# This implementation was adopted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/models/bert.py
# Commit id: abbc1311731867310635f9edc2a9ec18317c8c48

# Copyright (c) 2022, Tri Dao.
# This BERT implementation is based on our MLPerf 2.0 and MLPerf 2.1 BERT implementation.
# https://github.com/mlcommons/training_results_v2.0/blob/main/HazyResearch/benchmarks/bert/implementations/pytorch/modeling.py
# https://github.com/mlcommons/training_results_v2.1/blob/main/Azure-HazyResearch/benchmarks/bert/implementations/ND96amsr_A100_v4/modeling.py

# Inspired by https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py

import logging
import re
from collections import OrderedDict
from collections.abc import Sequence
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from einops import rearrange
from transformers import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput
from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaLMHead

from transformers.models.bert.modeling_bert import (
    BaseModelOutputWithPoolingAndCrossAttentions,
    BertForPreTrainingOutput,
)

from typing import Optional, Tuple, Union

from .xlm_padding import (
    index_first_axis,
    index_first_axis_residual,
    pad_input,
    unpad_input,
)
from .configuration_xlm_roberta import XLMRobertaFlashConfig
from .block import Block
from .embedding import XLMRobertaEmbeddings
from .mha import MHA
from .mlp import FusedMLP, Mlp
from .stochastic_depth import StochasticDepth


try:
    from flash_attn.ops.fused_dense import FusedDense
except ImportError:
    FusedDense = None

try:
    from flash_attn.ops.triton.layer_norm import layer_norm_fn
except ImportError:
    layer_norm_fn = None


try:
    from flash_attn.losses.cross_entropy import CrossEntropyLoss
except ImportError:
    CrossEntropyLoss = None


logger = logging.getLogger(__name__)


def create_mixer_cls(config, cross_attn=False, return_residual=False):
    use_flash_attn = getattr(config, "use_flash_attn", False)
    fused_bias_fc = getattr(config, "fused_bias_fc", False)
    rotary_kwargs = {}
    if config.position_embedding_type == "rotary":
        rotary_kwargs["rotary_emb_dim"] = getattr(
            config, "rotary_emb_dim", config.hidden_size
        )
        rotary_kwargs["rotary_emb_base"] = getattr(config, "rotary_emb_base", 10000.0)
        rotary_kwargs["rotary_emb_scale_base"] = getattr(
            config, "rotary_emb_scale_base", None
        )
        rotary_kwargs["rotary_emb_interleaved"] = getattr(
            config, "rotary_emb_interleaved", False
        )
    mixer_cls = partial(
        MHA,
        num_heads=config.num_attention_heads,
        cross_attn=cross_attn,
        dropout=config.attention_probs_dropout_prob,
        causal=False,
        fused_bias_fc=fused_bias_fc,
        use_flash_attn=use_flash_attn,
        return_residual=return_residual,
        **rotary_kwargs,
    )
    return mixer_cls


def create_mlp_cls(config, layer_idx=None, return_residual=False):
    inner_dim = config.intermediate_size
    fused_mlp = getattr(config, "fused_mlp", False)
    if fused_mlp:
        assert config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"], (
            "fused_mlp only " "supports approximate gelu"
        )
    if not fused_mlp:
        approximate = (
            "tanh"
            if config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"]
            else "none"
        )
        mlp_cls = partial(
            Mlp,
            hidden_features=inner_dim,
            activation=partial(F.gelu, approximate=approximate),
            return_residual=return_residual,
        )
    else:
        if FusedMLP is None:
            raise ImportError("fused_dense is not installed")
        mlp_checkpoint_lvl = getattr(config, "mlp_checkpoint_lvl", 0)
        # mlp_checkpoint_lvl could be a list, which contains the checkpoint_lvl for each layer
        if isinstance(mlp_checkpoint_lvl, Sequence):
            assert layer_idx is not None
            mlp_checkpoint_lvl = mlp_checkpoint_lvl[layer_idx]
        mlp_cls = partial(
            FusedMLP,
            hidden_features=inner_dim,
            checkpoint_lvl=mlp_checkpoint_lvl,
            return_residual=return_residual,
        )
    return mlp_cls


def create_block(config, layer_idx=None):
    last_layer_subset = getattr(config, "last_layer_subset", False)
    cross_attn = last_layer_subset and layer_idx == config.num_hidden_layers - 1
    # TD [2022-12-19]: For cross attention (last layer), we actually want to return the
    # residual x_kv, not residual x. But it's annoying to change the API (and it only affects
    # one layer) so we just choose not to return residual in this case.
    return_residual = not cross_attn
    mixer_cls = create_mixer_cls(config, cross_attn, return_residual=return_residual)
    mlp_cls = create_mlp_cls(config, layer_idx, return_residual=return_residual)
    norm_cls = partial(nn.LayerNorm, eps=config.layer_norm_eps)
    block = Block(
        config.hidden_size,
        mixer_cls,
        mlp_cls,
        norm_cls=norm_cls,
        prenorm=False,
        resid_dropout1=config.hidden_dropout_prob,
        resid_dropout2=config.hidden_dropout_prob,
        fused_dropout_add_ln=getattr(config, "fused_dropout_add_ln", False),
        return_residual=return_residual,
    )
    return block


# https://github.com/huggingface/transformers/blob/7032e0203262ebb2ebf55da8d2e01f873973e835/src/transformers/models/bert/modeling_bert.py#L748
def _init_weights(module, initializer_range=0.02):
    if isinstance(module, nn.Linear):
        nn.init.normal_(module.weight, std=initializer_range)
        if module.bias is not None:
            nn.init.zeros_(module.bias)
    elif isinstance(module, nn.Embedding):
        nn.init.normal_(module.weight, std=initializer_range)
        if module.padding_idx is not None:
            nn.init.zeros_(module.weight[module.padding_idx])


class XLMRobertaEncoder(nn.Module):
    def __init__(self, config: XLMRobertaFlashConfig):
        super().__init__()
        self.use_flash_attn = getattr(config, "use_flash_attn", False)
        self.layers = nn.ModuleList(
            [create_block(config, layer_idx=i) for i in range(config.num_hidden_layers)]
        )
        self._grad_checkpointing = False

    @property
    def gradient_checkpointing(self):
        return self._grad_checkpointing

    @gradient_checkpointing.setter
    def gradient_checkpointing(self, value):
        self._grad_checkpointing = value

    def forward(self, hidden_states, key_padding_mask=None, subset_mask=None):
        """If subset_mask is not None, we only want output for the subset of the sequence.
        This means that we only compute the last layer output for these tokens.
        subset_mask: (batch, seqlen), dtype=torch.bool
        """
        if key_padding_mask is None or not self.use_flash_attn:
            mixer_kwargs = (
                {"key_padding_mask": key_padding_mask.bool()}
                if key_padding_mask is not None
                else None
            )
            for layer in self.layers:
                if self._grad_checkpointing:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        layer,
                        hidden_states,
                        use_reentrant=False,
                        mixer_kwargs=mixer_kwargs,
                    )
                else:
                    hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
            if subset_mask is not None:
                hidden_states = hidden_states[subset_mask]
        else:
            batch, seqlen = hidden_states.shape[:2]
            hidden_states, indices, cu_seqlens, max_seqlen_in_batch = unpad_input(
                hidden_states, key_padding_mask
            )
            mixer_kwargs = {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen_in_batch}
            if subset_mask is None:
                for layer in self.layers:
                    if self._grad_checkpointing:
                        hidden_states = torch.utils.checkpoint.checkpoint(
                            layer,
                            hidden_states,
                            use_reentrant=False,
                            mixer_kwargs=mixer_kwargs,
                        )
                    else:
                        hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
                hidden_states = pad_input(hidden_states, indices, batch, seqlen)
            else:
                for layer in self.layers[:-1]:
                    if self._grad_checkpointing:
                        hidden_states = torch.utils.checkpoint.checkpoint(
                            layer,
                            hidden_states,
                            use_reentrant=False,
                            mixer_kwargs=mixer_kwargs,
                        )
                    else:
                        hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
                if key_padding_mask is not None:
                    subset_idx = torch.nonzero(
                        subset_mask[key_padding_mask], as_tuple=False
                    ).flatten()
                    subset_seqlens = (subset_mask & key_padding_mask).sum(
                        dim=-1, dtype=torch.int32
                    )
                    subset_cu_seqlens = F.pad(
                        torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32),
                        (1, 0),
                    )
                else:
                    subset_idx = torch.nonzero(subset_mask, as_tuple=False).flatten()
                    subset_seqlens = subset_mask.sum(dim=-1, dtype=torch.int32)
                    subset_cu_seqlens = F.pad(
                        torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32),
                        (1, 0),
                    )
                hidden_states_subset, hidden_states = index_first_axis_residual(
                    hidden_states, subset_idx
                )
                # It's ok to set max_seqlen_q to be much larger
                mixer_kwargs = {
                    "x_kv": hidden_states,
                    "cu_seqlens": subset_cu_seqlens,
                    "max_seqlen": max_seqlen_in_batch,
                    "cu_seqlens_k": cu_seqlens,
                    "max_seqlen_k": max_seqlen_in_batch,
                }
                if self._grad_checkpointing:
                    torch.utils.checkpoint.checkpoint(
                        self.layers[-1],
                        hidden_states_subset,
                        use_reentrant=False,
                        mixer_kwargs=mixer_kwargs,
                    )
                else:
                    hidden_states = self.layers[-1](
                        hidden_states_subset, mixer_kwargs=mixer_kwargs
                    )
        return hidden_states


class XLMRobertaPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        fused_bias_fc = getattr(config, "fused_bias_fc", False)
        if fused_bias_fc and FusedDense is None:
            raise ImportError("fused_dense is not installed")
        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
        self.dense = linear_cls(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states, pool=True):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0] if pool else hidden_states
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class XLMRobertaPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        fused_bias_fc = getattr(config, "fused_bias_fc", False)
        if fused_bias_fc and FusedDense is None:
            raise ImportError("fused_dense is not installed")
        self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
        if self.fused_dropout_add_ln and layer_norm_fn is None:
            raise ImportError("Triton is not installed")
        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
        self.dense = linear_cls(config.hidden_size, config.hidden_size)
        approximate = (
            "tanh"
            if config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"]
            else "none"
        )
        self.transform_act_fn = nn.GELU(approximate=approximate)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        if not self.fused_dropout_add_ln:
            hidden_states = self.layer_norm(hidden_states)
        else:
            hidden_states = layer_norm_fn(
                hidden_states,
                self.layer_norm.weight,
                self.layer_norm.bias,
                eps=self.layer_norm.eps,
            )
        return hidden_states


class XLMRobertaLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        fused_bias_fc = getattr(config, "fused_bias_fc", False)
        if fused_bias_fc and FusedDense is None:
            raise ImportError("fused_dense is not installed")
        linear_cls = nn.Linear if not fused_bias_fc else FusedDense

        self.transform = XLMRobertaPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = linear_cls(config.hidden_size, config.vocab_size, bias=True)

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class XLMRobertaPreTrainingHeads(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = XLMRobertaLMPredictionHead(config)
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, sequence_output, pooled_output):
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score


class XLMRobertaPreTrainedModel(PreTrainedModel):
    """An abstract class to handle weights initialization and
    a simple interface for dowloading and loading pretrained models.
    """

    config_class = XLMRobertaFlashConfig
    base_model_prefix = "roberta"
    supports_gradient_checkpointing = True

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, XLMRobertaEncoder):
            module.gradient_checkpointing = value


class XLMRobertaModel(XLMRobertaPreTrainedModel):
    def __init__(self, config: XLMRobertaFlashConfig, add_pooling_layer=True):
        super().__init__(config)
        self.pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
        if config.vocab_size % self.pad_vocab_size_multiple != 0:
            config.vocab_size += self.pad_vocab_size_multiple - (
                config.vocab_size % self.pad_vocab_size_multiple
            )
        self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
        if self.fused_dropout_add_ln and layer_norm_fn is None:
            raise ImportError("Triton is not installed")
        assert config.hidden_act in [
            "gelu",
            "gelu_new",
            "gelu_fast",
            "gelu_pytorch_tanh",
        ]

        self.embeddings = XLMRobertaEmbeddings(
            config.hidden_size,
            config.vocab_size,
            config.max_position_embeddings,
            config.type_vocab_size,
            padding_idx=config.pad_token_id,
        )
        self.emb_drop = nn.Dropout(config.hidden_dropout_prob)
        self.emb_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.encoder = XLMRobertaEncoder(config)
        self.pooler = XLMRobertaPooler(config) if add_pooling_layer else None

        self.apply(partial(_init_weights, initializer_range=config.initializer_range))

    def forward(
        self,
        input_ids,
        position_ids=None,
        token_type_ids=None,
        attention_mask=None,
        masked_tokens_mask=None,
        return_dict=None,
        **kwargs,
    ):
        """If masked_tokens_mask is not None (i.e. last_layer_subset == True in XLMForPreTraining),
        we only want the output for the masked tokens. This means that we only compute the last
        layer output for these tokens.
        masked_tokens_mask: (batch, seqlen), dtype=torch.bool
        """

        if kwargs:
            for key, value in kwargs.items():
                if value is not None:
                    logger.warning(
                        "Flash attention implementation does not support kwargs: %s",
                        key,
                    )

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        hidden_states = self.embeddings(
            input_ids, position_ids=position_ids, token_type_ids=token_type_ids
        )
        # TD [2022-12:18]: Don't need to force residual in fp32
        # BERT puts embedding LayerNorm before embedding dropout.
        if not self.fused_dropout_add_ln:
            hidden_states = self.emb_ln(hidden_states)
        else:
            hidden_states = layer_norm_fn(
                hidden_states, self.emb_ln.weight, self.emb_ln.bias, eps=self.emb_ln.eps
            )
        hidden_states = self.emb_drop(hidden_states)

        if masked_tokens_mask is not None:
            batch_size, seqlen = input_ids.shape[:2]
            # We also need the first column for the CLS token
            first_col_mask = torch.zeros(
                batch_size, seqlen, dtype=torch.bool, device=input_ids.device
            )
            first_col_mask[:, 0] = True
            subset_mask = masked_tokens_mask | first_col_mask
        else:
            subset_mask = None

        sequence_output = self.encoder(
            hidden_states, key_padding_mask=attention_mask, subset_mask=subset_mask
        )

        if masked_tokens_mask is None:
            pooled_output = (
                self.pooler(sequence_output) if self.pooler is not None else None
            )
        else:
            # TD [2022-03-01]: the indexing here is very tricky.
            if attention_mask is not None:
                subset_idx = subset_mask[attention_mask]
                pool_input = sequence_output[first_col_mask[attention_mask][subset_idx]]
                sequence_output = sequence_output[
                    masked_tokens_mask[attention_mask][subset_idx]
                ]
            else:
                pool_input = sequence_output[first_col_mask[subset_mask]]
                sequence_output = sequence_output[masked_tokens_mask[subset_mask]]
            pooled_output = (
                self.pooler(pool_input, pool=False) if self.pooler is not None else None
            )

        if not return_dict:
            return sequence_output, pooled_output

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
        )


class XLMRobertaForMaskedLM(XLMRobertaPreTrainedModel):
    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]

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

        if config.is_decoder:
            logger.warning(
                "If you want to use `XLMRobertaForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
        self.lm_head = XLMRobertaLMHead(config)

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

    def get_input_embeddings(self):
        return self.roberta.embeddings.word_embeddings

    def get_output_embeddings(self):
        return self.lm_head.decoder

    def set_output_embeddings(self, new_embeddings):
        self.lm_head.decoder = new_embeddings

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
            Used to hide legacy arguments that have been deprecated.
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(prediction_scores.device)
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(
                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
            )

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return (
                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
            )

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


# class XLMRobertaForPreTraining(XLMRobertaPreTrainedModel):
#     def __init__(self, config: XLMRobertaFlashConfig):
#         super().__init__(config)
#         # If dense_seq_output, we only need to pass the hidden states for the masked out tokens
#         # (around 15%) to the classifier heads.
#         self.dense_seq_output = getattr(config, "dense_seq_output", False)
#         # If last_layer_subset, we only need the compute the last layer for a subset of tokens
#         # (e.g., the tokens we need to compute the masked LM loss and the next-sentence prediction).
#         self.last_layer_subset = getattr(config, "last_layer_subset", False)
#         if self.last_layer_subset:
#             assert self.dense_seq_output, "last_layer_subset requires dense_seq_output"
#         use_xentropy = getattr(config, "use_xentropy", False)
#         if use_xentropy and CrossEntropyLoss is None:
#             raise ImportError("xentropy_cuda is not installed")
#         loss_cls = (
#             nn.CrossEntropyLoss
#             if not use_xentropy
#             else partial(CrossEntropyLoss, inplace_backward=True)
#         )
#
#         self.xlm = XLMRobertaModel(config)
#         self.cls = XLMRobertaPreTrainingHeads(config)
#         self.mlm_loss = loss_cls(ignore_index=0)
#         self.nsp_loss = loss_cls(ignore_index=-1)
#
#         # Initialize weights and apply final processing
#         self.apply(partial(_init_weights, initializer_range=config.initializer_range))
#         self.tie_weights()
#
#     def tie_weights(self):
#         self.cls.predictions.decoder.weight = self.xlm.embeddings.word_embeddings.weight
#
#     def forward(
#         self,
#         input_ids,
#         position_ids=None,
#         token_type_ids=None,
#         attention_mask=None,
#         labels=None,
#         next_sentence_label=None,
#     ):
#         """
#         If labels are provided, they must be 0 for masked out tokens (as specified in the attention
#         mask).
#         Outputs:
#             if `labels` and `next_sentence_label` are not `None`:
#                 Outputs the total_loss which is the sum of the masked language modeling loss and the next
#                 sentence classification loss.
#             if `labels` or `next_sentence_label` is `None`:
#                 Outputs a tuple comprising
#                 - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
#                 - the next sentence classification logits of shape [batch_size, 2].
#
#         """
#         masked_tokens_mask = labels > 0 if (self.last_layer_subset and labels is not None) else None
#         outputs = self.xlm(
#             input_ids,
#             position_ids=position_ids,
#             token_type_ids=token_type_ids,
#             attention_mask=attention_mask.bool() if attention_mask is not None else None,
#             masked_tokens_mask=masked_tokens_mask,
#         )
#         sequence_output, pooled_output = outputs.last_hidden_state, outputs.pooler_output
#         if self.dense_seq_output and labels is not None:
#             masked_token_idx = torch.nonzero(labels.flatten() > 0, as_tuple=False).flatten()
#             if not self.last_layer_subset:
#                 sequence_output = index_first_axis(
#                     rearrange(sequence_output, "b s d -> (b s) d"), masked_token_idx
#                 )
#         prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
#
#         total_loss = None
#         if labels is not None and next_sentence_label is not None:
#             if (
#                 self.dense_seq_output and labels is not None
#             ):  # prediction_scores are already flattened
#                 masked_lm_loss = self.mlm_loss(
#                     prediction_scores, labels.flatten()[masked_token_idx]
#                 )
#             else:
#                 masked_lm_loss = self.mlm_loss(
#                     rearrange(prediction_scores, "... v -> (...) v"),
#                     rearrange(labels, "... -> (...)"),
#                 )
#             next_sentence_loss = self.nsp_loss(
#                 rearrange(seq_relationship_score, "... t -> (...) t"),
#                 rearrange(next_sentence_label, "... -> (...)"),
#             )
#             total_loss = masked_lm_loss.float() + next_sentence_loss.float()
#
#         return BertForPreTrainingOutput(
#             loss=total_loss,
#             prediction_logits=prediction_scores,
#             seq_relationship_logits=seq_relationship_score,
#         )


def remap_state_dict(state_dict, config: PretrainedConfig):
    """
    Map the state_dict of a Huggingface BERT model to be flash_attn compatible.
    """

    # LayerNorm
    def key_mapping_ln_gamma_beta(key):
        key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key)
        key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key)
        return key

    state_dict = OrderedDict(
        (key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items()
    )

    # Layers
    def key_mapping_layers(key):
        return re.sub(r"^bert.encoder.layer.", "bert.encoder.layers.", key)

    state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())

    # LayerNorm
    def key_mapping_ln(key):
        key = re.sub(r"^bert.embeddings.LayerNorm.", "bert.emb_ln.", key)
        key = re.sub(
            r"^bert.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)",
            r"bert.encoder.layers.\1.norm1.\2",
            key,
        )
        key = re.sub(
            r"^bert.encoder.layers.(\d+).output.LayerNorm.(weight|bias)",
            r"bert.encoder.layers.\1.norm2.\2",
            key,
        )
        key = re.sub(
            r"^cls.predictions.transform.LayerNorm.(weight|bias)",
            r"cls.predictions.transform.layer_norm.\1",
            key,
        )
        return key

    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())

    # MLP
    def key_mapping_mlp(key):
        key = re.sub(
            r"^bert.encoder.layers.(\d+).intermediate.dense.(weight|bias)",
            r"bert.encoder.layers.\1.mlp.fc1.\2",
            key,
        )
        key = re.sub(
            r"^bert.encoder.layers.(\d+).output.dense.(weight|bias)",
            r"bert.encoder.layers.\1.mlp.fc2.\2",
            key,
        )
        return key

    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())

    # Attention
    last_layer_subset = getattr(config, "last_layer_subset", False)
    for d in range(config.num_hidden_layers):
        Wq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.weight")
        Wk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.weight")
        Wv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.weight")
        bq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.bias")
        bk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.bias")
        bv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.bias")
        if not (last_layer_subset and d == config.num_hidden_layers - 1):
            state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.weight"] = torch.cat(
                [Wq, Wk, Wv], dim=0
            )
            state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.bias"] = torch.cat(
                [bq, bk, bv], dim=0
            )
        else:
            state_dict[f"bert.encoder.layers.{d}.mixer.Wq.weight"] = Wq
            state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.weight"] = torch.cat(
                [Wk, Wv], dim=0
            )
            state_dict[f"bert.encoder.layers.{d}.mixer.Wq.bias"] = bq
            state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.bias"] = torch.cat(
                [bk, bv], dim=0
            )

    def key_mapping_attn(key):
        return re.sub(
            r"^bert.encoder.layers.(\d+).attention.output.dense.(weight|bias)",
            r"bert.encoder.layers.\1.mixer.out_proj.\2",
            key,
        )

    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())

    def key_mapping_decoder_bias(key):
        return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key)

    state_dict = OrderedDict(
        (key_mapping_decoder_bias(k), v) for k, v in state_dict.items()
    )

    # Word embedding
    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
    if pad_vocab_size_multiple > 1:
        word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
        state_dict["bert.embeddings.word_embeddings.weight"] = F.pad(
            word_embeddings, (0, 0, 0, config.vocab_size - word_embeddings.shape[0])
        )
        decoder_weight = state_dict["cls.predictions.decoder.weight"]
        state_dict["cls.predictions.decoder.weight"] = F.pad(
            decoder_weight, (0, 0, 0, config.vocab_size - decoder_weight.shape[0])
        )
        # If the vocab was padded, we want to set the decoder bias for those padded indices to be
        # strongly negative (i.e. the decoder shouldn't predict those indices).
        # TD [2022-05-09]: I don't think it affects the MLPerf training.
        decoder_bias = state_dict["cls.predictions.decoder.bias"]
        state_dict["cls.predictions.decoder.bias"] = F.pad(
            decoder_bias, (0, config.vocab_size - decoder_bias.shape[0]), value=-100.0
        )

    return state_dict


def inv_remap_state_dict(state_dict, config: PretrainedConfig):
    """
    Map the state_dict of a flash_attn model to be Huggingface BERT compatible.

    This function is meant to be the inverse of remap_state_dict.
    """
    # Word embedding
    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
    if pad_vocab_size_multiple > 1:
        word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
        decoder_weight = state_dict["cls.predictions.decoder.weight"]
        decoder_bias = state_dict["cls.predictions.decoder.bias"]
        # unpad embeddings
        state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings[
            : config.orig_vocab_size, :
        ]
        state_dict["cls.predictions.decoder.weight"] = decoder_weight[
            : config.orig_vocab_size, :
        ]
        state_dict["cls.predictions.decoder.bias"] = decoder_bias[
            : config.orig_vocab_size
        ]

    for d in range(config.num_hidden_layers):
        last_layer_subset = getattr(config, "last_layer_subset", False)
        if not last_layer_subset or d != (config.num_hidden_layers - 1):
            Wqkv_weights = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wqkv.weight")
            Wqkv_biases = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wqkv.bias")
            state_dict[f"bert.encoder.layers.{d}.attention.self.query.weight"] = (
                Wqkv_weights[: Wqkv_weights.shape[0] // 3, :]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.key.weight"] = (
                Wqkv_weights[
                    Wqkv_weights.shape[0] // 3 : 2 * Wqkv_weights.shape[0] // 3, :
                ]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.value.weight"] = (
                Wqkv_weights[2 * Wqkv_weights.shape[0] // 3 :, :]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.query.bias"] = (
                Wqkv_biases[: Wqkv_biases.shape[0] // 3]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.key.bias"] = (
                Wqkv_biases[Wqkv_biases.shape[0] // 3 : 2 * Wqkv_biases.shape[0] // 3]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.value.bias"] = (
                Wqkv_biases[2 * Wqkv_biases.shape[0] // 3 :]
            )
        else:
            Wq_weight = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wq.weight")
            Wkv_weights = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wkv.weight")
            Wq_bias = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wq.bias")
            Wkv_biases = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wkv.bias")
            state_dict[f"bert.encoder.layers.{d}.attention.self.query.weight"] = (
                Wq_weight
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.key.weight"] = (
                Wkv_weights[: Wkv_weights.shape[0] // 2, :]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.value.weight"] = (
                Wkv_weights[Wkv_weights.shape[0] // 2 :, :]
            )
            state_dict[f"bert.encoder.layers.{d}.attention.self.query.bias"] = Wq_bias
            state_dict[f"bert.encoder.layers.{d}.attention.self.key.bias"] = Wkv_biases[
                : Wkv_biases.shape[0] // 2
            ]
            state_dict[f"bert.encoder.layers.{d}.attention.self.value.bias"] = (
                Wkv_biases[Wkv_biases.shape[0] // 2 :]
            )

    def inv_key_mapping_ln(key):
        key = re.sub(r"bert.emb_ln.", "bert.embeddings.LayerNorm.", key)
        key = re.sub(
            r"bert.encoder.layers.(\d+).norm1.(weight|bias)",
            r"bert.encoder.layers.\1.attention.output.LayerNorm.\2",
            key,
        )
        key = re.sub(
            r"bert.encoder.layers.(\d+).norm2.(weight|bias)",
            r"bert.encoder.layers.\1.output.LayerNorm.\2",
            key,
        )
        key = re.sub(
            r"cls.predictions.transform.layer_norm.(weight|bias)",
            r"cls.predictions.transform.LayerNorm.\1",
            key,
        )
        return key

    def inv_key_mapping_ln_gamma_beta(key):
        key = re.sub(r"LayerNorm.weight$", "LayerNorm.gamma", key)
        key = re.sub(r"LayerNorm.bias$", "LayerNorm.beta", key)
        return key

    def inv_key_mapping_layers(key):
        return re.sub(r"bert.encoder.layers.", "bert.encoder.layer.", key)

    def inv_key_mapping_mlp(key):
        key = re.sub(
            r"bert.encoder.layer.(\d+).mlp.fc1.(weight|bias)",
            r"bert.encoder.layer.\1.intermediate.dense.\2",
            key,
        )
        key = re.sub(
            r"bert.encoder.layer.(\d+).mlp.fc2.(weight|bias)",
            r"bert.encoder.layer.\1.output.dense.\2",
            key,
        )
        return key

    def inv_key_mapping_attn(key):
        return re.sub(
            r"bert.encoder.layer.(\d+).mixer.out_proj.(weight|bias)",
            r"bert.encoder.layer.\1.attention.output.dense.\2",
            key,
        )

    def inv_key_mapping_decoder_bias(key):
        return re.sub(r"cls.predictions.decoder.bias", "cls.predictions.bias", key)

    state_dict = OrderedDict(
        (inv_key_mapping_ln(key), value) for key, value in state_dict.items()
    )
    state_dict = OrderedDict(
        (inv_key_mapping_ln_gamma_beta(key), value) for key, value in state_dict.items()
    )
    state_dict = OrderedDict(
        (inv_key_mapping_layers(key), value) for key, value in state_dict.items()
    )
    state_dict = OrderedDict(
        (inv_key_mapping_mlp(key), value) for key, value in state_dict.items()
    )
    state_dict = OrderedDict(
        (inv_key_mapping_attn(key), value) for key, value in state_dict.items()
    )
    state_dict = OrderedDict(
        (inv_key_mapping_decoder_bias(key), value) for key, value in state_dict.items()
    )

    return state_dict


# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->XLMRoberta
class XLMRobertaClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        classifier_dropout = (
            config.classifier_dropout
            if config.classifier_dropout is not None
            else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

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


# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
class XLMRobertaForSequenceClassification(XLMRobertaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
        self.classifier = XLMRobertaClassificationHead(config)

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

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        outputs = self.roberta(
            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_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (
                    labels.dtype == torch.long or labels.dtype == torch.int
                ):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )