# Copyright The Lightning 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.
from collections.abc import Sequence
from typing import Any, Callable, List, Optional, Tuple, Union, cast

import torch
from torch import Tensor
from torch.nn import Module

from torchmetrics.functional.text.bert import (
    _postprocess_multiple_references,
    _preprocess_multiple_references,
    bert_score,
)
from torchmetrics.functional.text.helper_embedding_metric import _preprocess_text
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = ["BERTScore.plot"]

# Default model recommended in the original implementation.
_DEFAULT_MODEL: str = "roberta-large"

if _SKIP_SLOW_DOCTEST and _TRANSFORMERS_GREATER_EQUAL_4_4:
    from transformers import AutoModel, AutoTokenizer

    def _download_model_for_bert_score() -> None:
        """Download intensive operations."""
        AutoTokenizer.from_pretrained(_DEFAULT_MODEL, resume_download=True)
        AutoModel.from_pretrained(_DEFAULT_MODEL, resume_download=True)

    if not _try_proceed_with_timeout(_download_model_for_bert_score):
        __doctest_skip__ = ["BERTScore", "BERTScore.plot"]
else:
    __doctest_skip__ = ["BERTScore", "BERTScore.plot"]


def _get_input_dict(input_ids: List[Tensor], attention_mask: List[Tensor]) -> dict[str, Tensor]:
    """Create an input dictionary of ``input_ids`` and ``attention_mask`` for BERTScore calculation."""
    return {"input_ids": torch.cat(input_ids), "attention_mask": torch.cat(attention_mask)}


class BERTScore(Metric):
    """`Bert_score Evaluating Text Generation`_ for measuring text similarity.

    BERT leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
    sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and
    system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for
    evaluating different language generation tasks. This implementation follows the original implementation from
    `BERT_score`_.

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds``: Predicted sentence(s). Can be one of:

        * A single predicted sentence as a string (``str``)
        * A sequence of predicted sentences (``Sequence[str]``)

    - ``target``: Target/reference sentence(s). Can be one of:

        * A single reference sentence as a string (``str``)
        * A sequence of reference sentences (``Sequence[str]``)
        * A sequence of sequences of reference sentences for multi-reference evaluation (``Sequence[Sequence[str]]``)

    As output of ``forward`` and ``compute`` the metric returns the following output:

    - ``score`` (:class:`~Dict`): A dictionary containing the keys ``precision``, ``recall`` and ``f1`` with
      corresponding values

    Args:
        preds (Union[str, Sequence[str]]): A single predicted sentence or a sequence of predicted sentences.
        target (Union[str, Sequence[str], Sequence[Sequence[str]]]): A single target sentence, a sequence of target
            sentences, or a sequence of sequences of target sentences for multiple references per prediction.
        model_type: A name or a model path used to load ``transformers`` pretrained model.
        num_layers: A layer of representation to use.
        all_layers:
            An indication of whether the representation from all model's layers should be used.
            If ``all_layers=True``, the argument ``num_layers`` is ignored.
        model:  A user's own model. Must be of `torch.nn.Module` instance.
        user_tokenizer:
            A user's own tokenizer used with the own model. This must be an instance with the ``__call__`` method.
            This method must take an iterable of sentences (`List[str]`) and must return a python dictionary
            containing `"input_ids"` and `"attention_mask"` represented by :class:`~torch.Tensor`.
            It is up to the user's model of whether `"input_ids"` is a :class:`~torch.Tensor` of input ids or embedding
            vectors. This tokenizer must prepend an equivalent of ``[CLS]`` token and append an equivalent of ``[SEP]``
            token as ``transformers`` tokenizer does.
        user_forward_fn:
            A user's own forward function used in a combination with ``user_model``. This function must take
            ``user_model`` and a python dictionary of containing ``"input_ids"`` and ``"attention_mask"`` represented
            by :class:`~torch.Tensor` as an input and return the model's output represented by the single
            :class:`~torch.Tensor`.
        verbose: An indication of whether a progress bar to be displayed during the embeddings' calculation.
        idf: An indication whether normalization using inverse document frequencies should be used.
        device: A device to be used for calculation.
        max_length: A maximum length of input sequences. Sequences longer than ``max_length`` are to be trimmed.
        batch_size: A batch size used for model processing.
        num_threads: A number of threads to use for a dataloader.
        return_hash: An indication of whether the correspodning ``hash_code`` should be returned.
        lang: A language of input sentences.
        rescale_with_baseline:
            An indication of whether bertscore should be rescaled with a pre-computed baseline.
            When a pretrained model from ``transformers`` model is used, the corresponding baseline is downloaded
            from the original ``bert-score`` package from `BERT_score`_ if available.
            In other cases, please specify a path to the baseline csv/tsv file, which must follow the formatting
            of the files from `BERT_score`_.
        baseline_path: A path to the user's own local csv/tsv file with the baseline scale.
        baseline_url: A url path to the user's own  csv/tsv file with the baseline scale.
        truncation: An indication of whether the input sequences should be truncated to the ``max_length``.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from pprint import pprint
        >>> from torchmetrics.text.bert import BERTScore
        >>> preds = ["hello there", "general kenobi"]
        >>> target = ["hello there", "master kenobi"]
        >>> bertscore = BERTScore()
        >>> pprint(bertscore(preds, target))
        {'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])}

    Example:
        >>> from pprint import pprint
        >>> from torchmetrics.text.bert import BERTScore
        >>> preds = ["hello there", "general kenobi"]
        >>> target = [["hello there", "master kenobi"], ["hello there", "master kenobi"]]
        >>> bertscore = BERTScore()
        >>> pprint(bertscore(preds, target))
        {'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])}

    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0

    preds_input_ids: List[Tensor]
    preds_attention_mask: List[Tensor]
    target_input_ids: List[Tensor]
    target_attention_mask: List[Tensor]

    def __init__(
        self,
        model_name_or_path: Optional[str] = None,
        num_layers: Optional[int] = None,
        all_layers: bool = False,
        model: Optional[Module] = None,
        user_tokenizer: Optional[Any] = None,
        user_forward_fn: Optional[Callable[[Module, dict[str, Tensor]], Tensor]] = None,
        verbose: bool = False,
        idf: bool = False,
        device: Optional[Union[str, torch.device]] = None,
        max_length: int = 512,
        batch_size: int = 64,
        num_threads: int = 0,
        return_hash: bool = False,
        lang: str = "en",
        rescale_with_baseline: bool = False,
        baseline_path: Optional[str] = None,
        baseline_url: Optional[str] = None,
        truncation: bool = False,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.model_name_or_path = model_name_or_path or _DEFAULT_MODEL
        self.num_layers = num_layers
        self.all_layers = all_layers
        self.model = model
        self.user_forward_fn = user_forward_fn
        self.verbose = verbose
        self.idf = idf
        self.embedding_device = device
        self.max_length = max_length
        self.batch_size = batch_size
        self.num_threads = num_threads
        self.return_hash = return_hash
        self.lang = lang
        self.rescale_with_baseline = rescale_with_baseline
        self.baseline_path = baseline_path
        self.baseline_url = baseline_url
        self.truncation = truncation
        self.ref_group_boundaries: Optional[List[Tuple[int, int]]] = None

        if user_tokenizer:
            self.tokenizer = user_tokenizer
            self.user_tokenizer = True
        else:
            if not _TRANSFORMERS_GREATER_EQUAL_4_4:
                raise ModuleNotFoundError(
                    "`BERTScore` metric with default tokenizers requires `transformers` package be installed."
                    " Either install with `pip install transformers>=4.4` or `pip install torchmetrics[text]`."
                )
            from transformers import AutoTokenizer

            if model_name_or_path is None:
                rank_zero_warn(
                    "The argument `model_name_or_path` was not specified while it is required when the default"
                    " `transformers` model is used."
                    f" It will use the default recommended model - {_DEFAULT_MODEL!r}."
                )
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
            self.user_tokenizer = False

        self.add_state("preds_input_ids", [], dist_reduce_fx="cat")
        self.add_state("preds_attention_mask", [], dist_reduce_fx="cat")
        self.add_state("target_input_ids", [], dist_reduce_fx="cat")
        self.add_state("target_attention_mask", [], dist_reduce_fx="cat")

    def update(
        self, preds: Union[str, Sequence[str]], target: Union[str, Sequence[str], Sequence[Sequence[str]]]
    ) -> None:
        """Store predictions/references for computing BERT scores.

        It is necessary to store sentences in a tokenized form to ensure the DDP mode working.

        """
        if isinstance(preds, str):
            preds = [preds]
        if isinstance(target, str):
            target = [target]
        if not isinstance(preds, list):
            preds = list(preds)
        if not isinstance(target, list):
            target = list(target)

        if len(preds) != len(target):
            raise ValueError(
                "Expected number of predicted and reference sentences to be the same, but got"
                f"{len(preds)} and {len(target)}"
            )

        if isinstance(preds, list) and len(preds) > 0 and isinstance(target, list) and len(target) > 0:
            preds, target, self.ref_group_boundaries = _preprocess_multiple_references(preds, target)

        preds_dict, _ = _preprocess_text(
            preds,
            self.tokenizer,
            self.max_length,
            truncation=self.truncation,
            sort_according_length=False,
            own_tokenizer=self.user_tokenizer,
        )
        target_dict, _ = _preprocess_text(
            cast(List[str], target),
            self.tokenizer,
            self.max_length,
            truncation=self.truncation,
            sort_according_length=False,
            own_tokenizer=self.user_tokenizer,
        )

        self.preds_input_ids.append(preds_dict["input_ids"])
        self.preds_attention_mask.append(preds_dict["attention_mask"])
        self.target_input_ids.append(target_dict["input_ids"])
        self.target_attention_mask.append(target_dict["attention_mask"])

    def compute(self) -> dict[str, Union[Tensor, List[float], str]]:
        """Calculate BERT scores."""
        preds = {
            "input_ids": dim_zero_cat(self.preds_input_ids),
            "attention_mask": dim_zero_cat(self.preds_attention_mask),
        }
        target = {
            "input_ids": dim_zero_cat(self.target_input_ids),
            "attention_mask": dim_zero_cat(self.target_attention_mask),
        }

        output_dict = bert_score(
            preds=preds,
            target=target,
            model_name_or_path=self.model_name_or_path,
            num_layers=self.num_layers,
            all_layers=self.all_layers,
            model=self.model,
            user_tokenizer=self.tokenizer if self.user_tokenizer else None,
            user_forward_fn=self.user_forward_fn,
            verbose=self.verbose,
            idf=self.idf,
            device=self.embedding_device,
            max_length=self.max_length,
            batch_size=self.batch_size,
            num_threads=self.num_threads,
            return_hash=self.return_hash,
            lang=self.lang,
            rescale_with_baseline=self.rescale_with_baseline,
            baseline_path=self.baseline_path,
            baseline_url=self.baseline_url,
        )

        if (
            self.ref_group_boundaries is not None
            and isinstance(output_dict["precision"], Tensor)
            and isinstance(output_dict["recall"], Tensor)
            and isinstance(output_dict["f1"], Tensor)
        ):
            output_dict["precision"], output_dict["recall"], output_dict["f1"] = _postprocess_multiple_references(
                output_dict["precision"], output_dict["recall"], output_dict["f1"], self.ref_group_boundaries
            )

        return output_dict

    def plot(
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
    ) -> _PLOT_OUT_TYPE:
        """Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> from torchmetrics.text.bert import BERTScore
            >>> preds = ["hello there", "general kenobi"]
            >>> target = ["hello there", "master kenobi"]
            >>> metric = BERTScore()
            >>> metric.update(preds, target)
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> from torch import tensor
            >>> from torchmetrics.text.bert import BERTScore
            >>> preds = ["hello there", "general kenobi"]
            >>> target = ["hello there", "master kenobi"]
            >>> metric = BERTScore()
            >>> values = []
            >>> for _ in range(10):
            ...     val = metric(preds, target)
            ...     val = {k: tensor(v).mean() for k,v in val.items()}  # convert into single value per key
            ...     values.append(val)
            >>> fig_, ax_ = metric.plot(values)

        """
        if val is None:  # default average score across sentences
            val = self.compute()  # type: ignore
            val = {k: torch.tensor(v).mean() for k, v in val.items()}  # type: ignore
        return self._plot(val, ax)