


'''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_ :     List[str]								=      True
except ImportError:
 UpperCAmelCase_ :     int								=      False


try:
 from torch.hub import _get_torch_home

 UpperCAmelCase_ :     Optional[int]								=      _get_torch_home()
except ImportError:
 UpperCAmelCase_ :     str								=      os.path.expanduser(
     os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
 )

UpperCAmelCase_ :     int								=      os.path.join(torch_cache_home, 'transformers')

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


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

 __A  :		Union[str, Any]       =			[]
 with open(UpperCamelCase__			) as f:
  for object in f.readlines():
   vg_attrs.append(object.split(','			)[0].lower().strip()			)
 return vg_classes, vg_attrs


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

class 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		List[str]    							=						{}


 def __init__(      self      ,      __lowercase      ,      __lowercase = "root"      ,      __lowercase=0						):

  """simple docstring"""







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

  __A  :		List[str]       =			d


 def __repr__(      self						):

  """simple docstring"""







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


 def __setattr__(      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  return self._pointer


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  with open(F"""{file_name}"""      ,      'w'						) as stream:
   dump(__lowercase      ,      __lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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


 @staticmethod
 def  snake_case__   (      __lowercase						):

  """simple docstring"""







  with open(__lowercase						) as stream:
   __A  :		Any       =			load(__lowercase      ,      Loader=__lowercase						)
  return data


 def __str__(      self						):

  """simple docstring"""







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


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      **__lowercase						):

  """simple docstring"""







  __A     ,__A  :		str       =			cls.get_config_dict(__lowercase      ,      **__lowercase						)
  return cls(__lowercase						)







 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			kwargs.pop('cache_dir'      ,      __lowercase						)
  __A  :		Tuple       =			kwargs.pop('force_download'      ,      __lowercase						)
  __A  :		Optional[int]       =			kwargs.pop('resume_download'      ,      __lowercase						)
  __A  :		Tuple       =			kwargs.pop('proxies'      ,      __lowercase						)
  __A  :		int       =			kwargs.pop('local_files_only'      ,      __lowercase						)

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

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

   __A  :		List[str]       =			Config.load_yaml(__lowercase						)

  except EnvironmentError:
   __A  :		Dict       =			'Can\'t load config for'
   raise EnvironmentError(__lowercase						)

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

  return Config.load_yaml(__lowercase						), kwargs


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


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					Tuple=True			):
 __A  :		Any       =			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 __A  :		List[str]       =			'/' not in model_id
 if legacy_format:
  return f"""{endpoint}/{model_id}-{filename}"""
 else:
  return f"""{endpoint}/{model_id}/{filename}"""


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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[Any]=None,				UpperCamelCase__       :					List[Any]=False,				UpperCamelCase__       :					List[str]=None,				UpperCamelCase__       :					Optional[int]=10,				UpperCamelCase__       :					Dict=False,				UpperCamelCase__       :					List[str]=None,				UpperCamelCase__       :					Optional[Any]=False,				):
 if cache_dir is None:
  __A  :		List[str]       =			TRANSFORMERS_CACHE
 if isinstance(UpperCamelCase__,				UpperCamelCase__			):
  __A  :		List[Any]       =			str(UpperCamelCase__			)

 os.makedirs(UpperCamelCase__,				exist_ok=UpperCamelCase__			)

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

 __A  :		int       =			url_to_filename(UpperCamelCase__,				UpperCamelCase__			)

 # get cache path to put the file
 __A  :		List[Any]       =			os.path.join(UpperCamelCase__,				UpperCamelCase__			)

 # 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(UpperCamelCase__			):
   return cache_path
  else:
   __A  :		str       =			[
       file
       for file in fnmatch.filter(os.listdir(UpperCamelCase__			),				filename + '.*'			)
       if not file.endswith('.json'			) and not file.endswith('.lock'			)
   ]
   if len(UpperCamelCase__			) > 0:
    return os.path.join(UpperCamelCase__,				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(UpperCamelCase__			) 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(UpperCamelCase__			):
  # If the download just completed while the lock was activated.
  if os.path.exists(UpperCamelCase__			) and not force_download:
   # Even if returning early like here, the lock will be released.
   return cache_path

  if resume_download:
   __A  :		Optional[int]       =			cache_path + '.incomplete'

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

   __A  :		List[str]       =			_resumable_file_manager
   if os.path.exists(UpperCamelCase__			):
    __A  :		int       =			os.stat(UpperCamelCase__			).st_size
   else:
    __A  :		Union[str, Any]       =			0
  else:
   __A  :		Dict       =			partial(tempfile.NamedTemporaryFile,				dir=UpperCamelCase__,				delete=UpperCamelCase__			)
   __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',				UpperCamelCase__,				temp_file.name,				)

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

  os.replace(temp_file.name,				UpperCamelCase__			)

  __A  :		str       =			{'url': url, 'etag': etag}
  __A  :		Dict       =			cache_path + '.json'
  with open(UpperCamelCase__,				'w'			) as meta_file:
   json.dump(UpperCamelCase__,				UpperCamelCase__			)

 return cache_path


def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Dict=None			):
 __A  :		Tuple       =			url.encode('utf-8'			)
 __A  :		int       =			shaaaa(UpperCamelCase__			)
 __A  :		Tuple       =			url_hash.hexdigest()

 if etag:
  __A  :		Tuple       =			etag.encode('utf-8'			)
  __A  :		Optional[Any]       =			shaaaa(UpperCamelCase__			)
  filename += "." + etag_hash.hexdigest()

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

 return filename


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

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

 if extract_compressed_file:
  if not is_zipfile(UpperCamelCase__			) and not tarfile.is_tarfile(UpperCamelCase__			):
   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  :		List[str]       =			os.path.split(UpperCamelCase__			)
  __A  :		Optional[Any]       =			output_file.replace('.',				'-'			) + '-extracted'
  __A  :		str       =			os.path.join(UpperCamelCase__,				UpperCamelCase__			)

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

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

  return output_path_extracted

 return output_path


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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Dict       =			requests.get(UpperCamelCase__			)
 __A  :		Optional[int]       =			np.array(Image.open(BytesIO(response.content			)			)			)
 return img


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


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


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


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










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			0.0

  # Start at index 1 and do index 0 at the end.
  for i in range(1      ,      self.order + 1						):
   result += (
       self.b_coeffs[i] * self.input_history[i - 1]
       - self.a_coeffs[i] * self.output_history[i - 1]
   )

  __A  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






