





'''simple docstring'''

import math







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_ = 0		,							lowerCAmelCase_ = 0							):

    """simple docstring"""






    lowercase         = end or len(lowerCAmelCase_							)
    for i in range(lowerCAmelCase_		,							lowerCAmelCase_							):
        lowercase         = i
        lowercase         = array[i]
        while temp_index != start and temp_index_value < array[temp_index - 1]:
            lowercase         = array[temp_index - 1]
            temp_index -= 1
        lowercase         = temp_index_value
    return array







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):  # Max Heap

    """simple docstring"""






    lowercase         = index
    lowercase         = 2 * index + 1  # Left Node
    lowercase         = 2 * index + 2  # Right Node

    if left_index < heap_size and array[largest] < array[left_index]:
        lowercase         = left_index

    if right_index < heap_size and array[largest] < array[right_index]:
        lowercase         = right_index

    if largest != index:
        lowercase   ,							lowercase         = array[largest], array[index]
        heapify(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = len(lowerCAmelCase_							)

    for i in range(n // 2		,							-1		,							-1							):
        heapify(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)

    for i in range(n - 1		,							0		,							-1							):
        lowercase   ,							lowercase         = array[0], array[i]
        heapify(lowerCAmelCase_		,							0		,							lowerCAmelCase_							)

    return array







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    if (array[first_index] > array[middle_index]) != (
        array[first_index] > array[last_index]
    ):
        return array[first_index]
    elif (array[middle_index] > array[first_index]) != (
        array[middle_index] > array[last_index]
    ):
        return array[middle_index]
    else:
        return array[last_index]







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = low
    lowercase         = high
    while True:
        while array[i] < pivot:
            i += 1
        j -= 1
        while pivot < array[j]:
            j -= 1
        if i >= j:
            return i
        lowercase   ,							lowercase         = array[j], array[i]
        i += 1







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    if len(lowerCAmelCase_							) == 0:
        return array
    lowercase         = 2 * math.ceil(math.loga(len(lowerCAmelCase_							)							)							)
    lowercase         = 16
    return intro_sort(lowerCAmelCase_		,							0		,							len(lowerCAmelCase_							)		,							lowerCAmelCase_		,							lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    while end - start > size_threshold:
        if max_depth == 0:
            return heap_sort(lowerCAmelCase_							)
        max_depth -= 1
        lowercase         = median_of_a(lowerCAmelCase_		,							lowerCAmelCase_		,							start + ((end - start) // 2) + 1		,							end - 1							)
        lowercase         = partition(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
        intro_sort(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
        lowercase         = p
    return insertion_sort(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __lowerCamelCase : List[str]											=			input("Enter numbers separated by a comma : ").strip()
   __lowerCamelCase : List[str]											=			[float(item) for item in user_input.split(",")]
   print(sort(unsorted))











'''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

			__lowerCamelCase : Optional[Any]											=			True
except ImportError:
			__lowerCamelCase : Dict											=			False


try:
			from torch.hub import _get_torch_home

			__lowerCamelCase : int											=			_get_torch_home()
except ImportError:
			__lowerCamelCase : int											=			os.path.expanduser(
			    os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
			)

__lowerCamelCase : Tuple											=			os.path.join(torch_cache_home, "transformers")

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







def 							UpperCAmelCase_    (	lowerCAmelCase_=OBJECTS		,							lowerCAmelCase_=ATTRIBUTES							):

				"""simple docstring"""






				lowercase         = []
				with open(lowerCAmelCase_							) as f:
								for object in f.readlines():
												vg_classes.append(object.split(","							)[0].lower().strip()							)

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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = OrderedDict()
				with open(lowerCAmelCase_		,							"rb"							) as f:
								lowercase         = pkl.load(lowerCAmelCase_							)["model"]
				for k in copy.deepcopy(list(ckp.keys()							)							):
								lowercase         = ckp.pop(lowerCAmelCase_							)
								if isinstance(lowerCAmelCase_		,							np.ndarray							):
												lowercase         = torch.tensor(lowerCAmelCase_							)
								else:
												assert isinstance(lowerCAmelCase_		,							torch.tensor							), type(lowerCAmelCase_							)
								lowercase         = v
				return r

class       UpperCAmelCase :
		UpperCAmelCase					:	int 			=		{}




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

						lowercase         = d




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




		def __setattr__(self      :	str							,			A__      :	List[str]							,			A__      :	Dict				)					-> int:
						lowercase         = val
						lowercase         = val
						lowercase         = key.split("."				)
						lowercase         = len(A__				) - 1
						lowercase         = 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:
																		lowercase         = val
														else:
																		lowercase         = pointer[l]




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Any:
						return self._pointer




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[int]							,			A__      :	int				)					-> int:
						with open(f'{file_name}'							,			"w"				) as stream:
										dump(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[int]							,			A__      :	List[str]				)					-> List[str]:
						with open(f'{file_name}'							,			"w"				) as stream:
										json.dump(A__							,			A__				)




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	Tuple				)					-> Optional[int]:
						with open(A__				) as stream:
										lowercase         = load(A__							,			Loader=A__				)
						return data




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




		@classmethod
		def 				UpperCAmelCase__   (cls      :	Any							,			A__      :	str							,			**A__      :	Optional[Any]				)					-> Optional[int]:
						lowercase   ,							lowercase         = cls.get_config_dict(A__							,			**A__				)
						return cls(A__				)





		@classmethod
		def 				UpperCAmelCase__   (cls      :	List[str]							,			A__      :	str							,			**A__      :	Optional[int]				)					-> int:
						lowercase         = kwargs.pop("cache_dir"							,			A__				)
						lowercase         = kwargs.pop("force_download"							,			A__				)
						lowercase         = kwargs.pop("resume_download"							,			A__				)
						lowercase         = kwargs.pop("proxies"							,			A__				)
						lowercase         = kwargs.pop("local_files_only"							,			A__				)

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

						try:
										# Load from URL or cache if already cached
										lowercase         = 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

										lowercase         = Config.load_yaml(A__				)

						except EnvironmentError:
										lowercase         = "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_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.load("dump.pt"		,							map_location=in_tensor.device							)
				lowercase         = in_tensor.numpy()
				lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = urlparse(lowerCAmelCase_							)
				return parsed.scheme in ("http", "https")







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=True							):

				"""simple docstring"""






				lowercase         = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				lowercase         = "/" not in model_id
				if legacy_format:
								return f'{endpoint}/{model_id}-{filename}'
				else:
								return f'{endpoint}/{model_id}/{filename}'







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=0		,							lowerCAmelCase_=None		,							):

				"""simple docstring"""






				lowercase         = "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
				lowercase         = {"user-agent": ua}
				if resume_size > 0:
								lowercase         = "bytes=%d-" % (resume_size,)
				lowercase         = requests.get(lowerCAmelCase_		,							stream=lowerCAmelCase_		,							proxies=lowerCAmelCase_		,							headers=lowerCAmelCase_							)
				if response.status_code == 416:  # Range not satisfiable
								return
				lowercase         = response.headers.get("Content-Length"							)
				lowercase         = resume_size + int(lowerCAmelCase_							) if content_length is not None else None
				lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=10		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							):

				"""simple docstring"""






				if cache_dir is None:
								lowercase         = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)

				os.makedirs(lowerCAmelCase_		,							exist_ok=lowerCAmelCase_							)

				lowercase         = None
				if not local_files_only:
								try:
												lowercase         = requests.head(lowerCAmelCase_		,							allow_redirects=lowerCAmelCase_		,							proxies=lowerCAmelCase_		,							timeout=lowerCAmelCase_							)
												if response.status_code == 200:
																lowercase         = response.headers.get("ETag"							)
								except (EnvironmentError, requests.exceptions.Timeout):
												# etag is already None
												pass

				lowercase         = url_to_filename(lowerCAmelCase_		,							lowerCAmelCase_							)

				# get cache path to put the file
				lowercase         = 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:
												lowercase         = [
												    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.
				lowercase         = 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:
												lowercase         = cache_path + ".incomplete"

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

												lowercase         = _resumable_file_manager
												if os.path.exists(lowerCAmelCase_							):
																lowercase         = os.stat(lowerCAmelCase_							).st_size
												else:
																lowercase         = 0
								else:
												lowercase         = partial(tempfile.NamedTemporaryFile		,							dir=lowerCAmelCase_		,							delete=lowerCAmelCase_							)
												lowercase         = 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_							)

								lowercase         = {"url": url, "etag": etag}
								lowercase         = cache_path + ".json"
								with open(lowerCAmelCase_		,							"w"							) as meta_file:
												json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)

				return cache_path







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None							):

				"""simple docstring"""






				lowercase         = url.encode("utf-8"							)
				lowercase         = shaaaa(lowerCAmelCase_							)
				lowercase         = url_hash.hexdigest()

				if etag:
								lowercase         = etag.encode("utf-8"							)
								lowercase         = shaaaa(lowerCAmelCase_							)
								filename += "." + etag_hash.hexdigest()

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

				return filename







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=False		,							lowerCAmelCase_=False		,							):

				"""simple docstring"""






				if cache_dir is None:
								lowercase         = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)

				if is_remote_url(lowerCAmelCase_							):
								# URL, so get it from the cache (downloading if necessary)
								lowercase         = 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.
								lowercase         = 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/"
								lowercase   ,							lowercase         = os.path.split(lowerCAmelCase_							)
								lowercase         = output_file.replace("."		,							"-"							) + "-extracted"
								lowercase         = 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
								lowercase         = 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_							):
																lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=","							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = requests.get(lowerCAmelCase_							)
				lowercase         = np.array(Image.open(BytesIO(response.content							)							)							)
				return img







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_="RGB"							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=1							):

				"""simple docstring"""






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




