


"""simple docstring"""






import copy

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


UpperCamelCase__				:Tuple			   =	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      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"rag"
		A    =							True
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=" / "					,				SCREAMING_SNAKE_CASE__=" // "					,				SCREAMING_SNAKE_CASE__=5					,				SCREAMING_SNAKE_CASE__=3_00					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__="wiki_dpr"					,				SCREAMING_SNAKE_CASE__="train"					,				SCREAMING_SNAKE_CASE__="compressed"					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(
					    bos_token_id=SCREAMING_SNAKE_CASE__					,				pad_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				decoder_start_token_id=SCREAMING_SNAKE_CASE__					,				forced_eos_token_id=SCREAMING_SNAKE_CASE__					,				is_encoder_decoder=SCREAMING_SNAKE_CASE__					,				prefix=SCREAMING_SNAKE_CASE__					,				vocab_size=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					assert (
					    "question_encoder" in kwargs and "generator" in kwargs
					), "Config has to be initialized with question_encoder and generator config"
					_UpperCamelCase					:Dict					     =	kwargs.pop('''question_encoder'''   )
					_UpperCamelCase					:str					     =	question_encoder_config.pop('''model_type'''   )
					_UpperCamelCase					:Optional[int]					     =	kwargs.pop('''generator'''   )
					_UpperCamelCase					:List[str]					     =	decoder_config.pop('''model_type'''   )

					from ..auto.configuration_auto import AutoConfig

					_UpperCamelCase					:Optional[int]					     =	AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	reduce_loss
					_UpperCamelCase					:Union[str, Any]					     =	label_smoothing
					_UpperCamelCase					:Optional[Any]					     =	exclude_bos_score
					_UpperCamelCase					:str					     =	do_marginalize

					_UpperCamelCase					:Tuple					     =	title_sep
					_UpperCamelCase					:Optional[int]					     =	doc_sep
					_UpperCamelCase					:Any					     =	n_docs
					_UpperCamelCase					:Any					     =	max_combined_length

					_UpperCamelCase					:List[str]					     =	dataset
					_UpperCamelCase					:Union[str, Any]					     =	dataset_split
					_UpperCamelCase					:int					     =	index_name

					_UpperCamelCase					:Dict					     =	retrieval_vector_size
					_UpperCamelCase					:Tuple					     =	retrieval_batch_size
					_UpperCamelCase					:List[Any]					     =	passages_path
					_UpperCamelCase					:Any					     =	index_path
					_UpperCamelCase					:Dict					     =	use_dummy_dataset

					_UpperCamelCase					:Dict					     =	output_retrieved

					_UpperCamelCase					:Union[str, Any]					     =	do_deduplication

					_UpperCamelCase					:List[str]					     =	use_cache

					if self.forced_eos_token_id is None:
								_UpperCamelCase					:Any					     =	getattr(self.generator					,				'''forced_eos_token_id'''					,				SCREAMING_SNAKE_CASE__   )
		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	PretrainedConfig:

					"""simple docstring"""



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





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



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





"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError('''You need to specify an `image_processor`.'''   )
					if tokenizer is None:
								raise ValueError('''You need to specify a `tokenizer`.'''   )

					super().__init__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor_class





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor

