

'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase :      List[Any]					 =     r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A__  )
class 							A__    (       A__  ):
  A__		 =				'rag'
  A__		 =				True





  def __init__(		self      :	Optional[Any]							,					_a      :	Union[str, Any]=None							,					_a      :	Optional[Any]=True							,					_a      :	Any=None							,					_a      :	Dict=None							,					_a      :	Optional[Any]=None							,					_a      :	Tuple=None							,					_a      :	List[Any]=None							,					_a      :	Optional[Any]=" / "							,					_a      :	str=" // "							,					_a      :	Tuple=5							,					_a      :	Optional[int]=300							,					_a      :	Optional[Any]=768							,					_a      :	Union[str, Any]=8							,					_a      :	Dict="wiki_dpr"							,					_a      :	Tuple="train"							,					_a      :	Any="compressed"							,					_a      :	Union[str, Any]=None							,					_a      :	Optional[int]=None							,					_a      :	Optional[int]=False							,					_a      :	Any=False							,					_a      :	str=0.0							,					_a      :	Optional[int]=True							,					_a      :	Optional[int]=False							,					_a      :	Optional[int]=False							,					_a      :	int=False							,					_a      :	Any=True							,					_a      :	Union[str, Any]=None							,					**_a      :	List[str]							,					)	->    str:





       '''simple docstring'''






       super().__init__(
           bos_token_id=_a							,					pad_token_id=_a							,					eos_token_id=_a							,					decoder_start_token_id=_a							,					forced_eos_token_id=_a							,					is_encoder_decoder=_a							,					prefix=_a							,					vocab_size=_a							,					**_a							,					)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('question_encoder' )
       _SCREAMING_SNAKE_CASE							    =question_encoder_config.pop('model_type' )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('generator' )
       _SCREAMING_SNAKE_CASE							    =decoder_config.pop('model_type' )

       from ..auto.configuration_auto import AutoConfig

       _SCREAMING_SNAKE_CASE							    =AutoConfig.for_model(_a							,					**_a )
       _SCREAMING_SNAKE_CASE							    =AutoConfig.for_model(_a							,					**_a )

       _SCREAMING_SNAKE_CASE							    =reduce_loss
       _SCREAMING_SNAKE_CASE							    =label_smoothing
       _SCREAMING_SNAKE_CASE							    =exclude_bos_score
       _SCREAMING_SNAKE_CASE							    =do_marginalize

       _SCREAMING_SNAKE_CASE							    =title_sep
       _SCREAMING_SNAKE_CASE							    =doc_sep
       _SCREAMING_SNAKE_CASE							    =n_docs
       _SCREAMING_SNAKE_CASE							    =max_combined_length

       _SCREAMING_SNAKE_CASE							    =dataset
       _SCREAMING_SNAKE_CASE							    =dataset_split
       _SCREAMING_SNAKE_CASE							    =index_name

       _SCREAMING_SNAKE_CASE							    =retrieval_vector_size
       _SCREAMING_SNAKE_CASE							    =retrieval_batch_size
       _SCREAMING_SNAKE_CASE							    =passages_path
       _SCREAMING_SNAKE_CASE							    =index_path
       _SCREAMING_SNAKE_CASE							    =use_dummy_dataset

       _SCREAMING_SNAKE_CASE							    =output_retrieved

       _SCREAMING_SNAKE_CASE							    =do_deduplication

       _SCREAMING_SNAKE_CASE							    =use_cache

       if self.forced_eos_token_id is None:
            _SCREAMING_SNAKE_CASE							    =getattr(self.generator							,					'forced_eos_token_id'							,					_a )





  @classmethod
  def 						A  (		cls      :	List[str]							,					_a      :	PretrainedConfig							,					_a      :	PretrainedConfig							,					**_a      :	Tuple )	->    PretrainedConfig:





       '''simple docstring'''






       return cls(question_encoder=question_encoder_config.to_dict()							,					generator=generator_config.to_dict()							,					**_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.question_encoder.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.generator.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output



'''simple docstring'''
from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowerCamelCase :      Union[str, Any]					 =     TypeVar("KT")
lowerCamelCase :      Dict					 =     TypeVar("VT")







class 							A__    (       Generic[KT, VT]  ):





  def __init__(		self      :	str							,					_a      :	KT | str = "root"							,					_a      :	VT | None = None )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =key
       _SCREAMING_SNAKE_CASE							    =value
       _SCREAMING_SNAKE_CASE							    =[]





  def __repr__(		self      :	Union[str, Any] )	->    str:





       '''simple docstring'''






       return f"Node({self.key}: {self.value})"





  @property
  def 						A  (		self      :	int )	->    int:





       '''simple docstring'''






       return len(self.forward )







class 							A__    (       Generic[KT, VT]  ):





  def __init__(		self      :	Optional[Any]							,					_a      :	float = 0.5							,					_a      :	int = 16 )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =Node[KT, VT]()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =p
       _SCREAMING_SNAKE_CASE							    =max_level





  def __str__(		self      :	Tuple )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =list(self )

       if len(_a ) == 0:
            return f"SkipList(level={self.level})"

       _SCREAMING_SNAKE_CASE							    =max((len(str(_a ) ) for item in items)							,					default=4 )
       _SCREAMING_SNAKE_CASE							    =max(_a							,					4 ) + 4

       _SCREAMING_SNAKE_CASE							    =self.head
       _SCREAMING_SNAKE_CASE							    =[]

       _SCREAMING_SNAKE_CASE							    =node.forward.copy()
       lines.append(f"[{node.key}]".ljust(_a							,					'-' ) + '* ' * len(_a ) )
       lines.append(' ' * label_size + '| ' * len(_a ) )

       while len(node.forward ) != 0:
            _SCREAMING_SNAKE_CASE							    =node.forward[0]

            lines.append(
                f"[{node.key}]".ljust(_a							,					'-' )
                + ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
            lines.append(' ' * label_size + '| ' * len(_a ) )
            _SCREAMING_SNAKE_CASE							    =node.forward

       lines.append('None'.ljust(_a ) + '* ' * len(_a ) )
       return f"SkipList(level={self.level})\n" + "\n".join(_a )





  def __iter__(		self      :	Dict )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.head

       while len(node.forward ) != 0:
            yield node.forward[0].key
            _SCREAMING_SNAKE_CASE							    =node.forward[0]





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =1
       while random() < self.p and level < self.max_level:
            level += 1

       return level





  def 						A  (		self      :	Any							,					_a      :	Any )	->    tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]

       _SCREAMING_SNAKE_CASE							    =self.head

       for i in reversed(range(self.level ) ):
            # i < node.level - When node level is lesser than `i` decrement `i`.
            # node.forward[i].key < key - Jumping to node with key value higher
            #                             or equal to searched key would result
            #                             in skipping searched key.
            while i < node.level and node.forward[i].key < key:
                 _SCREAMING_SNAKE_CASE							    =node.forward[i]
            # Each leftmost node (relative to searched node) will potentially have to
            # be updated.
            update_vector.append(_a )

       update_vector.reverse()  # Note that we were inserting values in reverse order.

       # len(node.forward) != 0 - If current node doesn't contain any further
       #                          references then searched key is not present.
       # node.forward[0].key == key - Next node key should be equal to search key
       #                              if key is present.
       if len(node.forward ) != 0 and node.forward[0].key == key:
            return node.forward[0], update_vector
       else:
            return None, update_vector





  def 						A  (		self      :	Union[str, Any]							,					_a      :	KT )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )

       if node is not None:
            for i, update_node in enumerate(_a ):
                 # Remove or replace all references to removed node.
                 if update_node.level > i and update_node.forward[i].key == key:
                      if node.level > i:
                           _SCREAMING_SNAKE_CASE							    =node.forward[i]
                      else:
                           _SCREAMING_SNAKE_CASE							    =update_node.forward[:i]





  def 						A  (		self      :	Optional[Any]							,					_a      :	KT							,					_a      :	VT )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )
       if node is not None:
            _SCREAMING_SNAKE_CASE							    =value
       else:
            _SCREAMING_SNAKE_CASE							    =self.random_level()

            if level > self.level:
                 # After level increase we have to add additional nodes to head.
                 for _ in range(self.level - 1							,					_a ):
                      update_vector.append(self.head )
                 _SCREAMING_SNAKE_CASE							    =level

            _SCREAMING_SNAKE_CASE							    =Node(_a							,					_a )

            for i, update_node in enumerate(update_vector[:level] ):
                 # Change references to pass through new node.
                 if update_node.level > i:
                      new_node.forward.append(update_node.forward[i] )

                 if update_node.level < i + 1:
                      update_node.forward.append(_a )
                 else:
                      _SCREAMING_SNAKE_CASE							    =new_node





  def 						A  (		self      :	List[str]							,					_a      :	VT )	->    VT | None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )

       if node is not None:
            return node.value

       return None


def        _lowerCAmelCase					(						)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert('Key1'							, 3				)
     skip_list.insert('Key2'							, 12				)
     skip_list.insert('Key3'							, 41				)
     skip_list.insert('Key4'							, -19				)

     _SCREAMING_SNAKE_CASE							    =skip_list.head
     _SCREAMING_SNAKE_CASE							    ={}
     while node.level != 0:
          _SCREAMING_SNAKE_CASE							    =node.forward[0]
          _SCREAMING_SNAKE_CASE							    =node.value

     assert len(_UpperCamelCase				) == 4
     assert all_values["Key1"] == 3
     assert all_values["Key2"] == 12
     assert all_values["Key3"] == 41
     assert all_values["Key4"] == -19


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert('Key1'							, 10				)
     skip_list.insert('Key1'							, 12				)

     skip_list.insert('Key5'							, 7				)
     skip_list.insert('Key7'							, 10				)
     skip_list.insert('Key10'							, 5				)

     skip_list.insert('Key7'							, 7				)
     skip_list.insert('Key5'							, 5				)
     skip_list.insert('Key10'							, 10				)

     _SCREAMING_SNAKE_CASE							    =skip_list.head
     _SCREAMING_SNAKE_CASE							    ={}
     while node.level != 0:
          _SCREAMING_SNAKE_CASE							    =node.forward[0]
          _SCREAMING_SNAKE_CASE							    =node.value

     if len(_UpperCamelCase				) != 4:
          print()
     assert len(_UpperCamelCase				) == 4
     assert all_values["Key1"] == 12
     assert all_values["Key7"] == 7
     assert all_values["Key5"] == 5
     assert all_values["Key10"] == 10


def        _lowerCAmelCase					(						)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     assert skip_list.find('Some key'				) is None


def        _lowerCAmelCase					(						)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key2'							, 20				)
     assert skip_list.find('Key2'				) == 20

     skip_list.insert('Some Key'							, 10				)
     skip_list.insert('Key2'							, 8				)
     skip_list.insert('V'							, 13				)

     assert skip_list.find('Y'				) is None
     assert skip_list.find('Key2'				) == 8
     assert skip_list.find('Some Key'				) == 10
     assert skip_list.find('V'				) == 13


def        _lowerCAmelCase					(						)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.delete('Some key'				)

     assert len(skip_list.head.forward				) == 0


def        _lowerCAmelCase					(						)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 14				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('V'				)
     skip_list.delete('Key2'				)

     assert skip_list.find('V'				) is None
     assert skip_list.find('Key2'				) is None


def        _lowerCAmelCase					(						)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 14				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('V'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) == 14
     assert skip_list.find('Key1'				) == 12
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('X'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) == 12
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('Key1'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) is None
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('Key2'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) is None
     assert skip_list.find('Key2'				) is None


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 1_42				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('X'				)

     def traverse_keys(_UpperCamelCase						:     Dict				):
          yield node.key
          for forward_node in node.forward:
               yield from traverse_keys(_UpperCamelCase				)

     assert len(set(traverse_keys(skip_list.head				)				)				) == 4


def        _lowerCAmelCase					(						)      ->			Union[str, Any]:
     """simple docstring"""




     def is_sorted(_UpperCamelCase						:     str				):
          return all(next_item >= item for item, next_item in zip(_UpperCamelCase							, lst[1:]				)				)

     _SCREAMING_SNAKE_CASE							    =SkipList()
     for i in range(10				):
          skip_list.insert(_UpperCamelCase							, _UpperCamelCase				)
     assert is_sorted(list(_UpperCamelCase				)				)
     skip_list.delete(5				)
     skip_list.delete(8				)
     skip_list.delete(2				)
     assert is_sorted(list(_UpperCamelCase				)				)
     skip_list.insert(-12							, -12				)
     skip_list.insert(77							, 77				)
     assert is_sorted(list(_UpperCamelCase				)				)


def        _lowerCAmelCase					(						)      ->			List[str]:
     """simple docstring"""




     for _ in range(1_00				):
          # Repeat test 100 times due to the probabilistic nature of skip list
          # random values == random bugs
          test_insert()
          test_insert_overrides_existing_value()

          test_searching_empty_list_returns_none()
          test_search()

          test_deleting_item_from_empty_list_do_nothing()
          test_deleted_items_are_not_founded_by_find_method()
          test_delete_removes_only_given_key()
          test_delete_doesnt_leave_dead_nodes()

          test_iter_always_yields_sorted_values()


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert(2							, '2'				)
     skip_list.insert(4							, '4'				)
     skip_list.insert(6							, '4'				)
     skip_list.insert(4							, '5'				)
     skip_list.insert(8							, '4'				)
     skip_list.insert(9							, '4'				)

     skip_list.delete(4				)

     print(_UpperCamelCase				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()
