"""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

  __UpperCamelCase       :    str         =		True
except ImportError:
  __UpperCamelCase       :    Optional[int]         =		False


try:
  from torch.hub import _get_torch_home

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

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

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


def   __UpperCAmelCase     (	_snake_case		:       Tuple=OBJECTS,				_snake_case		:       int=ATTRIBUTES       ):
   _lowercase							     =       []
   with open(_snake_case       ) as f:
      for object in f.readlines():
         vg_classes.append(object.split(","       )[0].lower().strip()       )

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


def   __UpperCAmelCase     (	_snake_case		:       int       ):
   _lowercase							     =       OrderedDict()
   with open(_snake_case,				"rb"       ) as f:
      _lowercase							     =       pkl.load(_snake_case       )["model"]
   for k in copy.deepcopy(list(ckp.keys()       )       ):
      _lowercase							     =       ckp.pop(_snake_case       )
      if isinstance(_snake_case,				np.ndarray       ):
         _lowercase							     =       torch.tensor(_snake_case       )
      else:
         assert isinstance(_snake_case,				torch.tensor       ), type(_snake_case       )
      _lowercase							     =       v
   return r
class   UpperCAmelCase_	:
    snake_case_											=      {}
    def __init__(	self			:       List[Any]   ,						_lowercase			:       dict   ,						_lowercase			:       str = "root"   ,						_lowercase			:       str=0		)						->    int:
       _lowercase							     =       name
       _lowercase							     =       level
       _lowercase							     =       {}
       for k, v in dictionary.items():
          if v is None:
             raise ValueError()
          _lowercase							     =       copy.deepcopy(_lowercase		)
          _lowercase							     =       copy.deepcopy(_lowercase		)
          if isinstance(_lowercase   ,						_lowercase		):
             _lowercase							     =       Config(_lowercase   ,						name=_lowercase   ,						level=level + 1		)
          _lowercase							     =       v
          setattr(self   ,						_lowercase   ,						_lowercase		)

       _lowercase							     =       d
    def __repr__(	self			:       Optional[int]		)						->    Tuple:
       return str(list((self._pointer.keys())		)		)
    def __setattr__(	self			:       Dict   ,						_lowercase			:       Tuple   ,						_lowercase			:       Optional[int]		)						->    List[str]:
       _lowercase							     =       val
       _lowercase							     =       val
       _lowercase							     =       key.split("."		)
       _lowercase							     =       len(_lowercase		) - 1
       _lowercase							     =       self._pointer
       if len(_lowercase		) > 1:
          for i, l in enumerate(_lowercase		):
             if hasattr(self   ,						_lowercase		) and isinstance(getattr(self   ,						_lowercase		)   ,						_lowercase		):
                setattr(getattr(self   ,						_lowercase		)   ,						".".join(levels[i:]		)   ,						_lowercase		)
             if l == last_level:
                _lowercase							     =       val
             else:
                _lowercase							     =       pointer[l]
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Union[str, Any]:
       return self._pointer
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]		)						->    Tuple:
       with open(f"""{file_name}"""   ,						"w"		) as stream:
          dump(_lowercase   ,						_lowercase		)
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]		)						->    str:
       with open(f"""{file_name}"""   ,						"w"		) as stream:
          json.dump(_lowercase   ,						_lowercase		)
    @staticmethod
    def 		_lowerCamelCase      (	_lowercase			:       Optional[Any]		)						->    Optional[Any]:
       with open(_lowercase		) as stream:
          _lowercase							     =       load(_lowercase   ,						Loader=_lowercase		)
       return data
    def __str__(	self			:       int		)						->    List[str]:
       _lowercase							     =       "    "
       if self._name != "root":
          _lowercase							     =       f"""{t * (self._level-1)}{self._name}:\n"""
       else:
          _lowercase							     =       ""
       _lowercase							     =       self._level
       for i, (k, v) in enumerate(self._pointer.items()		):
          if isinstance(_lowercase   ,						_lowercase		):
             r += f"""{t * (self._level)}{v}\n"""
             self._level += 1
          else:
             r += f"""{t * (self._level)}{k}: {v} ({type(_lowercase		).__name__})\n"""
          _lowercase							     =       level
       return r[:-1]
    @classmethod
    def 		_lowerCamelCase      (	cls			:       List[str]   ,						_lowercase			:       str   ,						**_lowercase			:       Union[str, Any]		)						->    Tuple:
       _lowercase     ,  _lowercase							     =       cls.get_config_dict(_lowercase   ,						**_lowercase		)
       return cls(_lowercase		)




    @classmethod
    def 		_lowerCamelCase      (	cls			:       Any   ,						_lowercase			:       str   ,						**_lowercase			:       Dict		)						->    Any:
       _lowercase							     =       kwargs.pop("cache_dir"   ,						_lowercase		)
       _lowercase							     =       kwargs.pop("force_download"   ,						_lowercase		)
       _lowercase							     =       kwargs.pop("resume_download"   ,						_lowercase		)
       _lowercase							     =       kwargs.pop("proxies"   ,						_lowercase		)
       _lowercase							     =       kwargs.pop("local_files_only"   ,						_lowercase		)

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

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

          _lowercase							     =       Config.load_yaml(_lowercase		)

       except EnvironmentError:
          _lowercase							     =       "Can't load config for"
          raise EnvironmentError(_lowercase		)

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

       return Config.load_yaml(_lowercase		), kwargs


def   __UpperCAmelCase     (	_snake_case		:       str       ):
   _lowercase							     =       torch.load("dump.pt",				map_location=in_tensor.device       )
   _lowercase							     =       in_tensor.numpy()
   _lowercase							     =       out_tensor.numpy()[0]
   print(na.shape,				na[0, 0, :5]       )
   print(na.shape,				na[0, 0, :5]       )
   assert np.allclose(_snake_case,				_snake_case,				rtol=0.0_1,				atol=0.1       ), (
       f"""{sum([1 for x in np.isclose(_snake_case,				_snake_case,				rtol=0.0_1,				atol=0.1       ).flatten() if x is False]       )/len(na.flatten()       )*1_0_0:.4f} %"""
       " element-wise mismatch"
   )
   raise Exception("tensors are all good"       )

   # Hugging face functions below


def   __UpperCAmelCase     (	_snake_case		:       int       ):
   _lowercase							     =       urlparse(_snake_case       )
   return parsed.scheme in ("http", "https")


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Optional[int]=True       ):
   _lowercase							     =       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   _lowercase							     =       "/" not in model_id
   if legacy_format:
      return f"""{endpoint}/{model_id}-{filename}"""
   else:
      return f"""{endpoint}/{model_id}/{filename}"""


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Any,				_snake_case		:       Optional[int]=None,				_snake_case		:       int=0,				_snake_case		:       Union[str, Any]=None,				):
   _lowercase							     =       "python/{}".format(sys.version.split()[0]       )
   if _torch_available:
      ua += "; torch/{}".format(torch.__version__       )
   if isinstance(_snake_case,				_snake_case       ):
      ua += "; " + "; ".join("{}/{}".format(_snake_case,				_snake_case       ) for k, v in user_agent.items()       )
   elif isinstance(_snake_case,				_snake_case       ):
      ua += "; " + user_agent
   _lowercase							     =       {"user-agent": ua}
   if resume_size > 0:
      _lowercase							     =       "bytes=%d-" % (resume_size,)
   _lowercase							     =       requests.get(_snake_case,				stream=_snake_case,				proxies=_snake_case,				headers=_snake_case       )
   if response.status_code == 4_1_6:  # Range not satisfiable
      return
   _lowercase							     =       response.headers.get("Content-Length"       )
   _lowercase							     =       resume_size + int(_snake_case       ) if content_length is not None else None
   _lowercase							     =       tqdm(
       unit="B",				unit_scale=_snake_case,				total=_snake_case,				initial=_snake_case,				desc="Downloading",				)
   for chunk in response.iter_content(chunk_size=1_0_2_4       ):
      if chunk:  # filter out keep-alive new chunks
         progress.update(len(_snake_case       )       )
         temp_file.write(_snake_case       )
   progress.close()


def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str=None,				_snake_case		:       Union[str, Any]=False,				_snake_case		:       Dict=None,				_snake_case		:       List[Any]=1_0,				_snake_case		:       str=False,				_snake_case		:       Union[str, Any]=None,				_snake_case		:       Optional[Any]=False,				):
   if cache_dir is None:
      _lowercase							     =       TRANSFORMERS_CACHE
   if isinstance(_snake_case,				_snake_case       ):
      _lowercase							     =       str(_snake_case       )

   os.makedirs(_snake_case,				exist_ok=_snake_case       )

   _lowercase							     =       None
   if not local_files_only:
      try:
         _lowercase							     =       requests.head(_snake_case,				allow_redirects=_snake_case,				proxies=_snake_case,				timeout=_snake_case       )
         if response.status_code == 2_0_0:
            _lowercase							     =       response.headers.get("ETag"       )
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   _lowercase							     =       url_to_filename(_snake_case,				_snake_case       )

   # get cache path to put the file
   _lowercase							     =       os.path.join(_snake_case,				_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(_snake_case       ):
         return cache_path
      else:
         _lowercase							     =       [
             file
             for file in fnmatch.filter(os.listdir(_snake_case       ),				filename + ".*"       )
             if not file.endswith(".json"       ) and not file.endswith(".lock"       )
         ]
         if len(_snake_case       ) > 0:
            return os.path.join(_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(_snake_case       ) and not force_download:
      return cache_path

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

      if resume_download:
         _lowercase							     =       cache_path + ".incomplete"

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

         _lowercase							     =       _resumable_file_manager
         if os.path.exists(_snake_case       ):
            _lowercase							     =       os.stat(_snake_case       ).st_size
         else:
            _lowercase							     =       0
      else:
         _lowercase							     =       partial(tempfile.NamedTemporaryFile,				dir=_snake_case,				delete=_snake_case       )
         _lowercase							     =       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",				_snake_case,				temp_file.name,				)

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

      os.replace(temp_file.name,				_snake_case       )

      _lowercase							     =       {"url": url, "etag": etag}
      _lowercase							     =       cache_path + ".json"
      with open(_snake_case,				"w"       ) as meta_file:
         json.dump(_snake_case,				_snake_case       )

   return cache_path


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       Union[str, Any]=None       ):
   _lowercase							     =       url.encode("utf-8"       )
   _lowercase							     =       shaaaa(_snake_case       )
   _lowercase							     =       url_hash.hexdigest()

   if etag:
      _lowercase							     =       etag.encode("utf-8"       )
      _lowercase							     =       shaaaa(_snake_case       )
      filename += "." + etag_hash.hexdigest()

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

   return filename


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Optional[Any]=None,				_snake_case		:       Union[str, Any]=False,				_snake_case		:       Dict=None,				_snake_case		:       Optional[int]=False,				_snake_case		:       Tuple=None,				_snake_case		:       Optional[int]=False,				_snake_case		:       int=False,				_snake_case		:       Optional[int]=False,				):
   if cache_dir is None:
      _lowercase							     =       TRANSFORMERS_CACHE
   if isinstance(_snake_case,				_snake_case       ):
      _lowercase							     =       str(_snake_case       )
   if isinstance(_snake_case,				_snake_case       ):
      _lowercase							     =       str(_snake_case       )

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

   if extract_compressed_file:
      if not is_zipfile(_snake_case       ) and not tarfile.is_tarfile(_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/"
      _lowercase     ,  _lowercase							     =       os.path.split(_snake_case       )
      _lowercase							     =       output_file.replace(".",				"-"       ) + "-extracted"
      _lowercase							     =       os.path.join(_snake_case,				_snake_case       )

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

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

      return output_path_extracted

   return output_path


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


def   __UpperCAmelCase     (	_snake_case		:       Tuple       ):
   _lowercase							     =       requests.get(_snake_case       )
   _lowercase							     =       np.array(Image.open(BytesIO(response.content       )       )       )
   return img


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


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


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


def   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       Optional[int]=1       ):
   return (images[i : i + batch] for i in range(0,				len(_snake_case       ),				_snake_case       ))
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()