
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__						:	List[str]	     = True
except ImportError:
   UpperCAmelCase__						:	List[str]	     = False


try:
   from torch.hub import _get_torch_home

   UpperCAmelCase__						:	str	     = _get_torch_home()
except ImportError:
   UpperCAmelCase__						:	Optional[Any]	     = os.path.expanduser(
       os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
   )

UpperCAmelCase__						:	Any	     = os.path.join(torch_cache_home, 'transformers')

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






def     lowerCamelCase__				(   a=OBJECTS						,						a=ATTRIBUTES ) ->  Any:
   _A:    Union[str, Any]						     =			[]
   with open(a ) as f:
      for object in f.readlines():
         vg_classes.append(object.split(''',''' )[0].lower().strip() )

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






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






class UpperCAmelCase  :





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       {}


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				dict     ,     lowerCAmelCase_	:				str = "root"     ,     lowerCAmelCase_	:				str=0	):

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

      _A:    Union[str, Any]						     =			d


   def __repr__(   self	:				Any	):

      """simple docstring"""
      return str(list((self._pointer.keys())	)	)


   def __setattr__(   self	:				Dict     ,     lowerCAmelCase_	:				Union[str, Any]     ,     lowerCAmelCase_	:				Dict	):

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


   def 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      return self._pointer


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				Dict	):

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


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				int	):

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


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				int	):

      """simple docstring"""
      with open(lowerCAmelCase_	) as stream:
         _A:    str						     =			load(lowerCAmelCase_     ,     Loader=lowerCAmelCase_	)
      return data


   def __str__(   self	:				str	):

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


   @classmethod
   def 					__magic_name__							(   cls	:				Optional[Any]     ,     lowerCAmelCase_	:				str     ,     **lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A     ,    _A:    List[str]						     =			cls.get_config_dict(lowerCAmelCase_     ,     **lowerCAmelCase_	)
      return cls(lowerCAmelCase_	)


   @classmethod
   def 					__magic_name__							(   cls	:				List[str]     ,     lowerCAmelCase_	:				str     ,     **lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Any						     =			kwargs.pop('''cache_dir'''     ,     lowerCAmelCase_	)
      _A:    List[str]						     =			kwargs.pop('''force_download'''     ,     lowerCAmelCase_	)
      _A:    List[str]						     =			kwargs.pop('''resume_download'''     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			kwargs.pop('''proxies'''     ,     lowerCAmelCase_	)
      _A:    List[Any]						     =			kwargs.pop('''local_files_only'''     ,     lowerCAmelCase_	)

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

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

         _A:    List[Any]						     =			Config.load_yaml(lowerCAmelCase_	)

      except EnvironmentError:
         _A:    Union[str, Any]						     =			'''Can\'t load config for'''
         raise EnvironmentError(lowerCAmelCase_	)

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

      return Config.load_yaml(lowerCAmelCase_	), kwargs






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

   # Hugging face functions below






def     lowerCamelCase__				(   a ) ->  Any:
   _A:    List[str]						     =			urlparse(a )
   return parsed.scheme in ("http", "https")






def     lowerCamelCase__				(   a						,						a						,						a=True ) ->  str:
   _A:    List[Any]						     =			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   _A:    int						     =			'''/''' not in model_id
   if legacy_format:
      return f"""{endpoint}/{model_id}-{filename}"""
   else:
      return f"""{endpoint}/{model_id}/{filename}"""






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






def     lowerCamelCase__				(   a						,						a=None						,						a=False						,						a=None						,						a=10						,						a=False						,						a=None						,						a=False						,						) ->  Optional[int]:
   if cache_dir is None:
      _A:    Optional[int]						     =			TRANSFORMERS_CACHE
   if isinstance(a						,						a ):
      _A:    str						     =			str(a )

   os.makedirs(a						,						exist_ok=a )

   _A:    Any						     =			None
   if not local_files_only:
      try:
         _A:    int						     =			requests.head(a						,						allow_redirects=a						,						proxies=a						,						timeout=a )
         if response.status_code == 2_00:
            _A:    List[str]						     =			response.headers.get('''ETag''' )
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   _A:    List[str]						     =			url_to_filename(a						,						a )

   # get cache path to put the file
   _A:    List[str]						     =			os.path.join(a						,						a )

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

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

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

      if resume_download:
         _A:    List[Any]						     =			cache_path + '''.incomplete'''

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

         _A:    int						     =			_resumable_file_manager
         if os.path.exists(a ):
            _A:    str						     =			os.stat(a ).st_size
         else:
            _A:    str						     =			0
      else:
         _A:    Optional[int]						     =			partial(tempfile.NamedTemporaryFile						,						dir=a						,						delete=a )
         _A:    List[str]						     =			0

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

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

      os.replace(temp_file.name						,						a )

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

   return cache_path






def     lowerCamelCase__				(   a						,						a=None ) ->  Dict:
   _A:    Optional[Any]						     =			url.encode('''utf-8''' )
   _A:    Tuple						     =			shaaaa(a )
   _A:    str						     =			url_hash.hexdigest()

   if etag:
      _A:    int						     =			etag.encode('''utf-8''' )
      _A:    Tuple						     =			shaaaa(a )
      filename += "." + etag_hash.hexdigest()

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

   return filename






def     lowerCamelCase__				(   a						,						a=None						,						a=False						,						a=None						,						a=False						,						a=None						,						a=False						,						a=False						,						a=False						,						) ->  Any:
   if cache_dir is None:
      _A:    Any						     =			TRANSFORMERS_CACHE
   if isinstance(a						,						a ):
      _A:    int						     =			str(a )
   if isinstance(a						,						a ):
      _A:    str						     =			str(a )

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

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

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

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

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

      return output_path_extracted

   return output_path






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






def     lowerCamelCase__				(   a ) ->  Dict:
   _A:    Any						     =			requests.get(a )
   _A:    Tuple						     =			np.array(Image.open(BytesIO(response.content ) ) )
   return img






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






def     lowerCamelCase__				(   ) ->  int:
   print(f"""{os.path.abspath(os.path.join(a						,						os.pardir ) )}/demo.ipynb""" )






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






def     lowerCamelCase__				(   a						,						a=1 ) ->  List[Any]:
   return (images[i : i + batch] for i in range(0						,						len(a )						,						a ))





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



