


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:						Dict   				=       True
except ImportError:
	_lowerCAmelCase:						Tuple   				=       False


try:
	from torch.hub import _get_torch_home

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

_lowerCAmelCase:						Optional[int]   				=       os.path.join(torch_cache_home, 'transformers')

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






def   _lowercase(					__a				:						List[Any]=OBJECTS    ,     __a				:						List[str]=ATTRIBUTES     ):
							a__      =[]
							with open(__a     ) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(','     )[0].lower().strip()     )

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






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




class       lowercase_					:
				snake_case	    ={}





				def __init__(				self       ,  lowercase_       ,  lowercase_ = "root"       ,  lowercase_=0)  -> Any:
											a__      =name
											a__      =level
											a__      ={}
											for k, v in dictionary.items():
																		if v is None:
																									raise ValueError()
																		a__      =copy.deepcopy(lowercase_)
																		a__      =copy.deepcopy(lowercase_)
																		if isinstance(lowercase_       ,  lowercase_):
																									a__      =Config(lowercase_       ,  name=lowercase_       ,  level=level + 1)
																		a__      =v
																		setattr(self       ,  lowercase_       ,  lowercase_)

											a__      =d





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





				def __setattr__(				self       ,  lowercase_       ,  lowercase_)  -> Dict:
											a__      =val
											a__      =val
											a__      =key.split('.')
											a__      =len(lowercase_) - 1
											a__      =self._pointer
											if len(lowercase_) > 1:
																		for i, l in enumerate(lowercase_):
																									if hasattr(self       ,  lowercase_) and isinstance(getattr(self       ,  lowercase_)       ,  lowercase_):
																																setattr(getattr(self       ,  lowercase_)       ,  '.'.join(levels[i:])       ,  lowercase_)
																									if l == last_level:
																																a__      =val
																									else:
																																a__      =pointer[l]





				def 						__UpperCamelCase    (				self)  -> List[str]:
											return self._pointer





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Any:
											with open(F"""{file_name}"""       ,  'w') as stream:
																		dump(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Union[str, Any]:
											with open(F"""{file_name}"""       ,  'w') as stream:
																		json.dump(lowercase_       ,  lowercase_)





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_)  -> Any:
											with open(lowercase_) as stream:
																		a__      =load(lowercase_       ,  Loader=lowercase_)
											return data





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





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> Union[str, Any]:
											a__    ,     a__      =cls.get_config_dict(lowercase_       ,  **lowercase_)
											return cls(lowercase_)





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> Tuple:
											a__      =kwargs.pop('cache_dir'       ,  lowercase_)
											a__      =kwargs.pop('force_download'       ,  lowercase_)
											a__      =kwargs.pop('resume_download'       ,  lowercase_)
											a__      =kwargs.pop('proxies'       ,  lowercase_)
											a__      =kwargs.pop('local_files_only'       ,  lowercase_)

											if os.path.isdir(lowercase_):
																		a__      =os.path.join(lowercase_       ,  lowercase_)
											elif os.path.isfile(lowercase_) or is_remote_url(lowercase_):
																		a__      =pretrained_model_name_or_path
											else:
																		a__      =hf_bucket_url(lowercase_       ,  filename=lowercase_       ,  use_cdn=lowercase_)

											try:
																		# Load from URL or cache if already cached
																		a__      =cached_path(
																		    lowercase_       ,  cache_dir=lowercase_       ,  force_download=lowercase_       ,  proxies=lowercase_       ,  resume_download=lowercase_       ,  local_files_only=lowercase_       ,  )
																		# Load config dict
																		if resolved_config_file is None:
																									raise EnvironmentError

																		a__      =Config.load_yaml(lowercase_)

											except EnvironmentError:
																		a__      ='Can\'t load config for'
																		raise EnvironmentError(lowercase_)

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

											return Config.load_yaml(lowercase_), kwargs







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






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						Any=True     ):
							a__      =CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
							a__      ='/' not in model_id
							if legacy_format:
														return f"""{endpoint}/{model_id}-{filename}"""
							else:
														return f"""{endpoint}/{model_id}/{filename}"""






def   _lowercase(					__a				:						Optional[int]    ,     __a				:						Dict    ,     __a				:						str=None    ,     __a				:						List[str]=0    ,     __a				:						Optional[Any]=None    ,     ):
							a__      ='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__      ={'user-agent': ua}
							if resume_size > 0:
														a__      ='bytes=%d-' % (resume_size,)
							a__      =requests.get(__a    ,     stream=__a    ,     proxies=__a    ,     headers=__a     )
							if response.status_code == 416:  # Range not satisfiable
														return
							a__      =response.headers.get('Content-Length'     )
							a__      =resume_size + int(__a     ) if content_length is not None else None
							a__      =tqdm(
							    unit='B'    ,     unit_scale=__a    ,     total=__a    ,     initial=__a    ,     desc='Downloading'    ,     )
							for chunk in response.iter_content(chunk_size=1024     ):
														if chunk:  # filter out keep-alive new chunks
																					progress.update(len(__a     )     )
																					temp_file.write(__a     )
							progress.close()






def   _lowercase(					__a				:						Optional[int]    ,     __a				:						int=None    ,     __a				:						Optional[int]=False    ,     __a				:						str=None    ,     __a				:						Optional[Any]=10    ,     __a				:						Optional[Any]=False    ,     __a				:						Optional[Any]=None    ,     __a				:						int=False    ,     ):
							if cache_dir is None:
														a__      =TRANSFORMERS_CACHE
							if isinstance(__a    ,     __a     ):
														a__      =str(__a     )

							os.makedirs(__a    ,     exist_ok=__a     )

							a__      =None
							if not local_files_only:
														try:
																					a__      =requests.head(__a    ,     allow_redirects=__a    ,     proxies=__a    ,     timeout=__a     )
																					if response.status_code == 200:
																												a__      =response.headers.get('ETag'     )
														except (EnvironmentError, requests.exceptions.Timeout):
																					# etag is already None
																					pass

							a__      =url_to_filename(__a    ,     __a     )

							# get cache path to put the file
							a__      =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__      =[
																					    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__      =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__      =cache_path + '.incomplete'

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

																					a__      =_resumable_file_manager
																					if os.path.exists(__a     ):
																												a__      =os.stat(__a     ).st_size
																					else:
																												a__      =0
														else:
																					a__      =partial(tempfile.NamedTemporaryFile    ,     dir=__a    ,     delete=__a     )
																					a__      =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__      ={'url': url, 'etag': etag}
														a__      =cache_path + '.json'
														with open(__a    ,     'w'     ) as meta_file:
																					json.dump(__a    ,     __a     )

							return cache_path






def   _lowercase(					__a				:						Dict    ,     __a				:						List[Any]=None     ):
							a__      =url.encode('utf-8'     )
							a__      =shaaaa(__a     )
							a__      =url_hash.hexdigest()

							if etag:
														a__      =etag.encode('utf-8'     )
														a__      =shaaaa(__a     )
														filename += "." + etag_hash.hexdigest()

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

							return filename






def   _lowercase(					__a				:						int    ,     __a				:						int=None    ,     __a				:						str=False    ,     __a				:						List[str]=None    ,     __a				:						Dict=False    ,     __a				:						Union[str, Any]=None    ,     __a				:						Any=False    ,     __a				:						List[Any]=False    ,     __a				:						Optional[Any]=False    ,     ):
							if cache_dir is None:
														a__      =TRANSFORMERS_CACHE
							if isinstance(__a    ,     __a     ):
														a__      =str(__a     )
							if isinstance(__a    ,     __a     ):
														a__      =str(__a     )

							if is_remote_url(__a     ):
														# URL, so get it from the cache (downloading if necessary)
														a__      =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__      =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__      =os.path.split(__a     )
														a__      =output_file.replace('.'    ,     '-'     ) + '-extracted'
														a__      =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__      =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__      =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   _lowercase(					__a				:						List[str]    ,     __a				:						List[str]=","     ):
							assert isinstance(__a    ,     __a     )
							if os.path.isfile(__a     ):
														with open(__a     ) as f:
																					a__      =eval(f.read()     )
							else:
														a__      =requests.get(__a     )
														try:
																					a__      =requests.json()
														except Exception:
																					a__      =req.content.decode()
																					assert data is not None, "could not connect"
																					try:
																												a__      =eval(__a     )
																					except Exception:
																												a__      =data.split('\n'     )
														req.close()
							return data






def   _lowercase(					__a				:						List[Any]     ):
							a__      =requests.get(__a     )
							a__      =np.array(Image.open(BytesIO(response.content     )     )     )
							return img






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






def   _lowercase(					):
							print(f"""{os.path.abspath(os.path.join(__a    ,     os.pardir     )     )}/demo.ipynb"""     )






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







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





from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

