
"""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__           =	True
except ImportError:
							lowerCamelCase__           =	False


try:
							from torch.hub import _get_torch_home

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

lowerCamelCase__           =	os.path.join(torch_cache_home, """transformers""")

lowerCamelCase__           =	"""https://cdn.huggingface.co"""
lowerCamelCase__           =	"""https://s3.amazonaws.com/models.huggingface.co/bert"""
lowerCamelCase__           =	"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
lowerCamelCase__           =	os.path.join(PATH, """config.yaml""")
lowerCamelCase__           =	os.path.join(PATH, """attributes.txt""")
lowerCamelCase__           =	os.path.join(PATH, """objects.txt""")
lowerCamelCase__           =	os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
lowerCamelCase__           =	os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
lowerCamelCase__           =	os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
lowerCamelCase__           =	"""pytorch_model.bin"""
lowerCamelCase__           =	"""config.yaml"""
def 					__lowerCAmelCase       (_UpperCamelCase=OBJECTS ,						_UpperCamelCase=ATTRIBUTES	):
	__lowerCAmelCase    :		Dict		    =   []
	with open(_UpperCamelCase	) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','	)[0].lower().strip()	)

	__lowerCAmelCase    :		str		    =   []
	with open(_UpperCamelCase	) as f:
		for object in f.readlines():
			vg_attrs.append(object.split(','	)[0].lower().strip()	)
	return vg_classes, vg_attrs
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   OrderedDict()
	with open(_UpperCamelCase ,						'rb'	) as f:
		__lowerCAmelCase    :		Union[str, Any]		    =   pkl.load(_UpperCamelCase	)['model']
	for k in copy.deepcopy(list(ckp.keys()	)	):
		__lowerCAmelCase    :		str		    =   ckp.pop(_UpperCamelCase	)
		if isinstance(_UpperCamelCase ,						np.ndarray	):
			__lowerCAmelCase    :		Dict		    =   torch.tensor(_UpperCamelCase	)
		else:
			assert isinstance(_UpperCamelCase ,						torch.tensor	), type(_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   v
	return r




class 				A__ :
		A_		: str            =			{}

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

			__lowerCAmelCase    :		int		    =   d

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

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

		def 							__lowerCamelCase							(   self				):
			return self._pointer

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			with open(f"{file_name}" ,	'w'				) as stream:
				dump(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			with open(f"{file_name}" ,	'w'				) as stream:
				json.dump(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE				):
			with open(_SCREAMING_SNAKE_CASE				) as stream:
				__lowerCAmelCase    :		Dict		    =   load(_SCREAMING_SNAKE_CASE ,	Loader=_SCREAMING_SNAKE_CASE				)
			return data

		def __str__(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   '    '
			if self._name != "root":
				__lowerCAmelCase    :		List[Any]		    =   f"{t * (self._level-1)}{self._name}:\n"
			else:
				__lowerCAmelCase    :		Union[str, Any]		    =   ''
			__lowerCAmelCase    :		List[Any]		    =   self._level
			for i, (k, v) in enumerate(self._pointer.items()				):
				if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					r += f"{t * (self._level)}{v}\n"
					self._level += 1
				else:
					r += f"{t * (self._level)}{k}: {v} ({type(_SCREAMING_SNAKE_CASE				).__name__})\n"
				__lowerCAmelCase    :		Optional[Any]		    =   level
			return r[:-1]

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   cls.get_config_dict(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
			return cls(_SCREAMING_SNAKE_CASE				)







		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   kwargs.pop('cache_dir' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   kwargs.pop('force_download' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   kwargs.pop('resume_download' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   kwargs.pop('proxies' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   kwargs.pop('local_files_only' ,	_SCREAMING_SNAKE_CASE				)

			if os.path.isdir(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		List[str]		    =   os.path.join(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			elif os.path.isfile(_SCREAMING_SNAKE_CASE				) or is_remote_url(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   pretrained_model_name_or_path
			else:
				__lowerCAmelCase    :		Union[str, Any]		    =   hf_bucket_url(_SCREAMING_SNAKE_CASE ,	filename=_SCREAMING_SNAKE_CASE ,	use_cdn=_SCREAMING_SNAKE_CASE				)

			try:
				# Load from URL or cache if already cached
				__lowerCAmelCase    :		Optional[int]		    =   cached_path(
				    _SCREAMING_SNAKE_CASE ,	cache_dir=_SCREAMING_SNAKE_CASE ,	force_download=_SCREAMING_SNAKE_CASE ,	proxies=_SCREAMING_SNAKE_CASE ,	resume_download=_SCREAMING_SNAKE_CASE ,	local_files_only=_SCREAMING_SNAKE_CASE ,	)
				# Load config dict
				if resolved_config_file is None:
					raise EnvironmentError

				__lowerCAmelCase    :		Tuple		    =   Config.load_yaml(_SCREAMING_SNAKE_CASE				)

			except EnvironmentError:
				__lowerCAmelCase    :		str		    =   'Can\'t load config for'
				raise EnvironmentError(_SCREAMING_SNAKE_CASE				)

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

			return Config.load_yaml(_SCREAMING_SNAKE_CASE				), kwargs



def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   torch.load('dump.pt' ,						map_location=in_tensor.device	)
	__lowerCAmelCase    :		Tuple		    =   in_tensor.numpy()
	__lowerCAmelCase    :		Dict		    =   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()	)*100:.4f} %"
	    " element-wise mismatch"
	)
	raise Exception('tensors are all good'	)

	# Hugging face functions below
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   urlparse(_UpperCamelCase	)
	return parsed.scheme in ("http", "https")
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=True	):
	__lowerCAmelCase    :		List[str]		    =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	__lowerCAmelCase    :		Optional[int]		    =   '/' not in model_id
	if legacy_format:
		return F"{endpoint}/{model_id}-{filename}"
	else:
		return F"{endpoint}/{model_id}/{filename}"
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=0 ,						_UpperCamelCase=None ,						):
	__lowerCAmelCase    :		Union[str, Any]		    =   '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
	__lowerCAmelCase    :		Optional[Any]		    =   {'user-agent': ua}
	if resume_size > 0:
		__lowerCAmelCase    :		Union[str, Any]		    =   'bytes=%d-' % (resume_size,)
	__lowerCAmelCase    :		int		    =   requests.get(_UpperCamelCase ,						stream=_UpperCamelCase ,						proxies=_UpperCamelCase ,						headers=_UpperCamelCase	)
	if response.status_code == 416:  # Range not satisfiable
		return
	__lowerCAmelCase    :		Dict		    =   response.headers.get('Content-Length'	)
	__lowerCAmelCase    :		int		    =   resume_size + int(_UpperCamelCase	) if content_length is not None else None
	__lowerCAmelCase    :		Union[str, Any]		    =   tqdm(
	    unit='B' ,						unit_scale=_UpperCamelCase ,						total=_UpperCamelCase ,						initial=_UpperCamelCase ,						desc='Downloading' ,						)
	for chunk in response.iter_content(chunk_size=1024	):
		if chunk:  # filter out keep-alive new chunks
			progress.update(len(_UpperCamelCase	)	)
			temp_file.write(_UpperCamelCase	)
	progress.close()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=10 ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						):
	if cache_dir is None:
		__lowerCAmelCase    :		Any		    =   TRANSFORMERS_CACHE
	if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   str(_UpperCamelCase	)

	os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   None
	if not local_files_only:
		try:
			__lowerCAmelCase    :		Optional[Any]		    =   requests.head(_UpperCamelCase ,						allow_redirects=_UpperCamelCase ,						proxies=_UpperCamelCase ,						timeout=_UpperCamelCase	)
			if response.status_code == 200:
				__lowerCAmelCase    :		Tuple		    =   response.headers.get('ETag'	)
		except (EnvironmentError, requests.exceptions.Timeout):
			# etag is already None
			pass

	__lowerCAmelCase    :		Optional[int]		    =   url_to_filename(_UpperCamelCase ,						_UpperCamelCase	)

	# get cache path to put the file
	__lowerCAmelCase    :		List[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:
			__lowerCAmelCase    :		List[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.
	__lowerCAmelCase    :		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:
			__lowerCAmelCase    :		List[str]		    =   cache_path + '.incomplete'

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

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

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

	return cache_path
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None	):
	__lowerCAmelCase    :		int		    =   url.encode('utf-8'	)
	__lowerCAmelCase    :		str		    =   shaaaa(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   url_hash.hexdigest()

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

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

	return filename
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=None ,						_UpperCamelCase=False ,						_UpperCamelCase=False ,						_UpperCamelCase=False ,						):
	if cache_dir is None:
		__lowerCAmelCase    :		List[Any]		    =   TRANSFORMERS_CACHE
	if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   str(_UpperCamelCase	)
	if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   str(_UpperCamelCase	)

	if is_remote_url(_UpperCamelCase	):
		# URL, so get it from the cache (downloading if necessary)
		__lowerCAmelCase    :		Optional[Any]		    =   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.
		__lowerCAmelCase    :		Optional[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/"
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   os.path.split(_UpperCamelCase	)
		__lowerCAmelCase    :		Optional[int]		    =   output_file.replace('.' ,						'-'	) + '-extracted'
		__lowerCAmelCase    :		Optional[Any]		    =   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
		__lowerCAmelCase    :		Union[str, Any]		    =   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	):
				__lowerCAmelCase    :		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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=","	):
	assert isinstance(_UpperCamelCase ,						_UpperCamelCase	)
	if os.path.isfile(_UpperCamelCase	):
		with open(_UpperCamelCase	) as f:
			__lowerCAmelCase    :		Optional[Any]		    =   eval(f.read()	)
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   requests.get(_UpperCamelCase	)
		try:
			__lowerCAmelCase    :		Union[str, Any]		    =   requests.json()
		except Exception:
			__lowerCAmelCase    :		List[Any]		    =   req.content.decode()
			assert data is not None, "could not connect"
			try:
				__lowerCAmelCase    :		str		    =   eval(_UpperCamelCase	)
			except Exception:
				__lowerCAmelCase    :		int		    =   data.split('\n'	)
		req.close()
	return data
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   requests.get(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   np.array(Image.open(BytesIO(response.content	)	)	)
	return img
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   url.split('/'	)[-1]
	if fn not in os.listdir(os.getcwd()	):
		wget.download(_UpperCamelCase	)
	with open(_UpperCamelCase ,						'rb'	) as stream:
		__lowerCAmelCase    :		int		    =   pkl.load(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   weights.pop('model'	)
	__lowerCAmelCase    :		Dict		    =   {}
	for k, v in model.items():
		__lowerCAmelCase    :		List[str]		    =   torch.from_numpy(_UpperCamelCase	)
		if "running_var" in k:
			__lowerCAmelCase    :		List[str]		    =   torch.tensor([0]	)
			__lowerCAmelCase    :		Optional[int]		    =   k.replace('running_var' ,						'num_batches_tracked'	)
			__lowerCAmelCase    :		Dict		    =   zero
	return new
def 					__lowerCAmelCase       ():
	print(F"{os.path.abspath(os.path.join(_UpperCamelCase ,						os.pardir	)	)}/demo.ipynb"	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase="RGB"	):
	assert isinstance(_UpperCamelCase ,						_UpperCamelCase	)
	if os.path.isfile(_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   cva.imread(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		Any		    =   get_image_from_url(_UpperCamelCase	)
		assert img is not None, F"could not connect to: {im}"
	__lowerCAmelCase    :		Optional[int]		    =   cva.cvtColor(_UpperCamelCase ,						cva.COLOR_BGR2RGB	)
	if input_format == "RGB":
		__lowerCAmelCase    :		Optional[int]		    =   img[:, :, ::-1]
	return img
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=1	):
	return (images[i : i + batch] for i in range(0 ,						len(_UpperCamelCase	) ,						_UpperCamelCase	))

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()