#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# 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 typing import Optional

from transformers import PreTrainedModel, RobertaConfig, RobertaModel
from transformers.file_utils import is_torch_available, requires_backends

if is_torch_available():
    import torch


class AnceEncoder(PreTrainedModel):
    config_class = RobertaConfig
    base_model_prefix = 'ance_encoder'
    load_tf_weights = None
    _keys_to_ignore_on_load_missing = [r'position_ids']
    _keys_to_ignore_on_load_unexpected = [r'pooler', r'classifier']

    def __init__(self, config: RobertaConfig):
        requires_backends(self, 'torch')
        super().__init__(config)
        self.config = config
        self.roberta = RobertaModel(config)
        self.embeddingHead = torch.nn.Linear(config.hidden_size, 768)
        self.norm = torch.nn.LayerNorm(768)
        self.init_weights()

    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
    def _init_weights(self, module):
        """ Initialize the weights """
        if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, torch.nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, torch.nn.Linear) and module.bias is not None:
            module.bias.data.zero_()

    def init_weights(self):
        self.roberta.init_weights()
        self.embeddingHead.apply(self._init_weights)
        self.norm.apply(self._init_weights)

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ):
        input_shape = input_ids.size()
        device = input_ids.device
        if attention_mask is None:
            attention_mask = (
                torch.ones(input_shape, device=device)
                if input_ids is None
                else (input_ids != self.roberta.config.pad_token_id)
            )
        outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
        sequence_output = outputs.last_hidden_state
        pooled_output = sequence_output[:, 0, :]
        pooled_output = self.norm(self.embeddingHead(pooled_output))
        return pooled_output