# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Classes to support Encoder-Decoder architectures """


from typing import Optional

from .configuration_encoder_decoder import EncoderDecoderConfig
from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
from .modeling_outputs import Seq2SeqLMOutput
from .modeling_utils import PreTrainedModel
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "EncoderDecoderConfig"

ENCODER_DECODER_START_DOCSTRING = r"""
    This class can be used to inialize a sequence-to-sequnece model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via :meth:`~transformers.AutoModel.from_pretrained` function and the decoder is loaded via :meth:`~transformers.AutoModelForCausalLM.from_pretrained` function.
    Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, *i.e.* summarization.

    The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
    Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.

    After such an Encoder Decoder model has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#module>`__ sub-class. Use it as a
    regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

    Parameters:
        config (:class:`~transformers.T5Config`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

ENCODER_DECODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary for the encoder.
            Indices can be obtained using :class:`~transformers.PretrainedTokenizer`.
            See :meth:`~transformers.PreTrainedTokenizer.encode` and
            :meth:`~transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices for the encoder.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        encoder_outputs (:obj:`tuple(torch.FloatTensor)`, `optional`, defaults to :obj:`None`):
            This tuple must consist of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
            `last_hidden_state` (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`) is a tensor of hidden-states at the output of the last layer of the encoder.
            Used in the cross-attention of the decoder.
        decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
            Provide for sequence to sequence training to the decoder.
            Indices can be obtained using :class:`transformers.PretrainedTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
            Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
        decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Labels for computing the masked language modeling loss for the decoder.
            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
            in ``[0, ..., config.vocab_size]``
        return_dict (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.Seq2SeqLMOutput` instead of a
            plain tuple.
        kwargs: (`optional`) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
            - Without a prefix which will be input as ``**encoder_kwargs`` for the encoder forward function.
            - With a `decoder_` prefix which will be input as ``**decoder_kwargs`` for the decoder forward function.
"""


@add_start_docstrings(ENCODER_DECODER_START_DOCSTRING)
class EncoderDecoderModel(PreTrainedModel):
    r"""
    :class:`~transformers.EncoderDecoder` is a generic model class that will be
    instantiated as a transformer architecture with one of the base model
    classes of the library as encoder and another one as
    decoder when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
    class method for the encoder and `AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path)` class method for the decoder.
    """
    config_class = EncoderDecoderConfig
    base_model_prefix = "encoder_decoder"

    def __init__(
        self,
        config: Optional[PretrainedConfig] = None,
        encoder: Optional[PreTrainedModel] = None,
        decoder: Optional[PreTrainedModel] = None,
    ):
        assert config is not None or (
            encoder is not None and decoder is not None
        ), "Either a configuration or an Encoder and a decoder has to be provided"
        if config is None:
            config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
        else:
            assert isinstance(config, self.config_class), "config: {} has to be of type {}".format(
                config, self.config_class
            )
        # initialize with config
        super().__init__(config)

        if encoder is None:
            from .modeling_auto import AutoModel

            encoder = AutoModel.from_config(config.encoder)

        if decoder is None:
            from .modeling_auto import AutoModelForCausalLM

            decoder = AutoModelForCausalLM.from_config(config.decoder)

        self.encoder = encoder
        self.decoder = decoder
        assert (
            self.encoder.get_output_embeddings() is None
        ), "The encoder {} should not have a LM Head. Please use a model without LM Head"

        # tie encoder, decoder weights if config set accordingly
        self.tie_weights()

    def tie_weights(self):
        # tie encoder & decoder if needed
        if self.config.tie_encoder_decoder:
            # tie encoder and decoder base model
            decoder_base_model_prefix = self.decoder.base_model_prefix
            self._tie_encoder_decoder_weights(
                self.encoder, self.decoder._modules[decoder_base_model_prefix], self.decoder.base_model_prefix
            )

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def get_output_embeddings(self):
        return self.decoder.get_output_embeddings()

    @classmethod
    def from_encoder_decoder_pretrained(
        cls,
        encoder_pretrained_model_name_or_path: str = None,
        decoder_pretrained_model_name_or_path: str = None,
        *model_args,
        **kwargs
    ) -> PreTrainedModel:
        r"""Instantiates an encoder and a decoder from one or two base classes of the library from pre-trained model checkpoints.


        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
        To train the model, you need to first set it back in training mode with `model.train()`.

        Params:
            encoder_pretrained_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
                information necessary to initiate the encoder. Either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/encoder``.
                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            decoder_pretrained_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
                information necessary to initiate the decoder. Either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/decoder``.
                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            model_args: (`optional`) Sequence of positional arguments:
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method

            kwargs: (`optional`) Remaining dictionary of keyword arguments.
                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attentions=True``).
                - To update the encoder configuration, use the prefix `encoder_` for each configuration parameter
                - To update the decoder configuration, use the prefix `decoder_` for each configuration parameter
                - To update the parent model configuration, do not use a prefix for each configuration parameter
                Behave differently depending on whether a :obj:`config` is provided or automatically loaded.

        Examples::

            >>> from transformers import EncoderDecoderModel
            >>> # initialize a bert2bert from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased')
            >>> # saving model after fine-tuning
            >>> model.save_pretrained("./bert2bert")
            >>> # load fine-tuned model
            >>> model = EncoderDecoderModel.from_pretrained("./bert2bert")

        """

        kwargs_encoder = {
            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
        }

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        # remove encoder, decoder kwargs from kwargs
        for key in kwargs_encoder.keys():
            del kwargs["encoder_" + key]
        for key in kwargs_decoder.keys():
            del kwargs["decoder_" + key]

        # Load and initialize the encoder and decoder
        # The distinction between encoder and decoder at the model level is made
        # by the value of the flag `is_decoder` that we need to set correctly.
        encoder = kwargs_encoder.pop("model", None)
        if encoder is None:
            assert (
                encoder_pretrained_model_name_or_path is not None
            ), "If `model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has to be defined"
            from .modeling_auto import AutoModel

            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
        encoder.config.is_decoder = False

        decoder = kwargs_decoder.pop("model", None)
        if decoder is None:
            assert (
                decoder_pretrained_model_name_or_path is not None
            ), "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has to be defined"
            from .modeling_auto import AutoModelForCausalLM

            if "config" not in kwargs_decoder:
                from .configuration_auto import AutoConfig

                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                    logger.info(
                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
                    )
                    decoder_config.is_decoder = True
                    decoder_config.add_cross_attention = True

                kwargs_decoder["config"] = decoder_config

            if kwargs_decoder["config"].is_decoder is False or decoder_config.add_cross_attention is False:
                logger.warning(
                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a `decoder_config` to `.from_encoder_decoder_pretrained(...)`"
                )

            decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)

        # instantiate config with corresponding kwargs
        config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
        return cls(encoder=encoder, decoder=decoder, config=config)

    @add_start_docstrings_to_callable(ENCODER_DECODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        inputs_embeds=None,
        attention_mask=None,
        encoder_outputs=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        decoder_inputs_embeds=None,
        labels=None,
        return_dict=None,
        **kwargs,
    ):
        r"""
        Returns:

        Examples::

            >>> from transformers import EncoderDecoderModel, BertTokenizer
            >>> import torch

            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert from pre-trained checkpoints

            >>> # forward
            >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
            >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)

            >>> # training
            >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=input_ids, return_dict=True)
            >>> loss, logits = outputs.loss, outputs.logits

            >>> # save and load from pretrained
            >>> model.save_pretrained("bert2bert")
            >>> model = EncoderDecoderModel.from_pretrained("bert2bert")

            >>> # generation
            >>> generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                return_dict=return_dict,
                **kwargs_encoder,
            )

        hidden_states = encoder_outputs[0]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            inputs_embeds=decoder_inputs_embeds,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            labels=labels,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # TODO(PVP): currently it is not possible to use `past`
        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=decoder_outputs.loss,
            logits=decoder_outputs.logits,
            past_key_values=None,  # TODO(PVP) - need to implement cache for BERT, etc... before this works
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

        return decoder_outputs + encoder_outputs

    def prepare_inputs_for_generation(self, input_ids, past, attention_mask, encoder_outputs, **kwargs):
        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids)
        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
        input_dict = {
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "decoder_input_ids": decoder_inputs["input_ids"],
            "encoder_outputs": encoder_outputs,
        }

        # Ideally all models should have a `use_cache`
        # leave following to ifs until all have it implemented
        if "use_cache" in decoder_inputs:
            input_dict["decoder_use_cache"] = decoder_inputs["use_cache"]

        if "past_key_values" in decoder_inputs:
            input_dict["past_key_values"] = decoder_inputs["past_key_values"]

        return input_dict

    def _reorder_cache(self, past, beam_idx):
        # apply decoder cache reordering here
        return self.decoder._reorder_cache(past, beam_idx)
