

"""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__			:					str							  =					False


try:
					from torch.hub import _get_torch_home

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

lowerCamelCase__			:					Dict							  =					os.path.join(torch_cache_home, '''transformers''')

lowerCamelCase__			:					List[Any]							  =					"""https://cdn.huggingface.co"""
lowerCamelCase__			:					int							  =					"""https://s3.amazonaws.com/models.huggingface.co/bert"""
lowerCamelCase__			:					Optional[int]							  =					"""/""".join(str(Path(__file__).resolve()).split('''/''')[:-1])
lowerCamelCase__			:					int							  =					os.path.join(PATH, '''config.yaml''')
lowerCamelCase__			:					List[Any]							  =					os.path.join(PATH, '''attributes.txt''')
lowerCamelCase__			:					Union[str, Any]							  =					os.path.join(PATH, '''objects.txt''')
lowerCamelCase__			:					int							  =					os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
lowerCamelCase__			:					List[Any]							  =					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__			:					Union[str, Any]							  =					"""config.yaml"""
def  UpperCamelCase   (     _lowerCAmelCase       :  List[str]=OBJECTS,					_lowerCAmelCase       :  Union[str, Any]=ATTRIBUTES	)			->     Optional[int]:
	_UpperCAmelCase      :							Dict       =   []
	with open(_lowerCAmelCase	) as f:
		for object in f.readlines():
			vg_classes.append(object.split(""","""	)[0].lower().strip()	)

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






class       _UpperCAmelCase     :
			__a	:    Optional[Any]			   =							{}
			def __init__(						self ,						_A ,						_A = "root" ,						_A=0       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   name
				_UpperCAmelCase      :							Union[str, Any]       =   level
				_UpperCAmelCase      :							Optional[Any]       =   {}
				for k, v in dictionary.items():
					if v is None:
						raise ValueError()
					_UpperCAmelCase      :							Optional[int]       =   copy.deepcopy(snake_case__       )
					_UpperCAmelCase      :							int       =   copy.deepcopy(snake_case__       )
					if isinstance(snake_case__ ,						snake_case__       ):
						_UpperCAmelCase      :							Optional[Any]       =   Config(snake_case__ ,						name=snake_case__ ,						level=level + 1       )
					_UpperCAmelCase      :							List[Any]       =   v
					setattr(self ,						snake_case__ ,						snake_case__       )

				_UpperCAmelCase      :							int       =   d
			def __repr__(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return str(list((self._pointer.keys())       )       )
			def __setattr__(						self ,						_A ,						_A       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   val
				_UpperCAmelCase      :							Optional[Any]       =   val
				_UpperCAmelCase      :							Any       =   key.split("""."""       )
				_UpperCAmelCase      :							Optional[Any]       =   len(snake_case__       ) - 1
				_UpperCAmelCase      :							Dict       =   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      :							Optional[int]       =   val
						else:
							_UpperCAmelCase      :							Any       =   pointer[l]
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				return self._pointer
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      List[str]:






				'''simple docstring'''







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






				'''simple docstring'''







				with open(f'''{file_name}''' ,						"""w"""       ) as stream:
					json.dump(snake_case__ ,						snake_case__       )
			@staticmethod
			def 							__snake_case	(						_A       )      ->      Optional[int]:






				'''simple docstring'''







				with open(snake_case__       ) as stream:
					_UpperCAmelCase      :							Dict       =   load(snake_case__ ,						Loader=snake_case__       )
				return data
			def __str__(						self       )      ->      int:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[int]       =   cls.get_config_dict(snake_case__ ,						**snake_case__       )
				return cls(snake_case__       )
			@classmethod
			def 							__snake_case	(						cls ,						_A ,						**_A       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   kwargs.pop("""cache_dir""" ,						snake_case__       )
				_UpperCAmelCase      :							Optional[int]       =   kwargs.pop("""force_download""" ,						snake_case__       )
				_UpperCAmelCase      :							int       =   kwargs.pop("""resume_download""" ,						snake_case__       )
				_UpperCAmelCase      :							str       =   kwargs.pop("""proxies""" ,						snake_case__       )
				_UpperCAmelCase      :							Union[str, Any]       =   kwargs.pop("""local_files_only""" ,						snake_case__       )

				if os.path.isdir(snake_case__       ):
					_UpperCAmelCase      :							Dict       =   os.path.join(snake_case__ ,						snake_case__       )
				elif os.path.isfile(snake_case__       ) or is_remote_url(snake_case__       ):
					_UpperCAmelCase      :							Dict       =   pretrained_model_name_or_path
				else:
					_UpperCAmelCase      :							Optional[Any]       =   hf_bucket_url(snake_case__ ,						filename=snake_case__ ,						use_cdn=snake_case__       )

				try:
					# Load from URL or cache if already cached
					_UpperCAmelCase      :							List[Any]       =   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      :							int       =   Config.load_yaml(snake_case__       )

				except EnvironmentError:
					_UpperCAmelCase      :							List[Any]       =   """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  UpperCamelCase   (     _lowerCAmelCase       :  Any	)			->     List[Any]:
	_UpperCAmelCase      :							Any       =   torch.load("""dump.pt""",					map_location=in_tensor.device	)
	_UpperCAmelCase      :							Optional[Any]       =   in_tensor.numpy()
	_UpperCAmelCase      :							List[Any]       =   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       :  int	)			->     Optional[int]:
	_UpperCAmelCase      :							str       =   urlparse(_lowerCAmelCase	)
	return parsed.scheme in ("http", "https")
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  List[str]=True	)			->     List[str]:
	_UpperCAmelCase      :							Union[str, Any]       =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	_UpperCAmelCase      :							Any       =   """/""" not in model_id
	if legacy_format:
		return f'''{endpoint}/{model_id}-{filename}'''
	else:
		return f'''{endpoint}/{model_id}/{filename}'''
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  Tuple=None,					_lowerCAmelCase       :  Optional[Any]=0,					_lowerCAmelCase       :  Dict=None,					)			->     Union[str, Any]:
	_UpperCAmelCase      :							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
	_UpperCAmelCase      :							List[Any]       =   {"""user-agent""": ua}
	if resume_size > 0:
		_UpperCAmelCase      :							Tuple       =   """bytes=%d-""" % (resume_size,)
	_UpperCAmelCase      :							Tuple       =   requests.get(_lowerCAmelCase,					stream=_lowerCAmelCase,					proxies=_lowerCAmelCase,					headers=_lowerCAmelCase	)
	if response.status_code == 416:  # Range not satisfiable
		return
	_UpperCAmelCase      :							Optional[int]       =   response.headers.get("""Content-Length"""	)
	_UpperCAmelCase      :							str       =   resume_size + int(_lowerCAmelCase	) if content_length is not None else None
	_UpperCAmelCase      :							str       =   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       :  List[Any],					_lowerCAmelCase       :  Optional[int]=None,					_lowerCAmelCase       :  List[str]=False,					_lowerCAmelCase       :  Optional[Any]=None,					_lowerCAmelCase       :  Optional[int]=10,					_lowerCAmelCase       :  Optional[int]=False,					_lowerCAmelCase       :  Dict=None,					_lowerCAmelCase       :  Optional[int]=False,					)			->     str:
	if cache_dir is None:
		_UpperCAmelCase      :							Optional[Any]       =   TRANSFORMERS_CACHE
	if isinstance(_lowerCAmelCase,					_lowerCAmelCase	):
		_UpperCAmelCase      :							Optional[Any]       =   str(_lowerCAmelCase	)

	os.makedirs(_lowerCAmelCase,					exist_ok=_lowerCAmelCase	)

	_UpperCAmelCase      :							Optional[int]       =   None
	if not local_files_only:
		try:
			_UpperCAmelCase      :							Optional[Any]       =   requests.head(_lowerCAmelCase,					allow_redirects=_lowerCAmelCase,					proxies=_lowerCAmelCase,					timeout=_lowerCAmelCase	)
			if response.status_code == 200:
				_UpperCAmelCase      :							List[Any]       =   response.headers.get("""ETag"""	)
		except (EnvironmentError, requests.exceptions.Timeout):
			# etag is already None
			pass

	_UpperCAmelCase      :							Dict       =   url_to_filename(_lowerCAmelCase,					_lowerCAmelCase	)

	# get cache path to put the file
	_UpperCAmelCase      :							Dict       =   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:
			_UpperCAmelCase      :							str       =   [
			    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.
	_UpperCAmelCase      :							Dict       =   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:
			_UpperCAmelCase      :							Any       =   cache_path + """.incomplete"""

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

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

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

	return cache_path
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  List[Any]=None	)			->     Optional[int]:
	_UpperCAmelCase      :							Optional[Any]       =   url.encode("""utf-8"""	)
	_UpperCAmelCase      :							List[Any]       =   shaaaa(_lowerCAmelCase	)
	_UpperCAmelCase      :							List[Any]       =   url_hash.hexdigest()

	if etag:
		_UpperCAmelCase      :							Any       =   etag.encode("""utf-8"""	)
		_UpperCAmelCase      :							Tuple       =   shaaaa(_lowerCAmelCase	)
		filename += "." + etag_hash.hexdigest()

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

	return filename
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  Optional[Any]=None,					_lowerCAmelCase       :  List[Any]=False,					_lowerCAmelCase       :  Tuple=None,					_lowerCAmelCase       :  Optional[int]=False,					_lowerCAmelCase       :  Optional[Any]=None,					_lowerCAmelCase       :  Dict=False,					_lowerCAmelCase       :  Dict=False,					_lowerCAmelCase       :  str=False,					)			->     Tuple:
	if cache_dir is None:
		_UpperCAmelCase      :							Tuple       =   TRANSFORMERS_CACHE
	if isinstance(_lowerCAmelCase,					_lowerCAmelCase	):
		_UpperCAmelCase      :							Tuple       =   str(_lowerCAmelCase	)
	if isinstance(_lowerCAmelCase,					_lowerCAmelCase	):
		_UpperCAmelCase      :							Union[str, Any]       =   str(_lowerCAmelCase	)

	if is_remote_url(_lowerCAmelCase	):
		# URL, so get it from the cache (downloading if necessary)
		_UpperCAmelCase      :							Any       =   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.
		_UpperCAmelCase      :							Any       =   url_or_filename
	elif urlparse(_lowerCAmelCase	).scheme == "":
		# File, but it doesn't exist.
		raise EnvironmentError("""file {} not found""".format(_lowerCAmelCase	)	)
	else:
		# Something unknown
		raise ValueError("""unable to parse {} as a URL or as a local path""".format(_lowerCAmelCase	)	)

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

		# Path where we extract compressed archives
		# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
		_UpperCAmelCase  ,				_UpperCAmelCase      :							Tuple       =   os.path.split(_lowerCAmelCase	)
		_UpperCAmelCase      :							str       =   output_file.replace(""".""",					"""-"""	) + """-extracted"""
		_UpperCAmelCase      :							Optional[Any]       =   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
		_UpperCAmelCase      :							Union[str, Any]       =   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	):
				_UpperCAmelCase      :							List[Any]       =   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       :  Optional[Any],					_lowerCAmelCase       :  Optional[Any]=","	)			->     List[Any]:
	assert isinstance(_lowerCAmelCase,					_lowerCAmelCase	)
	if os.path.isfile(_lowerCAmelCase	):
		with open(_lowerCAmelCase	) as f:
			_UpperCAmelCase      :							str       =   eval(f.read()	)
	else:
		_UpperCAmelCase      :							List[str]       =   requests.get(_lowerCAmelCase	)
		try:
			_UpperCAmelCase      :							Optional[Any]       =   requests.json()
		except Exception:
			_UpperCAmelCase      :							Tuple       =   req.content.decode()
			assert data is not None, "could not connect"
			try:
				_UpperCAmelCase      :							Optional[Any]       =   eval(_lowerCAmelCase	)
			except Exception:
				_UpperCAmelCase      :							Union[str, Any]       =   data.split("""\n"""	)
		req.close()
	return data
def  UpperCamelCase   (     _lowerCAmelCase       :  List[str]	)			->     List[Any]:
	_UpperCAmelCase      :							str       =   requests.get(_lowerCAmelCase	)
	_UpperCAmelCase      :							Union[str, Any]       =   np.array(Image.open(BytesIO(response.content	)	)	)
	return img
def  UpperCamelCase   (     _lowerCAmelCase       :  str	)			->     Tuple:
	_UpperCAmelCase      :							List[str]       =   url.split("""/"""	)[-1]
	if fn not in os.listdir(os.getcwd()	):
		wget.download(_lowerCAmelCase	)
	with open(_lowerCAmelCase,					"""rb"""	) as stream:
		_UpperCAmelCase      :							Any       =   pkl.load(_lowerCAmelCase	)
	_UpperCAmelCase      :							Any       =   weights.pop("""model"""	)
	_UpperCAmelCase      :							Tuple       =   {}
	for k, v in model.items():
		_UpperCAmelCase      :							Union[str, Any]       =   torch.from_numpy(_lowerCAmelCase	)
		if "running_var" in k:
			_UpperCAmelCase      :							Optional[int]       =   torch.tensor([0]	)
			_UpperCAmelCase      :							Optional[Any]       =   k.replace("""running_var""",					"""num_batches_tracked"""	)
			_UpperCAmelCase      :							Any       =   zero
	return new
def  UpperCamelCase   (     )			->     str:
	print(f'''{os.path.abspath(os.path.join(_lowerCAmelCase,					os.pardir	)	)}/demo.ipynb'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  str="RGB"	)			->     Optional[int]:
	assert isinstance(_lowerCAmelCase,					_lowerCAmelCase	)
	if os.path.isfile(_lowerCAmelCase	):
		_UpperCAmelCase      :							List[Any]       =   cva.imread(_lowerCAmelCase	)
	else:
		_UpperCAmelCase      :							Tuple       =   get_image_from_url(_lowerCAmelCase	)
		assert img is not None, f'''could not connect to: {im}'''
	_UpperCAmelCase      :							int       =   cva.cvtColor(_lowerCAmelCase,					cva.COLOR_BGR2RGB	)
	if input_format == "RGB":
		_UpperCAmelCase      :							Any       =   img[:, :, ::-1]
	return img





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








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  float,					_lowerCAmelCase       :  float,					_lowerCAmelCase       :  float	)			->     float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative"""	)
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer"""	)
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





