


"""simple docstring"""







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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    List[Any]       ="dpr"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=512		,					snake_case__=2		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__="absolute"		,					snake_case__ = 0		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,					**snake_case__						)

         lowerCAmelCase					:					int								=							vocab_size
         lowerCAmelCase					:					List[Any]								=							hidden_size
         lowerCAmelCase					:					Dict								=							num_hidden_layers
         lowerCAmelCase					:					str								=							num_attention_heads
         lowerCAmelCase					:					str								=							hidden_act
         lowerCAmelCase					:					List[str]								=							intermediate_size
         lowerCAmelCase					:					Any								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					List[Any]								=							type_vocab_size
         lowerCAmelCase					:					Any								=							initializer_range
         lowerCAmelCase					:					Tuple								=							layer_norm_eps
         lowerCAmelCase					:					List[str]								=							projection_dim
         lowerCAmelCase					:					Tuple								=							position_embedding_type









"""simple docstring"""







import re

from filelock import FileLock


try:
     import nltk

     lowerCAmelCase__				    =       True
except (ImportError, ModuleNotFoundError):
     lowerCAmelCase__				    =       False

if NLTK_AVAILABLE:
     with FileLock('''.lock''') as lock:
          nltk.download('''punkt''', quiet=True)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  re.sub("<n>"    ,				""    ,				SCREAMING_SNAKE_CASE    )  # remove pegasus newline char
  assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
  return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE    )    )





