# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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.
"""
RetriBERT model
"""


import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


logger = logging.get_logger(__name__)


from .._archive_maps import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402


# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class RetriBertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = RetriBertConfig
    load_tf_weights = None
    base_model_prefix = "retribert"

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


RETRIBERT_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    """Bert Based model to embed queries or document for document retrieval.""",
    RETRIBERT_START_DOCSTRING,
)
class RetriBertModel(RetriBertPreTrainedModel):
    def __init__(self, config: RetriBertConfig) -> None:
        super().__init__(config)
        self.projection_dim = config.projection_dim

        self.bert_query = BertModel(config)
        self.bert_doc = None if config.share_encoders else BertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False)
        self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False)

        self.ce_loss = nn.CrossEntropyLoss(reduction="mean")

        # Initialize weights and apply final processing
        self.post_init()

    def embed_sentences_checkpointed(
        self,
        input_ids,
        attention_mask,
        sent_encoder,
        checkpoint_batch_size=-1,
    ):
        # reproduces BERT forward pass with checkpointing
        if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
            return sent_encoder(input_ids, attention_mask=attention_mask)[1]
        else:
            # prepare implicit variables
            device = input_ids.device
            input_shape = input_ids.size()
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
            head_mask = [None] * sent_encoder.config.num_hidden_layers
            extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask(
                attention_mask, input_shape
            )

            # define function for checkpointing
            def partial_encode(*inputs):
                encoder_outputs = sent_encoder.encoder(
                    inputs[0],
                    attention_mask=inputs[1],
                    head_mask=head_mask,
                )
                sequence_output = encoder_outputs[0]
                pooled_output = sent_encoder.pooler(sequence_output)
                return pooled_output

            # run embedding layer on everything at once
            embedding_output = sent_encoder.embeddings(
                input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None
            )
            # run encoding and pooling on one mini-batch at a time
            pooled_output_list = []
            for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
                b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask)
                pooled_output_list.append(pooled_output)
            return torch.cat(pooled_output_list, dim=0)

    def embed_questions(
        self,
        input_ids,
        attention_mask=None,
        checkpoint_batch_size=-1,
    ):
        q_reps = self.embed_sentences_checkpointed(
            input_ids,
            attention_mask,
            self.bert_query,
            checkpoint_batch_size,
        )
        return self.project_query(q_reps)

    def embed_answers(
        self,
        input_ids,
        attention_mask=None,
        checkpoint_batch_size=-1,
    ):
        a_reps = self.embed_sentences_checkpointed(
            input_ids,
            attention_mask,
            self.bert_query if self.bert_doc is None else self.bert_doc,
            checkpoint_batch_size,
        )
        return self.project_doc(a_reps)

    def forward(
        self,
        input_ids_query: torch.LongTensor,
        attention_mask_query: Optional[torch.FloatTensor],
        input_ids_doc: torch.LongTensor,
        attention_mask_doc: Optional[torch.FloatTensor],
        checkpoint_batch_size: int = -1,
    ) -> torch.FloatTensor:
        r"""
        Args:
            input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary for the queries in a batch.

                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary for the documents in a batch.
            attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on documents padding token indices.
            checkpoint_batch_size (`int`, *optional*, defaults to `-1`):
                If greater than 0, uses gradient checkpointing to only compute sequence representation on
                `checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to
                all document representations in the batch.

        Return:
            `torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its
            corresponding document and each document to its corresponding query in the batch
        """
        device = input_ids_query.device
        q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size)
        a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size)
        compare_scores = torch.mm(q_reps, a_reps.t())
        loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device))
        loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device))
        loss = (loss_qa + loss_aq) / 2
        return loss
