

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							:						str     			=		True
except ImportError:
						lowerCAmelCase							:						List[str]     			=		False


try:
						from torch.hub import _get_torch_home

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

lowerCAmelCase							:						Any     			=		os.path.join(torch_cache_home, """transformers""")

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







def     A_   (					_UpperCAmelCase=OBJECTS       ,			_UpperCAmelCase=ATTRIBUTES					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				List[Any]							= []
						with open(__lowerCamelCase					) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(","					)[0].lower().strip()					)

						SCREAMING_SNAKE_CASE_:				Optional[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     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





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



class 						__lowercase       :




			"""simple docstring"""







			_UpperCAmelCase     :							int    							=  {}



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

									SCREAMING_SNAKE_CASE_:				str							= d



			def __repr__(				self						:  List[Any]):
									return str(list((self._pointer.keys())))



			def __setattr__(				self						:  Tuple     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				List[str]							= val
									SCREAMING_SNAKE_CASE_:				Any							= val
									SCREAMING_SNAKE_CASE_:				List[Any]							= key.split(".")
									SCREAMING_SNAKE_CASE_:				Optional[int]							= len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_:				str							= self._pointer
									if len(lowerCAmelCase__) > 1:
															for i, l in enumerate(lowerCAmelCase__):
																					if hasattr(self     ,		lowerCAmelCase__) and isinstance(getattr(self     ,		lowerCAmelCase__)     ,		lowerCAmelCase__):
																											setattr(getattr(self     ,		lowerCAmelCase__)     ,		".".join(levels[i:])     ,		lowerCAmelCase__)
																					if l == last_level:
																											SCREAMING_SNAKE_CASE_:				Optional[Any]							= val
																					else:
																											SCREAMING_SNAKE_CASE_:				Any							= pointer[l]



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									return self._pointer



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Union[str, Any]):
									with open(F"{file_name}"     ,		"w") as stream:
															dump(lowerCAmelCase__     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Union[str, Any]):
									with open(F"{file_name}"     ,		"w") as stream:
															json.dump(lowerCAmelCase__     ,		lowerCAmelCase__)



			@staticmethod
			def  _SCREAMING_SNAKE_CASE    (				lowerCAmelCase__						:  str):
									with open(lowerCAmelCase__) as stream:
															SCREAMING_SNAKE_CASE_:				int							= load(lowerCAmelCase__     ,		Loader=lowerCAmelCase__)
									return data



			def __str__(				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= "    "
									if self._name != "root":
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= F"{t * (self._level-1)}{self._name}:\n"
									else:
															SCREAMING_SNAKE_CASE_:				Any							= ""
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self._level
									for i, (k, v) in enumerate(self._pointer.items()):
															if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
																					r += F"{t * (self._level)}{v}\n"
																					self._level += 1
															else:
																					r += F"{t * (self._level)}{k}: {v} ({type(lowerCAmelCase__).__name__})\n"
															SCREAMING_SNAKE_CASE_:				str							= level
									return r[:-1]



			@classmethod
			def  _SCREAMING_SNAKE_CASE    (				cls						:  Any     ,		lowerCAmelCase__						:  str     ,		**lowerCAmelCase__						:  int):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Dict							= cls.get_config_dict(lowerCAmelCase__     ,		**lowerCAmelCase__)
									return cls(lowerCAmelCase__)




			@classmethod
			def  _SCREAMING_SNAKE_CASE    (				cls						:  Optional[int]     ,		lowerCAmelCase__						:  Any     ,		**lowerCAmelCase__						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				int							= kwargs.pop("cache_dir"     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= kwargs.pop("force_download"     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Dict							= kwargs.pop("resume_download"     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				int							= kwargs.pop("proxies"     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= kwargs.pop("local_files_only"     ,		lowerCAmelCase__)

									if os.path.isdir(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Dict							= os.path.join(lowerCAmelCase__     ,		lowerCAmelCase__)
									elif os.path.isfile(lowerCAmelCase__) or is_remote_url(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Tuple							= pretrained_model_name_or_path
									else:
															SCREAMING_SNAKE_CASE_:				Dict							= hf_bucket_url(lowerCAmelCase__     ,		filename=lowerCAmelCase__     ,		use_cdn=lowerCAmelCase__)

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

															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= Config.load_yaml(lowerCAmelCase__)

									except EnvironmentError:
															SCREAMING_SNAKE_CASE_:				Dict							= "Can't load config for"
															raise EnvironmentError(lowerCAmelCase__)

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

									return Config.load_yaml(lowerCAmelCase__), kwargs







def     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				str							= torch.load("dump.pt"       ,			map_location=in_tensor.device					)
						SCREAMING_SNAKE_CASE_:				Tuple							= in_tensor.numpy()
						SCREAMING_SNAKE_CASE_:				int							= out_tensor.numpy()[0]
						print(na.shape       ,			na[0, 0, :5]					)
						print(na.shape       ,			na[0, 0, :5]					)
						assert np.allclose(__lowerCamelCase       ,			__lowerCamelCase       ,			rtol=0.0_1       ,			atol=0.1					), (
						    f"{sum([1 for x in np.isclose(__lowerCamelCase       ,			__lowerCamelCase       ,			rtol=0.0_1       ,			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     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				str							= urlparse(__lowerCamelCase					)
						return parsed.scheme in ("http", "https")







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=True					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= "/" not in model_id
						if legacy_format:
												return f"{endpoint}/{model_id}-{filename}"
						else:
												return f"{endpoint}/{model_id}/{filename}"







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=0       ,			_UpperCAmelCase=None       ,			):

						'''simple docstring'''





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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=10       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=False       ,			):

						'''simple docstring'''





						if cache_dir is None:
												SCREAMING_SNAKE_CASE_:				Dict							= TRANSFORMERS_CACHE
						if isinstance(__lowerCamelCase       ,			__lowerCamelCase					):
												SCREAMING_SNAKE_CASE_:				Dict							= str(__lowerCamelCase					)

						os.makedirs(__lowerCamelCase       ,			exist_ok=__lowerCamelCase					)

						SCREAMING_SNAKE_CASE_:				List[Any]							= None
						if not local_files_only:
												try:
																		SCREAMING_SNAKE_CASE_:				Tuple							= requests.head(__lowerCamelCase       ,			allow_redirects=__lowerCamelCase       ,			proxies=__lowerCamelCase       ,			timeout=__lowerCamelCase					)
																		if response.status_code == 2_00:
																								SCREAMING_SNAKE_CASE_:				List[str]							= response.headers.get("ETag"					)
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						SCREAMING_SNAKE_CASE_:				Optional[int]							= url_to_filename(__lowerCamelCase       ,			__lowerCamelCase					)

						# get cache path to put the file
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= os.path.join(__lowerCamelCase       ,			__lowerCamelCase					)

						# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
						# try to get the last downloaded one
						if etag is None:
												if os.path.exists(__lowerCamelCase					):
																		return cache_path
												else:
																		SCREAMING_SNAKE_CASE_:				Optional[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.
						SCREAMING_SNAKE_CASE_:				Any							= cache_path + ".lock"
						with FileLock(__lowerCamelCase					):
												# If the download just completed while the lock was activated.
												if os.path.exists(__lowerCamelCase					) and not force_download:
																		# Even if returning early like here, the lock will be released.
																		return cache_path

												if resume_download:
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= cache_path + ".incomplete"

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

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

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

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

												os.replace(temp_file.name       ,			__lowerCamelCase					)

												SCREAMING_SNAKE_CASE_:				Any							= {"url": url, "etag": etag}
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= cache_path + ".json"
												with open(__lowerCamelCase       ,			"w"					) as meta_file:
																		json.dump(__lowerCamelCase       ,			__lowerCamelCase					)

						return cache_path







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=None					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Optional[int]							= url.encode("utf-8"					)
						SCREAMING_SNAKE_CASE_:				str							= shaaaa(__lowerCamelCase					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= url_hash.hexdigest()

						if etag:
												SCREAMING_SNAKE_CASE_:				List[Any]							= etag.encode("utf-8"					)
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= shaaaa(__lowerCamelCase					)
												filename += "." + etag_hash.hexdigest()

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

						return filename







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=False       ,			):

						'''simple docstring'''





						if cache_dir is None:
												SCREAMING_SNAKE_CASE_:				Dict							= TRANSFORMERS_CACHE
						if isinstance(__lowerCamelCase       ,			__lowerCamelCase					):
												SCREAMING_SNAKE_CASE_:				Tuple							= str(__lowerCamelCase					)
						if isinstance(__lowerCamelCase       ,			__lowerCamelCase					):
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= str(__lowerCamelCase					)

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

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

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

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

												# Prevent parallel extractions
												SCREAMING_SNAKE_CASE_:				str							= output_path + ".lock"
												with FileLock(__lowerCamelCase					):
																		shutil.rmtree(__lowerCamelCase       ,			ignore_errors=__lowerCamelCase					)
																		os.makedirs(__lowerCamelCase					)
																		if is_zipfile(__lowerCamelCase					):
																								with ZipFile(__lowerCamelCase       ,			"r"					) as zip_file:
																														zip_file.extractall(__lowerCamelCase					)
																														zip_file.close()
																		elif tarfile.is_tarfile(__lowerCamelCase					):
																								SCREAMING_SNAKE_CASE_:				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     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=","					):

						'''simple docstring'''





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







def     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Any							= requests.get(__lowerCamelCase					)
						SCREAMING_SNAKE_CASE_:				str							= np.array(Image.open(BytesIO(response.content					)					)					)
						return img







def     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





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







def     A_   (					):

						'''simple docstring'''





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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase="RGB"					):

						'''simple docstring'''





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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=1					):

						'''simple docstring'''





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









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






