




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

    snake_case_ : Optional[Any]    		=       True
except ImportError:
    snake_case_ : Any    		=       False


try:
    from torch.hub import _get_torch_home

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

snake_case_ : int    		=       os.path.join(torch_cache_home, "transformers")

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


def    A						(__A   :    Optional[Any]=OBJECTS   ,				__A   :    Optional[Any]=ATTRIBUTES       )   ->      Any:







     """simple docstring"""


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

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


def    A						(__A   :    int       )   ->      List[Any]:







     """simple docstring"""


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







class __snake_case			:
   UpperCAmelCase__  :    str          =  {}







   def __init__(      self		:						Tuple					,						_snake_case		:						dict					,						_snake_case		:						str = "root"					,						_snake_case		:						Optional[Any]=0):

        """simple docstring"""


        UpperCAmelCase_          =							name
        UpperCAmelCase_          =							level
        UpperCAmelCase_          =							{}
        for k, v in dictionary.items():
             if v is None:
                  raise ValueError()
             UpperCAmelCase_          =							copy.deepcopy(_snake_case)
             UpperCAmelCase_          =							copy.deepcopy(_snake_case)
             if isinstance(_snake_case					,						_snake_case):
                  UpperCAmelCase_          =							Config(_snake_case					,						name=_snake_case					,						level=level + 1)
             UpperCAmelCase_          =							v
             setattr(self					,						_snake_case					,						_snake_case)

        UpperCAmelCase_          =							d







   def __repr__(      self		:						Any):

        """simple docstring"""


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







   def __setattr__(      self		:						int					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							val
        UpperCAmelCase_          =							val
        UpperCAmelCase_          =							key.split('''.''')
        UpperCAmelCase_          =							len(_snake_case) - 1
        UpperCAmelCase_          =							self._pointer
        if len(_snake_case) > 1:
             for i, l in enumerate(_snake_case):
                  if hasattr(self					,						_snake_case) and isinstance(getattr(self					,						_snake_case)					,						_snake_case):
                       setattr(getattr(self					,						_snake_case)					,						'''.'''.join(levels[i:])					,						_snake_case)
                  if l == last_level:
                       UpperCAmelCase_          =							val
                  else:
                       UpperCAmelCase_          =							pointer[l]







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        return self._pointer







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						List[str]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						List[str]					,						_snake_case		:						Any):

        """simple docstring"""


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







   @staticmethod
   def lowerCamelCase						(      _snake_case		:						str):

        """simple docstring"""


        with open(_snake_case) as stream:
             UpperCAmelCase_          =							load(_snake_case					,						Loader=_snake_case)
        return data







   def __str__(      self		:						Optional[Any]):

        """simple docstring"""


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







   @classmethod
   def lowerCamelCase						(      cls		:						Any					,						_snake_case		:						str					,						**_snake_case		:						Any):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							cls.get_config_dict(_snake_case					,						**_snake_case)
        return cls(_snake_case)







   @classmethod
   def lowerCamelCase						(      cls		:						Union[str, Any]					,						_snake_case		:						str					,						**_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.pop('''cache_dir'''					,						_snake_case)
        UpperCAmelCase_          =							kwargs.pop('''force_download'''					,						_snake_case)
        UpperCAmelCase_          =							kwargs.pop('''resume_download'''					,						_snake_case)
        UpperCAmelCase_          =							kwargs.pop('''proxies'''					,						_snake_case)
        UpperCAmelCase_          =							kwargs.pop('''local_files_only'''					,						_snake_case)

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

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

             UpperCAmelCase_          =							Config.load_yaml(_snake_case)

        except EnvironmentError:
             UpperCAmelCase_          =							'''Can\'t load config for'''
             raise EnvironmentError(_snake_case)

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

        return Config.load_yaml(_snake_case), kwargs







def    A						(__A   :    List[str]       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							torch.load('''dump.pt'''   ,				map_location=in_tensor.device       )
     UpperCAmelCase_          =							in_tensor.numpy()
     UpperCAmelCase_          =							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()       )*100:.4f} %"""
         " element-wise mismatch"
     )
     raise Exception('''tensors are all good'''       )

     # Hugging face functions below


def    A						(__A   :    Union[str, Any]       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							urlparse(__A       )
     return parsed.scheme in ("http", "https")


def    A						(__A   :    str   ,				__A   :    str   ,				__A   :    Any=True       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     UpperCAmelCase_          =							'''/''' not in model_id
     if legacy_format:
          return F"""{endpoint}/{model_id}-{filename}"""
     else:
          return F"""{endpoint}/{model_id}/{filename}"""


def    A						(__A   :    List[Any]   ,				__A   :    str   ,				__A   :    Dict=None   ,				__A   :    Optional[Any]=0   ,				__A   :    Union[str, Any]=None   ,				)   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							'''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
     UpperCAmelCase_          =							{'''user-agent''': ua}
     if resume_size > 0:
          UpperCAmelCase_          =							'''bytes=%d-''' % (resume_size,)
     UpperCAmelCase_          =							requests.get(__A   ,				stream=__A   ,				proxies=__A   ,				headers=__A       )
     if response.status_code == 416:  # Range not satisfiable
          return
     UpperCAmelCase_          =							response.headers.get('''Content-Length'''       )
     UpperCAmelCase_          =							resume_size + int(__A       ) if content_length is not None else None
     UpperCAmelCase_          =							tqdm(
         unit='''B'''   ,				unit_scale=__A   ,				total=__A   ,				initial=__A   ,				desc='''Downloading'''   ,				)
     for chunk in response.iter_content(chunk_size=1024       ):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(__A       )       )
               temp_file.write(__A       )
     progress.close()


def    A						(__A   :    List[Any]   ,				__A   :    Tuple=None   ,				__A   :    Optional[Any]=False   ,				__A   :    int=None   ,				__A   :    List[Any]=10   ,				__A   :    Union[str, Any]=False   ,				__A   :    List[Any]=None   ,				__A   :    Union[str, Any]=False   ,				)   ->      Any:







     """simple docstring"""


     if cache_dir is None:
          UpperCAmelCase_          =							TRANSFORMERS_CACHE
     if isinstance(__A   ,				__A       ):
          UpperCAmelCase_          =							str(__A       )

     os.makedirs(__A   ,				exist_ok=__A       )

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

     UpperCAmelCase_          =							url_to_filename(__A   ,				__A       )

     # get cache path to put the file
     UpperCAmelCase_          =							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:
               UpperCAmelCase_          =							[
                   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.
     UpperCAmelCase_          =							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:
               UpperCAmelCase_          =							cache_path + '''.incomplete'''

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

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

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

     return cache_path


def    A						(__A   :    Union[str, Any]   ,				__A   :    Optional[Any]=None       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							url.encode('''utf-8'''       )
     UpperCAmelCase_          =							shaaaa(__A       )
     UpperCAmelCase_          =							url_hash.hexdigest()

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

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

     return filename


def    A						(__A   :    List[Any]   ,				__A   :    Optional[int]=None   ,				__A   :    Optional[int]=False   ,				__A   :    Optional[int]=None   ,				__A   :    int=False   ,				__A   :    List[str]=None   ,				__A   :    Dict=False   ,				__A   :    Union[str, Any]=False   ,				__A   :    str=False   ,				)   ->      Any:







     """simple docstring"""


     if cache_dir is None:
          UpperCAmelCase_          =							TRANSFORMERS_CACHE
     if isinstance(__A   ,				__A       ):
          UpperCAmelCase_          =							str(__A       )
     if isinstance(__A   ,				__A       ):
          UpperCAmelCase_          =							str(__A       )

     if is_remote_url(__A       ):
          # URL, so get it from the cache (downloading if necessary)
          UpperCAmelCase_          =							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.
          UpperCAmelCase_          =							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/"
          UpperCAmelCase_     ,       UpperCAmelCase_          =							os.path.split(__A       )
          UpperCAmelCase_          =							output_file.replace('''.'''   ,				'''-'''       ) + '''-extracted'''
          UpperCAmelCase_          =							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
          UpperCAmelCase_          =							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       ):
                    UpperCAmelCase_          =							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    A						(__A   :    List[Any]   ,				__A   :    List[Any]=","       )   ->      Union[str, Any]:







     """simple docstring"""


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


def    A						(__A   :    int       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							requests.get(__A       )
     UpperCAmelCase_          =							np.array(Image.open(BytesIO(response.content       )       )       )
     return img


def    A						(__A   :    Dict       )   ->      Tuple:







     """simple docstring"""


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


def    A						()   ->      Optional[int]:







     """simple docstring"""


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


def    A						(__A   :    Any   ,				__A   :    Optional[Any]="RGB"       )   ->      int:







     """simple docstring"""


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


def    A						(__A   :    List[Any]   ,				__A   :    Dict=1       )   ->      Union[str, Any]:







     """simple docstring"""


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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






