"""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

 lowerCAmelCase_:					List[str]     =					True
except ImportError:
 lowerCAmelCase_:					Dict     =					False


try:
 from torch.hub import _get_torch_home

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

lowerCAmelCase_:					List[str]     =					os.path.join(torch_cache_home, "transformers")

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







def 					__a   (		A=OBJECTS    ,				A=ATTRIBUTES      ):






     '''simple docstring'''



     lowercase__				 =							[]
     with open(A      ) as f:
          for object in f.readlines():
               vg_classes.append(object.split(","      )[0].lower().strip()      )

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







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							OrderedDict()
     with open(A    ,				"rb"      ) as f:
          lowercase__				 =							pkl.load(A      )["model"]
     for k in copy.deepcopy(list(ckp.keys()      )      ):
          lowercase__				 =							ckp.pop(A      )
          if isinstance(A    ,				np.ndarray      ):
               lowercase__				 =							torch.tensor(A      )
          else:
               assert isinstance(A    ,				torch.tensor      ), type(A      )
          lowercase__				 =							v
     return r






class    a__    :
   snake_case_							=      {}


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = "root",				_UpperCAmelCase=0  ):
        '''simple docstring'''



        lowercase__				 =							name
        lowercase__				 =							level
        lowercase__				 =							{}
        for k, v in dictionary.items():
             if v is None:
                  raise ValueError()
             lowercase__				 =							copy.deepcopy(_UpperCAmelCase  )
             lowercase__				 =							copy.deepcopy(_UpperCAmelCase  )
             if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ):
                  lowercase__				 =							Config(_UpperCAmelCase,				name=_UpperCAmelCase,				level=level + 1  )
             lowercase__				 =							v
             setattr(self,				_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							d


   def __repr__(     self  ):
        '''simple docstring'''



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


   def __setattr__(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							val
        lowercase__				 =							val
        lowercase__				 =							key.split("."  )
        lowercase__				 =							len(_UpperCAmelCase  ) - 1
        lowercase__				 =							self._pointer
        if len(_UpperCAmelCase  ) > 1:
             for i, l in enumerate(_UpperCAmelCase  ):
                  if hasattr(self,				_UpperCAmelCase  ) and isinstance(getattr(self,				_UpperCAmelCase  ),				_UpperCAmelCase  ):
                       setattr(getattr(self,				_UpperCAmelCase  ),				".".join(levels[i:]  ),				_UpperCAmelCase  )
                  if l == last_level:
                       lowercase__				 =							val
                  else:
                       lowercase__				 =							pointer[l]


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return self._pointer


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   @staticmethod
   def 	snake_case__	(     _UpperCAmelCase  ):
        '''simple docstring'''



        with open(_UpperCAmelCase  ) as stream:
             lowercase__				 =							load(_UpperCAmelCase,				Loader=_UpperCAmelCase  )
        return data


   def __str__(     self  ):
        '''simple docstring'''



        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(_UpperCAmelCase,				_UpperCAmelCase  ):
                  r += F'''{t * (self._level)}{v}\n'''
                  self._level += 1
             else:
                  r += F'''{t * (self._level)}{k}: {v} ({type(_UpperCAmelCase  ).__name__})\n'''
             lowercase__				 =							level
        return r[:-1]


   @classmethod
   def 	snake_case__	(     cls,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							cls.get_config_dict(_UpperCAmelCase,				**_UpperCAmelCase  )
        return cls(_UpperCAmelCase  )


   @classmethod
   def 	snake_case__	(     cls,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							kwargs.pop("cache_dir",				_UpperCAmelCase  )
        lowercase__				 =							kwargs.pop("force_download",				_UpperCAmelCase  )
        lowercase__				 =							kwargs.pop("resume_download",				_UpperCAmelCase  )
        lowercase__				 =							kwargs.pop("proxies",				_UpperCAmelCase  )
        lowercase__				 =							kwargs.pop("local_files_only",				_UpperCAmelCase  )

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

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

             lowercase__				 =							Config.load_yaml(_UpperCAmelCase  )

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

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

        return Config.load_yaml(_UpperCAmelCase  ), kwargs







def 					__a   (		A      ):






     '''simple docstring'''



     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(A    ,				A    ,				rtol=0.01    ,				atol=0.1      ), (
         f'''{sum([1 for x in np.isclose(A    ,				A    ,				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   (		A      ):






     '''simple docstring'''



     lowercase__				 =							urlparse(A      )
     return parsed.scheme in ("http", "https")







def 					__a   (		A    ,				A    ,				A=True      ):






     '''simple docstring'''



     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 					__a   (		A    ,				A    ,				A=None    ,				A=0    ,				A=None    ,				):






     '''simple docstring'''



     lowercase__				 =							"python/{}".format(sys.version.split()[0]      )
     if _torch_available:
          ua += "; torch/{}".format(torch.__version__      )
     if isinstance(A    ,				A      ):
          ua += "; " + "; ".join("{}/{}".format(A    ,				A      ) for k, v in user_agent.items()      )
     elif isinstance(A    ,				A      ):
          ua += "; " + user_agent
     lowercase__				 =							{"user-agent": ua}
     if resume_size > 0:
          lowercase__				 =							"bytes=%d-" % (resume_size,)
     lowercase__				 =							requests.get(A    ,				stream=A    ,				proxies=A    ,				headers=A      )
     if response.status_code == 4_16:  # Range not satisfiable
          return
     lowercase__				 =							response.headers.get("Content-Length"      )
     lowercase__				 =							resume_size + int(A      ) if content_length is not None else None
     lowercase__				 =							tqdm(
         unit="B"    ,				unit_scale=A    ,				total=A    ,				initial=A    ,				desc="Downloading"    ,				)
     for chunk in response.iter_content(chunk_size=10_24      ):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(A      )      )
               temp_file.write(A      )
     progress.close()







def 					__a   (		A    ,				A=None    ,				A=False    ,				A=None    ,				A=10    ,				A=False    ,				A=None    ,				A=False    ,				):






     '''simple docstring'''



     if cache_dir is None:
          lowercase__				 =							TRANSFORMERS_CACHE
     if isinstance(A    ,				A      ):
          lowercase__				 =							str(A      )

     os.makedirs(A    ,				exist_ok=A      )

     lowercase__				 =							None
     if not local_files_only:
          try:
               lowercase__				 =							requests.head(A    ,				allow_redirects=A    ,				proxies=A    ,				timeout=A      )
               if response.status_code == 2_00:
                    lowercase__				 =							response.headers.get("ETag"      )
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     lowercase__				 =							url_to_filename(A    ,				A      )

     # get cache path to put the file
     lowercase__				 =							os.path.join(A    ,				A      )

     # 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(A      ):
               return cache_path
          else:
               lowercase__				 =							[
                   file
                   for file in fnmatch.filter(os.listdir(A      )    ,				filename + ".*"      )
                   if not file.endswith(".json"      ) and not file.endswith(".lock"      )
               ]
               if len(A      ) > 0:
                    return os.path.join(A    ,				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(A      ) and not force_download:
          return cache_path

     # Prevent parallel downloads of the same file with a lock.
     lowercase__				 =							cache_path + ".lock"
     with FileLock(A      ):
          # If the download just completed while the lock was activated.
          if os.path.exists(A      ) 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(A    ,				"a+b"      ) as f:
                         yield f

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

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

          os.replace(temp_file.name    ,				A      )

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

     return cache_path







def 					__a   (		A    ,				A=None      ):






     '''simple docstring'''



     lowercase__				 =							url.encode("utf-8"      )
     lowercase__				 =							shaaaa(A      )
     lowercase__				 =							url_hash.hexdigest()

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

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

     return filename







def 					__a   (		A    ,				A=None    ,				A=False    ,				A=None    ,				A=False    ,				A=None    ,				A=False    ,				A=False    ,				A=False    ,				):






     '''simple docstring'''



     if cache_dir is None:
          lowercase__				 =							TRANSFORMERS_CACHE
     if isinstance(A    ,				A      ):
          lowercase__				 =							str(A      )
     if isinstance(A    ,				A      ):
          lowercase__				 =							str(A      )

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

     if extract_compressed_file:
          if not is_zipfile(A      ) and not tarfile.is_tarfile(A      ):
               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(A      )
          lowercase__				 =							output_file.replace("."    ,				"-"      ) + "-extracted"
          lowercase__				 =							os.path.join(A    ,				A      )

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

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

          return output_path_extracted

     return output_path







def 					__a   (		A    ,				A=","      ):






     '''simple docstring'''



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







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							requests.get(A      )
     lowercase__				 =							np.array(Image.open(BytesIO(response.content      )      )      )
     return img







def 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		):






     '''simple docstring'''



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







def 					__a   (		A    ,				A="RGB"      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A=1      ):






     '''simple docstring'''



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





"""simple docstring"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(A      ) % 6)
     else:
          lowercase__				 =							b""

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]    ,				2      )]
             for index in range(0    ,				len(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							encoded_data.count("="      )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase__				 =							encoded_data[:-padding]

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




