




'''simple docstring'''




import copy

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


a_      :		Optional[Any]							      =       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   __UpperCamelCase       ( lowerCamelCase__				):
       lowercase	:    Dict      						='rag'
       lowercase	:    Dict      						=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,		):



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

              from ..auto.configuration_auto import AutoConfig

              lowerCamelCase_							=AutoConfig.for_model(lowerCAmelCase,		**lowerCAmelCase  )
              lowerCamelCase_							=AutoConfig.for_model(lowerCAmelCase,		**lowerCAmelCase  )

              lowerCamelCase_							=reduce_loss
              lowerCamelCase_							=label_smoothing
              lowerCamelCase_							=exclude_bos_score
              lowerCamelCase_							=do_marginalize

              lowerCamelCase_							=title_sep
              lowerCamelCase_							=doc_sep
              lowerCamelCase_							=n_docs
              lowerCamelCase_							=max_combined_length

              lowerCamelCase_							=dataset
              lowerCamelCase_							=dataset_split
              lowerCamelCase_							=index_name

              lowerCamelCase_							=retrieval_vector_size
              lowerCamelCase_							=retrieval_batch_size
              lowerCamelCase_							=passages_path
              lowerCamelCase_							=index_path
              lowerCamelCase_							=use_dummy_dataset

              lowerCamelCase_							=output_retrieved

              lowerCamelCase_							=do_deduplication

              lowerCamelCase_							=use_cache

              if self.forced_eos_token_id is None:
                     lowerCamelCase_							=getattr(self.generator,		'''forced_eos_token_id''',		lowerCAmelCase  )






       @classmethod
       def      lowercase__     (							cls,		lowerCAmelCase,		lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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











'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






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

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))