



"""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

   SCREAMING_SNAKE_CASE_         =  True
except ImportError:
   SCREAMING_SNAKE_CASE_         =  False


try:
   from torch.hub import _get_torch_home

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

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

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




def       lowercase__  (   lowerCAmelCase			:   str=OBJECTS	,			lowerCAmelCase			:   Optional[int]=ATTRIBUTES	)	-> str:






    """simple docstring"""




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

    UpperCAmelCase       =   []
    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       lowercase__  (   lowerCAmelCase			:   str	)	-> int:






    """simple docstring"""




    UpperCAmelCase       =   OrderedDict()
    with open(SCREAMING_SNAKE_CASE_	,			'rb'	) as f:
        UpperCAmelCase       =   pkl.load(SCREAMING_SNAKE_CASE_	)['model']
    for k in copy.deepcopy(list(ckp.keys()	)	):
        UpperCAmelCase       =   ckp.pop(SCREAMING_SNAKE_CASE_	)
        if isinstance(SCREAMING_SNAKE_CASE_	,			np.ndarray	):
            UpperCAmelCase       =   torch.tensor(SCREAMING_SNAKE_CASE_	)
        else:
            assert isinstance(SCREAMING_SNAKE_CASE_	,			torch.tensor	), type(SCREAMING_SNAKE_CASE_	)
        UpperCAmelCase       =   v
    return r
class        _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		{}




      def __init__(							self				,    lowercase_				,    lowercase_ = "root"				,    lowercase_=0							)  ->   int:
          UpperCAmelCase       =   name
          UpperCAmelCase       =   level
          UpperCAmelCase       =   {}
          for k, v in dictionary.items():
              if v is None:
                  raise ValueError()
              UpperCAmelCase       =   copy.deepcopy(lowercase_							)
              UpperCAmelCase       =   copy.deepcopy(lowercase_							)
              if isinstance(lowercase_				,    lowercase_							):
                  UpperCAmelCase       =   Config(lowercase_				,    name=lowercase_				,    level=level + 1							)
              UpperCAmelCase       =   v
              setattr(self				,    lowercase_				,    lowercase_							)

          UpperCAmelCase       =   d




      def __repr__(							self							)  ->   Tuple:
          return str(list((self._pointer.keys())							)							)




      def __setattr__(							self				,    lowercase_				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   val
          UpperCAmelCase       =   val
          UpperCAmelCase       =   key.split('.'							)
          UpperCAmelCase       =   len(lowercase_							) - 1
          UpperCAmelCase       =   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:
                      UpperCAmelCase       =   val
                  else:
                      UpperCAmelCase       =   pointer[l]




      def       a_ (							self							)  ->   Union[str, Any]:
          return self._pointer




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[str]:






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




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[str]:






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




      @staticmethod
      def       a_ (							lowercase_							)  ->   Tuple:






          with open(lowercase_							) as stream:
              UpperCAmelCase       =   load(lowercase_				,    Loader=lowercase_							)
          return data




      def __str__(							self							)  ->   Dict:
          UpperCAmelCase       =   '    '
          if self._name != "root":
              UpperCAmelCase       =   F"{t * (self._level-1)}{self._name}:\n"
          else:
              UpperCAmelCase       =   ''
          UpperCAmelCase       =   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"
              UpperCAmelCase       =   level
          return r[:-1]




      @classmethod
      def       a_ (							cls				,    lowercase_				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   cls.get_config_dict(lowercase_				,    **lowercase_							)
          return cls(lowercase_							)




      @classmethod
      def       a_ (							cls				,    lowercase_				,    **lowercase_							)  ->   Any:
          UpperCAmelCase       =   kwargs.pop('cache_dir'				,    lowercase_							)
          UpperCAmelCase       =   kwargs.pop('force_download'				,    lowercase_							)
          UpperCAmelCase       =   kwargs.pop('resume_download'				,    lowercase_							)
          UpperCAmelCase       =   kwargs.pop('proxies'				,    lowercase_							)
          UpperCAmelCase       =   kwargs.pop('local_files_only'				,    lowercase_							)

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

          try:
              # Load from URL or cache if already cached
              UpperCAmelCase       =   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

              UpperCAmelCase       =   Config.load_yaml(lowercase_							)

          except EnvironmentError:
              UpperCAmelCase       =   '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__  (   lowerCAmelCase			:   Any	)	-> Dict:






    """simple docstring"""




    UpperCAmelCase       =   torch.load('dump.pt'	,			map_location=in_tensor.device	)
    UpperCAmelCase       =   in_tensor.numpy()
    UpperCAmelCase       =   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=0.01	,			atol=0.1	), (
        F"{sum([1 for x in np.isclose(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	,			rtol=0.01	,			atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*100:.4f} %"
        " element-wise mismatch"
    )
    raise Exception('tensors are all good'	)

    # Hugging face functions below




def       lowercase__  (   lowerCAmelCase			:   Dict	)	-> int:






    """simple docstring"""




    UpperCAmelCase       =   urlparse(SCREAMING_SNAKE_CASE_	)
    return parsed.scheme in ("http", "https")




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   Tuple=True	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    UpperCAmelCase       =   '/' not in model_id
    if legacy_format:
        return F"{endpoint}/{model_id}-{filename}"
    else:
        return F"{endpoint}/{model_id}/{filename}"




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   str=None	,			lowerCAmelCase			:   Any=0	,			lowerCAmelCase			:   Union[str, Any]=None	,			)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   '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
    UpperCAmelCase       =   {'user-agent': ua}
    if resume_size > 0:
        UpperCAmelCase       =   'bytes=%d-' % (resume_size,)
    UpperCAmelCase       =   requests.get(SCREAMING_SNAKE_CASE_	,			stream=SCREAMING_SNAKE_CASE_	,			proxies=SCREAMING_SNAKE_CASE_	,			headers=SCREAMING_SNAKE_CASE_	)
    if response.status_code == 416:  # Range not satisfiable
        return
    UpperCAmelCase       =   response.headers.get('Content-Length'	)
    UpperCAmelCase       =   resume_size + int(SCREAMING_SNAKE_CASE_	) if content_length is not None else None
    UpperCAmelCase       =   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=1_024	):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(SCREAMING_SNAKE_CASE_	)	)
            temp_file.write(SCREAMING_SNAKE_CASE_	)
    progress.close()




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   int=False	,			lowerCAmelCase			:   Any=None	,			lowerCAmelCase			:   Optional[int]=10	,			lowerCAmelCase			:   Optional[int]=False	,			lowerCAmelCase			:   str=None	,			lowerCAmelCase			:   Tuple=False	,			)	-> Optional[int]:






    """simple docstring"""




    if cache_dir is None:
        UpperCAmelCase       =   TRANSFORMERS_CACHE
    if isinstance(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	):
        UpperCAmelCase       =   str(SCREAMING_SNAKE_CASE_	)

    os.makedirs(SCREAMING_SNAKE_CASE_	,			exist_ok=SCREAMING_SNAKE_CASE_	)

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

    UpperCAmelCase       =   url_to_filename(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)

    # get cache path to put the file
    UpperCAmelCase       =   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:
            UpperCAmelCase       =   [
                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.
    UpperCAmelCase       =   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:
            UpperCAmelCase       =   cache_path + '.incomplete'

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

            UpperCAmelCase       =   _resumable_file_manager
            if os.path.exists(SCREAMING_SNAKE_CASE_	):
                UpperCAmelCase       =   os.stat(SCREAMING_SNAKE_CASE_	).st_size
            else:
                UpperCAmelCase       =   0
        else:
            UpperCAmelCase       =   partial(tempfile.NamedTemporaryFile	,			dir=SCREAMING_SNAKE_CASE_	,			delete=SCREAMING_SNAKE_CASE_	)
            UpperCAmelCase       =   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_	)

        UpperCAmelCase       =   {'url': url, 'etag': etag}
        UpperCAmelCase       =   cache_path + '.json'
        with open(SCREAMING_SNAKE_CASE_	,			'w'	) as meta_file:
            json.dump(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)

    return cache_path




def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Optional[int]=None	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   url.encode('utf-8'	)
    UpperCAmelCase       =   shaaaa(SCREAMING_SNAKE_CASE_	)
    UpperCAmelCase       =   url_hash.hexdigest()

    if etag:
        UpperCAmelCase       =   etag.encode('utf-8'	)
        UpperCAmelCase       =   shaaaa(SCREAMING_SNAKE_CASE_	)
        filename += "." + etag_hash.hexdigest()

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

    return filename




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Tuple=None	,			lowerCAmelCase			:   Any=False	,			lowerCAmelCase			:   int=None	,			lowerCAmelCase			:   Any=False	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   List[Any]=False	,			lowerCAmelCase			:   List[Any]=False	,			lowerCAmelCase			:   Tuple=False	,			)	-> Optional[Any]:






    """simple docstring"""




    if cache_dir is None:
        UpperCAmelCase       =   TRANSFORMERS_CACHE
    if isinstance(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	):
        UpperCAmelCase       =   str(SCREAMING_SNAKE_CASE_	)
    if isinstance(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	):
        UpperCAmelCase       =   str(SCREAMING_SNAKE_CASE_	)

    if is_remote_url(SCREAMING_SNAKE_CASE_	):
        # URL, so get it from the cache (downloading if necessary)
        UpperCAmelCase       =   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.
        UpperCAmelCase       =   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/"
        UpperCAmelCase     ,							UpperCAmelCase       =   os.path.split(SCREAMING_SNAKE_CASE_	)
        UpperCAmelCase       =   output_file.replace('.'	,			'-'	) + '-extracted'
        UpperCAmelCase       =   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
        UpperCAmelCase       =   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_	):
                UpperCAmelCase       =   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       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[Any]=","	)	-> str:






    """simple docstring"""




    assert isinstance(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    if os.path.isfile(SCREAMING_SNAKE_CASE_	):
        with open(SCREAMING_SNAKE_CASE_	) as f:
            UpperCAmelCase       =   eval(f.read()	)
    else:
        UpperCAmelCase       =   requests.get(SCREAMING_SNAKE_CASE_	)
        try:
            UpperCAmelCase       =   requests.json()
        except Exception:
            UpperCAmelCase       =   req.content.decode()
            assert data is not None, "could not connect"
            try:
                UpperCAmelCase       =   eval(SCREAMING_SNAKE_CASE_	)
            except Exception:
                UpperCAmelCase       =   data.split('\n'	)
        req.close()
    return data




def       lowercase__  (   lowerCAmelCase			:   Any	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   requests.get(SCREAMING_SNAKE_CASE_	)
    UpperCAmelCase       =   np.array(Image.open(BytesIO(response.content	)	)	)
    return img




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	)	-> Dict:






    """simple docstring"""




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




def       lowercase__  (   )	-> Tuple:






    """simple docstring"""




    print(F"{os.path.abspath(os.path.join(SCREAMING_SNAKE_CASE_	,			os.pardir	)	)}/demo.ipynb"	)




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Tuple="RGB"	)	-> str:






    """simple docstring"""




    assert isinstance(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_	)
    if os.path.isfile(SCREAMING_SNAKE_CASE_	):
        UpperCAmelCase       =   cva.imread(SCREAMING_SNAKE_CASE_	)
    else:
        UpperCAmelCase       =   get_image_from_url(SCREAMING_SNAKE_CASE_	)
        assert img is not None, F"could not connect to: {im}"
    UpperCAmelCase       =   cva.cvtColor(SCREAMING_SNAKE_CASE_	,			cva.COLOR_BGR2RGB	)
    if input_format == "RGB":
        UpperCAmelCase       =   img[:, :, ::-1]
    return img







def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   Tuple=1	)	-> List[str]:






    """simple docstring"""




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





"""simple docstring"""

import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

SCREAMING_SNAKE_CASE_         =  180




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   k_size // 2
    UpperCAmelCase     ,							UpperCAmelCase       =   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    UpperCAmelCase       =   (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(lowerCAmelCase	) + np.square(lowerCAmelCase	)) / (2 * np.square(lowerCAmelCase	))	)
    )
    return g




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   np.zeros(lowerCAmelCase	)

    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            UpperCAmelCase       =   gradient_direction[row, col]

            if (
                0 <= direction < PI / 8
                or 15 * PI / 8 <= direction <= 2 * PI
                or 7 * PI / 8 <= direction <= 9 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row, col - 1]
                UpperCAmelCase       =   sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row + 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col]
                UpperCAmelCase       =   sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    UpperCAmelCase       =   sobel_grad[row, col]

    return destination




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   List[str]	)	-> Any:






    """simple docstring"""




    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            if destination[row, col] >= threshold_high:
                UpperCAmelCase       =   strong
            elif destination[row, col] <= threshold_low:
                UpperCAmelCase       =   0
            else:
                UpperCAmelCase       =   weak




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> List[str]:






    """simple docstring"""




    for row in range(1	,			image_shape[0]	):
        for col in range(1	,			image_shape[1]	):
            if destination[row, col] == weak:
                if 255 in (
                    destination[row, col + 1],
                    destination[row, col - 1],
                    destination[row - 1, col],
                    destination[row + 1, col],
                    destination[row - 1, col - 1],
                    destination[row + 1, col - 1],
                    destination[row - 1, col + 1],
                    destination[row + 1, col + 1],
                ):
                    UpperCAmelCase       =   strong
                else:
                    UpperCAmelCase       =   0




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int=15	,			lowerCAmelCase			:   Dict=30	,			lowerCAmelCase			:   str=128	,			lowerCAmelCase			:   Optional[Any]=255	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   img_convolve(lowerCAmelCase	,			gen_gaussian_kernel(9	,			sigma=1.4	)	)
    # get the gradient and degree by sobel_filter
    UpperCAmelCase     ,							UpperCAmelCase       =   sobel_filter(lowerCAmelCase	)
    UpperCAmelCase       =   PI + np.radadeg(lowerCAmelCase	)

    UpperCAmelCase       =   suppress_non_maximum(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	)

    detect_high_low_threshold(
        image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    track_edge(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    return destination


if __name__ == "__main__":
   # read original image in gray mode
   SCREAMING_SNAKE_CASE_         =  cva.imread(r'''../image_data/lena.jpg''', 0)
   # canny edge detection
   SCREAMING_SNAKE_CASE_         =  canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)



