





"""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 			=  True
except ImportError:
		A 			=  False


try:
		from torch.hub import _get_torch_home

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

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

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

def     __A      (			a_						:int=OBJECTS   ,       a_						:str=ATTRIBUTES)		->		Any:
				__a :						Dict			       =  []
				with open(a_) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''',''')[0].lower().strip())

				__a :						List[Any]			       =  []
				with open(a_) as f:
								for object in f.readlines():
												vg_attrs.append(object.split(''',''')[0].lower().strip())
				return vg_classes, vg_attrs

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




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  {}







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

									__a :						Tuple			       =  d







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







					def __setattr__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  val
									__a :						Optional[Any]			       =  val
									__a :						List[str]			       =  key.split('''.'''     )
									__a :						Union[str, Any]			       =  len(_UpperCAmelCase     ) - 1
									__a :						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:
																					__a :						str			       =  val
																	else:
																					__a :						Any			       =  pointer[l]







					def  _lowerCamelCase		(						self     ):
									return self._pointer







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







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







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase     ):
									with open(_UpperCAmelCase     ) as stream:
													__a :						int			       =  load(_UpperCAmelCase	,			Loader=_UpperCAmelCase     )
									return data







					def __str__(						self     ):
									__a :						Tuple			       =  '''    '''
									if self._name != "root":
													__a :						Union[str, Any]			       =  f"""{t * (self._level-1)}{self._name}:\n"""
									else:
													__a :						Optional[Any]			       =  ''''''
									__a :						Optional[Any]			       =  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"""
													__a :						Any			       =  level
									return r[:-1]







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a       ,						__a :						Tuple			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )
									return cls(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a :						Dict			       =  kwargs.pop('''cache_dir'''	,			_UpperCAmelCase     )
									__a :						int			       =  kwargs.pop('''force_download'''	,			_UpperCAmelCase     )
									__a :						Tuple			       =  kwargs.pop('''resume_download'''	,			_UpperCAmelCase     )
									__a :						Dict			       =  kwargs.pop('''proxies'''	,			_UpperCAmelCase     )
									__a :						Tuple			       =  kwargs.pop('''local_files_only'''	,			_UpperCAmelCase     )

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

									try:
													# Load from URL or cache if already cached
													__a :						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

													__a :						Tuple			       =  Config.load_yaml(_UpperCAmelCase     )

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

def     __A      (			a_						:str   ,       a_						:str   ,       a_						:str=True)		->		str:
				__a :						Any			       =  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				__a :						Optional[int]			       =  '''/''' not in model_id
				if legacy_format:
								return F"""{endpoint}/{model_id}-{filename}"""
				else:
								return F"""{endpoint}/{model_id}/{filename}"""

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

def     __A      (			a_						:Dict   ,       a_						:Tuple=None   ,       a_						:Optional[Any]=False   ,       a_						:List[Any]=None   ,       a_						:Union[str, Any]=10   ,       a_						:Union[str, Any]=False   ,       a_						:List[Any]=None   ,       a_						:Dict=False   ,       )		->		Any:
				if cache_dir is None:
								__a :						int			       =  TRANSFORMERS_CACHE
				if isinstance(a_   ,       a_):
								__a :						List[str]			       =  str(a_)

				os.makedirs(a_   ,       exist_ok=a_)

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

				__a :						Any			       =  url_to_filename(a_   ,       a_)

				# get cache path to put the file
				__a :						Tuple			       =  os.path.join(a_   ,       a_)

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

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

								if resume_download:
												__a :						Optional[int]			       =  cache_path + '''.incomplete'''

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

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

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

								os.replace(temp_file.name   ,       a_)

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

				return cache_path

def     __A      (			a_						:Union[str, Any]   ,       a_						:List[str]=None)		->		Any:
				__a :						Union[str, Any]			       =  url.encode('''utf-8''')
				__a :						Tuple			       =  shaaaa(a_)
				__a :						Optional[Any]			       =  url_hash.hexdigest()

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

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

				return filename

def     __A      (			a_						:Optional[Any]   ,       a_						:Optional[int]=None   ,       a_						:Tuple=False   ,       a_						:Optional[int]=None   ,       a_						:Union[str, Any]=False   ,       a_						:Tuple=None   ,       a_						:List[Any]=False   ,       a_						:str=False   ,       a_						:List[Any]=False   ,       )		->		Any:
				if cache_dir is None:
								__a :						Dict			       =  TRANSFORMERS_CACHE
				if isinstance(a_   ,       a_):
								__a :						int			       =  str(a_)
				if isinstance(a_   ,       a_):
								__a :						List[Any]			       =  str(a_)

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

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

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

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

								return output_path_extracted

				return output_path

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

def     __A      (			a_						:List[str])		->		str:
				__a :						List[Any]			       =  requests.get(a_)
				__a :						Tuple			       =  np.array(Image.open(BytesIO(response.content)))
				return img

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

def     __A      (			)		->		int:
				print(F"""{os.path.abspath(os.path.join(a_   ,       os.pardir))}/demo.ipynb""")

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






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






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[Any]			       =  max(self.edges.values()     ) + 1
													for edge, weight in self.edges.items():
																	if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																					if weight < min_weight:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

				__a :						int			       =  sum(graph.edges.values())
				__a :						int			       =  sum(subgraph.edges.values())

				return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')