


import copy

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


_A		:				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(__snake_case   )
class        a					(			__snake_case   ):
     UpperCAmelCase__ :  Optional[Any]						      =	"rag"
     UpperCAmelCase__ :  str						      =	True





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				int=None    ,	SCREAMING_SNAKE_CASE_					:				Dict=" / "    ,	SCREAMING_SNAKE_CASE_					:				Tuple=" // "    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=5    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=300    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=768    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=8    ,	SCREAMING_SNAKE_CASE_					:				List[str]="wiki_dpr"    ,	SCREAMING_SNAKE_CASE_					:				Dict="train"    ,	SCREAMING_SNAKE_CASE_					:				List[str]="compressed"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False    ,	SCREAMING_SNAKE_CASE_					:				List[str]=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=0.0    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False    ,	SCREAMING_SNAKE_CASE_					:				Dict=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	**SCREAMING_SNAKE_CASE_					:				Tuple    ,	):
        super().__init__(
            bos_token_id=_lowercase    ,	pad_token_id=_lowercase    ,	eos_token_id=_lowercase    ,	decoder_start_token_id=_lowercase    ,	forced_eos_token_id=_lowercase    ,	is_encoder_decoder=_lowercase    ,	prefix=_lowercase    ,	vocab_size=_lowercase    ,	**_lowercase    ,	)
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        __lowerCamelCase:				Optional[int]     =	kwargs.pop("""question_encoder"""							)
        __lowerCamelCase:				List[str]     =	question_encoder_config.pop("""model_type"""							)
        __lowerCamelCase:				List[Any]     =	kwargs.pop("""generator"""							)
        __lowerCamelCase:				List[Any]     =	decoder_config.pop("""model_type"""							)

        from ..auto.configuration_auto import AutoConfig

        __lowerCamelCase:				Optional[Any]     =	AutoConfig.for_model(_lowercase    ,	**_lowercase							)
        __lowerCamelCase:				Optional[Any]     =	AutoConfig.for_model(_lowercase    ,	**_lowercase							)

        __lowerCamelCase:				Dict     =	reduce_loss
        __lowerCamelCase:				Tuple     =	label_smoothing
        __lowerCamelCase:				List[Any]     =	exclude_bos_score
        __lowerCamelCase:				Optional[int]     =	do_marginalize

        __lowerCamelCase:				int     =	title_sep
        __lowerCamelCase:				Any     =	doc_sep
        __lowerCamelCase:				str     =	n_docs
        __lowerCamelCase:				int     =	max_combined_length

        __lowerCamelCase:				List[str]     =	dataset
        __lowerCamelCase:				Optional[int]     =	dataset_split
        __lowerCamelCase:				Optional[Any]     =	index_name

        __lowerCamelCase:				Tuple     =	retrieval_vector_size
        __lowerCamelCase:				Dict     =	retrieval_batch_size
        __lowerCamelCase:				Optional[int]     =	passages_path
        __lowerCamelCase:				Optional[Any]     =	index_path
        __lowerCamelCase:				int     =	use_dummy_dataset

        __lowerCamelCase:				List[str]     =	output_retrieved

        __lowerCamelCase:				List[Any]     =	do_deduplication

        __lowerCamelCase:				int     =	use_cache

        if self.forced_eos_token_id is None:
           __lowerCamelCase:				str     =	getattr(self.generator    ,	"""forced_eos_token_id"""    ,	_lowercase							)





     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				int    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        return cls(question_encoder=question_encoder_config.to_dict()    ,	generator=generator_config.to_dict()    ,	**_lowercase							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Union[str, Any]     =	copy.deepcopy(self.__dict__							)
        __lowerCamelCase:				List[str]     =	self.question_encoder.to_dict()
        __lowerCamelCase:				Any     =	self.generator.to_dict()
        __lowerCamelCase:				Optional[Any]     =	self.__class__.model_type
        return output







from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


