# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" RAG model configuration """

import copy

from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings


RAG_CONFIG_DOC = r"""
    :class:`~transformers.RagConfig` stores the configuration of a `RagModel`.
    Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
    to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
    for more information.

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


@add_start_docstrings(RAG_CONFIG_DOC)
class RagConfig(PretrainedConfig):
    model_type = "rag"

    def __init__(
        self,
        vocab_size=None,
        is_encoder_decoder=True,
        prefix=None,
        bos_token_id=None,
        pad_token_id=None,
        eos_token_id=None,
        decoder_start_token_id=None,
        title_sep=" / ",
        doc_sep=" // ",
        n_docs=5,
        max_combined_length=300,
        retrieval_vector_size=768,
        retrieval_batch_size=8,
        dataset="wiki_dpr",
        dataset_split="train",
        index_name="compressed",
        index_path=None,
        passages_path=None,
        use_dummy_dataset=False,
        reduce_loss=False,
        label_smoothing=0.0,
        do_deduplication=True,
        exclude_bos_score=False,
        do_marginalize=False,
        output_retrieved=False,
        **kwargs
    ):
        super().__init__(
            bos_token_id=bos_token_id,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            decoder_start_token_id=decoder_start_token_id,
            is_encoder_decoder=is_encoder_decoder,
            prefix=prefix,
            vocab_size=vocab_size,
            **kwargs,
        )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        question_encoder_config = kwargs.pop("question_encoder")
        question_encoder_model_type = question_encoder_config.pop("model_type")
        decoder_config = kwargs.pop("generator")
        decoder_model_type = decoder_config.pop("model_type")

        from .configuration_auto import AutoConfig

        self.question_encoder = AutoConfig.for_model(question_encoder_model_type, **question_encoder_config)
        self.generator = AutoConfig.for_model(decoder_model_type, **decoder_config)

        self.reduce_loss = reduce_loss
        self.label_smoothing = label_smoothing
        self.exclude_bos_score = exclude_bos_score
        self.do_marginalize = do_marginalize

        self.title_sep = title_sep
        self.doc_sep = doc_sep
        self.n_docs = n_docs
        self.max_combined_length = max_combined_length

        self.dataset = dataset
        self.dataset_split = dataset_split
        self.index_name = index_name

        self.retrieval_vector_size = retrieval_vector_size
        self.retrieval_batch_size = retrieval_batch_size
        self.passages_path = passages_path
        self.index_path = index_path
        self.use_dummy_dataset = use_dummy_dataset

        self.output_retrieved = output_retrieved

        self.do_deduplication = do_deduplication

    @classmethod
    def from_question_encoder_generator_configs(
        cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.

        Returns:
            :class:`EncoderDecoderConfig`: An instance of a configuration object
        """
        return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **kwargs)

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default :meth:`~transformers.PretrainedConfig.to_dict`.

        Returns:
            :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["question_encoder"] = self.question_encoder.to_dict()
        output["generator"] = self.generator.to_dict()
        output["model_type"] = self.__class__.model_type
        return output
