





'''simple docstring'''



import copy

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


_SCREAMING_SNAKE_CASE   =      R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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






@add_start_docstrings(lowerCAmelCase__  )
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			str     =    "rag"
	a      :			int     =    True


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=" / "       ,_lowerCamelCase=" // "       ,_lowerCamelCase=5       ,_lowerCamelCase=300       ,_lowerCamelCase=768       ,_lowerCamelCase=8       ,_lowerCamelCase="wiki_dpr"       ,_lowerCamelCase="train"       ,_lowerCamelCase="compressed"       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=0.0       ,_lowerCamelCase=True       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							List[str]:







				'''simple docstring'''





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

				from ..auto.configuration_auto import AutoConfig

				__lowercase			  =							AutoConfig.for_model(_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							AutoConfig.for_model(_lowerCamelCase       ,**_lowerCamelCase	)

				__lowercase			  =							reduce_loss
				__lowercase			  =							label_smoothing
				__lowercase			  =							exclude_bos_score
				__lowercase			  =							do_marginalize

				__lowercase			  =							title_sep
				__lowercase			  =							doc_sep
				__lowercase			  =							n_docs
				__lowercase			  =							max_combined_length

				__lowercase			  =							dataset
				__lowercase			  =							dataset_split
				__lowercase			  =							index_name

				__lowercase			  =							retrieval_vector_size
				__lowercase			  =							retrieval_batch_size
				__lowercase			  =							passages_path
				__lowercase			  =							index_path
				__lowercase			  =							use_dummy_dataset

				__lowercase			  =							output_retrieved

				__lowercase			  =							do_deduplication

				__lowercase			  =							use_cache

				if self.forced_eos_token_id is None:
							__lowercase			  =							getattr(self.generator       ,'''forced_eos_token_id'''       ,_lowerCamelCase	)


	@classmethod
	def   _UpperCAmelCase       (cls       ,_lowerCamelCase       ,_lowerCamelCase       ,**_lowerCamelCase	)							->							PretrainedConfig:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





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












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4





