code: 
  
  
  '''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			:		Optional[Any] =			True
  except ImportError:
  	__lowercase			:		List[Any] =			False
  
  
  try:
  	from torch.hub import _get_torch_home
  
  	__lowercase			:		Any =			_get_torch_home()
  except ImportError:
  	__lowercase			:		Tuple =			os.path.expanduser(
  	    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
  	)
  
  __lowercase			:		Any =			os.path.join(torch_cache_home, 'transformers')
  
  __lowercase			:		Tuple =			'https://cdn.huggingface.co'
  __lowercase			:		List[str] =			'https://s3.amazonaws.com/models.huggingface.co/bert'
  __lowercase			:		List[Any] =			'/'.join(str(Path(__file__).resolve()).split('/')[:-1])
  __lowercase			:		str =			os.path.join(PATH, 'config.yaml')
  __lowercase			:		Tuple =			os.path.join(PATH, 'attributes.txt')
  __lowercase			:		Optional[Any] =			os.path.join(PATH, 'objects.txt')
  __lowercase			:		List[Any] =			os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
  __lowercase			:		Tuple =			os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
  __lowercase			:		int =			os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
  __lowercase			:		str =			'pytorch_model.bin'
  __lowercase			:		Dict =			'config.yaml'
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]=OBJECTS		,   _SCREAMING_SNAKE_CASE   :     int=ATTRIBUTES     ):
  					__a    : Tuple      					=						[]
  					with open(_SCREAMING_SNAKE_CASE     ) as f:
  										for object in f.readlines():
  															vg_classes.append(object.split(','     )[0].lower().strip()     )
  
  					__a    : int      					=						[]
  					with open(_SCREAMING_SNAKE_CASE     ) as f:
  										for object in f.readlines():
  															vg_attrs.append(object.split(','     )[0].lower().strip()     )
  					return vg_classes, vg_attrs
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int     ):
  					__a    : Optional[Any]      					=						OrderedDict()
  					with open(_SCREAMING_SNAKE_CASE		,   'rb'     ) as f:
  										__a    : List[str]      					=						pkl.load(_SCREAMING_SNAKE_CASE     )['model']
  					for k in copy.deepcopy(list(ckp.keys()     )     ):
  										__a    : List[str]      					=						ckp.pop(_SCREAMING_SNAKE_CASE     )
  										if isinstance(_SCREAMING_SNAKE_CASE		,   np.ndarray     ):
  															__a    : Optional[Any]      					=						torch.tensor(_SCREAMING_SNAKE_CASE     )
  										else:
  															assert isinstance(_SCREAMING_SNAKE_CASE		,   torch.tensor     ), type(_SCREAMING_SNAKE_CASE     )
  										__a    : Optional[int]      					=						v
  					return r
  
  
  
  
  
  class 							__UpperCamelCase       :
  	A_			    =					{}
  
  
  
  
  	def __init__(     self		,       __a		,       __a = "root"		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						name
  						__a    : Dict      					=						level
  						__a    : Optional[int]      					=						{}
  						for k, v in dictionary.items():
  											if v is None:
  																raise ValueError()
  											__a    : Tuple      					=						copy.deepcopy(__a							)
  											__a    : str      					=						copy.deepcopy(__a							)
  											if isinstance(__a		,       __a							):
  																__a    : str      					=						Config(__a		,       name=__a		,       level=level + 1							)
  											__a    : Optional[Any]      					=						v
  											setattr(self		,       __a		,       __a							)
  
  						__a    : Any      					=						d
  
  
  
  
  	def __repr__(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return str(list((self._pointer.keys())							)							)
  
  
  
  
  	def __setattr__(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						val
  						__a    : Optional[int]      					=						val
  						__a    : Optional[int]      					=						key.split('.'							)
  						__a    : int      					=						len(__a							) - 1
  						__a    : Dict      					=						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    : List[Any]      					=						pointer[l]
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self._pointer
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						with open(f"""{file_name}"""		,       'w'							) as stream:
  											dump(__a		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						with open(f"""{file_name}"""		,       'w'							) as stream:
  											json.dump(__a		,       __a							)
  
  
  
  
  	@staticmethod
  	def    __UpperCAmelCase							(     __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						with open(__a							) as stream:
  											__a    : Optional[int]      					=						load(__a		,       Loader=__a							)
  						return data
  
  
  
  
  	def __str__(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						'    '
  						if self._name != "root":
  											__a    : List[str]      					=						f"""{t * (self._level-1)}{self._name}:\n"""
  						else:
  											__a    : Dict      					=						''
  						__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    : Union[str, Any]      					=						level
  						return r[:-1]
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls		,       __a		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						cls.get_config_dict(__a		,       **__a							)
  						return cls(__a							)
  
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls		,       __a		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						kwargs.pop('cache_dir'		,       __a							)
  						__a    : str      					=						kwargs.pop('force_download'		,       __a							)
  						__a    : Any      					=						kwargs.pop('resume_download'		,       __a							)
  						__a    : List[str]      					=						kwargs.pop('proxies'		,       __a							)
  						__a    : Tuple      					=						kwargs.pop('local_files_only'		,       __a							)
  
  						if os.path.isdir(__a							):
  											__a    : Any      					=						os.path.join(__a		,       __a							)
  						elif os.path.isfile(__a							) or is_remote_url(__a							):
  											__a    : Any      					=						pretrained_model_name_or_path
  						else:
  											__a    : Optional[Any]      					=						hf_bucket_url(__a		,       filename=__a		,       use_cdn=__a							)
  
  						try:
  											# Load from URL or cache if already cached
  											__a    : Optional[int]      					=						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    : int      					=						Config.load_yaml(__a							)
  
  						except EnvironmentError:
  											__a    : List[str]      					=						'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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					__a    : List[Any]      					=						torch.load('dump.pt'		,   map_location=in_tensor.device     )
  					__a    : int      					=						in_tensor.numpy()
  					__a    : Optional[int]      					=						out_tensor.numpy()[0]
  					print(na.shape		,   na[0, 0, :5]     )
  					print(na.shape		,   na[0, 0, :5]     )
  					assert np.allclose(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   rtol=0.0_1		,   atol=0.1     ), (
  					    F"""{sum([1 for x in np.isclose(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					__a    : List[Any]      					=						urlparse(_SCREAMING_SNAKE_CASE     )
  					return parsed.scheme in ("http", "https")
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     str=True     ):
  					__a    : Dict      					=						CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  					__a    : int      					=						'/' not in model_id
  					if legacy_format:
  										return F"""{endpoint}/{model_id}-{filename}"""
  					else:
  										return F"""{endpoint}/{model_id}/{filename}"""
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   _SCREAMING_SNAKE_CASE   :     str=0		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=None		,   ):
  					__a    : Any      					=						'python/{}'.format(sys.version.split()[0]     )
  					if _torch_available:
  										ua += "; torch/{}".format(torch.__version__     )
  					if isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										ua += "; " + "; ".join('{}/{}'.format(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for k, v in user_agent.items()     )
  					elif isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										ua += "; " + user_agent
  					__a    : List[str]      					=						{'user-agent': ua}
  					if resume_size > 0:
  										__a    : int      					=						'bytes=%d-' % (resume_size,)
  					__a    : int      					=						requests.get(_SCREAMING_SNAKE_CASE		,   stream=_SCREAMING_SNAKE_CASE		,   proxies=_SCREAMING_SNAKE_CASE		,   headers=_SCREAMING_SNAKE_CASE     )
  					if response.status_code == 416:  # Range not satisfiable
  										return
  					__a    : List[str]      					=						response.headers.get('Content-Length'     )
  					__a    : List[Any]      					=						resume_size + int(_SCREAMING_SNAKE_CASE     ) if content_length is not None else None
  					__a    : Optional[Any]      					=						tqdm(
  					    unit='B'		,   unit_scale=_SCREAMING_SNAKE_CASE		,   total=_SCREAMING_SNAKE_CASE		,   initial=_SCREAMING_SNAKE_CASE		,   desc='Downloading'		,   )
  					for chunk in response.iter_content(chunk_size=1_024     ):
  										if chunk:  # filter out keep-alive new chunks
  															progress.update(len(_SCREAMING_SNAKE_CASE     )     )
  															temp_file.write(_SCREAMING_SNAKE_CASE     )
  					progress.close()
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=False		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=10		,   _SCREAMING_SNAKE_CASE   :     int=False		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     str=False		,   ):
  					if cache_dir is None:
  										__a    : List[str]      					=						TRANSFORMERS_CACHE
  					if isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : Any      					=						str(_SCREAMING_SNAKE_CASE     )
  
  					os.makedirs(_SCREAMING_SNAKE_CASE		,   exist_ok=_SCREAMING_SNAKE_CASE     )
  
  					__a    : List[str]      					=						None
  					if not local_files_only:
  										try:
  															__a    : List[Any]      					=						requests.head(_SCREAMING_SNAKE_CASE		,   allow_redirects=_SCREAMING_SNAKE_CASE		,   proxies=_SCREAMING_SNAKE_CASE		,   timeout=_SCREAMING_SNAKE_CASE     )
  															if response.status_code == 200:
  																				__a    : Any      					=						response.headers.get('ETag'     )
  										except (EnvironmentError, requests.exceptions.Timeout):
  															# etag is already None
  															pass
  
  					__a    : List[Any]      					=						url_to_filename(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					# get cache path to put the file
  					__a    : Dict      					=						os.path.join(_SCREAMING_SNAKE_CASE		,   _SCREAMING_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(_SCREAMING_SNAKE_CASE     ):
  															return cache_path
  										else:
  															__a    : int      					=						[
  															    file
  															    for file in fnmatch.filter(os.listdir(_SCREAMING_SNAKE_CASE     )		,   filename + '.*'     )
  															    if not file.endswith('.json'     ) and not file.endswith('.lock'     )
  															]
  															if len(_SCREAMING_SNAKE_CASE     ) > 0:
  																				return os.path.join(_SCREAMING_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(_SCREAMING_SNAKE_CASE     ) and not force_download:
  										return cache_path
  
  					# Prevent parallel downloads of the same file with a lock.
  					__a    : List[str]      					=						cache_path + '.lock'
  					with FileLock(_SCREAMING_SNAKE_CASE     ):
  										# If the download just completed while the lock was activated.
  										if os.path.exists(_SCREAMING_SNAKE_CASE     ) and not force_download:
  															# Even if returning early like here, the lock will be released.
  															return cache_path
  
  										if resume_download:
  															__a    : List[str]      					=						cache_path + '.incomplete'
  
  															@contextmanager
  															def _resumable_file_manager():
  																				with open(_SCREAMING_SNAKE_CASE		,   'a+b'     ) as f:
  																									yield f
  
  															__a    : List[Any]      					=						_resumable_file_manager
  															if os.path.exists(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Tuple      					=						os.stat(_SCREAMING_SNAKE_CASE     ).st_size
  															else:
  																				__a    : Optional[Any]      					=						0
  										else:
  															__a    : int      					=						partial(tempfile.NamedTemporaryFile		,   dir=_SCREAMING_SNAKE_CASE		,   delete=_SCREAMING_SNAKE_CASE     )
  															__a    : Dict      					=						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'		,   _SCREAMING_SNAKE_CASE		,   temp_file.name		,   )
  
  															http_get(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   proxies=_SCREAMING_SNAKE_CASE		,   resume_size=_SCREAMING_SNAKE_CASE		,   user_agent=_SCREAMING_SNAKE_CASE		,   )
  
  										os.replace(temp_file.name		,   _SCREAMING_SNAKE_CASE     )
  
  										__a    : Optional[int]      					=						{'url': url, 'etag': etag}
  										__a    : Dict      					=						cache_path + '.json'
  										with open(_SCREAMING_SNAKE_CASE		,   'w'     ) as meta_file:
  															json.dump(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					return cache_path
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Dict=None     ):
  					__a    : Dict      					=						url.encode('utf-8'     )
  					__a    : Dict      					=						shaaaa(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						url_hash.hexdigest()
  
  					if etag:
  										__a    : Optional[int]      					=						etag.encode('utf-8'     )
  										__a    : Any      					=						shaaaa(_SCREAMING_SNAKE_CASE     )
  										filename += "." + etag_hash.hexdigest()
  
  					if url.endswith('.h5'     ):
  										filename += ".h5"
  
  					return filename
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Tuple=False		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=False		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None		,   _SCREAMING_SNAKE_CASE   :     Dict=False		,   _SCREAMING_SNAKE_CASE   :     List[str]=False		,   _SCREAMING_SNAKE_CASE   :     Any=False		,   ):
  					if cache_dir is None:
  										__a    : List[Any]      					=						TRANSFORMERS_CACHE
  					if isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : str      					=						str(_SCREAMING_SNAKE_CASE     )
  					if isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : Tuple      					=						str(_SCREAMING_SNAKE_CASE     )
  
  					if is_remote_url(_SCREAMING_SNAKE_CASE     ):
  										# URL, so get it from the cache (downloading if necessary)
  										__a    : List[str]      					=						get_from_cache(
  										    _SCREAMING_SNAKE_CASE		,   cache_dir=_SCREAMING_SNAKE_CASE		,   force_download=_SCREAMING_SNAKE_CASE		,   proxies=_SCREAMING_SNAKE_CASE		,   resume_download=_SCREAMING_SNAKE_CASE		,   user_agent=_SCREAMING_SNAKE_CASE		,   local_files_only=_SCREAMING_SNAKE_CASE		,   )
  					elif os.path.exists(_SCREAMING_SNAKE_CASE     ):
  										# File, and it exists.
  										__a    : Optional[int]      					=						url_or_filename
  					elif urlparse(_SCREAMING_SNAKE_CASE     ).scheme == "":
  										# File, but it doesn't exist.
  										raise EnvironmentError('file {} not found'.format(_SCREAMING_SNAKE_CASE     )     )
  					else:
  										# Something unknown
  										raise ValueError('unable to parse {} as a URL or as a local path'.format(_SCREAMING_SNAKE_CASE     )     )
  
  					if extract_compressed_file:
  										if not is_zipfile(_SCREAMING_SNAKE_CASE     ) and not tarfile.is_tarfile(_SCREAMING_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    : Any      					=						os.path.split(_SCREAMING_SNAKE_CASE     )
  										__a    : Any      					=						output_file.replace('.'		,   '-'     ) + '-extracted'
  										__a    : Tuple      					=						os.path.join(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  										if os.path.isdir(_SCREAMING_SNAKE_CASE     ) and os.listdir(_SCREAMING_SNAKE_CASE     ) and not force_extract:
  															return output_path_extracted
  
  										# Prevent parallel extractions
  										__a    : Dict      					=						output_path + '.lock'
  										with FileLock(_SCREAMING_SNAKE_CASE     ):
  															shutil.rmtree(_SCREAMING_SNAKE_CASE		,   ignore_errors=_SCREAMING_SNAKE_CASE     )
  															os.makedirs(_SCREAMING_SNAKE_CASE     )
  															if is_zipfile(_SCREAMING_SNAKE_CASE     ):
  																				with ZipFile(_SCREAMING_SNAKE_CASE		,   'r'     ) as zip_file:
  																									zip_file.extractall(_SCREAMING_SNAKE_CASE     )
  																									zip_file.close()
  															elif tarfile.is_tarfile(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Optional[Any]      					=						tarfile.open(_SCREAMING_SNAKE_CASE     )
  																				tar_file.extractall(_SCREAMING_SNAKE_CASE     )
  																				tar_file.close()
  															else:
  																				raise EnvironmentError('Archive format of {} could not be identified'.format(_SCREAMING_SNAKE_CASE     )     )
  
  										return output_path_extracted
  
  					return output_path
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     str=","     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					if os.path.isfile(_SCREAMING_SNAKE_CASE     ):
  										with open(_SCREAMING_SNAKE_CASE     ) as f:
  															__a    : Optional[int]      					=						eval(f.read()     )
  					else:
  										__a    : Tuple      					=						requests.get(_SCREAMING_SNAKE_CASE     )
  										try:
  															__a    : Optional[Any]      					=						requests.json()
  										except Exception:
  															__a    : Dict      					=						req.content.decode()
  															assert data is not None, "could not connect"
  															try:
  																				__a    : Union[str, Any]      					=						eval(_SCREAMING_SNAKE_CASE     )
  															except Exception:
  																				__a    : str      					=						data.split('\n'     )
  										req.close()
  					return data
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict     ):
  					__a    : str      					=						requests.get(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						np.array(Image.open(BytesIO(response.content     )     )     )
  					return img
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple     ):
  					__a    : Any      					=						url.split('/'     )[-1]
  					if fn not in os.listdir(os.getcwd()     ):
  										wget.download(_SCREAMING_SNAKE_CASE     )
  					with open(_SCREAMING_SNAKE_CASE		,   'rb'     ) as stream:
  										__a    : Optional[Any]      					=						pkl.load(_SCREAMING_SNAKE_CASE     )
  					__a    : str      					=						weights.pop('model'     )
  					__a    : str      					=						{}
  					for k, v in model.items():
  										__a    : str      					=						torch.from_numpy(_SCREAMING_SNAKE_CASE     )
  										if "running_var" in k:
  															__a    : Any      					=						torch.tensor([0]     )
  															__a    : List[Any]      					=						k.replace('running_var'		,   'num_batches_tracked'     )
  															__a    : Any      					=						zero
  					return new
  
  
  
  
  
  
  def 						lowerCamelCase							():
  					print(F"""{os.path.abspath(os.path.join(_SCREAMING_SNAKE_CASE		,   os.pardir     )     )}/demo.ipynb"""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]="RGB"     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					if os.path.isfile(_SCREAMING_SNAKE_CASE     ):
  										__a    : Dict      					=						cva.imread(_SCREAMING_SNAKE_CASE     )
  					else:
  										__a    : Optional[int]      					=						get_image_from_url(_SCREAMING_SNAKE_CASE     )
  										assert img is not None, F"""could not connect to: {im}"""
  					__a    : str      					=						cva.cvtColor(_SCREAMING_SNAKE_CASE		,   cva.COLOR_BGR2RGB     )
  					if input_format == "RGB":
  										__a    : Tuple      					=						img[:, :, ::-1]
  					return img
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=1     ):
  					return (images[i : i + batch] for i in range(0		,   len(_SCREAMING_SNAKE_CASE     )		,   _SCREAMING_SNAKE_CASE     ))
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  					# Splits the attention layers into three variables.
  					if attention_paths_to_split is not None:
  										for path, path_map in attention_paths_to_split.items():
  															__a    : Union[str, Any]      					=						old_checkpoint[path]
  															__a    : List[str]      					=						old_tensor.shape[0] // 3
  
  															__a    : Dict      					=						(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)
  
  															__a    : List[str]      					=						old_tensor.shape[0] // config['num_head_channels'] // 3
  
  															__a    : str      					=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
  															__a					,       __a					,       __a    : Dict      					=						old_tensor.split(channels // num_heads		,   dim=1     )
  
  															__a    : Optional[int]      					=						query.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : Tuple      					=						key.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						value.reshape(_SCREAMING_SNAKE_CASE     )
  
  					for path in paths:
  										__a    : Tuple      					=						path['new']
  
  										# These have already been assigned
  										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  															continue
  
  										if additional_replacements is not None:
  															for replacement in additional_replacements:
  																				__a    : int      					=						new_path.replace(replacement['old']		,   replacement['new']     )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  										__a    : Any      					=						old_checkpoint[path['old']]
  										__a    : Tuple      					=						['proj_attn.weight']
  										__a    : Tuple      					=						['proj_out.weight', 'proj_in.weight']
  										if any(k in new_path for k in names     ):
  															__a    : Tuple      					=						weight[:, :, 0]
  										elif any(k in new_path for k in names_a     ) and len(weight.shape     ) > 2 and ".attentions." not in new_path:
  															__a    : int      					=						weight[:, :, 0]
  										else:
  															__a    : Union[str, Any]      					=						weight
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Dict      					=						old_item
  
  										#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  										#         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
  										#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  										#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
  										#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
  										mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=1     ):
  					if n_shave_prefix_segments >= 0:
  										return ".".join(path.split('.'     )[n_shave_prefix_segments:]     )
  					else:
  										return ".".join(path.split('.'     )[:n_shave_prefix_segments]     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Optional[Any]      					=						[]
  					for old_item in old_list:
  										mapping.append({'old': old_item, 'new': old_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Optional[int]      					=						old_item.replace('in_layers.0'		,   'norm1'     )
  										__a    : Optional[Any]      					=						new_item.replace('in_layers.2'		,   'conv1'     )
  
  										__a    : str      					=						new_item.replace('out_layers.0'		,   'norm2'     )
  										__a    : str      					=						new_item.replace('out_layers.3'		,   'conv2'     )
  
  										__a    : List[Any]      					=						new_item.replace('emb_layers.1'		,   'time_emb_proj'     )
  										__a    : List[Any]      					=						new_item.replace('skip_connection'		,   'conv_shortcut'     )
  
  										__a    : int      					=						shave_segments(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE     )
  
  										if "temopral_conv" not in old_item:
  															mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=False     ):
  					__a    : Dict      					=						{}
  					__a    : Tuple      					=						list(checkpoint.keys()     )
  
  					__a    : Optional[int]      					=						'model.diffusion_model.'
  
  					# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  					if sum(k.startswith('model_ema'     ) for k in keys     ) > 100 and extract_ema:
  										print(F"""Checkpoint {path} has both EMA and non-EMA weights."""     )
  										print(
  										    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
  										    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'     )
  										for key in keys:
  															if key.startswith('model.diffusion_model'     ):
  																				__a    : int      					=						'model_ema.' + ''.join(key.split('.'     )[1:]     )
  																				__a    : Optional[int]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  					else:
  										if sum(k.startswith('model_ema'     ) for k in keys     ) > 100:
  															print(
  															    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
  															    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'     )
  
  										for key in keys:
  															__a    : Optional[Any]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						{}
  
  					__a    : Optional[Any]      					=						unet_state_dict['time_embed.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['time_embed.0.bias']
  					__a    : Any      					=						unet_state_dict['time_embed.2.weight']
  					__a    : Dict      					=						unet_state_dict['time_embed.2.bias']
  
  					if config["class_embed_type"] is None:
  										# No parameters to port
  										...
  					elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  										__a    : List[Any]      					=						unet_state_dict['label_emb.0.0.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.0.bias']
  										__a    : Optional[Any]      					=						unet_state_dict['label_emb.0.2.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.2.bias']
  					else:
  										raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""     )
  
  					__a    : str      					=						unet_state_dict['input_blocks.0.0.weight']
  					__a    : Tuple      					=						unet_state_dict['input_blocks.0.0.bias']
  
  					__a    : Optional[int]      					=						[v for v in unet_state_dict if v.startswith('input_blocks.0.1'     )]
  					__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  					assign_to_checkpoint(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						unet_state_dict['out.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.0.bias']
  					__a    : Optional[int]      					=						unet_state_dict['out.2.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.2.bias']
  
  					# Retrieves the keys for the input blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'input_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the middle blocks only
  					__a    : Dict      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'middle_block' in layer}     )
  					__a    : Optional[Any]      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the output blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'output_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					for i in range(1		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						(i - 1) // (config['layers_per_block'] + 1)
  										__a    : Tuple      					=						(i - 1) % (config['layers_per_block'] + 1)
  
  										__a    : Optional[Any]      					=						[
  										    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  										]
  										__a    : List[str]      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  										__a    : Tuple      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
  										if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
  															__a    : Optional[Any]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.weight"""     )
  															__a    : Optional[int]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.bias"""     )
  
  										__a    : str      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : Tuple      					=						{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										__a    : Optional[int]      					=						[key for key in resnets if 'temopral_conv' in key]
  										__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : List[Any]      					=						{
  										    'old': F"""input_blocks.{i}.0.temopral_conv""",
  										    'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  										}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : Optional[Any]      					=						{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : str      					=						{
  															    'old': F"""input_blocks.{i}.2""",
  															    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						middle_blocks[0]
  					__a    : str      					=						[key for key in resnet_a if 'temopral_conv' in key]
  					__a    : Dict      					=						middle_blocks[1]
  					__a    : str      					=						middle_blocks[2]
  					__a    : List[str]      					=						middle_blocks[3]
  					__a    : Any      					=						[key for key in resnet_a if 'temopral_conv' in key]
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Dict      					=						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Dict      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : str      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					for i in range(_SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						i // (config['layers_per_block'] + 1)
  										__a    : Optional[int]      					=						i % (config['layers_per_block'] + 1)
  										__a    : Any      					=						[shave_segments(_SCREAMING_SNAKE_CASE		,   2     ) for name in output_blocks[i]]
  										__a    : Optional[Any]      					=						{}
  
  										for layer in output_block_layers:
  															__a					,       __a    : Optional[int]      					=						layer.split('.'     )[0], shave_segments(_SCREAMING_SNAKE_CASE		,   1     )
  															if layer_id in output_block_list:
  																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE     )
  															else:
  																				__a    : int      					=						[layer_name]
  
  										if len(_SCREAMING_SNAKE_CASE     ) > 1:
  															__a    : Any      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
  															__a    : List[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
  															__a    : Optional[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
  															__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[Any]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						[key for key in resnets if 'temopral_conv' in key]
  															__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						{
  															    'old': F"""output_blocks.{i}.0.temopral_conv""",
  															    'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : List[str]      					=						{k: sorted(_SCREAMING_SNAKE_CASE     ) for k, v in output_block_list.items()}
  															if ["conv.bias", "conv.weight"] in output_block_list.values():
  																				__a    : Tuple      					=						list(output_block_list.values()     ).index(['conv.bias', 'conv.weight']     )
  																				__a    : str      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.weight"""
  																				]
  																				__a    : Tuple      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.bias"""
  																				]
  
  																				# Clear attentions as they have been attributed above.
  																				if len(_SCREAMING_SNAKE_CASE     ) == 2:
  																									__a    : Optional[Any]      					=						[]
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Tuple      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : int      					=						{
  																				    'old': F"""output_blocks.{i}.1""",
  																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Optional[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : Optional[Any]      					=						{
  																				    'old': F"""output_blocks.{i}.2""",
  																				    'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  										else:
  															__a    : int      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=1     )
  															for path in resnet_0_paths:
  																				__a    : str      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), path['old']]     )
  																				__a    : Dict      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'resnets', str(_SCREAMING_SNAKE_CASE     ), path['new']]     )
  																				__a    : Optional[int]      					=						unet_state_dict[old_path]
  
  															__a    : Union[str, Any]      					=						[l for l in output_block_layers if 'temopral_conv' in l]
  															for path in temopral_conv_paths:
  																				__a    : Any      					=						path.split('temopral_conv.'     )[-1]
  																				__a    : int      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), str(_SCREAMING_SNAKE_CASE     ), 'temopral_conv', pruned_path]     )
  																				__a    : str      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'temp_convs', str(_SCREAMING_SNAKE_CASE     ), pruned_path]     )
  																				__a    : Optional[Any]      					=						unet_state_dict[old_path]
  
  					return new_checkpoint
  
  
  if __name__ == "__main__":
  	__lowercase			:		Optional[int] =			argparse.ArgumentParser()
  
  	parser.add_argument(
  	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  	)
  	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  	__lowercase			:		str =			parser.parse_args()
  
  	__lowercase			:		Dict =			torch.load(args.checkpoint_path, map_location='cpu')
  	__lowercase			:		Union[str, Any] =			UNetaDConditionModel()
  
  	__lowercase			:		Optional[Any] =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	__lowercase			:		Optional[int] =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	__lowercase			:		List[str] =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
  	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
  	# load state_dict
  	unet.load_state_dict(converted_ckpt)
  
  	unet.save_pretrained(args.dump_path)
  
  	# -- finish converting the unet --
  
  
  
  