
"""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

		_lowercase											=			True
except ImportError:
		_lowercase											=			False


try:
		from torch.hub import _get_torch_home

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

_lowercase											=			os.path.join(torch_cache_home, '''transformers''')

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






def  _snake_case			(						snake_case__				:       Optional[Any]=OBJECTS		, snake_case__				:       List[str]=ATTRIBUTES       ):
	A :    Dict  				=				[]
	with open(snake_case__       ) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','       )[0].lower().strip()       )

	A :    Union[str, Any]  				=				[]
	with open(snake_case__       ) as f:
		for object in f.readlines():
			vg_attrs.append(object.split(','       )[0].lower().strip()       )
	return vg_classes, vg_attrs






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







class 			lowerCAmelCase_			:



						'''simple docstring'''


						_lowerCamelCase:      int =      {}


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

							A :    Tuple  				=				d


						def __repr__(	self       : Tuple )       ->				Union[str, Any]:
							return str(list((self._pointer.keys()) ) )


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


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


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : Optional[Any]					,A_       : Union[str, Any] )       ->				int:
							with open(F'{file_name}'					,'w' ) as stream:
								dump(A_					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : int					,A_       : str )       ->				Optional[Any]:
							with open(F'{file_name}'					,'w' ) as stream:
								json.dump(A_					,A_ )


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : List[str] )       ->				str:
							with open(A_ ) as stream:
								A :    Any  				=				load(A_					,Loader=A_ )
							return data


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


						@classmethod
						def   _SCREAMING_SNAKE_CASE (	cls       : List[str]					,A_       : str					,**A_       : Dict )       ->				Dict:
							A				, A :    List[str]  				=				cls.get_config_dict(A_					,**A_ )
							return cls(A_ )




						@classmethod
						def   _SCREAMING_SNAKE_CASE (	cls       : Dict					,A_       : str					,**A_       : Optional[int] )       ->				Any:
							A :    Union[str, Any]  				=				kwargs.pop('cache_dir'					,A_ )
							A :    List[Any]  				=				kwargs.pop('force_download'					,A_ )
							A :    List[str]  				=				kwargs.pop('resume_download'					,A_ )
							A :    Optional[int]  				=				kwargs.pop('proxies'					,A_ )
							A :    Union[str, Any]  				=				kwargs.pop('local_files_only'					,A_ )

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

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

								A :    List[str]  				=				Config.load_yaml(A_ )

							except EnvironmentError:
								A :    Optional[Any]  				=				'Can\'t load config for'
								raise EnvironmentError(A_ )

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

							return Config.load_yaml(A_ ), kwargs






def  _snake_case			(						snake_case__				:       Dict       ):
	A :    Tuple  				=				torch.load('dump.pt'		, map_location=in_tensor.device       )
	A :    Dict  				=				in_tensor.numpy()
	A :    Optional[Any]  				=				out_tensor.numpy()[0]
	print(na.shape		, na[0, 0, :5]       )
	print(na.shape		, na[0, 0, :5]       )
	assert np.allclose(snake_case__		, snake_case__		, rtol=0.01		, atol=0.1       ), (
	    F'{sum([1 for x in np.isclose(snake_case__		, snake_case__		, 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  _snake_case			(						snake_case__				:       List[Any]       ):
	A :    Union[str, Any]  				=				urlparse(snake_case__       )
	return parsed.scheme in ("http", "https")






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       str		, snake_case__				:       Optional[int]=True       ):
	A :    List[str]  				=				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	A :    Union[str, Any]  				=				'/' not in model_id
	if legacy_format:
		return F'{endpoint}/{model_id}-{filename}'
	else:
		return F'{endpoint}/{model_id}/{filename}'






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






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Dict=None		, snake_case__				:       Any=False		, snake_case__				:       Union[str, Any]=None		, snake_case__				:       List[str]=10		, snake_case__				:       int=False		, snake_case__				:       int=None		, snake_case__				:       List[str]=False		, ):
	if cache_dir is None:
		A :    List[str]  				=				TRANSFORMERS_CACHE
	if isinstance(snake_case__		, snake_case__       ):
		A :    Tuple  				=				str(snake_case__       )

	os.makedirs(snake_case__		, exist_ok=snake_case__       )

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

	A :    Dict  				=				url_to_filename(snake_case__		, snake_case__       )

	# get cache path to put the file
	A :    List[Any]  				=				os.path.join(snake_case__		, snake_case__       )

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

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

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

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

			A :    Tuple  				=				_resumable_file_manager
			if os.path.exists(snake_case__       ):
				A :    Tuple  				=				os.stat(snake_case__       ).st_size
			else:
				A :    int  				=				0
		else:
			A :    Optional[Any]  				=				partial(tempfile.NamedTemporaryFile		, dir=snake_case__		, delete=snake_case__       )
			A :    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'		, snake_case__		, temp_file.name		, )

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

		os.replace(temp_file.name		, snake_case__       )

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

	return cache_path






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       Tuple=None       ):
	A :    Optional[Any]  				=				url.encode('utf-8'       )
	A :    Tuple  				=				shaaaa(snake_case__       )
	A :    Tuple  				=				url_hash.hexdigest()

	if etag:
		A :    Optional[Any]  				=				etag.encode('utf-8'       )
		A :    Dict  				=				shaaaa(snake_case__       )
		filename += "." + etag_hash.hexdigest()

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

	return filename






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       List[Any]=None		, snake_case__				:       Optional[int]=False		, snake_case__				:       List[str]=None		, snake_case__				:       Tuple=False		, snake_case__				:       Optional[Any]=None		, snake_case__				:       List[str]=False		, snake_case__				:       Optional[Any]=False		, snake_case__				:       Any=False		, ):
	if cache_dir is None:
		A :    Any  				=				TRANSFORMERS_CACHE
	if isinstance(snake_case__		, snake_case__       ):
		A :    Dict  				=				str(snake_case__       )
	if isinstance(snake_case__		, snake_case__       ):
		A :    Any  				=				str(snake_case__       )

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

	if extract_compressed_file:
		if not is_zipfile(snake_case__       ) and not tarfile.is_tarfile(snake_case__       ):
			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/"
		A				, A :    List[Any]  				=				os.path.split(snake_case__       )
		A :    List[str]  				=				output_file.replace('.'		, '-'       ) + '-extracted'
		A :    Dict  				=				os.path.join(snake_case__		, snake_case__       )

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

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

		return output_path_extracted

	return output_path






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






def  _snake_case			(						snake_case__				:       Union[str, Any]       ):
	A :    Dict  				=				requests.get(snake_case__       )
	A :    Tuple  				=				np.array(Image.open(BytesIO(response.content       )       )       )
	return img






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






def  _snake_case			(						):
	print(F'{os.path.abspath(os.path.join(snake_case__		, os.pardir       )       )}/demo.ipynb'       )






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       int="RGB"       ):
	assert isinstance(snake_case__		, snake_case__       )
	if os.path.isfile(snake_case__       ):
		A :    Optional[Any]  				=				cva.imread(snake_case__       )
	else:
		A :    Optional[int]  				=				get_image_from_url(snake_case__       )
		assert img is not None, F'could not connect to: {im}'
	A :    List[str]  				=				cva.cvtColor(snake_case__		, cva.COLOR_BGR2RGB       )
	if input_format == "RGB":
		A :    Dict  				=				img[:, :, ::-1]
	return img






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Tuple=1       ):
	return (images[i : i + batch] for i in range(0		, len(snake_case__       )		, snake_case__       ))

"""simple docstring"""


from __future__ import annotations






def  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record