import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist

import transformers
class Pooler(nn.Module):
    """
    Parameter-free poolers to get the sentence embedding
    'cls': [CLS] representation with BERT/RoBERTa's MLP pooler.
    'cls_before_pooler': [CLS] representation without the original MLP pooler.
    'avg': average of the last layers' hidden states at each token.
    'avg_top2': average of the last two layers.
    'avg_first_last': average of the first and the last layers.
    """
    def __init__(self, pooler_type):
        super().__init__()
        self.pooler_type = pooler_type
        assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type

    def forward(self, attention_mask, outputs):
        last_hidden = outputs.last_hidden_state
        # pooler_output = outputs.pooler_output
        hidden_states = outputs.hidden_states

        if self.pooler_type in ['cls_before_pooler', 'cls']:
            return last_hidden[:, 0]
        elif self.pooler_type == "avg":
            return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1))
        elif self.pooler_type == "avg_first_last":
            first_hidden = hidden_states[0]
            last_hidden = hidden_states[-1]
            pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
            return pooled_result
        elif self.pooler_type == "avg_top2":
            second_last_hidden = hidden_states[-2]
            last_hidden = hidden_states[-1]
            pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
            return pooled_result
        else:
            raise NotImplementedError