




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     : str      =True
except ImportError:
      __SCREAMING_SNAKE_CASE     : str      =False


try:
      from torch.hub import _get_torch_home

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

__SCREAMING_SNAKE_CASE     : Any      =os.path.join(torch_cache_home, """transformers""")

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

def       lowerCAmelCase_      (	lowerCamelCase:							str=OBJECTS  ,					lowerCamelCase:							Optional[int]=ATTRIBUTES    )       ->      Optional[Any]:


      '''simple docstring'''




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

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

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Optional[int]:


      '''simple docstring'''




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






class       A      :
     """simple docstring"""

     _snake_case:						Optional[int]  ={}




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

           lowerCamelCase__   	=					d




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




     def __setattr__(     self	: Dict						,	A__	: Optional[int]						,	A__	: Optional[int]      )					->							Dict:
           lowerCamelCase__   	=					val
           lowerCamelCase__   	=					val
           lowerCamelCase__   	=					key.split('.'      )
           lowerCamelCase__   	=					len(UpperCAmelCase_      ) - 1
           lowerCamelCase__   	=					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:
                             lowerCamelCase__   	=					val
                       else:
                             lowerCamelCase__   	=					pointer[l]




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




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: int						,	A__	: str      )					->							Union[str, Any]:
           with open(F"""{file_name}"""						,	'w'      ) as stream:
                 dump(UpperCAmelCase_						,	UpperCAmelCase_      )




     def 			__snake_case (     self	: Optional[Any]						,	A__	: str						,	A__	: Any      )					->							Any:
           with open(F"""{file_name}"""						,	'w'      ) as stream:
                 json.dump(UpperCAmelCase_						,	UpperCAmelCase_      )




     @staticmethod
     def 			__snake_case (     A__	: Union[str, Any]      )					->							int:
           with open(UpperCAmelCase_      ) as stream:
                 lowerCamelCase__   	=					load(UpperCAmelCase_						,	Loader=UpperCAmelCase_      )
           return data




     def __str__(     self	: Dict      )					->							str:
           lowerCamelCase__   	=					"""    """
           if self._name != "root":
                 lowerCamelCase__   	=					F"""{t * (self._level-1)}{self._name}:\n"""
           else:
                 lowerCamelCase__   	=					""""""
           lowerCamelCase__   	=					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"""
                 lowerCamelCase__   	=					level
           return r[:-1]




     @classmethod
     def 			__snake_case (     cls	: Optional[Any]						,	A__	: str						,	**A__	: Dict      )					->							List[str]:
           lowerCamelCase__   	=					cls.get_config_dict(UpperCAmelCase_						,	**UpperCAmelCase_      )
           return cls(UpperCAmelCase_      )




     @classmethod
     def 			__snake_case (     cls	: Optional[Any]						,	A__	: List[str]						,	**A__	: Any      )					->							Any:
           lowerCamelCase__   	=					kwargs.pop('cache_dir'						,	UpperCAmelCase_      )
           lowerCamelCase__   	=					kwargs.pop('force_download'						,	UpperCAmelCase_      )
           lowerCamelCase__   	=					kwargs.pop('resume_download'						,	UpperCAmelCase_      )
           lowerCamelCase__   	=					kwargs.pop('proxies'						,	UpperCAmelCase_      )
           lowerCamelCase__   	=					kwargs.pop('local_files_only'						,	UpperCAmelCase_      )

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

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

                 lowerCamelCase__   	=					Config.load_yaml(UpperCAmelCase_      )

           except EnvironmentError:
                 lowerCamelCase__   	=					"""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       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					torch.load('dump.pt'  ,					map_location=in_tensor.device    )
      lowerCamelCase__   	=					in_tensor.numpy()
      lowerCamelCase__   	=					out_tensor.numpy()[0]
      print(na.shape  ,					na[0, 0, :5]    )
      print(na.shape  ,					na[0, 0, :5]    )
      assert np.allclose(__UpperCAmelCase  ,					__UpperCAmelCase  ,					rtol=0.01  ,					atol=0.1    ), (
          f"""{sum([1 for x in np.isclose(__UpperCAmelCase  ,					__UpperCAmelCase  ,					rtol=0.01  ,					atol=0.1    ).flatten() if x is False]    )/len(na.flatten()    )*1_00:.4f} %"""
          " element-wise mismatch"
      )
      raise Exception('tensors are all good'    )

      # Hugging face functions below

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					urlparse(__UpperCAmelCase    )
      return parsed.scheme in ("http", "https")

def       lowerCAmelCase_      (	lowerCamelCase:							Union[str, Any]  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							Union[str, Any]=True    )       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
      lowerCamelCase__   	=					"""/""" not in model_id
      if legacy_format:
            return f"""{endpoint}/{model_id}-{filename}"""
      else:
            return f"""{endpoint}/{model_id}/{filename}"""

def       lowerCAmelCase_      (	lowerCamelCase:							int  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							str=None  ,					lowerCamelCase:							int=0  ,					lowerCamelCase:							List[Any]=None  ,					)       ->      Any:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]  ,					lowerCamelCase:							Union[str, Any]=None  ,					lowerCamelCase:							Optional[Any]=False  ,					lowerCamelCase:							Any=None  ,					lowerCamelCase:							Optional[int]=10  ,					lowerCamelCase:							int=False  ,					lowerCamelCase:							str=None  ,					lowerCamelCase:							int=False  ,					)       ->      List[Any]:


      '''simple docstring'''




      if cache_dir is None:
            lowerCamelCase__   	=					TRANSFORMERS_CACHE
      if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase    ):
            lowerCamelCase__   	=					str(__UpperCAmelCase    )

      os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase    )

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

      lowerCamelCase__   	=					url_to_filename(__UpperCAmelCase  ,					__UpperCAmelCase    )

      # get cache path to put the file
      lowerCamelCase__   	=					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase    )

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

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

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

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

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

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

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

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

            os.replace(temp_file.name  ,					__UpperCAmelCase    )

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

      return cache_path

def       lowerCAmelCase_      (	lowerCamelCase:							Any  ,					lowerCamelCase:							Union[str, Any]=None    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					url.encode('utf-8'    )
      lowerCamelCase__   	=					shaaaa(__UpperCAmelCase    )
      lowerCamelCase__   	=					url_hash.hexdigest()

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

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

      return filename

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							str=None  ,					lowerCamelCase:							int=False  ,					lowerCamelCase:							List[str]=None  ,					lowerCamelCase:							List[str]=False  ,					lowerCamelCase:							Tuple=None  ,					lowerCamelCase:							Optional[Any]=False  ,					lowerCamelCase:							List[str]=False  ,					lowerCamelCase:							Tuple=False  ,					)       ->      Optional[Any]:


      '''simple docstring'''




      if cache_dir is None:
            lowerCamelCase__   	=					TRANSFORMERS_CACHE
      if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase    ):
            lowerCamelCase__   	=					str(__UpperCAmelCase    )
      if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase    ):
            lowerCamelCase__   	=					str(__UpperCAmelCase    )

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

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

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

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

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

            return output_path_extracted

      return output_path

def       lowerCAmelCase_      (	lowerCamelCase:							int  ,					lowerCamelCase:							List[str]=","    )       ->      Optional[Any]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple    )       ->      Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					requests.get(__UpperCAmelCase    )
      lowerCamelCase__   	=					np.array(Image.open(BytesIO(response.content    )    )    )
      return img

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]    )       ->      List[str]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	)       ->      List[Any]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							int  ,					lowerCamelCase:							Dict="RGB"    )       ->      Optional[Any]:


      '''simple docstring'''




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


def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							Optional[int]=1    )       ->      Optional[int]:


      '''simple docstring'''




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




"""simple docstring"""







import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase:  List[str]			     =				logging.getLogger(__name__)


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: List[Any]     =   argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""      )
		parser.add_argument(
		    """--dataset_name"""   ,				type=__UpperCAmelCase   ,				default="""wikitext"""   ,				help="""Name of the training. Explore datasets at: hf.co/datasets."""   ,				)
		parser.add_argument(
		    """--dataset_config"""   ,				type=__UpperCAmelCase   ,				default="""wikitext-103-raw-v1"""   ,				help="""Configuration name of the dataset."""      )
		parser.add_argument(
		    """--batch_size"""   ,				type=__UpperCAmelCase   ,				default=1000   ,				help="""Batch size during training."""   ,				)
		parser.add_argument(
		    """--vocab_size"""   ,				type=__UpperCAmelCase   ,				default=10048   ,				help="""Size of the desired vocabulary."""   ,				)
		parser.add_argument(
		    """--limit"""   ,				default=__UpperCAmelCase   ,				type=__UpperCAmelCase   ,				help="""Limit the number of shards (used for debugging)."""   ,				)
		parser.add_argument(
		    """--export_to_hub"""   ,				action="""store_true"""   ,				)

		_lowercase				: Any     =   parser.parse_args()
		return args


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   datasets.load_dataset(args.dataset_name   ,				args.dataset_config   ,				split="""train"""      )

		if args.limit is not None:
				_lowercase				: Tuple     =   min(len(__UpperCAmelCase      )   ,				args.limit      )
				_lowercase				: Optional[Any]     =   dataset.select(range(__UpperCAmelCase      )      )
				logger.info(F"""Limiting the dataset to {args.limit} entries."""      )

		def batch_iterator():
				for i in range(0   ,				len(__UpperCAmelCase      )   ,				args.batch_size      ):
						yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
		_lowercase				: Optional[int]     =   Tokenizer(Unigram()      )
		_lowercase				: List[str]     =   normalizers.Sequence([normalizers.Replace("""``"""   ,				"""\""""      ), normalizers.Replace("""''"""   ,				"""\""""      )]      )
		_lowercase				: str     =   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		_lowercase				: int     =   UnigramTrainer(
		    unk_token="""<unk>"""   ,				special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]   ,				vocab_size=args.vocab_size   ,				)

		logger.info("""Training the tokenizer."""      )
		tokenizer.train_from_iterator(batch_iterator()   ,				trainer=__UpperCAmelCase      )
		logger.info("""Tokenizer training complete!"""      )

		_lowercase				: int     =   tokenizer.token_to_id("""[CLS]"""      )
		_lowercase				: Optional[Any]     =   tokenizer.token_to_id("""[SEP]"""      )
		_lowercase				: Optional[int]     =   processors.TemplateProcessing(
		    single="""[CLS]:0 $A:0 [SEP]:0"""   ,				pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""   ,				special_tokens=[
		        ("""[CLS]""", cls_token_id),
		        ("""[SEP]""", sep_token_id),
		    ]   ,				)
		_lowercase				: Union[str, Any]     =   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("""Exporting the trained tokenzier to Hub."""      )
				_lowercase				: List[Any]     =   AlbertTokenizerFast(tokenizer_object=__UpperCAmelCase      )
				new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


if __name__ == "__main__":
		UpperCAmelCase:  List[Any]			     =				parse_args()
		main(args)



