




'''simple docstring'''


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_			:     str							    =     True
except ImportError:
    lowerCAmelCase_			:     Any							    =     False


try:
    from torch.hub import _get_torch_home

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

lowerCAmelCase_			:     Optional[Any]							    =     os.path.join(torch_cache_home, 'transformers')

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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]=OBJECTS   ,						UpperCamelCase__						:				Tuple=ATTRIBUTES							):




     """simple docstring"""


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

     a_    :       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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]							):




     """simple docstring"""


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







class     SCREAMING_SNAKE_CASE						:
      __magic_name__     :					Optional[Any]             =				{}






      def __init__(   self      :     Union[str, Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Tuple = "root"	,	lowercase__      :     Dict=0					):


           '''simple docstring'''

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

           a_    :       Optional[Any]    	=     d






      def __repr__(   self      :     List[Any]					):


           '''simple docstring'''

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






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


           '''simple docstring'''

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






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           return self._pointer






      def        lowercase_	(   self      :     int	,	lowercase__      :     Any	,	lowercase__      :     Dict					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     int	,	lowercase__      :     Any	,	lowercase__      :     List[Any]					):


           '''simple docstring'''

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






      @staticmethod
      def        lowercase_	(   lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           with open(lowercase__					) as stream:
                a_    :       List[str]    	=     load(lowercase__	,	Loader=lowercase__					)
           return data






      def __str__(   self      :     Any					):


           '''simple docstring'''

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






      @classmethod
      def        lowercase_	(   cls      :     Tuple	,	lowercase__      :     List[Any]	,	**lowercase__      :     List[str]					):


           '''simple docstring'''

           a_	,    a_    :       Any    	=     cls.get_config_dict(lowercase__	,	**lowercase__					)
           return cls(lowercase__					)






      @classmethod
      def        lowercase_	(   cls      :     Any	,	lowercase__      :     Tuple	,	**lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       int    	=     kwargs.pop("""cache_dir"""	,	lowercase__					)
           a_    :       int    	=     kwargs.pop("""force_download"""	,	lowercase__					)
           a_    :       Optional[Any]    	=     kwargs.pop("""resume_download"""	,	lowercase__					)
           a_    :       List[str]    	=     kwargs.pop("""proxies"""	,	lowercase__					)
           a_    :       int    	=     kwargs.pop("""local_files_only"""	,	lowercase__					)

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

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

                a_    :       str    	=     Config.load_yaml(lowercase__					)

           except EnvironmentError:
                a_    :       int    	=     """Can't load config for"""
                raise EnvironmentError(lowercase__					)

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

           return Config.load_yaml(lowercase__					), kwargs



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[int]							):




     """simple docstring"""


     a_    :       int    	=     torch.load("""dump.pt"""   ,						map_location=in_tensor.device							)
     a_    :       Tuple    	=     in_tensor.numpy()
     a_    :       List[str]    	=     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       int    	=     urlparse(_A							)
     return parsed.scheme in ("http", "https")



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Any=True							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     a_    :       Dict    	=     """/""" not in model_id
     if legacy_format:
          return F"{endpoint}/{model_id}-{filename}"
     else:
          return F"{endpoint}/{model_id}/{filename}"



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				List[str]   ,						UpperCamelCase__						:				Optional[int]   ,						UpperCamelCase__						:				List[str]=None   ,						UpperCamelCase__						:				Union[str, Any]=0   ,						UpperCamelCase__						:				List[str]=None   ,						):




     """simple docstring"""


     a_    :       Union[str, 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_    :       str    	=     {"""user-agent""": ua}
     if resume_size > 0:
          a_    :       Optional[int]    	=     """bytes=%d-""" % (resume_size,)
     a_    :       Dict    	=     requests.get(_A   ,						stream=_A   ,						proxies=_A   ,						headers=_A							)
     if response.status_code == 416:  # Range not satisfiable
          return
     a_    :       Union[str, Any]    	=     response.headers.get("""Content-Length"""							)
     a_    :       Dict    	=     resume_size + int(_A							) if content_length is not None else None
     a_    :       Any    	=     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Tuple=None   ,						UpperCamelCase__						:				int=False   ,						UpperCamelCase__						:				List[str]=None   ,						UpperCamelCase__						:				Dict=10   ,						UpperCamelCase__						:				Optional[int]=False   ,						UpperCamelCase__						:				List[Any]=None   ,						UpperCamelCase__						:				List[Any]=False   ,						):




     """simple docstring"""


     if cache_dir is None:
          a_    :       Any    	=     TRANSFORMERS_CACHE
     if isinstance(_A   ,						_A							):
          a_    :       Dict    	=     str(_A							)

     os.makedirs(_A   ,						exist_ok=_A							)

     a_    :       Tuple    	=     None
     if not local_files_only:
          try:
               a_    :       Optional[int]    	=     requests.head(_A   ,						allow_redirects=_A   ,						proxies=_A   ,						timeout=_A							)
               if response.status_code == 200:
                    a_    :       Tuple    	=     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_    :       Optional[int]    	=     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_    :       int    	=     [
                   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_    :       str    	=     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_    :       str    	=     cache_path + """.incomplete"""

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

               a_    :       Optional[int]    	=     _resumable_file_manager
               if os.path.exists(_A							):
                    a_    :       Tuple    	=     os.stat(_A							).st_size
               else:
                    a_    :       int    	=     0
          else:
               a_    :       int    	=     partial(tempfile.NamedTemporaryFile   ,						dir=_A   ,						delete=_A							)
               a_    :       int    	=     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_    :       List[Any]    	=     {"""url""": url, """etag""": etag}
          a_    :       Tuple    	=     cache_path + """.json"""
          with open(_A   ,						"""w"""							) as meta_file:
               json.dump(_A   ,						_A							)

     return cache_path



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict   ,						UpperCamelCase__						:				Any=None							):




     """simple docstring"""


     a_    :       str    	=     url.encode("""utf-8"""							)
     a_    :       Union[str, Any]    	=     shaaaa(_A							)
     a_    :       str    	=     url_hash.hexdigest()

     if etag:
          a_    :       Union[str, Any]    	=     etag.encode("""utf-8"""							)
          a_    :       List[str]    	=     shaaaa(_A							)
          filename += "." + etag_hash.hexdigest()

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

     return filename



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Any   ,						UpperCamelCase__						:				List[str]=None   ,						UpperCamelCase__						:				List[Any]=False   ,						UpperCamelCase__						:				Tuple=None   ,						UpperCamelCase__						:				List[str]=False   ,						UpperCamelCase__						:				Tuple=None   ,						UpperCamelCase__						:				str=False   ,						UpperCamelCase__						:				str=False   ,						UpperCamelCase__						:				List[Any]=False   ,						):




     """simple docstring"""


     if cache_dir is None:
          a_    :       Dict    	=     TRANSFORMERS_CACHE
     if isinstance(_A   ,						_A							):
          a_    :       str    	=     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_    :       Tuple    	=     os.path.split(_A							)
          a_    :       Tuple    	=     output_file.replace("""."""   ,						"""-"""							) + """-extracted"""
          a_    :       str    	=     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_    :       Optional[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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Any   ,						UpperCamelCase__						:				Dict=","							):




     """simple docstring"""


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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]							):




     """simple docstring"""


     a_    :       str    	=     requests.get(_A							)
     a_    :       int    	=     np.array(Image.open(BytesIO(response.content							)							)							)
     return img



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]							):




     """simple docstring"""


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



def    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict   ,						UpperCamelCase__						:				int="RGB"							):




     """simple docstring"""


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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				Any=1							):




     """simple docstring"""


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






'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_			:     int							    =     {
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Union[str, Any]							    =     [
     'DPRContextEncoderTokenizerFast',
     'DPRQuestionEncoderTokenizerFast',
     'DPRReaderTokenizerFast',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     List[str]							    =     [
     'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPRContextEncoder',
     'DPRPretrainedContextEncoder',
     'DPRPreTrainedModel',
     'DPRPretrainedQuestionEncoder',
     'DPRPretrainedReader',
     'DPRQuestionEncoder',
     'DPRReader',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Dict							    =     [
     'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDPRContextEncoder',
     'TFDPRPretrainedContextEncoder',
     'TFDPRPretrainedQuestionEncoder',
     'TFDPRPretrainedReader',
     'TFDPRQuestionEncoder',
     'TFDPRReader',
 ]


if TYPE_CHECKING:
 from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
 from .tokenization_dpr import (
     DPRContextEncoderTokenizer,
     DPRQuestionEncoderTokenizer,
     DPRReaderOutput,
     DPRReaderTokenizer,
 )

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_dpr_fast import (
      DPRContextEncoderTokenizerFast,
      DPRQuestionEncoderTokenizerFast,
      DPRReaderTokenizerFast,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dpr import (
      DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPRContextEncoder,
      DPRPretrainedContextEncoder,
      DPRPreTrainedModel,
      DPRPretrainedQuestionEncoder,
      DPRPretrainedReader,
      DPRQuestionEncoder,
      DPRReader,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_dpr import (
      TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDPRContextEncoder,
      TFDPRPretrainedContextEncoder,
      TFDPRPretrainedQuestionEncoder,
      TFDPRPretrainedReader,
      TFDPRQuestionEncoder,
      TFDPRReader,
  )

else:
 import sys

 lowerCAmelCase_			:     int							    =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
