
"""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					:List[str]    = True
except ImportError:
  a					:Tuple    = False


try:
  from torch.hub import _get_torch_home

  a					:Optional[int]    = _get_torch_home()
except ImportError:
  a					:List[Any]    = os.path.expanduser(
      os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
  )

a					:Optional[Any]    = os.path.join(torch_cache_home, "transformers")

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




def  _lowercase					(    __lowerCAmelCase=OBJECTS  ,					__lowerCAmelCase=ATTRIBUTES							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[]
      with open(__lowerCAmelCase							) as f:
            for object in f.readlines():
                  vg_classes.append(object.split(""","""							)[0].lower().strip()							)

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




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

class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:str       = {}

     def __init__(						self	,    _a	,    _a = "root"	,    _a=0  )  ->      List[Any]:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d

     def __repr__(						self  )  ->      Optional[int]:


           """simple docstring"""


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

     def __setattr__(						self	,    _a	,    _a  )  ->      Optional[int]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           return self._pointer

     def 					_a			(						self	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


           with open(f'''{file_name}'''	,    """w"""  ) as stream:
                 dump(_a	,    _a  )

     def 					_a			(						self	,    _a	,    _a  )  ->      int:


           """simple docstring"""


           with open(f'''{file_name}'''	,    """w"""  ) as stream:
                 json.dump(_a	,    _a  )

     @staticmethod
     def 					_a			(						_a  )  ->      Optional[Any]:


           """simple docstring"""


           with open(_a  ) as stream:
                 SCREAMING_SNAKE_CASE__   :      int      		=	load(_a	,    Loader=_a  )
           return data

     def __str__(						self  )  ->      Optional[int]:


           """simple docstring"""


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

     @classmethod
     def 					_a			(						cls	,    _a	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	cls.get_config_dict(_a	,    **_a  )
           return cls(_a  )





     @classmethod
     def 					_a			(						cls	,    _a	,    **_a  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	kwargs.pop("""cache_dir"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	kwargs.pop("""force_download"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	kwargs.pop("""resume_download"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	kwargs.pop("""proxies"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	kwargs.pop("""local_files_only"""	,    _a  )

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

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

                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	Config.load_yaml(_a  )

           except EnvironmentError:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""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  _lowercase					(    __lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      int      		=	torch.load("""dump.pt"""  ,					map_location=in_tensor.device							)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	in_tensor.numpy()
      SCREAMING_SNAKE_CASE__   :      Any      		=	out_tensor.numpy()[0]
      print(na.shape  ,					na[0, 0, :5]							)
      print(na.shape  ,					na[0, 0, :5]							)
      assert np.allclose(__lowerCAmelCase  ,					__lowerCAmelCase  ,					rtol=0.01  ,					atol=0.1							), (
          F'''{sum([1 for x in np.isclose(__lowerCAmelCase  ,					__lowerCAmelCase  ,					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					(    __lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	urlparse(__lowerCAmelCase							)
      return parsed.scheme in ("http", "https")




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




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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=False  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=10  ,					__lowerCAmelCase=False  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=False  ,					)       ->				Union[str, Any]:
      if cache_dir is None:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	TRANSFORMERS_CACHE
      if isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	str(__lowerCAmelCase							)

      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)

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

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	url_to_filename(__lowerCAmelCase  ,					__lowerCAmelCase							)

      # get cache path to put the file
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

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

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

            if resume_download:
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	cache_path + """.incomplete"""

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

                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	_resumable_file_manager
                  if os.path.exists(__lowerCAmelCase							):
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	os.stat(__lowerCAmelCase							).st_size
                  else:
                        SCREAMING_SNAKE_CASE__   :      str      		=	0
            else:
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	partial(tempfile.NamedTemporaryFile  ,					dir=__lowerCAmelCase  ,					delete=__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      Optional[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"""  ,					__lowerCAmelCase  ,					temp_file.name  ,					)

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

            os.replace(temp_file.name  ,					__lowerCAmelCase							)

            SCREAMING_SNAKE_CASE__   :      List[str]      		=	{"""url""": url, """etag""": etag}
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	cache_path + """.json"""
            with open(__lowerCAmelCase  ,					"""w"""							) as meta_file:
                  json.dump(__lowerCAmelCase  ,					__lowerCAmelCase							)

      return cache_path




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=None							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	url.encode("""utf-8"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	shaaaa(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	url_hash.hexdigest()

      if etag:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	etag.encode("""utf-8"""							)
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	shaaaa(__lowerCAmelCase							)
            filename += "." + etag_hash.hexdigest()

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

      return filename




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

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

      if extract_compressed_file:
            if not is_zipfile(__lowerCAmelCase							) and not tarfile.is_tarfile(__lowerCAmelCase							):
                  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/"
            SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.split(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	output_file.replace("""."""  ,					"""-"""							) + """-extracted"""
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

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

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

            return output_path_extracted

      return output_path




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




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




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




def  _lowercase					(    )       ->				Optional[Any]:
      print(F'''{os.path.abspath(os.path.join(__lowerCAmelCase  ,					os.pardir							)							)}/demo.ipynb'''							)




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




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







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      if divisor % 5 == 0 or divisor % 2 == 0:
            return 0
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	1
      SCREAMING_SNAKE_CASE__   :      int      		=	1
      while repunit:
            SCREAMING_SNAKE_CASE__   :      str      		=	(10 * repunit + 1) % divisor
            repunit_index += 1
      return repunit_index




def  _lowercase					(    __lowerCAmelCase = 100_0000							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	limit - 1
      if divisor % 2 == 0:
            divisor += 1
      while least_divisible_repunit(__lowerCAmelCase							) <= limit:
            divisor += 2
      return divisor


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




