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  snake_case  (       snake_case__			:Dict=OBJECTS   ,	snake_case__			:int=ATTRIBUTES)						->   Optional[Any]:
       _A        =  []
       with open(snake_case__) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(""",""")[0].lower().strip())

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






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



class 				a							:







 """simple docstring"""






 lowerCamelCase	:List[Any]    			=		{}



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = "root"		,				lowerCAmelCase_=0							)  ->					Union[str, Any]:
        _A        =  name
        _A        =  level
        _A        =  {}
        for k, v in dictionary.items():
               if v is None:
                      raise ValueError()
               _A        =  copy.deepcopy(lowerCAmelCase_							)
               _A        =  copy.deepcopy(lowerCAmelCase_							)
               if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
                      _A        =  Config(lowerCAmelCase_		,				name=lowerCAmelCase_		,				level=level + 1							)
               _A        =  v
               setattr(self		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  d



 def __repr__(					self							)  ->					Optional[int]:
        return str(list((self._pointer.keys())							)							)



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



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        return self._pointer



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Tuple:
        with open(F'''{file_name}'''		,				"""w"""							) as stream:
               dump(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        with open(F'''{file_name}'''		,				"""w"""							) as stream:
               json.dump(lowerCAmelCase_		,				lowerCAmelCase_							)



 @staticmethod
 def      UpperCAmelCase							(					lowerCAmelCase_							)  ->					Tuple:
        with open(lowerCAmelCase_							) as stream:
               _A        =  load(lowerCAmelCase_		,				Loader=lowerCAmelCase_							)
        return data



 def __str__(					self							)  ->					Dict:
        _A        =  """    """
        if self._name != "root":
               _A        =  F'''{t * (self._level-1)}{self._name}:\n'''
        else:
               _A        =  """"""
        _A        =  self._level
        for i, (k, v) in enumerate(self._pointer.items()							):
               if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
                      r += F'''{t * (self._level)}{v}\n'''
                      self._level += 1
               else:
                      r += F'''{t * (self._level)}{k}: {v} ({type(lowerCAmelCase_							).__name__})\n'''
               _A        =  level
        return r[:-1]



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        _A   ,  _A        =  cls.get_config_dict(lowerCAmelCase_		,				**lowerCAmelCase_							)
        return cls(lowerCAmelCase_							)



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Optional[Any]:
        _A        =  kwargs.pop("""cache_dir"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""force_download"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""resume_download"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""proxies"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""local_files_only"""		,				lowerCAmelCase_							)

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

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

               _A        =  Config.load_yaml(lowerCAmelCase_							)

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

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

        return Config.load_yaml(lowerCAmelCase_							), kwargs






def  snake_case  (       snake_case__			:str)						->   Tuple:
       _A        =  torch.load("""dump.pt"""   ,	map_location=in_tensor.device)
       _A        =  in_tensor.numpy()
       _A        =  out_tensor.numpy()[0]
       print(na.shape   ,	na[0, 0, :5])
       print(na.shape   ,	na[0, 0, :5])
       assert np.allclose(snake_case__   ,	snake_case__   ,	rtol=0.01   ,	atol=0.1), (
           F'''{sum([1 for x in np.isclose(snake_case__   ,	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  snake_case  (       snake_case__			:str)						->   Union[str, Any]:
       _A        =  urlparse(snake_case__)
       return parsed.scheme in ("http", "https")






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






def  snake_case  (       snake_case__			:Dict   ,	snake_case__			:Tuple   ,	snake_case__			:List[str]=None   ,	snake_case__			:int=0   ,	snake_case__			:Dict=None   ,	)						->   List[str]:
       _A        =  """python/{}""".format(sys.version.split()[0])
       if _torch_available:
              ua += "; torch/{}".format(torch.__version__)
       if isinstance(snake_case__   ,	snake_case__):
              ua += "; " + "; ".join("""{}/{}""".format(snake_case__   ,	snake_case__) for k, v in user_agent.items())
       elif isinstance(snake_case__   ,	snake_case__):
              ua += "; " + user_agent
       _A        =  {"""user-agent""": ua}
       if resume_size > 0:
              _A        =  """bytes=%d-""" % (resume_size,)
       _A        =  requests.get(snake_case__   ,	stream=snake_case__   ,	proxies=snake_case__   ,	headers=snake_case__)
       if response.status_code == 416:  # Range not satisfiable
              return
       _A        =  response.headers.get("""Content-Length""")
       _A        =  resume_size + int(snake_case__) if content_length is not None else None
       _A        =  tqdm(
           unit="""B"""   ,	unit_scale=snake_case__   ,	total=snake_case__   ,	initial=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(snake_case__))
                     temp_file.write(snake_case__)
       progress.close()






def  snake_case  (       snake_case__			:str   ,	snake_case__			:Dict=None   ,	snake_case__			:Dict=False   ,	snake_case__			:Optional[int]=None   ,	snake_case__			:Optional[int]=10   ,	snake_case__			:Optional[int]=False   ,	snake_case__			:Tuple=None   ,	snake_case__			:Dict=False   ,	)						->   List[str]:
       if cache_dir is None:
              _A        =  TRANSFORMERS_CACHE
       if isinstance(snake_case__   ,	snake_case__):
              _A        =  str(snake_case__)

       os.makedirs(snake_case__   ,	exist_ok=snake_case__)

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

       _A        =  url_to_filename(snake_case__   ,	snake_case__)

       # get cache path to put the file
       _A        =  os.path.join(snake_case__   ,	snake_case__)

       # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
       # try to get the last downloaded one
       if etag is None:
              if os.path.exists(snake_case__):
                     return cache_path
              else:
                     _A        =  [
                         file
                         for file in fnmatch.filter(os.listdir(snake_case__)   ,	filename + """.*""")
                         if not file.endswith(""".json""") and not file.endswith(""".lock""")
                     ]
                     if len(snake_case__) > 0:
                            return os.path.join(snake_case__   ,	matching_files[-1])
                     else:
                            # If files cannot be found and local_files_only=True,
                            # the models might've been found if local_files_only=False
                            # Notify the user about that
                            if local_files_only:
                                   raise ValueError(
                                       """Cannot find the requested files in the cached path and outgoing traffic has been"""
                                       """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
                                       """ to False.""")
                            return None

    # From now on, etag is not None.
       if os.path.exists(snake_case__) and not force_download:
              return cache_path

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

              if resume_download:
                     _A        =  cache_path + """.incomplete"""

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

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

              # Download to temporary file, then copy to cache dir once finished.
              # Otherwise you get corrupt cache entries if the download gets interrupted.
              with temp_file_manager() as temp_file:
                     print(
                         """%s not found in cache or force_download set to True, downloading to %s"""   ,	snake_case__   ,	temp_file.name   ,	)

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

              os.replace(temp_file.name   ,	snake_case__)

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

       return cache_path






def  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:List[str]=None)						->   Dict:
       _A        =  url.encode("""utf-8""")
       _A        =  shaaaa(snake_case__)
       _A        =  url_hash.hexdigest()

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

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

       return filename






def  snake_case  (       snake_case__			:List[Any]   ,	snake_case__			:Optional[Any]=None   ,	snake_case__			:Dict=False   ,	snake_case__			:int=None   ,	snake_case__			:str=False   ,	snake_case__			:Dict=None   ,	snake_case__			:Tuple=False   ,	snake_case__			:Union[str, Any]=False   ,	snake_case__			:List[Any]=False   ,	)						->   Tuple:
       if cache_dir is None:
              _A        =  TRANSFORMERS_CACHE
       if isinstance(snake_case__   ,	snake_case__):
              _A        =  str(snake_case__)
       if isinstance(snake_case__   ,	snake_case__):
              _A        =  str(snake_case__)

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

       if extract_compressed_file:
              if not is_zipfile(snake_case__) and not tarfile.is_tarfile(snake_case__):
                     return output_path

              # Path where we extract compressed archives
              # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
              _A   ,  _A        =  os.path.split(snake_case__)
              _A        =  output_file.replace("""."""   ,	"""-""") + """-extracted"""
              _A        =  os.path.join(snake_case__   ,	snake_case__)

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

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

              return output_path_extracted

       return output_path






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






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






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






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






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







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





def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  set()

       # Replace all the whitespace in our sentence
       _A        =  input_str.replace(""" """   ,	"""""")
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower())
       return len(snake_case__) == 26






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  [False] * 26
       for char in input_str:
              if char.islower():
                     _A        =  True
              elif char.isupper():
                     _A        =  True
       return all(snake_case__)






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       return len({char for char in input_str.lower() if char.isalpha()}) == 26






def  snake_case  (       )						->   None:
       from timeit import timeit

       _A        =  """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
       print(timeit("""is_pangram()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_faster()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_fastest()"""   ,	setup=snake_case__))
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()




