



'''simple docstring'''

import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
		import torch

		_A					:     Tuple      =True
except ImportError:
		_A					:     Optional[int]      =False


try:
		from torch.hub import _get_torch_home

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

_A					:     Tuple      =os.path.join(torch_cache_home, '''transformers''')

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







def     __UpperCamelCase					(				_lowercase=OBJECTS,					_lowercase=ATTRIBUTES       )			->      Optional[int]:
	_lowercase       :							Dict  	=		[]
	with open(_lowercase       ) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','       )[0].lower().strip()       )

	_lowercase       :							Optional[int]  	=		[]
	with open(_lowercase       ) as f:
		for object in f.readlines():
			vg_attrs.append(object.split(','       )[0].lower().strip()       )
	return vg_classes, vg_attrs







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



class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    {}




					def __init__(	self					:						Any  ,	UpperCamelCase_					:						dict  ,	UpperCamelCase_					:						str = "root"  ,	UpperCamelCase_					:						str=0       )	->							Tuple:



						'''simple docstring'''





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

						_lowercase       :							str  	=		d




					def __repr__(	self					:						Union[str, Any]       )	->							Any:



						'''simple docstring'''





						return str(list((self._pointer.keys())       )       )




					def __setattr__(	self					:						Any  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Union[str, Any]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[int]       )	->							List[str]:



						'''simple docstring'''





						return self._pointer




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[Any]  ,	UpperCamelCase_					:						Any       )	->							int:



						'''simple docstring'''





						with open(F'''{file_name}'''  ,	'w'       ) as stream:
							dump(UpperCamelCase_  ,	UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						List[Any]  ,	UpperCamelCase_					:						Dict       )	->							int:



						'''simple docstring'''





						with open(F'''{file_name}'''  ,	'w'       ) as stream:
							json.dump(UpperCamelCase_  ,	UpperCamelCase_       )




					@staticmethod
					def     __UpperCAmelCase  (	UpperCamelCase_					:						Any       )	->							Union[str, Any]:



						'''simple docstring'''





						with open(UpperCamelCase_       ) as stream:
							_lowercase       :							Tuple  	=		load(UpperCamelCase_  ,	Loader=UpperCamelCase_       )
						return data




					def __str__(	self					:						Dict       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Optional[Any]  	=		'    '
						if self._name != "root":
							_lowercase       :							Any  	=		F'''{t * (self._level-1)}{self._name}:\n'''
						else:
							_lowercase       :							Optional[int]  	=		''
						_lowercase       :							int  	=		self._level
						for i, (k, v) in enumerate(self._pointer.items()       ):
							if isinstance(UpperCamelCase_  ,	UpperCamelCase_       ):
								r += F'''{t * (self._level)}{v}\n'''
								self._level += 1
							else:
								r += F'''{t * (self._level)}{k}: {v} ({type(UpperCamelCase_       ).__name__})\n'''
							_lowercase       :							Optional[Any]  	=		level
						return r[:-1]




					@classmethod
					def     __UpperCAmelCase  (	cls					:						Optional[Any]  ,	UpperCamelCase_					:						str  ,	**UpperCamelCase_					:						Optional[int]       )	->							Any:



						'''simple docstring'''





						_lowercase	,     _lowercase       :							Optional[int]  	=		cls.get_config_dict(UpperCamelCase_  ,	**UpperCamelCase_       )
						return cls(UpperCamelCase_       )




					@classmethod
					def     __UpperCAmelCase  (	cls					:						Optional[Any]  ,	UpperCamelCase_					:						str  ,	**UpperCamelCase_					:						Dict       )	->							int:



						'''simple docstring'''





						_lowercase       :							Union[str, Any]  	=		kwargs.pop('cache_dir'  ,	UpperCamelCase_       )
						_lowercase       :							Optional[Any]  	=		kwargs.pop('force_download'  ,	UpperCamelCase_       )
						_lowercase       :							Any  	=		kwargs.pop('resume_download'  ,	UpperCamelCase_       )
						_lowercase       :							int  	=		kwargs.pop('proxies'  ,	UpperCamelCase_       )
						_lowercase       :							int  	=		kwargs.pop('local_files_only'  ,	UpperCamelCase_       )

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

						try:
							# Load from URL or cache if already cached
							_lowercase       :							List[str]  	=		cached_path(
							    UpperCamelCase_  ,	cache_dir=UpperCamelCase_  ,	force_download=UpperCamelCase_  ,	proxies=UpperCamelCase_  ,	resume_download=UpperCamelCase_  ,	local_files_only=UpperCamelCase_  ,	)
							# Load config dict
							if resolved_config_file is None:
								raise EnvironmentError

							_lowercase       :							Dict  	=		Config.load_yaml(UpperCamelCase_       )

						except EnvironmentError:
							_lowercase       :							List[str]  	=		'Can\'t load config for'
							raise EnvironmentError(UpperCamelCase_       )

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

						return Config.load_yaml(UpperCamelCase_       ), kwargs







def     __UpperCamelCase					(				_lowercase       )			->      Optional[Any]:
	_lowercase       :							Optional[int]  	=		torch.load('dump.pt',					map_location=in_tensor.device       )
	_lowercase       :							List[str]  	=		in_tensor.numpy()
	_lowercase       :							Union[str, Any]  	=		out_tensor.numpy()[0]
	print(na.shape,					na[0, 0, :5]       )
	print(na.shape,					na[0, 0, :5]       )
	assert np.allclose(_lowercase,					_lowercase,					rtol=0.0_1,					atol=0.1       ), (
	    f'''{sum([1 for x in np.isclose(_lowercase,					_lowercase,					rtol=0.0_1,					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					(				_lowercase       )			->      str:
	_lowercase       :							Optional[Any]  	=		urlparse(_lowercase       )
	return parsed.scheme in ("http", "https")







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase=True       )			->      str:
	_lowercase       :							List[str]  	=		CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	_lowercase       :							List[str]  	=		'/' not in model_id
	if legacy_format:
		return f'''{endpoint}/{model_id}-{filename}'''
	else:
		return f'''{endpoint}/{model_id}/{filename}'''







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase=None,					_lowercase=0,					_lowercase=None,					)			->      Dict:
	_lowercase       :							List[Any]  	=		'python/{}'.format(sys.version.split()[0]       )
	if _torch_available:
		ua += "; torch/{}".format(torch.__version__       )
	if isinstance(_lowercase,					_lowercase       ):
		ua += "; " + "; ".join('{}/{}'.format(_lowercase,					_lowercase       ) for k, v in user_agent.items()       )
	elif isinstance(_lowercase,					_lowercase       ):
		ua += "; " + user_agent
	_lowercase       :							List[str]  	=		{'user-agent': ua}
	if resume_size > 0:
		_lowercase       :							List[Any]  	=		'bytes=%d-' % (resume_size,)
	_lowercase       :							List[Any]  	=		requests.get(_lowercase,					stream=_lowercase,					proxies=_lowercase,					headers=_lowercase       )
	if response.status_code == 416:  # Range not satisfiable
		return
	_lowercase       :							str  	=		response.headers.get('Content-Length'       )
	_lowercase       :							str  	=		resume_size + int(_lowercase       ) if content_length is not None else None
	_lowercase       :							Any  	=		tqdm(
	    unit='B',					unit_scale=_lowercase,					total=_lowercase,					initial=_lowercase,					desc='Downloading',					)
	for chunk in response.iter_content(chunk_size=1024       ):
		if chunk:  # filter out keep-alive new chunks
			progress.update(len(_lowercase       )       )
			temp_file.write(_lowercase       )
	progress.close()







def     __UpperCamelCase					(				_lowercase,					_lowercase=None,					_lowercase=False,					_lowercase=None,					_lowercase=10,					_lowercase=False,					_lowercase=None,					_lowercase=False,					)			->      Tuple:
	if cache_dir is None:
		_lowercase       :							List[str]  	=		TRANSFORMERS_CACHE
	if isinstance(_lowercase,					_lowercase       ):
		_lowercase       :							Dict  	=		str(_lowercase       )

	os.makedirs(_lowercase,					exist_ok=_lowercase       )

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

	_lowercase       :							Dict  	=		url_to_filename(_lowercase,					_lowercase       )

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

	# 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(_lowercase       ):
			return cache_path
		else:
			_lowercase       :							str  	=		[
			    file
			    for file in fnmatch.filter(os.listdir(_lowercase       ),					filename + '.*'       )
			    if not file.endswith('.json'       ) and not file.endswith('.lock'       )
			]
			if len(_lowercase       ) > 0:
				return os.path.join(_lowercase,					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(_lowercase       ) and not force_download:
		return cache_path

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

		if resume_download:
			_lowercase       :							Optional[Any]  	=		cache_path + '.incomplete'

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

			_lowercase       :							int  	=		_resumable_file_manager
			if os.path.exists(_lowercase       ):
				_lowercase       :							Union[str, Any]  	=		os.stat(_lowercase       ).st_size
			else:
				_lowercase       :							int  	=		0
		else:
			_lowercase       :							str  	=		partial(tempfile.NamedTemporaryFile,					dir=_lowercase,					delete=_lowercase       )
			_lowercase       :							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',					_lowercase,					temp_file.name,					)

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

		os.replace(temp_file.name,					_lowercase       )

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

	return cache_path







def     __UpperCamelCase					(				_lowercase,					_lowercase=None       )			->      List[Any]:
	_lowercase       :							Optional[Any]  	=		url.encode('utf-8'       )
	_lowercase       :							Optional[int]  	=		shaaaa(_lowercase       )
	_lowercase       :							Dict  	=		url_hash.hexdigest()

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

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

	return filename







def     __UpperCamelCase					(				_lowercase,					_lowercase=None,					_lowercase=False,					_lowercase=None,					_lowercase=False,					_lowercase=None,					_lowercase=False,					_lowercase=False,					_lowercase=False,					)			->      Optional[int]:
	if cache_dir is None:
		_lowercase       :							List[str]  	=		TRANSFORMERS_CACHE
	if isinstance(_lowercase,					_lowercase       ):
		_lowercase       :							List[str]  	=		str(_lowercase       )
	if isinstance(_lowercase,					_lowercase       ):
		_lowercase       :							Optional[int]  	=		str(_lowercase       )

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

	if extract_compressed_file:
		if not is_zipfile(_lowercase       ) and not tarfile.is_tarfile(_lowercase       ):
			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       :							Union[str, Any]  	=		os.path.split(_lowercase       )
		_lowercase       :							Optional[Any]  	=		output_file.replace('.',					'-'       ) + '-extracted'
		_lowercase       :							Dict  	=		os.path.join(_lowercase,					_lowercase       )

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

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

		return output_path_extracted

	return output_path







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







def     __UpperCamelCase					(				_lowercase       )			->      List[str]:
	_lowercase       :							Dict  	=		requests.get(_lowercase       )
	_lowercase       :							Optional[int]  	=		np.array(Image.open(BytesIO(response.content       )       )       )
	return img







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







def     __UpperCamelCase					(				)			->      Dict:
	print(f'''{os.path.abspath(os.path.join(_lowercase,					os.pardir       )       )}/demo.ipynb'''       )







def     __UpperCamelCase					(				_lowercase,					_lowercase="RGB"       )			->      Optional[Any]:
	assert isinstance(_lowercase,					_lowercase       )
	if os.path.isfile(_lowercase       ):
		_lowercase       :							int  	=		cva.imread(_lowercase       )
	else:
		_lowercase       :							Optional[int]  	=		get_image_from_url(_lowercase       )
		assert img is not None, f'''could not connect to: {im}'''
	_lowercase       :							Tuple  	=		cva.cvtColor(_lowercase,					cva.COLOR_BGR2RGB       )
	if input_format == "RGB":
		_lowercase       :							Optional[Any]  	=		img[:, :, ::-1]
	return img







def     __UpperCamelCase					(				_lowercase,					_lowercase=1       )			->      Any:
	return (images[i : i + batch] for i in range(0,					len(_lowercase       ),					_lowercase       ))





'''simple docstring'''

from __future__ import annotations

_A					:     Union[str, Any]      ='''Alexander Joslin'''







def     __UpperCamelCase					(				_lowercase       )			->      int:

	if number <= 0:
		_lowercase       :							Optional[Any]  	=		f'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_lowercase       )

	_lowercase       :							List[str]  	=		[number + 1] * (number + 1)

	# starting position
	_lowercase       :							str  	=		0
	for i in range(1,					_lowercase       ):
		_lowercase       :							Union[str, Any]  	=		min(table[i + 1],					table[i] + 1       )
		# check if out of bounds
		if i * 2 <= number:
			_lowercase       :							Optional[Any]  	=		min(table[i * 2],					table[i] + 1       )
		# check if out of bounds
		if i * 3 <= number:
			_lowercase       :							Any  	=		min(table[i * 3],					table[i] + 1       )
	return table[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
