# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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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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"""PyTorch BERT model with Patience-based Early Exit. """


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


logger = logging.getLogger(__name__)


class BertEncoderWithPabee(BertEncoder):
    def adaptive_forward(self, hidden_states, current_layer, attention_mask=None, head_mask=None):
        layer_outputs = self.layer[current_layer](hidden_states, attention_mask, head_mask[current_layer])

        hidden_states = layer_outputs[0]

        return hidden_states


@add_start_docstrings(
    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.",
    BERT_START_DOCSTRING,
)
class BertModelWithPabee(BertModel):
    """

    The model can behave as an encoder (with only self-attention) as well
    as a decoder, in which case a layer of cross-attention is added between
    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the
    :obj:`is_decoder` argument of the configuration set to :obj:`True`; an
    :obj:`encoder_hidden_states` is expected as an input to the forward pass.

    .. _`Attention is all you need`:
        https://arxiv.org/abs/1706.03762

    """

    def __init__(self, config):
        super().__init__(config)

        self.encoder = BertEncoderWithPabee(config)

        self.init_weights()
        self.patience = 0
        self.inference_instances_num = 0
        self.inference_layers_num = 0

        self.regression_threshold = 0

    def set_regression_threshold(self, threshold):
        self.regression_threshold = threshold

    def set_patience(self, patience):
        self.patience = patience

    def reset_stats(self):
        self.inference_instances_num = 0
        self.inference_layers_num = 0

    def log_stats(self):
        avg_inf_layers = self.inference_layers_num / self.inference_instances_num
        message = f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up = {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
        print(message)

    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_dropout=None,
        output_layers=None,
        regression=False,
    ):
        r"""
        Return:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
            last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
                Sequence of hidden-states at the output of the last layer of the model.
            pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
                Last layer hidden-state of the first token of the sequence (classification token)
                further processed by a Linear layer and a Tanh activation function. The Linear
                layer weights are trained from the next sentence prediction (classification)
                objective during pre-training.

                This output is usually *not* a good summary
                of the semantic content of the input, you're often better with averaging or pooling
                the sequence of hidden-states for the whole input sequence.
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
                Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
                of shape :obj:`(batch_size, sequence_length, hidden_size)`.

                Hidden-states of the model at the output of each layer plus the initial embedding outputs.
            attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
                Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
                :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

                Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
                heads.
        """

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )
        encoder_outputs = embedding_output

        if self.training:
            res = []
            for i in range(self.config.num_hidden_layers):
                encoder_outputs = self.encoder.adaptive_forward(
                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask
                )

                pooled_output = self.pooler(encoder_outputs)
                logits = output_layers[i](output_dropout(pooled_output))
                res.append(logits)
        elif self.patience == 0:  # Use all layers for inference
            encoder_outputs = self.encoder(
                embedding_output,
                attention_mask=extended_attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_extended_attention_mask,
            )
            pooled_output = self.pooler(encoder_outputs[0])
            res = [output_layers[self.config.num_hidden_layers - 1](pooled_output)]
        else:
            patient_counter = 0
            patient_result = None
            calculated_layer_num = 0
            for i in range(self.config.num_hidden_layers):
                calculated_layer_num += 1
                encoder_outputs = self.encoder.adaptive_forward(
                    encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask
                )

                pooled_output = self.pooler(encoder_outputs)
                logits = output_layers[i](pooled_output)
                if regression:
                    labels = logits.detach()
                    if patient_result is not None:
                        patient_labels = patient_result.detach()
                    if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
                        patient_counter += 1
                    else:
                        patient_counter = 0
                else:
                    labels = logits.detach().argmax(dim=1)
                    if patient_result is not None:
                        patient_labels = patient_result.detach().argmax(dim=1)
                    if (patient_result is not None) and torch.all(labels.eq(patient_labels)):
                        patient_counter += 1
                    else:
                        patient_counter = 0

                patient_result = logits
                if patient_counter == self.patience:
                    break
            res = [patient_result]
            self.inference_layers_num += calculated_layer_num
            self.inference_instances_num += 1

        return res


@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    BERT_START_DOCSTRING,
)
class BertForSequenceClassificationWithPabee(BertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.bert = BertModelWithPabee(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifiers = nn.ModuleList(
            [nn.Linear(config.hidden_size, self.config.num_labels) for _ in range(config.num_hidden_layers)]
        )

        self.init_weights()

    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        r"""
            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for computing the sequence classification/regression loss.
                Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
                If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
                Classification (or regression if config.num_labels==1) loss.
            logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
                Classification (or regression if config.num_labels==1) scores (before SoftMax).
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
                Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
                of shape :obj:`(batch_size, sequence_length, hidden_size)`.

                Hidden-states of the model at the output of each layer plus the initial embedding outputs.
            attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
                Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
                :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

                Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
                heads.

        Examples::

            from transformers import BertTokenizer, BertForSequenceClassification
            from pabee import BertForSequenceClassificationWithPabee
            from torch import nn
            import torch

            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            model = BertForSequenceClassificationWithPabee.from_pretrained('bert-base-uncased')

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

            loss, logits = outputs[:2]

        """

        logits = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_dropout=self.dropout,
            output_layers=self.classifiers,
            regression=self.num_labels == 1,
        )

        outputs = (logits[-1],)

        if labels is not None:
            total_loss = None
            total_weights = 0
            for ix, logits_item in enumerate(logits):
                if self.num_labels == 1:
                    #  We are doing regression
                    loss_fct = MSELoss()
                    loss = loss_fct(logits_item.view(-1), labels.view(-1))
                else:
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(logits_item.view(-1, self.num_labels), labels.view(-1))
                if total_loss is None:
                    total_loss = loss
                else:
                    total_loss += loss * (ix + 1)
                total_weights += ix + 1
            outputs = (total_loss / total_weights,) + outputs

        return outputs
