



"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		str 					=					word.split()

					def justify(SCREAMING_SNAKE_CASE__ :  list				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int			) -> str:
										snake_case_					:		Dict 					=					max_width - width
										snake_case_					:		Union[str, Any] 					=					len(SCREAMING_SNAKE_CASE__			)
										if len(SCREAMING_SNAKE_CASE__			) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															snake_case_					:		Tuple 					=					words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															snake_case_					:		str 					=					spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															snake_case_					:		Tuple 					=					(
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(SCREAMING_SNAKE_CASE__			):
																				num_spaces_between_words_list[i] += 1
															snake_case_					:		Union[str, Any] 					=					[]
															for i in range(SCREAMING_SNAKE_CASE__			):
																				# add the word
																				aligned_words_list.append(line[i]			)
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * """ """			)
															# just add the last word to the sentence
															aligned_words_list.append(line[-1]			)
															# join the aligned words list to form a justified line
															return "".join(SCREAMING_SNAKE_CASE__			)

					snake_case_					:		List[Any] 					=					[]
					snake_case_					:		list[str] 					=					[]
					snake_case_					:		Tuple 					=					0
					for word in words:
										if width + len(SCREAMING_SNAKE_CASE__			) + len(SCREAMING_SNAKE_CASE__			) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(SCREAMING_SNAKE_CASE__			)
															width += len(SCREAMING_SNAKE_CASE__			)
										else:
															# justify the line and add it to result
															answer.append(justify(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)			)
															# reset new line and new width
															snake_case_					:		List[Any] 					=					[word], len(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		List[str] 					=					max_width - width - len(SCREAMING_SNAKE_CASE__			)
					answer.append(""" """.join(SCREAMING_SNAKE_CASE__			) + (remaining_spaces + 1) * """ """			)
					return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()








"""simple docstring"""

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


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

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




