# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
RoBERTa: A Robustly Optimized BERT Pretraining Approach.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from fairseq import utils
from fairseq.models import (
    FairseqDecoder,
    FairseqLanguageModel,
    register_model,
    register_model_architecture,
)
from fairseq.modules import (
    LayerNorm,
    TransformerSentenceEncoder,
    NormSelect
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params

from .hub_interface import RobertaHubInterface


@register_model('roberta')
class RobertaModel(FairseqLanguageModel):

    @classmethod
    def hub_models(cls):
        return {
            'roberta.base': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz',
            'roberta.large': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz',
            'roberta.large.mnli': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz',
            'roberta.large.wsc': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz',
        }

    def __init__(self, args, encoder):
        super().__init__(encoder)
        self.args = args

        # We follow BERT's random weight initialization
        self.apply(init_bert_params)

        self.classification_heads = nn.ModuleDict()

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        parser.add_argument('--encoder-layers', type=int, metavar='L',
                            help='num encoder layers')
        parser.add_argument('--encoder-embed-dim', type=int, metavar='H',
                            help='encoder embedding dimension')
        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='F',
                            help='encoder embedding dimension for FFN')
        parser.add_argument('--encoder-attention-heads', type=int, metavar='A',
                            help='num encoder attention heads')
        parser.add_argument('--activation-fn',
                            choices=utils.get_available_activation_fns(),
                            help='activation function to use')
        parser.add_argument('--pooler-activation-fn',
                            choices=utils.get_available_activation_fns(),
                            help='activation function to use for pooler layer')
        parser.add_argument('--encoder-normalize-before', action='store_true',
                            help='apply layernorm before each encoder block')
        parser.add_argument('--dropout', type=float, metavar='D',
                            help='dropout probability')
        parser.add_argument('--attention-dropout', type=float, metavar='D',
                            help='dropout probability for attention weights')
        parser.add_argument('--activation-dropout', type=float, metavar='D',
                            help='dropout probability after activation in FFN')
        parser.add_argument('--pooler-dropout', type=float, metavar='D',
                            help='dropout probability in the masked_lm pooler layers')
        parser.add_argument('--max-positions', type=int,
                            help='number of positional embeddings to learn')
        parser.add_argument('--load-checkpoint-heads', action='store_true',
                            help='(re-)register and load heads when loading checkpoints')
        # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
        parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
                            help='LayerDrop probability for encoder')
        parser.add_argument('--encoder-layers-to-keep', default=None,
                            help='which layers to *keep* when pruning as a comma-separated list')

    @classmethod
    def build_model(cls, args, task):
        """Build a new model instance."""

        # make sure all arguments are present
        base_architecture(args)

        if not hasattr(args, 'max_positions'):
            args.max_positions = args.tokens_per_sample

        encoder = RobertaEncoder(args, task.source_dictionary)
        return cls(args, encoder)

    def forward(self, src_tokens, features_only=False, return_all_hiddens=False, classification_head_name=None, **kwargs):
        if classification_head_name is not None:
            features_only = True

        x, extra = self.decoder(src_tokens, features_only, return_all_hiddens, **kwargs)

        if classification_head_name is not None:
            x = self.classification_heads[classification_head_name](x)
        return x, extra

    def register_classification_head(self, name, num_classes=None, inner_dim=None, **kwargs):
        """Register a classification head."""
        if name in self.classification_heads:
            prev_num_classes = self.classification_heads[name].out_proj.out_features
            prev_inner_dim = self.classification_heads[name].dense.out_features
            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
                print(
                    'WARNING: re-registering head "{}" with num_classes {} (prev: {}) '
                    'and inner_dim {} (prev: {})'.format(
                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
                    )
                )
        self.classification_heads[name] = RobertaClassificationHead(
            self.args.encoder_embed_dim,
            inner_dim or self.args.encoder_embed_dim,
            num_classes,
            self.args.pooler_activation_fn,
            self.args.pooler_dropout,
        )

    @property
    def supported_targets(self):
        return {'self'}

    @classmethod
    def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='gpt2', **kwargs):
        from fairseq import hub_utils
        x = hub_utils.from_pretrained(
            model_name_or_path,
            checkpoint_file,
            data_name_or_path,
            archive_map=cls.hub_models(),
            bpe=bpe,
            load_checkpoint_heads=True,
            **kwargs,
        )
        return RobertaHubInterface(x['args'], x['task'], x['models'][0])

    def upgrade_state_dict_named(self, state_dict, name):
        super().upgrade_state_dict_named(state_dict, name)

        prefix = name + '.' if name != '' else ''
        current_head_names = [] if not hasattr(self, 'classification_heads') else \
            self.classification_heads.keys()

        # Handle new classification heads present in the state dict.
        keys_to_delete = []
        for k in state_dict.keys():
            if not k.startswith(prefix + 'classification_heads.'):
                continue

            head_name = k[len(prefix + 'classification_heads.'):].split('.')[0]
            num_classes = state_dict[prefix + 'classification_heads.' + head_name + '.out_proj.weight'].size(0)
            inner_dim = state_dict[prefix + 'classification_heads.' + head_name + '.dense.weight'].size(0)

            if getattr(self.args, 'load_checkpoint_heads', False):
                if head_name not in current_head_names:
                    self.register_classification_head(head_name, num_classes, inner_dim)
            else:
                if head_name not in current_head_names:
                    print(
                        'WARNING: deleting classification head ({}) from checkpoint '
                        'not present in current model: {}'.format(head_name, k)
                    )
                    keys_to_delete.append(k)
                elif (
                    num_classes != self.classification_heads[head_name].out_proj.out_features
                    or inner_dim != self.classification_heads[head_name].dense.out_features
                ):
                    print(
                        'WARNING: deleting classification head ({}) from checkpoint '
                        'with different dimensions than current model: {}'.format(head_name, k)
                    )
                    keys_to_delete.append(k)
        for k in keys_to_delete:
            del state_dict[k]

        # Copy any newly-added classification heads into the state dict
        # with their current weights.
        if hasattr(self, 'classification_heads'):
            cur_state = self.classification_heads.state_dict()
            for k, v in cur_state.items():
                if prefix + 'classification_heads.' + k not in state_dict:
                    print('Overwriting', prefix + 'classification_heads.' + k)
                    state_dict[prefix + 'classification_heads.' + k] = v


@register_model('xlmr')
class XLMRModel(RobertaModel):
    @classmethod
    def hub_models(cls):
        return {
            'xlmr.base.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.v0.tar.gz',
            'xlmr.large.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.v0.tar.gz',
        }

    @classmethod
    def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs):
        from fairseq import hub_utils
        x = hub_utils.from_pretrained(
            model_name_or_path,
            checkpoint_file,
            data_name_or_path,
            archive_map=cls.hub_models(),
            bpe=bpe,
            load_checkpoint_heads=True,
            **kwargs,
        )
        return RobertaHubInterface(x['args'], x['task'], x['models'][0])


class RobertaLMHead(nn.Module):
    """Head for masked language modeling."""

    def __init__(self, embed_dim, output_dim, activation_fn, weight=None, \
            num_attention_heads=1, encoder_norm_ff='layer'):
        super().__init__()
        self.dense = nn.Linear(embed_dim, embed_dim)
        self.activation_fn = utils.get_activation_fn(activation_fn)
        if encoder_norm_ff not in ['layer', 'group', 'ammlayer']:
            self.layer_norm = LayerNorm(embed_dim)
        else:
            self.layer_norm = NormSelect(encoder_norm_ff, embed_dim, num_attention_heads)
        if weight is None:
            weight = nn.Linear(embed_dim, output_dim, bias=False).weight
        self.weight = weight
        self.bias = nn.Parameter(torch.zeros(output_dim))

    def forward(self, features, masked_tokens=None, **kwargs):
        # Only project the unmasked tokens while training,
        # saves both memory and computation
        if masked_tokens is not None:
            features = features[masked_tokens, :]

        x = self.dense(features)
        x = self.activation_fn(x)
        x = self.layer_norm(x)
        # project back to size of vocabulary with bias
        x = F.linear(x, self.weight) + self.bias
        return x


class RobertaClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout):
        super().__init__()
        self.dense = nn.Linear(input_dim, inner_dim)
        self.activation_fn = utils.get_activation_fn(activation_fn)
        self.dropout = nn.Dropout(p=pooler_dropout)
        self.out_proj = nn.Linear(inner_dim, num_classes)

    def forward(self, features, **kwargs):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = self.activation_fn(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


class RobertaEncoder(FairseqDecoder):
    """RoBERTa encoder.

    Implements the :class:`~fairseq.models.FairseqDecoder` interface required
    by :class:`~fairseq.models.FairseqLanguageModel`.
    """

    def __init__(self, args, dictionary):
        super().__init__(dictionary)
        self.args = args

        # RoBERTa is a sentence encoder model, so users will intuitively trim
        # encoder layers. However, the implementation uses the fairseq decoder,
        # so we fix here.
        if args.encoder_layers_to_keep:
            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
            args.decoder_layers_to_keep = args.encoder_layers_to_keep
            args.encoder_layers_to_keep = None

        self.sentence_encoder = TransformerSentenceEncoder(
            padding_idx=dictionary.pad(),
            vocab_size=len(dictionary),
            num_encoder_layers=args.encoder_layers,
            embedding_dim=args.encoder_embed_dim,
            ffn_embedding_dim=args.encoder_ffn_embed_dim,
            num_attention_heads=args.encoder_attention_heads,
            dropout=args.dropout,
            attention_dropout=args.attention_dropout,
            activation_dropout=args.activation_dropout,
            layerdrop=args.encoder_layerdrop,
            max_seq_len=args.max_positions,
            num_segments=0,
            encoder_normalize_before=args.encoder_normalize_before,
            apply_bert_init=True,
            activation_fn=args.activation_fn,
            encoder_norm_self=args.encoder_norm_self,
            encoder_norm_ff=args.encoder_norm_ff,
        )
        self.lm_head = RobertaLMHead(
            embed_dim=args.encoder_embed_dim,
            output_dim=len(dictionary),
            activation_fn=args.activation_fn,
            weight=self.sentence_encoder.embed_tokens.weight,
            num_attention_heads=args.encoder_attention_heads,
            encoder_norm_ff=args.encoder_norm_ff,
        )

    def forward(self, src_tokens, features_only=False, return_all_hiddens=False, masked_tokens=None, **unused):
        """
        Args:
            src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
            features_only (bool, optional): skip LM head and just return
                features. If True, the output will be of shape
                `(batch, src_len, embed_dim)`.
            return_all_hiddens (bool, optional): also return all of the
                intermediate hidden states (default: False).

        Returns:
            tuple:
                - the LM output of shape `(batch, src_len, vocab)`
                - a dictionary of additional data, where 'inner_states'
                  is a list of hidden states.
        """
        x, extra = self.extract_features(src_tokens, return_all_hiddens)
        if not features_only:
            x = self.output_layer(x, masked_tokens=masked_tokens)
        return x, extra

    def extract_features(self, src_tokens, return_all_hiddens=False, **unused):
        inner_states, _ = self.sentence_encoder(
            src_tokens,
            last_state_only=not return_all_hiddens,
        )
        features = inner_states[-1]
        return features, {'inner_states': inner_states if return_all_hiddens else None}

    def output_layer(self, features, masked_tokens=None, **unused):
        return self.lm_head(features, masked_tokens)

    def max_positions(self):
        """Maximum output length supported by the encoder."""
        return self.args.max_positions


@register_model_architecture('roberta', 'roberta')
def base_architecture(args):
    args.encoder_layers = getattr(args, 'encoder_layers', 12)
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
    args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
    args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)

    args.activation_fn = getattr(args, 'activation_fn', 'gelu')
    args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')

    args.dropout = getattr(args, 'dropout', 0.1)
    args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
    args.activation_dropout = getattr(args, 'activation_dropout', 0.0)
    args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)


@register_model_architecture('roberta', 'roberta_base')
def roberta_base_architecture(args):
    base_architecture(args)


@register_model_architecture('roberta', 'roberta_large')
def roberta_large_architecture(args):
    args.encoder_layers = getattr(args, 'encoder_layers', 24)
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
    args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
    args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
    base_architecture(args)


@register_model_architecture('roberta', 'xlm')
def xlm_architecture(args):
    args.encoder_layers = getattr(args, 'encoder_layers', 16)
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1280)
    args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1280*4)
    args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)

    base_architecture(args)

 # tall and skinny model for testbed
@register_model_architecture('roberta', 'roberta_benchmark_skinny')
def roberta_benchmark_skinny_architecture(args):
    args.encoder_layers = getattr(args, 'encoder_layers', 12)
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 128)
    args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
    args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
    base_architecture(args)


@register_model_architecture('roberta', 'roberta_benchmark_fat')
def roberta_benchmark_fat_architecture(args):
    args.encoder_layers = getattr(args, 'encoder_layers', 4)
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
    args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
    args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)    
    base_architecture(args)
