



import copy

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


UpperCamelCase__ =						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(_UpperCAmelCase			)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  int      =			"""rag"""
			lowercase__      :  int      =			True





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

									__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"""			,     lowercase   )





			@classmethod
			def 		snake_case__			(				cls	:       Optional[Any]			,     lowercase	:       PretrainedConfig			,     lowercase	:       PretrainedConfig			,     **lowercase	:       int   )						->					PretrainedConfig:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       List[str]   )						->					Union[str, 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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ =						{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "LukeForEntityClassification",
			    "LukeForEntityPairClassification",
			    "LukeForEntitySpanClassification",
			    "LukeForMultipleChoice",
			    "LukeForQuestionAnswering",
			    "LukeForSequenceClassification",
			    "LukeForTokenClassification",
			    "LukeForMaskedLM",
			    "LukeModel",
			    "LukePreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





