

'''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

    _snake_case     =  True
except ImportError:
    _snake_case     =  False


try:
    from torch.hub import _get_torch_home

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

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

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







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

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







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


class    a__  :
   _SCREAMING_SNAKE_CASE    :     str	       =							{}


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





      """simple docstring"""





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

      _lowercase			:      List[str]			     =   d


   def __repr__( self  ):





      """simple docstring"""





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


   def __setattr__( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Any			     =   val
      _lowercase			:      int			     =   val
      _lowercase			:      Tuple			     =   key.split("."  )
      _lowercase			:      Union[str, Any]			     =   len(__a  ) - 1
      _lowercase			:      Any			     =   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:
               _lowercase			:      Optional[int]			     =   val
            else:
               _lowercase			:      str			     =   pointer[l]


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return self._pointer


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





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


   @staticmethod
   def 						_lowerCamelCase   ( _UpperCamelCase  ):





      """simple docstring"""





      with open(__a  ) as stream:
         _lowercase			:      Dict			     =   load(__a			,				Loader=__a  )
      return data


   def __str__( self  ):





      """simple docstring"""





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


   @classmethod
   def 						_lowerCamelCase   ( cls			,				_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Tuple			     =   cls.get_config_dict(__a			,				**__a  )
      return cls(__a  )






   @classmethod
   def 						_lowerCamelCase   ( cls			,				_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   kwargs.pop("cache_dir"			,				__a  )
      _lowercase			:      str			     =   kwargs.pop("force_download"			,				__a  )
      _lowercase			:      Optional[Any]			     =   kwargs.pop("resume_download"			,				__a  )
      _lowercase			:      str			     =   kwargs.pop("proxies"			,				__a  )
      _lowercase			:      int			     =   kwargs.pop("local_files_only"			,				__a  )

      if os.path.isdir(__a  ):
         _lowercase			:      str			     =   os.path.join(__a			,				__a  )
      elif os.path.isfile(__a  ) or is_remote_url(__a  ):
         _lowercase			:      List[str]			     =   pretrained_model_name_or_path
      else:
         _lowercase			:      int			     =   hf_bucket_url(__a			,				filename=__a			,				use_cdn=__a  )

      try:
         # Load from URL or cache if already cached
         _lowercase			:      Optional[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

         _lowercase			:      Tuple			     =   Config.load_yaml(__a  )

      except EnvironmentError:
         _lowercase			:      List[Any]			     =   "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 					_A		(	snake_case     )			->	List[str]:
   _lowercase			:      Dict			     =   torch.load("dump.pt"		,							map_location=in_tensor.device     )
   _lowercase			:      List[str]			     =   in_tensor.numpy()
   _lowercase			:      Optional[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()     )*1_00:.4f} %'''
       " element-wise mismatch"
   )
   raise Exception("tensors are all good"     )

   # Hugging face functions below







def 					_A		(	snake_case     )			->	Union[str, Any]:
   _lowercase			:      List[Any]			     =   urlparse(_lowerCamelCase     )
   return parsed.scheme in ("http", "https")







def 					_A		(	snake_case		,							snake_case		,							snake_case=True     )			->	Optional[int]:
   _lowercase			:      str			     =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   _lowercase			:      Any			     =   "/" not in model_id
   if legacy_format:
      return F'''{endpoint}/{model_id}-{filename}'''
   else:
      return F'''{endpoint}/{model_id}/{filename}'''







def 					_A		(	snake_case		,							snake_case		,							snake_case=None		,							snake_case=0		,							snake_case=None		,							)			->	Any:
   _lowercase			:      Optional[int]			     =   "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
   _lowercase			:      int			     =   {"user-agent": ua}
   if resume_size > 0:
      _lowercase			:      List[Any]			     =   "bytes=%d-" % (resume_size,)
   _lowercase			:      Tuple			     =   requests.get(_lowerCamelCase		,							stream=_lowerCamelCase		,							proxies=_lowerCamelCase		,							headers=_lowerCamelCase     )
   if response.status_code == 4_16:  # Range not satisfiable
      return
   _lowercase			:      List[str]			     =   response.headers.get("Content-Length"     )
   _lowercase			:      Any			     =   resume_size + int(_lowerCamelCase     ) if content_length is not None else None
   _lowercase			:      List[Any]			     =   tqdm(
       unit="B"		,							unit_scale=_lowerCamelCase		,							total=_lowerCamelCase		,							initial=_lowerCamelCase		,							desc="Downloading"		,							)
   for chunk in response.iter_content(chunk_size=10_24     ):
      if chunk:  # filter out keep-alive new chunks
         progress.update(len(_lowerCamelCase     )     )
         temp_file.write(_lowerCamelCase     )
   progress.close()







def 					_A		(	snake_case		,							snake_case=None		,							snake_case=False		,							snake_case=None		,							snake_case=10		,							snake_case=False		,							snake_case=None		,							snake_case=False		,							)			->	Dict:




   if cache_dir is None:
      _lowercase			:      int			     =   TRANSFORMERS_CACHE
   if isinstance(_lowerCamelCase		,							_lowerCamelCase     ):
      _lowercase			:      Any			     =   str(_lowerCamelCase     )

   os.makedirs(_lowerCamelCase		,							exist_ok=_lowerCamelCase     )

   _lowercase			:      Union[str, Any]			     =   None
   if not local_files_only:
      try:
         _lowercase			:      List[Any]			     =   requests.head(_lowerCamelCase		,							allow_redirects=_lowerCamelCase		,							proxies=_lowerCamelCase		,							timeout=_lowerCamelCase     )
         if response.status_code == 2_00:
            _lowercase			:      Any			     =   response.headers.get("ETag"     )
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   _lowercase			:      int			     =   url_to_filename(_lowerCamelCase		,							_lowerCamelCase     )

   # get cache path to put the file
   _lowercase			:      Dict			     =   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:
         _lowercase			:      int			     =   [
             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.
   _lowercase			:      int			     =   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:
         _lowercase			:      List[str]			     =   cache_path + ".incomplete"

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

         _lowercase			:      Optional[int]			     =   _resumable_file_manager
         if os.path.exists(_lowerCamelCase     ):
            _lowercase			:      str			     =   os.stat(_lowerCamelCase     ).st_size
         else:
            _lowercase			:      Union[str, Any]			     =   0
      else:
         _lowercase			:      int			     =   partial(tempfile.NamedTemporaryFile		,							dir=_lowerCamelCase		,							delete=_lowerCamelCase     )
         _lowercase			:      List[str]			     =   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     )

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

   return cache_path







def 					_A		(	snake_case		,							snake_case=None     )			->	str:
   _lowercase			:      List[Any]			     =   url.encode("utf-8"     )
   _lowercase			:      Union[str, Any]			     =   shaaaa(_lowerCamelCase     )
   _lowercase			:      str			     =   url_hash.hexdigest()

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

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

   return filename







def 					_A		(	snake_case		,							snake_case=None		,							snake_case=False		,							snake_case=None		,							snake_case=False		,							snake_case=None		,							snake_case=False		,							snake_case=False		,							snake_case=False		,							)			->	int:




   if cache_dir is None:
      _lowercase			:      Any			     =   TRANSFORMERS_CACHE
   if isinstance(_lowerCamelCase		,							_lowerCamelCase     ):
      _lowercase			:      int			     =   str(_lowerCamelCase     )
   if isinstance(_lowerCamelCase		,							_lowerCamelCase     ):
      _lowercase			:      int			     =   str(_lowerCamelCase     )

   if is_remote_url(_lowerCamelCase     ):
      # URL, so get it from the cache (downloading if necessary)
      _lowercase			:      Tuple			     =   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.
      _lowercase			:      str			     =   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/"
      _lowercase			:      Tuple			     =   os.path.split(_lowerCamelCase     )
      _lowercase			:      Union[str, Any]			     =   output_file.replace("."		,							"-"     ) + "-extracted"
      _lowercase			:      Optional[int]			     =   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
      _lowercase			:      Any			     =   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     ):
            _lowercase			:      List[Any]			     =   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 					_A		(	snake_case		,							snake_case=","     )			->	Tuple:
   assert isinstance(_lowerCamelCase		,							_lowerCamelCase     )
   if os.path.isfile(_lowerCamelCase     ):
      with open(_lowerCamelCase     ) as f:
         _lowercase			:      Optional[int]			     =   eval(f.read()     )
   else:
      _lowercase			:      Union[str, Any]			     =   requests.get(_lowerCamelCase     )
      try:
         _lowercase			:      str			     =   requests.json()
      except Exception:
         _lowercase			:      str			     =   req.content.decode()
         assert data is not None, "could not connect"
         try:
            _lowercase			:      Optional[Any]			     =   eval(_lowerCamelCase     )
         except Exception:
            _lowercase			:      int			     =   data.split("\n"     )
      req.close()
   return data







def 					_A		(	snake_case     )			->	Any:
   _lowercase			:      str			     =   requests.get(_lowerCamelCase     )
   _lowercase			:      Any			     =   np.array(Image.open(BytesIO(response.content     )     )     )
   return img







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







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







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







def 					_A		(	snake_case		,							snake_case=1     )			->	Optional[int]:
   return (images[i : i + batch] for i in range(0		,							len(_lowerCamelCase     )		,							_lowerCamelCase     ))











from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return 0.0






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]  )]  )
							_lowerCAmelCase     :							Optional[int]    =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]  )]  )
							return lowest, highest






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    512
							_lowerCAmelCase     :							Union[str, Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							Optional[Any]    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							int    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							str    =    np.abs(np.fft.fft(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Union[str, Any]    =    20 * np.logaa(_lowerCamelCase  )

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				, samplerate / 2 - 1  )
							plt.xlabel("Frequency (Hz)"  )
							plt.xscale("log"  )

							# Display within reasonable bounds
							_lowerCAmelCase     :							List[Any]    =    get_bounds(_lowerCamelCase				, _lowerCamelCase  )
							plt.ylim(max([-80, bounds[0]]  )				, min([80, bounds[1]]  )  )
							plt.ylabel("Gain (dB)"  )

							plt.plot(_lowerCamelCase  )
							plt.show()






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    512
							_lowerCAmelCase     :							Optional[Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							str    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							Optional[Any]    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							Optional[Any]    =    np.angle(np.fft.fft(_lowerCamelCase  )  )

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				, samplerate / 2 - 1  )
							plt.xlabel("Frequency (Hz)"  )
							plt.xscale("log"  )

							plt.ylim(-2 * pi				, 2 * pi  )
							plt.ylabel("Phase shift (Radians)"  )
							plt.plot(np.unwrap(_lowerCamelCase				, -2 * pi  )  )
							plt.show()
