
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

   lowercase_											=	True
except ImportError:
   lowercase_											=	False


try:
   from torch.hub import _get_torch_home

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

lowercase_											=	os.path.join(torch_cache_home, """transformers""")

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


def     a__			(    snake_case=OBJECTS   ,	snake_case=ATTRIBUTES    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[]
  with open(snake_case    ) as f:
    for object in f.readlines():
      vg_classes.append(object.split(''','''    )[0].lower().strip()    )

  __SCREAMING_SNAKE_CASE     :					Dict			=						[]
  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     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Dict			=						OrderedDict()
  with open(snake_case   ,	'''rb'''    ) as f:
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pkl.load(snake_case    )['''model''']
  for k in copy.deepcopy(list(ckp.keys()    )    ):
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						ckp.pop(snake_case    )
    if isinstance(snake_case   ,	np.ndarray    ):
      __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.tensor(snake_case    )
    else:
      assert isinstance(snake_case   ,	torch.tensor    ), type(snake_case    )
    __SCREAMING_SNAKE_CASE     :					List[str]			=						v
  return r





class   __UpperCamelCase				:






     """simple docstring"""







     lowerCAmelCase_     =	{}



     def __init__(							self    :    Optional[int]      ,		_A    :    dict      ,		_A    :    str = "root"      ,		_A    :    Dict=0				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						name
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						level
       __SCREAMING_SNAKE_CASE     :					int			=						{}
       for k, v in dictionary.items():
         if v is None:
           raise ValueError()
         __SCREAMING_SNAKE_CASE     :					Dict			=						copy.deepcopy(_A				)
         __SCREAMING_SNAKE_CASE     :					str			=						copy.deepcopy(_A				)
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						Config(_A      ,		name=_A      ,		level=level + 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						v
         setattr(self      ,		_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						d



     def __repr__(							self    :    Union[str, Any]				):







       """simple docstring"""




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



     def __setattr__(							self    :    Any      ,		_A    :    Tuple      ,		_A    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						val
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						val
       __SCREAMING_SNAKE_CASE     :					Dict			=						key.split('''.'''				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						len(_A				) - 1
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._pointer
       if len(_A				) > 1:
         for i, l in enumerate(_A				):
           if hasattr(self      ,		_A				) and isinstance(getattr(self      ,		_A				)      ,		_A				):
             setattr(getattr(self      ,		_A				)      ,		'''.'''.join(levels[i:]				)      ,		_A				)
           if l == last_level:
             __SCREAMING_SNAKE_CASE     :					Tuple			=						val
           else:
             __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						pointer[l]



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       return self._pointer



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[str]      ,		_A    :    List[str]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Any      ,		_A    :    List[str]				):







       """simple docstring"""




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



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    Optional[Any]				):







       """simple docstring"""




       with open(_A				) as stream:
         __SCREAMING_SNAKE_CASE     :					str			=						load(_A      ,		Loader=_A				)
       return data



     def __str__(							self    :    Dict				):







       """simple docstring"""




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



     @classmethod
     def 				UpperCAmelCase__    (							cls    :    Union[str, Any]      ,		_A    :    str      ,		**_A    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						cls.get_config_dict(_A      ,		**_A				)
       return cls(_A				)




     @classmethod
     def 				UpperCAmelCase__    (							cls    :    int      ,		_A    :    str      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''cache_dir'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						kwargs.pop('''force_download'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.pop('''resume_download'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.pop('''proxies'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						kwargs.pop('''local_files_only'''      ,		_A				)

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

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

         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						Config.load_yaml(_A				)

       except EnvironmentError:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						'''Can\'t load config for'''
         raise EnvironmentError(_A				)

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

       return Config.load_yaml(_A				), kwargs






def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.load('''dump.pt'''   ,	map_location=in_tensor.device    )
  __SCREAMING_SNAKE_CASE     :					int			=						in_tensor.numpy()
  __SCREAMING_SNAKE_CASE     :					Any			=						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     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						urlparse(snake_case    )
  return parsed.scheme in ("http", "https")


def     a__			(    snake_case   ,	snake_case   ,	snake_case=True    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					int			=						CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  __SCREAMING_SNAKE_CASE     :					Dict			=						'''/''' not in model_id
  if legacy_format:
    return F'''{endpoint}/{model_id}-{filename}'''
  else:
    return F'''{endpoint}/{model_id}/{filename}'''


def     a__			(    snake_case   ,	snake_case   ,	snake_case=None   ,	snake_case=0   ,	snake_case=None   ,	):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Any			=						'''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
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						{'''user-agent''': ua}
  if resume_size > 0:
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'''bytes=%d-''' % (resume_size,)
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						requests.get(snake_case   ,	stream=snake_case   ,	proxies=snake_case   ,	headers=snake_case    )
  if response.status_code == 416:  # Range not satisfiable
    return
  __SCREAMING_SNAKE_CASE     :					Any			=						response.headers.get('''Content-Length'''    )
  __SCREAMING_SNAKE_CASE     :					str			=						resume_size + int(snake_case    ) if content_length is not None else None
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						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     a__			(    snake_case   ,	snake_case=None   ,	snake_case=False   ,	snake_case=None   ,	snake_case=10   ,	snake_case=False   ,	snake_case=None   ,	snake_case=False   ,	):





  """simple docstring"""
  if cache_dir is None:
    __SCREAMING_SNAKE_CASE     :					str			=						TRANSFORMERS_CACHE
  if isinstance(snake_case   ,	snake_case    ):
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						str(snake_case    )

  os.makedirs(snake_case   ,	exist_ok=snake_case    )

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

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						url_to_filename(snake_case   ,	snake_case    )

  # get cache path to put the file
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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:
      __SCREAMING_SNAKE_CASE     :					List[str]			=						[
          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.
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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:
      __SCREAMING_SNAKE_CASE     :					Any			=						cache_path + '''.incomplete'''

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

      __SCREAMING_SNAKE_CASE     :					Any			=						_resumable_file_manager
      if os.path.exists(snake_case    ):
        __SCREAMING_SNAKE_CASE     :					str			=						os.stat(snake_case    ).st_size
      else:
        __SCREAMING_SNAKE_CASE     :					List[str]			=						0
    else:
      __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						partial(tempfile.NamedTemporaryFile   ,	dir=snake_case   ,	delete=snake_case    )
      __SCREAMING_SNAKE_CASE     :					List[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'''   ,	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    )

    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{'''url''': url, '''etag''': etag}
    __SCREAMING_SNAKE_CASE     :					Dict			=						cache_path + '''.json'''
    with open(snake_case   ,	'''w'''    ) as meta_file:
      json.dump(snake_case   ,	snake_case    )

  return cache_path


def     a__			(    snake_case   ,	snake_case=None    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Tuple			=						url.encode('''utf-8'''    )
  __SCREAMING_SNAKE_CASE     :					List[str]			=						shaaaa(snake_case    )
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						url_hash.hexdigest()

  if etag:
    __SCREAMING_SNAKE_CASE     :					str			=						etag.encode('''utf-8'''    )
    __SCREAMING_SNAKE_CASE     :					Tuple			=						shaaaa(snake_case    )
    filename += "." + etag_hash.hexdigest()

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

  return filename


def     a__			(    snake_case   ,	snake_case=None   ,	snake_case=False   ,	snake_case=None   ,	snake_case=False   ,	snake_case=None   ,	snake_case=False   ,	snake_case=False   ,	snake_case=False   ,	):





  """simple docstring"""
  if cache_dir is None:
    __SCREAMING_SNAKE_CASE     :					Dict			=						TRANSFORMERS_CACHE
  if isinstance(snake_case   ,	snake_case    ):
    __SCREAMING_SNAKE_CASE     :					int			=						str(snake_case    )
  if isinstance(snake_case   ,	snake_case    ):
    __SCREAMING_SNAKE_CASE     :					Any			=						str(snake_case    )

  if is_remote_url(snake_case    ):
    # URL, so get it from the cache (downloading if necessary)
    __SCREAMING_SNAKE_CASE     :					str			=						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.
    __SCREAMING_SNAKE_CASE     :					List[str]			=						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/"
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						os.path.split(snake_case    )
    __SCREAMING_SNAKE_CASE     :					int			=						output_file.replace('''.'''   ,	'''-'''    ) + '''-extracted'''
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						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
    __SCREAMING_SNAKE_CASE     :					Tuple			=						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    ):
        __SCREAMING_SNAKE_CASE     :					int			=						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     a__			(    snake_case   ,	snake_case=","    ):





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


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						requests.get(snake_case    )
  __SCREAMING_SNAKE_CASE     :					List[str]			=						np.array(Image.open(BytesIO(response.content    )    )    )
  return img


def     a__			(    snake_case    ):





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


def     a__			(    ):





  """simple docstring"""
  print(F'''{os.path.abspath(os.path.join(snake_case   ,	os.pardir    )    )}/demo.ipynb'''    )


def     a__			(    snake_case   ,	snake_case="RGB"    ):





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


def     a__			(    snake_case   ,	snake_case=1    ):





  """simple docstring"""
  return (images[i : i + batch] for i in range(0   ,	len(snake_case    )   ,	snake_case    ))


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
