






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

      lowerCamelCase			:					List[Any]         =	True
except ImportError:
      lowerCamelCase			:					Union[str, Any]         =	False


try:
      from torch.hub import _get_torch_home

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

lowerCamelCase			:					Dict         =	os.path.join(torch_cache_home, '''transformers''')

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





def       __lowerCAmelCase      (						__snake_case=OBJECTS					, __snake_case=ATTRIBUTES       ):
     __lowerCAmelCase								=					[]
     with open(UpperCAmelCase__       ) as f:
          for object in f.readlines():
               vg_classes.append(object.split(","       )[0].lower().strip()       )

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





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



class 							_UpperCamelCase			:
      snake_case_  =							{}


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = "root"					,						__UpperCamelCase=0	)->	int:
           __lowerCAmelCase								=					name
           __lowerCAmelCase								=					level
           __lowerCAmelCase								=					{}
           for k, v in dictionary.items():
                if v is None:
                     raise ValueError()
                __lowerCAmelCase								=					copy.deepcopy(__UpperCamelCase	)
                __lowerCAmelCase								=					copy.deepcopy(__UpperCamelCase	)
                if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					Config(__UpperCamelCase					,						name=__UpperCamelCase					,						level=level + 1	)
                __lowerCAmelCase								=					v
                setattr(self					,						__UpperCamelCase					,						__UpperCamelCase	)

           __lowerCAmelCase								=					d


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


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


      def  __UpperCAmelCase		(	self	)->	Dict:
           return self._pointer


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[int]:
           with open(F"""{file_name}"""					,						"w"	) as stream:
                dump(__UpperCamelCase					,						__UpperCamelCase	)


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


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	List[Any]:
           with open(__UpperCamelCase	) as stream:
                __lowerCAmelCase								=					load(__UpperCamelCase					,						Loader=__UpperCamelCase	)
           return data


      def __str__(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					"    "
           if self._name != "root":
                __lowerCAmelCase								=					F"""{t * (self._level-1)}{self._name}:\n"""
           else:
                __lowerCAmelCase								=					""
           __lowerCAmelCase								=					self._level
           for i, (k, v) in enumerate(self._pointer.items()	):
                if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     r += F"""{t * (self._level)}{v}\n"""
                     self._level += 1
                else:
                     r += F"""{t * (self._level)}{k}: {v} ({type(__UpperCamelCase	).__name__})\n"""
                __lowerCAmelCase								=					level
           return r[:-1]


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						**__UpperCamelCase	)->	List[str]:
           __lowerCAmelCase			, __lowerCAmelCase								=					cls.get_config_dict(__UpperCamelCase					,						**__UpperCamelCase	)
           return cls(__UpperCamelCase	)


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						**__UpperCamelCase	)->	List[Any]:
           __lowerCAmelCase								=					kwargs.pop("cache_dir"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					kwargs.pop("force_download"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					kwargs.pop("resume_download"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					kwargs.pop("proxies"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					kwargs.pop("local_files_only"					,						__UpperCamelCase	)

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

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

                __lowerCAmelCase								=					Config.load_yaml(__UpperCamelCase	)

           except EnvironmentError:
                __lowerCAmelCase								=					"Can't load config for"
                raise EnvironmentError(__UpperCamelCase	)

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

           return Config.load_yaml(__UpperCamelCase	), kwargs





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					torch.load("dump.pt"					, map_location=in_tensor.device       )
     __lowerCAmelCase								=					in_tensor.numpy()
     __lowerCAmelCase								=					out_tensor.numpy()[0]
     print(na.shape					, na[0, 0, :5]       )
     print(na.shape					, na[0, 0, :5]       )
     assert np.allclose(UpperCAmelCase__					, UpperCAmelCase__					, rtol=0.01					, atol=0.1       ), (
         F"""{sum([1 for x in np.isclose(UpperCAmelCase__					, UpperCAmelCase__					, rtol=0.01					, atol=0.1       ).flatten() if x is False]       )/len(na.flatten()       )*100:.4f} %"""
         " element-wise mismatch"
     )
     raise Exception("tensors are all good"       )

     # Hugging face functions below





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					urlparse(UpperCAmelCase__       )
     return parsed.scheme in ("http", "https")





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=True       ):
     __lowerCAmelCase								=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     __lowerCAmelCase								=					"/" not in model_id
     if legacy_format:
          return F"""{endpoint}/{model_id}-{filename}"""
     else:
          return F"""{endpoint}/{model_id}/{filename}"""





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





def       __lowerCAmelCase      (						__snake_case					, __snake_case=None					, __snake_case=False					, __snake_case=None					, __snake_case=10					, __snake_case=False					, __snake_case=None					, __snake_case=False					, ):
     if cache_dir is None:
          __lowerCAmelCase								=					TRANSFORMERS_CACHE
     if isinstance(UpperCAmelCase__					, UpperCAmelCase__       ):
          __lowerCAmelCase								=					str(UpperCAmelCase__       )

     os.makedirs(UpperCAmelCase__					, exist_ok=UpperCAmelCase__       )

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

     __lowerCAmelCase								=					url_to_filename(UpperCAmelCase__					, UpperCAmelCase__       )

     # get cache path to put the file
     __lowerCAmelCase								=					os.path.join(UpperCAmelCase__					, UpperCAmelCase__       )

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

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

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

          if resume_download:
               __lowerCAmelCase								=					cache_path + ".incomplete"

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

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

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

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

          os.replace(temp_file.name					, UpperCAmelCase__       )

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

     return cache_path





def       __lowerCAmelCase      (						__snake_case					, __snake_case=None       ):
     __lowerCAmelCase								=					url.encode("utf-8"       )
     __lowerCAmelCase								=					shaaaa(UpperCAmelCase__       )
     __lowerCAmelCase								=					url_hash.hexdigest()

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

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

     return filename





def       __lowerCAmelCase      (						__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					, ):
     if cache_dir is None:
          __lowerCAmelCase								=					TRANSFORMERS_CACHE
     if isinstance(UpperCAmelCase__					, UpperCAmelCase__       ):
          __lowerCAmelCase								=					str(UpperCAmelCase__       )
     if isinstance(UpperCAmelCase__					, UpperCAmelCase__       ):
          __lowerCAmelCase								=					str(UpperCAmelCase__       )

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

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

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

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

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

          return output_path_extracted

     return output_path





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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					requests.get(UpperCAmelCase__       )
     __lowerCAmelCase								=					np.array(Image.open(BytesIO(response.content       )       )       )
     return img





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





def       __lowerCAmelCase      (						):
     print(F"""{os.path.abspath(os.path.join(UpperCAmelCase__					, os.pardir       )       )}/demo.ipynb"""       )





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





def       __lowerCAmelCase      (						__snake_case					, __snake_case=1       ):
     return (images[i : i + batch] for i in range(0					, len(UpperCAmelCase__       )					, UpperCAmelCase__       ))














from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






