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

      UpperCamelCase													=		True
except ImportError:
      UpperCamelCase													=		False


try:
      from torch.hub import _get_torch_home

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

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

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

def     lowercase_       (    _lowerCamelCase							: List[Any]=OBJECTS	,			_lowerCamelCase							: int=ATTRIBUTES):
    lowercase__				:     int					      =       []
    with open(_lowerCamelCase) as f:
        for object in f.readlines():
            vg_classes.append(object.split(",")[0].lower().strip())

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

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



class 						snake_case_ :
   __A			:	Optional[int]        =       {}






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

       lowercase__				:     Dict					      =       d






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






   def __setattr__(   self       :  Dict			,		lowercase_       :  str			,		lowercase_       :  int						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       val
       lowercase__				:     Dict					      =       val
       lowercase__				:     Any					      =       key.split("."						)
       lowercase__				:     List[str]					      =       len(lowercase_						) - 1
       lowercase__				:     List[Any]					      =       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:
                   lowercase__				:     str					      =       val
               else:
                   lowercase__				:     Optional[int]					      =       pointer[l]






   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[int]:
       return self._pointer






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  List[str]			,		lowercase_       :  List[str]						)      ->		Optional[Any]:
       with open(F'''{file_name}'''			,		"w"						) as stream:
           dump(lowercase_			,		lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Any			,		lowercase_       :  List[Any]						)      ->		Optional[int]:
       with open(F'''{file_name}'''			,		"w"						) as stream:
           json.dump(lowercase_			,		lowercase_						)






   @staticmethod
   def    __UpperCamelCase  (   lowercase_       :  Union[str, Any]						)      ->		Tuple:
       with open(lowercase_						) as stream:
           lowercase__				:     Any					      =       load(lowercase_			,		Loader=lowercase_						)
       return data






   def __str__(   self       :  Any						)      ->		Union[str, Any]:
       lowercase__				:     Optional[int]					      =       "    "
       if self._name != "root":
           lowercase__				:     Optional[Any]					      =       F'''{t * (self._level-1)}{self._name}:\n'''
       else:
           lowercase__				:     str					      =       ""
       lowercase__				:     Union[str, Any]					      =       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'''
           lowercase__				:     List[Any]					      =       level
       return r[:-1]






   @classmethod
   def    __UpperCamelCase  (   cls       :  List[Any]			,		lowercase_       :  str			,		**lowercase_       :  str						)      ->		Tuple:
       lowercase__      ,     lowercase__				:     Tuple					      =       cls.get_config_dict(lowercase_			,		**lowercase_						)
       return cls(lowercase_						)







   @classmethod
   def    __UpperCamelCase  (   cls       :  List[str]			,		lowercase_       :  str			,		**lowercase_       :  Union[str, Any]						)      ->		Optional[int]:
       lowercase__				:     Tuple					      =       kwargs.pop("cache_dir"			,		lowercase_						)
       lowercase__				:     Any					      =       kwargs.pop("force_download"			,		lowercase_						)
       lowercase__				:     Tuple					      =       kwargs.pop("resume_download"			,		lowercase_						)
       lowercase__				:     int					      =       kwargs.pop("proxies"			,		lowercase_						)
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("local_files_only"			,		lowercase_						)

       if os.path.isdir(lowercase_						):
           lowercase__				:     List[str]					      =       os.path.join(lowercase_			,		lowercase_						)
       elif os.path.isfile(lowercase_						) or is_remote_url(lowercase_						):
           lowercase__				:     Optional[Any]					      =       pretrained_model_name_or_path
       else:
           lowercase__				:     List[str]					      =       hf_bucket_url(lowercase_			,		filename=lowercase_			,		use_cdn=lowercase_						)

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

           lowercase__				:     Dict					      =       Config.load_yaml(lowercase_						)

       except EnvironmentError:
           lowercase__				:     Union[str, Any]					      =       "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							: int):
    lowercase__				:     str					      =       torch.load("dump.pt"	,			map_location=in_tensor.device)
    lowercase__				:     Dict					      =       in_tensor.numpy()
    lowercase__				:     str					      =       out_tensor.numpy()[0]
    print(na.shape	,			na[0, 0, :5])
    print(na.shape	,			na[0, 0, :5])
    assert np.allclose(_lowerCamelCase	,			_lowerCamelCase	,			rtol=0.01	,			atol=0.1), (
        f'''{sum([1 for x in np.isclose(_lowerCamelCase	,			_lowerCamelCase	,			rtol=0.01	,			atol=0.1).flatten() if x is False])/len(na.flatten())*100:.4f} %'''
        " element-wise mismatch"
    )
    raise Exception("tensors are all good")

    # Hugging face functions below

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]):
    lowercase__				:     Any					      =       urlparse(_lowerCamelCase)
    return parsed.scheme in ("http", "https")

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]=True):
    lowercase__				:     Optional[Any]					      =       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    lowercase__				:     Dict					      =       "/" not in model_id
    if legacy_format:
        return f'''{endpoint}/{model_id}-{filename}'''
    else:
        return f'''{endpoint}/{model_id}/{filename}'''

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: List[str]=None	,			_lowerCamelCase							: Tuple=0	,			_lowerCamelCase							: int=None	,			):
    lowercase__				:     List[str]					      =       "python/{}".format(sys.version.split()[0])
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__)
    if isinstance(_lowerCamelCase	,			_lowerCamelCase):
        ua += "; " + "; ".join("{}/{}".format(_lowerCamelCase	,			_lowerCamelCase) for k, v in user_agent.items())
    elif isinstance(_lowerCamelCase	,			_lowerCamelCase):
        ua += "; " + user_agent
    lowercase__				:     str					      =       {"user-agent": ua}
    if resume_size > 0:
        lowercase__				:     int					      =       "bytes=%d-" % (resume_size,)
    lowercase__				:     Optional[int]					      =       requests.get(_lowerCamelCase	,			stream=_lowerCamelCase	,			proxies=_lowerCamelCase	,			headers=_lowerCamelCase)
    if response.status_code == 416:  # Range not satisfiable
        return
    lowercase__				:     Any					      =       response.headers.get("Content-Length")
    lowercase__				:     Optional[int]					      =       resume_size + int(_lowerCamelCase) if content_length is not None else None
    lowercase__				:     Any					      =       tqdm(
        unit="B"	,			unit_scale=_lowerCamelCase	,			total=_lowerCamelCase	,			initial=_lowerCamelCase	,			desc="Downloading"	,			)
    for chunk in response.iter_content(chunk_size=1024):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(_lowerCamelCase))
            temp_file.write(_lowerCamelCase)
    progress.close()

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Tuple=None	,			_lowerCamelCase							: Dict=False	,			_lowerCamelCase							: Optional[Any]=None	,			_lowerCamelCase							: int=10	,			_lowerCamelCase							: Union[str, Any]=False	,			_lowerCamelCase							: str=None	,			_lowerCamelCase							: int=False	,			):
    if cache_dir is None:
        lowercase__				:     Dict					      =       TRANSFORMERS_CACHE
    if isinstance(_lowerCamelCase	,			_lowerCamelCase):
        lowercase__				:     Dict					      =       str(_lowerCamelCase)

    os.makedirs(_lowerCamelCase	,			exist_ok=_lowerCamelCase)

    lowercase__				:     Optional[Any]					      =       None
    if not local_files_only:
        try:
            lowercase__				:     Optional[Any]					      =       requests.head(_lowerCamelCase	,			allow_redirects=_lowerCamelCase	,			proxies=_lowerCamelCase	,			timeout=_lowerCamelCase)
            if response.status_code == 200:
                lowercase__				:     int					      =       response.headers.get("ETag")
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    lowercase__				:     Optional[Any]					      =       url_to_filename(_lowerCamelCase	,			_lowerCamelCase)

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

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

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

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

        if resume_download:
            lowercase__				:     Optional[int]					      =       cache_path + ".incomplete"

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

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

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

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

        os.replace(temp_file.name	,			_lowerCamelCase)

        lowercase__				:     Union[str, Any]					      =       {"url": url, "etag": etag}
        lowercase__				:     List[Any]					      =       cache_path + ".json"
        with open(_lowerCamelCase	,			"w") as meta_file:
            json.dump(_lowerCamelCase	,			_lowerCamelCase)

    return cache_path

def     lowercase_       (    _lowerCamelCase							: int	,			_lowerCamelCase							: List[Any]=None):
    lowercase__				:     int					      =       url.encode("utf-8")
    lowercase__				:     str					      =       shaaaa(_lowerCamelCase)
    lowercase__				:     Optional[Any]					      =       url_hash.hexdigest()

    if etag:
        lowercase__				:     int					      =       etag.encode("utf-8")
        lowercase__				:     Union[str, Any]					      =       shaaaa(_lowerCamelCase)
        filename += "." + etag_hash.hexdigest()

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

    return filename

def     lowercase_       (    _lowerCamelCase							: Optional[int]	,			_lowerCamelCase							: Optional[Any]=None	,			_lowerCamelCase							: List[str]=False	,			_lowerCamelCase							: Tuple=None	,			_lowerCamelCase							: List[str]=False	,			_lowerCamelCase							: Tuple=None	,			_lowerCamelCase							: str=False	,			_lowerCamelCase							: List[Any]=False	,			_lowerCamelCase							: List[Any]=False	,			):
    if cache_dir is None:
        lowercase__				:     Tuple					      =       TRANSFORMERS_CACHE
    if isinstance(_lowerCamelCase	,			_lowerCamelCase):
        lowercase__				:     Optional[int]					      =       str(_lowerCamelCase)
    if isinstance(_lowerCamelCase	,			_lowerCamelCase):
        lowercase__				:     Optional[Any]					      =       str(_lowerCamelCase)

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

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

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        lowercase__      ,     lowercase__				:     Union[str, Any]					      =       os.path.split(_lowerCamelCase)
        lowercase__				:     List[str]					      =       output_file.replace("."	,			"-") + "-extracted"
        lowercase__				:     List[str]					      =       os.path.join(_lowerCamelCase	,			_lowerCamelCase)

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

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

        return output_path_extracted

    return output_path

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

def     lowercase_       (    _lowerCamelCase							: Tuple):
    lowercase__				:     int					      =       requests.get(_lowerCamelCase)
    lowercase__				:     str					      =       np.array(Image.open(BytesIO(response.content)))
    return img

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

def     lowercase_       (    ):
    print(f'''{os.path.abspath(os.path.join(_lowerCamelCase	,			os.pardir))}/demo.ipynb''')

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

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Optional[int]=1):
    return (images[i : i + batch] for i in range(0	,			len(_lowerCamelCase)	,			_lowerCamelCase))




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       self.padding
       if padding not in ("longest", "max_length"):
           raise ValueError("Padding must be either 'longest' or 'max_length'!"						)
       if max_length is not None and text_pair is not None:
           # Because we have to instantiate a Trimmer to do it properly
           raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"						)
       if max_length is None:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None:
           if text.shape.rank > 1:
               raise ValueError("text argument should not be multidimensional when a text pair is supplied!"						)
           if text_pair.shape.rank > 1:
               raise ValueError("text_pair should not be multidimensional!"						)
       if text.shape.rank == 2:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }



