

import copy

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


lowerCAmelCase__	   = 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(snake_case			)
class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       'rag'
			__lowerCamelCase        =       True


			def __init__(    self      ,      lowercase=None      ,      lowercase=True      ,      lowercase=None      ,      lowercase=None      ,      lowercase=None      ,      lowercase=None      ,      lowercase=None      ,      lowercase=" / "      ,      lowercase=" // "      ,      lowercase=5      ,      lowercase=300      ,      lowercase=768      ,      lowercase=8      ,      lowercase="wiki_dpr"      ,      lowercase="train"      ,      lowercase="compressed"      ,      lowercase=None      ,      lowercase=None      ,      lowercase=False      ,      lowercase=False      ,      lowercase=0.0      ,      lowercase=True      ,      lowercase=False      ,      lowercase=False      ,      lowercase=False      ,      lowercase=True      ,      lowercase=None      ,      **lowercase      ,      )  ->						Tuple:
							'''simple docstring'''






							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"
							A__        =			kwargs.pop("question_encoder"       )
							A__        =			question_encoder_config.pop("model_type"       )
							A__        =			kwargs.pop("generator"       )
							A__        =			decoder_config.pop("model_type"       )

							from ..auto.configuration_auto import AutoConfig

							A__        =			AutoConfig.for_model(lowercase      ,      **lowercase       )
							A__        =			AutoConfig.for_model(lowercase      ,      **lowercase       )

							A__        =			reduce_loss
							A__        =			label_smoothing
							A__        =			exclude_bos_score
							A__        =			do_marginalize

							A__        =			title_sep
							A__        =			doc_sep
							A__        =			n_docs
							A__        =			max_combined_length

							A__        =			dataset
							A__        =			dataset_split
							A__        =			index_name

							A__        =			retrieval_vector_size
							A__        =			retrieval_batch_size
							A__        =			passages_path
							A__        =			index_path
							A__        =			use_dummy_dataset

							A__        =			output_retrieved

							A__        =			do_deduplication

							A__        =			use_cache

							if self.forced_eos_token_id is None:
											A__        =			getattr(self.generator      ,      "forced_eos_token_id"      ,      lowercase       )


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      lowercase      ,      **lowercase       )  ->						PretrainedConfig:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






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




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

