

import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  _a									=  True
except ImportError:
  _a									=  False


try:
  from torch.hub import _get_torch_home

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

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     Dict=OBJECTS       ,   SCREAMING_SNAKE_CASE						:     Optional[int]=ATTRIBUTES						)       ->       Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:  Any        = []
 with open(SCREAMING_SNAKE_CASE						) as f:
  for object in f.readlines():
   vg_classes.append(object.split(','						)[0].lower().strip()						)

 __lowerCAmelCase:  Tuple        = []
 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 _a					(			SCREAMING_SNAKE_CASE						:     Tuple						)       ->       List[str]:

 """simple docstring"""






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






class 				A_					:
     _lowercase     :	List[str]        =      {}


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

      __lowerCAmelCase:  Dict        = d


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


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


     def     UpperCAmelCase    (				self     :						Dict   )				->      int:
      return self._pointer


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


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Dict   )				->      str:
      with open(F'''{file_name}'''     ,    'w'   ) as stream:
       json.dump(UpperCAmelCase     ,    UpperCAmelCase   )


     @staticmethod
     def     UpperCAmelCase    (				UpperCAmelCase     :						str   )				->      Tuple:
      with open(UpperCAmelCase   ) as stream:
       __lowerCAmelCase:  Optional[int]        = load(UpperCAmelCase     ,    Loader=UpperCAmelCase   )
      return data


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


     @classmethod
     def     UpperCAmelCase    (				cls     :						Dict     ,    UpperCAmelCase     :						str     ,    **UpperCAmelCase     :						int   )				->      Optional[Any]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  str        = cls.get_config_dict(UpperCAmelCase     ,    **UpperCAmelCase   )
      return cls(UpperCAmelCase   )


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

      if os.path.isdir(UpperCAmelCase   ):
       __lowerCAmelCase:  Tuple        = os.path.join(UpperCAmelCase     ,    UpperCAmelCase   )
      elif os.path.isfile(UpperCAmelCase   ) or is_remote_url(UpperCAmelCase   ):
       __lowerCAmelCase:  Optional[Any]        = pretrained_model_name_or_path
      else:
       __lowerCAmelCase:  Dict        = hf_bucket_url(UpperCAmelCase     ,    filename=UpperCAmelCase     ,    use_cdn=UpperCAmelCase   )

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

       __lowerCAmelCase:  str        = Config.load_yaml(UpperCAmelCase   )

      except EnvironmentError:
       __lowerCAmelCase:  Tuple        = 'Can\'t load config for'
       raise EnvironmentError(UpperCAmelCase   )

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

      return Config.load_yaml(UpperCAmelCase   ), kwargs







def _a					(			SCREAMING_SNAKE_CASE						:     Tuple						)       ->       Tuple:

 """simple docstring"""






 __lowerCAmelCase:  int        = torch.load('dump.pt'       ,   map_location=in_tensor.device						)
 __lowerCAmelCase:  Optional[Any]        = in_tensor.numpy()
 __lowerCAmelCase:  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()						)*1_00:.4f} %'''
     " element-wise mismatch"
 )
 raise Exception('tensors are all good'						)

 # Hugging face functions below







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = urlparse(SCREAMING_SNAKE_CASE						)
 return parsed.scheme in ("http", "https")







def _a					(			SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     List[Any]=True						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 __lowerCAmelCase:  List[str]        = '/' not in model_id
 if legacy_format:
  return f'''{endpoint}/{model_id}-{filename}'''
 else:
  return f'''{endpoint}/{model_id}/{filename}'''







def _a					(			SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Dict=0       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   )       ->       Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = '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
 __lowerCAmelCase:  Optional[Any]        = {'user-agent': ua}
 if resume_size > 0:
  __lowerCAmelCase:  List[str]        = 'bytes=%d-' % (resume_size,)
 __lowerCAmelCase:  Dict        = requests.get(SCREAMING_SNAKE_CASE       ,   stream=SCREAMING_SNAKE_CASE       ,   proxies=SCREAMING_SNAKE_CASE       ,   headers=SCREAMING_SNAKE_CASE						)
 if response.status_code == 4_16:  # Range not satisfiable
  return
 __lowerCAmelCase:  List[str]        = response.headers.get('Content-Length'						)
 __lowerCAmelCase:  Union[str, Any]        = resume_size + int(SCREAMING_SNAKE_CASE						) if content_length is not None else None
 __lowerCAmelCase:  int        = 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=10_24						):
  if chunk:  # filter out keep-alive new chunks
   progress.update(len(SCREAMING_SNAKE_CASE						)						)
   temp_file.write(SCREAMING_SNAKE_CASE						)
 progress.close()







def _a					(			SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     Dict=False       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     List[Any]=10       ,   SCREAMING_SNAKE_CASE						:     str=False       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=False       ,   )       ->       Dict:

 """simple docstring"""






 if cache_dir is None:
  __lowerCAmelCase:  Union[str, Any]        = TRANSFORMERS_CACHE
 if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  List[str]        = str(SCREAMING_SNAKE_CASE						)

 os.makedirs(SCREAMING_SNAKE_CASE       ,   exist_ok=SCREAMING_SNAKE_CASE						)

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

 __lowerCAmelCase:  str        = url_to_filename(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 # get cache path to put the file
 __lowerCAmelCase:  int        = 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:
   __lowerCAmelCase:  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.
 __lowerCAmelCase:  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:
   __lowerCAmelCase:  str        = cache_path + '.incomplete'

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

   __lowerCAmelCase:  List[str]        = _resumable_file_manager
   if os.path.exists(SCREAMING_SNAKE_CASE						):
    __lowerCAmelCase:  Tuple        = os.stat(SCREAMING_SNAKE_CASE						).st_size
   else:
    __lowerCAmelCase:  int        = 0
  else:
   __lowerCAmelCase:  Any        = partial(tempfile.NamedTemporaryFile       ,   dir=SCREAMING_SNAKE_CASE       ,   delete=SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  List[Any]        = 0

  # Download to temporary file, then copy to cache dir once finished.
  # Otherwise you get corrupt cache entries if the download gets interrupted.
  with temp_file_manager() as temp_file:
   print(
       '%s not found in cache or force_download set to True, downloading to %s'       ,   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						)

  __lowerCAmelCase:  List[str]        = {'url': url, 'etag': etag}
  __lowerCAmelCase:  int        = cache_path + '.json'
  with open(SCREAMING_SNAKE_CASE       ,   'w'						) as meta_file:
   json.dump(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 return cache_path







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     str=None						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = url.encode('utf-8'						)
 __lowerCAmelCase:  Tuple        = shaaaa(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Optional[Any]        = url_hash.hexdigest()

 if etag:
  __lowerCAmelCase:  int        = etag.encode('utf-8'						)
  __lowerCAmelCase:  List[Any]        = shaaaa(SCREAMING_SNAKE_CASE						)
  filename += "." + etag_hash.hexdigest()

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

 return filename







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Dict=None       ,   SCREAMING_SNAKE_CASE						:     str=False       ,   SCREAMING_SNAKE_CASE						:     Dict=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=False       ,   SCREAMING_SNAKE_CASE						:     Tuple=None       ,   SCREAMING_SNAKE_CASE						:     int=False       ,   SCREAMING_SNAKE_CASE						:     Any=False       ,   SCREAMING_SNAKE_CASE						:     int=False       ,   )       ->       Dict:

 """simple docstring"""






 if cache_dir is None:
  __lowerCAmelCase:  List[Any]        = TRANSFORMERS_CACHE
 if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  List[Any]        = str(SCREAMING_SNAKE_CASE						)
 if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  List[str]        = str(SCREAMING_SNAKE_CASE						)

 if is_remote_url(SCREAMING_SNAKE_CASE						):
  # URL, so get it from the cache (downloading if necessary)
  __lowerCAmelCase:  int        = 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.
  __lowerCAmelCase:  List[str]        = 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/"
  __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = os.path.split(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = output_file.replace('.'       ,   '-'						) + '-extracted'
  __lowerCAmelCase:  Dict        = 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
  __lowerCAmelCase:  Optional[Any]        = 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						):
    __lowerCAmelCase:  int        = 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 _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]=","						)       ->       Any:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     Dict						)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = requests.get(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = np.array(Image.open(BytesIO(response.content						)						)						)
 return img







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       List[str]:

 """simple docstring"""






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







def _a					(			)       ->       Any:

 """simple docstring"""






 print(f'''{os.path.abspath(os.path.join(SCREAMING_SNAKE_CASE       ,   os.pardir						)						)}/demo.ipynb'''						)







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str="RGB"						)       ->       Optional[int]:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=1						)       ->       str:

 """simple docstring"""






 return (images[i : i + batch] for i in range(0       ,   len(SCREAMING_SNAKE_CASE						)       ,   SCREAMING_SNAKE_CASE						))








import numpy as np







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





