# 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
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"""RAG Retriever model implementation."""

import os
import pickle
import time
from typing import Iterable, List, Optional, Tuple

import numpy as np

from ...file_utils import (
    cached_path,
    is_datasets_available,
    is_faiss_available,
    is_remote_url,
    requires_datasets,
    requires_faiss,
)
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
from .tokenization_rag import RagTokenizer


if is_datasets_available():
    from datasets import Dataset, load_dataset, load_from_disk

if is_faiss_available():
    import faiss


logger = logging.get_logger(__name__)


LEGACY_INDEX_PATH = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr/"


class Index:
    """
    A base class for the Indices encapsulated by the :class:`~transformers.RagRetriever`.
    """

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        """
        Returns a list of dictionaries, containing titles and text of the retrieved documents.

        Args:
            doc_ids (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`):
                A tensor of document indices.
        """
        raise NotImplementedError

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        """
        For each query in the batch, retrieves ``n_docs`` documents.

        Args:
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size):
                An array of query vectors.
            n_docs (:obj:`int`):
                The number of docs retrieved per query.

        Returns:
            :obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`: A tensor of indices of retrieved documents.
            :obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`: A tensor of vector representations of
            retrieved documents.
        """
        raise NotImplementedError

    def is_initialized(self):
        """
        Returns :obj:`True` if index is already initialized.
        """
        raise NotImplementedError

    def init_index(self):
        """
        A function responsible for loading the index into memory. Should be called only once per training run of a RAG
        model. E.g. if the model is trained on multiple GPUs in a distributed setup, only one of the workers will load
        the index.
        """
        raise NotImplementedError


class LegacyIndex(Index):
    """
    An index which can be deserialized from the files built using https://github.com/facebookresearch/DPR. We use
    default faiss index parameters as specified in that repository.

    Args:
        vector_size (:obj:`int`):
            The dimension of indexed vectors.
        index_path (:obj:`str`):
            A path to a `directory` containing index files compatible with
            :class:`~transformers.models.rag.retrieval_rag.LegacyIndex`
    """

    INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
    PASSAGE_FILENAME = "psgs_w100.tsv.pkl"

    def __init__(self, vector_size, index_path):
        self.index_id_to_db_id = []
        self.index_path = index_path
        self.passages = self._load_passages()
        self.vector_size = vector_size
        self.index = None
        self._index_initialized = False

    def _resolve_path(self, index_path, filename):
        assert os.path.isdir(index_path) or is_remote_url(index_path), "Please specify a valid ``index_path``."
        archive_file = os.path.join(index_path, filename)
        try:
            # Load from URL or cache if already cached
            resolved_archive_file = cached_path(archive_file)
        except EnvironmentError:
            msg = (
                f"Can't load '{archive_file}'. Make sure that:\n\n"
                f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}"
                f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
            )
            raise EnvironmentError(msg)
        if resolved_archive_file == archive_file:
            logger.info("loading file {}".format(archive_file))
        else:
            logger.info("loading file {} from cache at {}".format(archive_file, resolved_archive_file))
        return resolved_archive_file

    def _load_passages(self):
        logger.info("Loading passages from {}".format(self.index_path))
        passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
        with open(passages_path, "rb") as passages_file:
            passages = pickle.load(passages_file)
        return passages

    def _deserialize_index(self):
        logger.info("Loading index from {}".format(self.index_path))
        resolved_index_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index.dpr")
        self.index = faiss.read_index(resolved_index_path)
        resolved_meta_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index_meta.dpr")
        with open(resolved_meta_path, "rb") as metadata_file:
            self.index_id_to_db_id = pickle.load(metadata_file)
        assert (
            len(self.index_id_to_db_id) == self.index.ntotal
        ), "Deserialized index_id_to_db_id should match faiss index size"

    def is_initialized(self):
        return self._index_initialized

    def init_index(self):
        index = faiss.IndexHNSWFlat(self.vector_size + 1, 512)
        index.hnsw.efSearch = 128
        index.hnsw.efConstruction = 200
        self.index = index
        self._deserialize_index()
        self._index_initialized = True

    def get_doc_dicts(self, doc_ids: np.array):
        doc_list = []
        for doc_ids_i in doc_ids:
            ids = [str(int(doc_id)) for doc_id in doc_ids_i]
            docs = [self.passages[doc_id] for doc_id in ids]
            doc_list.append(docs)
        doc_dicts = []
        for docs in doc_list:
            doc_dict = {}
            doc_dict["title"] = [doc[1] for doc in docs]
            doc_dict["text"] = [doc[0] for doc in docs]
            doc_dicts.append(doc_dict)
        return doc_dicts

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        aux_dim = np.zeros(len(question_hidden_states), dtype="float32").reshape(-1, 1)
        query_nhsw_vectors = np.hstack((question_hidden_states, aux_dim))
        _, docs_ids = self.index.search(query_nhsw_vectors, n_docs)
        vectors = [[self.index.reconstruct(int(doc_id))[:-1] for doc_id in doc_ids] for doc_ids in docs_ids]
        ids = [[int(self.index_id_to_db_id[doc_id]) for doc_id in doc_ids] for doc_ids in docs_ids]
        return np.array(ids), np.array(vectors)


class HFIndexBase(Index):
    def __init__(self, vector_size, dataset, index_initialized=False):
        self.vector_size = vector_size
        self.dataset = dataset
        self._index_initialized = index_initialized
        self._check_dataset_format(with_index=index_initialized)
        dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True, dtype="float32")

    def _check_dataset_format(self, with_index: bool):
        if not isinstance(self.dataset, Dataset):
            raise ValueError("Dataset should be a datasets.Dataset object, but got {}".format(type(self.dataset)))
        if len({"title", "text", "embeddings"} - set(self.dataset.column_names)) > 0:
            raise ValueError(
                "Dataset should be a dataset with the following columns: "
                "title (str), text (str) and embeddings (arrays of dimension vector_size), "
                "but got columns {}".format(self.dataset.column_names)
            )
        if with_index and "embeddings" not in self.dataset.list_indexes():
            raise ValueError(
                "Missing faiss index in the dataset. Make sure you called `dataset.add_faiss_index` to compute it "
                "or `dataset.load_faiss_index` to load one from the disk."
            )

    def init_index(self):
        raise NotImplementedError()

    def is_initialized(self):
        return self._index_initialized

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        return [self.dataset[doc_ids[i].tolist()] for i in range(doc_ids.shape[0])]

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        _, ids = self.dataset.search_batch("embeddings", question_hidden_states, n_docs)
        docs = [self.dataset[[i for i in indices if i >= 0]] for indices in ids]
        vectors = [doc["embeddings"] for doc in docs]
        for i in range(len(vectors)):
            if len(vectors[i]) < n_docs:
                vectors[i] = np.vstack([vectors[i], np.zeros((n_docs - len(vectors[i]), self.vector_size))])
        return np.array(ids), np.array(vectors)  # shapes (batch_size, n_docs) and (batch_size, n_docs, d)


class CanonicalHFIndex(HFIndexBase):
    """
    A wrapper around an instance of :class:`~datasets.Datasets`. If ``index_path`` is set to ``None``, we load the
    pre-computed index available with the :class:`~datasets.arrow_dataset.Dataset`, otherwise, we load the index from
    the indicated path on disk.

    Args:
        vector_size (:obj:`int`): the dimension of the passages embeddings used by the index
        dataset_name (:obj:`str`, optional, defaults to ``wiki_dpr``):
            A datatset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids
            with ``datasets.list_datasets()``).
        dataset_split (:obj:`str`, optional, defaults to ``train``)
            Which split of the ``dataset`` to load.
        index_name (:obj:`str`, optional, defaults to ``train``)
            The index_name of the index associated with the ``dataset``. The index loaded from ``index_path`` will be
            saved under this name.
        index_path (:obj:`str`, optional, defaults to ``None``)
            The path to the serialized faiss index on disk.
        use_dummy_dataset (:obj:`bool`, optional, defaults to ``False``): If True, use the dummy configuration of the dataset for tests.
    """

    def __init__(
        self,
        vector_size: int,
        dataset_name: str = "wiki_dpr",
        dataset_split: str = "train",
        index_name: Optional[str] = None,
        index_path: Optional[str] = None,
        use_dummy_dataset=False,
    ):
        if int(index_path is None) + int(index_name is None) != 1:
            raise ValueError("Please provide `index_name` or `index_path`.")
        self.dataset_name = dataset_name
        self.dataset_split = dataset_split
        self.index_name = index_name
        self.index_path = index_path
        self.use_dummy_dataset = use_dummy_dataset
        logger.info("Loading passages from {}".format(self.dataset_name))
        dataset = load_dataset(
            self.dataset_name, with_index=False, split=self.dataset_split, dummy=self.use_dummy_dataset
        )
        super().__init__(vector_size, dataset, index_initialized=False)

    def init_index(self):
        if self.index_path is not None:
            logger.info("Loading index from {}".format(self.index_path))
            self.dataset.load_faiss_index("embeddings", file=self.index_path)
        else:
            logger.info("Loading index from {}".format(self.dataset_name + " with index name " + self.index_name))
            self.dataset = load_dataset(
                self.dataset_name,
                with_embeddings=True,
                with_index=True,
                split=self.dataset_split,
                index_name=self.index_name,
                dummy=self.use_dummy_dataset,
            )
            self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
        self._index_initialized = True


class CustomHFIndex(HFIndexBase):
    """
    A wrapper around an instance of :class:`~datasets.Datasets`. The dataset and the index are both loaded from the
    indicated paths on disk.

    Args:
        vector_size (:obj:`int`): the dimension of the passages embeddings used by the index
        dataset_path (:obj:`str`):
            The path to the serialized dataset on disk. The dataset should have 3 columns: title (str), text (str) and
            embeddings (arrays of dimension vector_size)
        index_path (:obj:`str`)
            The path to the serialized faiss index on disk.
    """

    def __init__(self, vector_size: int, dataset, index_path=None):
        super().__init__(vector_size, dataset, index_initialized=index_path is None)
        self.index_path = index_path

    @classmethod
    def load_from_disk(cls, vector_size, dataset_path, index_path):
        logger.info("Loading passages from {}".format(dataset_path))
        if dataset_path is None or index_path is None:
            raise ValueError(
                "Please provide ``dataset_path`` and ``index_path`` after calling ``dataset.save_to_disk(dataset_path)`` "
                "and ``dataset.get_index('embeddings').save(index_path)``."
            )
        dataset = load_from_disk(dataset_path)
        return cls(vector_size=vector_size, dataset=dataset, index_path=index_path)

    def init_index(self):
        if not self.is_initialized():
            logger.info("Loading index from {}".format(self.index_path))
            self.dataset.load_faiss_index("embeddings", file=self.index_path)
            self._index_initialized = True


class RagRetriever:
    """
    Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents
    contents, and it formats them to be used with a RagModel.

    Args:
        config (:class:`~transformers.RagConfig`):
            The configuration of the RAG model this Retriever is used with. Contains parameters indicating which
            ``Index`` to build. You can load your own custom dataset with ``config.index_name="custom"`` or use a
            canonical one (default) from the datasets library with ``config.index_name="wiki_dpr"`` for example.
        question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
            The tokenizer that was used to tokenize the question. It is used to decode the question and then use the
            generator_tokenizer.
        generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
            The tokenizer used for the generator part of the RagModel.
        index (:class:`~transformers.models.rag.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
            If specified, use this index instead of the one built using the configuration

    Examples::

        >>> # To load the default "wiki_dpr" dataset with 21M passages from wikipedia (index name is 'compressed' or 'exact')
        >>> from transformers import RagRetriever
        >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', dataset="wiki_dpr", index_name='compressed')

        >>> # To load your own indexed dataset built with the datasets library. More info on how to build the indexed dataset in examples/rag/use_own_knowledge_dataset.py
        >>> from transformers import RagRetriever
        >>> dataset = ...  # dataset must be a datasets.Datasets object with columns "title", "text" and "embeddings", and it must have a faiss index
        >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', indexed_dataset=dataset)

        >>> # To load your own indexed dataset built with the datasets library that was saved on disk. More info in examples/rag/use_own_knowledge_dataset.py
        >>> from transformers import RagRetriever
        >>> dataset_path = "path/to/my/dataset"  # dataset saved via `dataset.save_to_disk(...)`
        >>> index_path = "path/to/my/index.faiss"  # faiss index saved via `dataset.get_index("embeddings").save(...)`
        >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', index_name='custom', passages_path=dataset_path, index_path=index_path)

        >>> # To load the legacy index built originally for Rag's paper
        >>> from transformers import RagRetriever
        >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', index_name='legacy')

    """

    _init_retrieval = True

    def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None):
        requires_datasets(self)
        requires_faiss(self)
        super().__init__()
        self.index = index or self._build_index(config)
        self.generator_tokenizer = generator_tokenizer
        self.question_encoder_tokenizer = question_encoder_tokenizer

        self.n_docs = config.n_docs
        self.batch_size = config.retrieval_batch_size

        self.config = config
        if self._init_retrieval:
            self.init_retrieval()

    @staticmethod
    def _build_index(config):
        if config.index_name == "legacy":
            return LegacyIndex(
                config.retrieval_vector_size,
                config.index_path or LEGACY_INDEX_PATH,
            )
        elif config.index_name == "custom":
            return CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size,
                dataset_path=config.passages_path,
                index_path=config.index_path,
            )
        else:
            return CanonicalHFIndex(
                vector_size=config.retrieval_vector_size,
                dataset_name=config.dataset,
                dataset_split=config.dataset_split,
                index_name=config.index_name,
                index_path=config.index_path,
                use_dummy_dataset=config.use_dummy_dataset,
            )

    @classmethod
    def from_pretrained(cls, retriever_name_or_path, indexed_dataset=None, **kwargs):
        requires_datasets(cls)
        requires_faiss(cls)
        config = kwargs.pop("config", None) or RagConfig.from_pretrained(retriever_name_or_path, **kwargs)
        rag_tokenizer = RagTokenizer.from_pretrained(retriever_name_or_path, config=config)
        question_encoder_tokenizer = rag_tokenizer.question_encoder
        generator_tokenizer = rag_tokenizer.generator
        if indexed_dataset is not None:
            config.index_name = "custom"
            index = CustomHFIndex(config.retrieval_vector_size, indexed_dataset)
        else:
            index = cls._build_index(config)
        return cls(
            config,
            question_encoder_tokenizer=question_encoder_tokenizer,
            generator_tokenizer=generator_tokenizer,
            index=index,
        )

    def save_pretrained(self, save_directory):
        if isinstance(self.index, CustomHFIndex):
            if self.config.index_path is None:
                index_path = os.path.join(save_directory, "hf_dataset_index.faiss")
                self.index.dataset.get_index("embeddings").save(index_path)
                self.config.index_path = index_path
            if self.config.passages_path is None:
                passages_path = os.path.join(save_directory, "hf_dataset")
                # datasets don't support save_to_disk with indexes right now
                faiss_index = self.index.dataset._indexes.pop("embeddings")
                self.index.dataset.save_to_disk(passages_path)
                self.index.dataset._indexes["embeddings"] = faiss_index
                self.config.passages_path = passages_path
        self.config.save_pretrained(save_directory)
        rag_tokenizer = RagTokenizer(
            question_encoder=self.question_encoder_tokenizer,
            generator=self.generator_tokenizer,
        )
        rag_tokenizer.save_pretrained(save_directory)

    def init_retrieval(self):
        """
        Retriever initalization function. It loads the index into memory.
        """

        logger.info("initializing retrieval")
        self.index.init_index()

    def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):
        r"""
        Postprocessing retrieved ``docs`` and combining them with ``input_strings``.

        Args:
            docs  (:obj:`dict`):
                Retrieved documents.
            input_strings (:obj:`str`):
                Input strings decoded by ``preprocess_query``.
            prefix (:obj:`str`):
                Prefix added at the beginning of each input, typically used with T5-based models.

        Return:
            :obj:`tuple(tensors)`: a tuple consisting of two elements: contextualized ``input_ids`` and a compatible
            ``attention_mask``.
        """

        def cat_input_and_doc(doc_title, doc_text, input_string, prefix):
            # TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation
            # TODO(piktus): better handling of truncation
            if doc_title.startswith('"'):
                doc_title = doc_title[1:]
            if doc_title.endswith('"'):
                doc_title = doc_title[:-1]
            if prefix is None:
                prefix = ""
            out = (prefix + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string).replace(
                "  ", " "
            )
            return out

        rag_input_strings = [
            cat_input_and_doc(
                docs[i]["title"][j],
                docs[i]["text"][j],
                input_strings[i],
                prefix,
            )
            for i in range(len(docs))
            for j in range(n_docs)
        ]

        contextualized_inputs = self.generator_tokenizer.batch_encode_plus(
            rag_input_strings,
            max_length=self.config.max_combined_length,
            return_tensors=return_tensors,
            padding="max_length",
            truncation=True,
        )

        return contextualized_inputs["input_ids"], contextualized_inputs["attention_mask"]

    def _chunk_tensor(self, t: Iterable, chunk_size: int) -> List[Iterable]:
        return [t[i : i + chunk_size] for i in range(0, len(t), chunk_size)]

    def _main_retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, np.ndarray]:
        question_hidden_states_batched = self._chunk_tensor(question_hidden_states, self.batch_size)
        ids_batched = []
        vectors_batched = []
        for question_hidden_states in question_hidden_states_batched:
            start_time = time.time()
            ids, vectors = self.index.get_top_docs(question_hidden_states, n_docs)
            logger.debug(
                "index search time: {} sec, batch size {}".format(
                    time.time() - start_time, question_hidden_states.shape
                )
            )
            ids_batched.extend(ids)
            vectors_batched.extend(vectors)
        return (
            np.array(ids_batched),
            np.array(vectors_batched),
        )  # shapes (batch_size, n_docs) and (batch_size, n_docs, d)

    def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
        """
        Retrieves documents for specified ``question_hidden_states``.

        Args:
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`):
                A batch of query vectors to retrieve with.
            n_docs (:obj:`int`):
                The number of docs retrieved per query.

        Return:
            :obj:`Tuple[np.ndarray, np.ndarray, List[dict]]`: A tuple with the following objects:

            - **retrieved_doc_embeds** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`) -- The retrieval
              embeddings of the retrieved docs per query.
            - **doc_ids** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`) -- The ids of the documents in the
              index
            - **doc_dicts** (:obj:`List[dict]`): The :obj:`retrieved_doc_embeds` examples per query.
        """

        doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)

    def __call__(
        self,
        question_input_ids: List[List[int]],
        question_hidden_states: np.ndarray,
        prefix=None,
        n_docs=None,
        return_tensors=None,
    ) -> BatchEncoding:
        """
        Retrieves documents for specified :obj:`question_hidden_states`.

        Args:
            question_input_ids: (:obj:`List[List[int]]`) batch of input ids
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`:
                A batch of query vectors to retrieve with.
            prefix: (:obj:`str`, `optional`):
                The prefix used by the generator's tokenizer.
            n_docs (:obj:`int`, `optional`):
                The number of docs retrieved per query.
            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`, defaults to "pt"):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.

        Returns: :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following
        fields:

            - **context_input_ids** -- List of token ids to be fed to a model.

              `What are input IDs? <../glossary.html#input-ids>`__

            - **context_attention_mask** -- List of indices specifying which tokens should be attended to by the model
            (when :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names`).

              `What are attention masks? <../glossary.html#attention-mask>`__

            - **retrieved_doc_embeds** -- List of embeddings of the retrieved documents
            - **doc_ids** -- List of ids of the retrieved documents
        """

        n_docs = n_docs if n_docs is not None else self.n_docs
        prefix = prefix if prefix is not None else self.config.generator.prefix
        retrieved_doc_embeds, doc_ids, docs = self.retrieve(question_hidden_states, n_docs)

        input_strings = self.question_encoder_tokenizer.batch_decode(question_input_ids, skip_special_tokens=True)
        context_input_ids, context_attention_mask = self.postprocess_docs(
            docs, input_strings, prefix, n_docs, return_tensors=return_tensors
        )

        return BatchEncoding(
            {
                "context_input_ids": context_input_ids,
                "context_attention_mask": context_attention_mask,
                "retrieved_doc_embeds": retrieved_doc_embeds,
                "doc_ids": doc_ids,
            },
            tensor_type=return_tensors,
        )
