake_case     :				Tuple 						=					requests.get(UpperCAmelCase_    )
		try:
			__snake_case     :				Dict 						=					requests.json()
		except Exception:
			__snake_case     :				Optional[Any] 						=					req.content.decode()
			assert data is not None, "could not connect"
			try:
				__snake_case     :				str 						=					eval(UpperCAmelCase_    )
			except Exception:
				__snake_case     :				Optional[Any] 						=					data.split('\n'    )
		req.close()
	return data
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[int]    )				->     Any:

	'''simple docstring'''





	__snake_case     :				str 						=					requests.get(UpperCAmelCase_    )
	__snake_case     :				Any 						=					np.array(Image.open(BytesIO(response.content    )    )    )
	return img
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     List[Any]:

	'''simple docstring'''





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

	'''simple docstring'''





	print(F"{os.path.abspath(os.path.join(UpperCAmelCase_     ,				os.pardir    )    )}/demo.ipynb"    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							Optional[int]="RGB"    )				->     int:

	'''simple docstring'''





	assert isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    )
	if os.path.isfile(UpperCAmelCase_    ):
		__snake_case     :				Any 						=					cva.imread(UpperCAmelCase_    )
	else:
		__snake_case     :				Union[str, Any] 						=					get_image_from_url(UpperCAmelCase_    )
		assert img is not None, F"could not connect to: {im}"
	__snake_case     :				int 						=					cva.cvtColor(UpperCAmelCase_     ,				cva.COLOR_BGR2RGB    )
	if input_format == "RGB":
		__snake_case     :				Optional[Any] 						=					img[:, :, ::-1]
	return img

def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[int]     ,				UpperCAmelCase_       :							Tuple=1    )				->     Union[str, Any]:

	'''simple docstring'''





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






"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





