# coding=utf-8
# Copyright 2023 The Mega Authors and The HuggingFace Inc. team.
#
# 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.
"""PyTorch MEGA model."""

import math
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_outputs import (
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_mega import MegaConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "mnaylor/mega-base-wikitext"
_CONFIG_FOR_DOC = "MegaConfig"

MEGA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "mnaylor/mega-base-wikitext",
    # See all Mega models at https://huggingface.co/models?filter=mega
]


class MegaEmbeddings(nn.Module):
    """
    Mega's basic implementation does not incorporate token type embeddings, so this is a stripped-down version of
    RoBERTa's embeddings which optionally includes token types
    """

    def __init__(self, config: MegaConfig):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.use_token_types = config.add_token_type_embeddings
        if self.use_token_types:
            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
            # registering a buffer here allows model tracing when not passing optional token type IDs
            # more info at transformers issue #5664
            self.register_buffer(
                "token_type_ids", torch.zeros(config.max_positions, dtype=torch.long).expand((1, -1)), persistent=False
            )

        self.padding_idx = config.pad_token_id

    def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None):
        if (input_ids is None) and (inputs_embeds is None):
            raise ValueError("Must provide one of input_ids or inputs_embeds")
        elif input_ids is not None:
            input_shape = input_ids.size()
            device = input_ids.device

            # get the word embeddings if only IDs are provided
            inputs_embeds = self.word_embeddings(input_ids)
        else:
            input_shape = inputs_embeds.size()[:-1]
            device = inputs_embeds.device

        # the original Mega implementation did not include token type embeddings, so we add
        # an option to use them if desired; if embeddings are present and token type IDs are
        # not provided, we will use a registered buffer (which helps with tracing)
        if self.use_token_types:
            if token_type_ids is None:
                if hasattr(self, "token_type_ids"):
                    buffered_token_type_ids = self.token_type_ids[:, : input_shape[1]]
                    buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], input_shape[1])
                    token_type_ids = buffered_token_type_ids_expanded
                else:
                    token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

            # access token type embeddings
            token_type_embeddings = self.token_type_embeddings(token_type_ids)
            # add the token type embeddings to the word embeddings
            embeddings = inputs_embeds + token_type_embeddings
        else:
            embeddings = inputs_embeds
        return embeddings


class MegaSimpleRelativePositionalBias(nn.Module):
    """
    Simple relative positional embeddings copied from the Mega repo; renamed variables for better readability
    """

    def __init__(self, config: MegaConfig):
        super().__init__()
        self.config = config
        self.max_positions = self.config.max_positions if self.config.chunk_size < 0 else self.config.chunk_size
        self.rel_pos_bias = nn.Parameter(torch.Tensor(2 * config.max_positions - 1))

    def forward(self, seq_len):
        if seq_len > self.max_positions:
            raise ValueError("Sequence length {} going beyond max length {}".format(seq_len, self.max_positions))

        # seq_len * 2 - 1
        bias = self.rel_pos_bias[(self.max_positions - seq_len) : (self.max_positions + seq_len - 1)]
        # seq_len * 3 - 1
        tile = F.pad(bias, (0, seq_len))
        # (seq_len * 3 - 1) * seq_len
        tile = torch.tile(tile, (seq_len,))
        tile = tile[:-seq_len]
        # seq_len x (3 * seq_len - 2)
        tile = tile.view(seq_len, 3 * seq_len - 2)
        start = (2 * seq_len - 1) // 2
        end = tile.size(1) - start
        tile = tile[:, start:end]
        return tile


class MegaRotaryRelativePositionalBias(nn.Module):
    """
    Rotary relative bias for positional information; similar in concept to RoPE (i.e. RoFormer) but taken from the Mega
    repo due to differences in implementation.

    When initialized, produces a positional bias which ranges from position 0 to config.max_positions, but can
    extrapolate to longer sequences. Can be indexed according to input position IDs
    """

    def __init__(self, config: MegaConfig):
        super().__init__()
        if config.hidden_size % 2 != 0:
            raise RuntimeError("Rotary positional bias requires `hidden_size` to be a multiple of 2")
        self.config = config
        self.embed_dim = config.shared_representation_size
        self.max_positions = self.config.max_positions if self.config.chunk_size < 0 else self.config.chunk_size
        self.sine, self.cosine = MegaRotaryRelativePositionalBias.get_sinusoid_embeddings(
            config.max_positions, self.embed_dim
        )
        # alpha and beta parameters for the rotary bias; beta renamed to b_param to avoid clashes with tf/flax weight handling
        # in loading pretrained weights
        self.alpha = nn.Parameter(torch.Tensor(1, self.embed_dim))
        self.b_param = nn.Parameter(torch.Tensor(1, self.embed_dim))
        self.register_buffer("_float_tensor", torch.FloatTensor([0.0]))

    @staticmethod
    def get_sinusoid_embeddings(max_positions: int, embedding_dim: int):
        half_dim = embedding_dim // 2
        emb = math.log(10000) / half_dim
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
        emb = torch.arange(max_positions, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
        return torch.sin(emb), torch.cos(emb)

    def rotary(self, input):
        seq_len, embed_dim = input.size()
        chunk_1, chunk_2 = torch.chunk(input, 2, dim=-1)
        if self.sine is None or seq_len > self.sine.size(0):
            self.sine, self.cosine = MegaRotaryRelativePositionalBias.get_sinusoid_embeddings(seq_len, embed_dim)
            self.max_positions = seq_len
        self.sine = self.sine.to(self._float_tensor)
        self.cosine = self.cosine.to(self._float_tensor)

        sin = self.sine[:seq_len]
        cos = self.cosine[:seq_len]
        return torch.cat([chunk_1 * cos - chunk_2 * sin, chunk_2 * cos + chunk_1 * sin], dim=1)

    def forward(self, seq_len):
        rotary_alpha = self.rotary(self.alpha.expand(seq_len, self.embed_dim))
        rotary_beta = self.rotary(self.b_param.expand(seq_len, self.embed_dim))
        bias = torch.einsum("mk,nk->mn", rotary_alpha, rotary_beta)
        return bias


class MegaDropout(nn.Module):
    """
    A unified class for standard dropout functionality and featurewise dropout.

    The original fairseq Mega repo used 2 classes for these, which included some unnecessary handling of training logic
    and an unused `inplace` option. The original implementation used torch.nn.functional instead of submodules, which
    is retained here as well.
    """

    def __init__(self, dropout_probability, is_featurewise=False):
        super().__init__()
        self.dropout_probability = dropout_probability
        self.is_featurewise = is_featurewise

    def forward(self, input, batch_first: bool = False):
        if self.is_featurewise:
            if batch_first:
                # (batch_size X sequence_length X feature_dimension)
                # -> (batch_size X feature_dimension X sequence_length)
                # -> (batch_size X sequence_length X feature_dimension)
                return F.dropout2d(
                    input.transpose(-1, -2), p=self.dropout_probability, training=self.training
                ).transpose(-1, -2)
            else:
                if input.dim() != 3:
                    raise ValueError(
                        "Feature dropout inputs must be exactly 3-dimensional if inputs are ordered [sequence length, batch size, hidden dimension]"
                    )
                # (sequence_length X batch_size X feature_dimension)
                # -> (batch_size X feature_dimension X sequence_length)
                # -> (sequence_length X batch_size X feature_dimension)
                return F.dropout2d(input.permute(1, 2, 0), p=self.dropout_probability, training=self.training).permute(
                    2, 0, 1
                )
        else:
            return F.dropout(input, p=self.dropout_probability, training=self.training)


class MegaRMSNorm(nn.Module):
    """
    RMSNorm used in Mega implementation. Differs from T5's RMSNorm by applying the weight prior to taking the square
    root (as opposed to after in T5)
    """

    def __init__(self, number_features, eps=1e-6, affine=True):
        super().__init__()
        self.num_features = number_features
        self.eps = eps
        self.affine = affine
        if affine:
            self.weight = nn.Parameter(torch.Tensor(self.num_features))
        else:
            self.register_parameter("weight", None)

    def forward(self, input):
        mean_square = torch.mean(torch.square(input), dim=-1, keepdim=True)
        if self.weight is not None:
            input = input * self.weight

        input * torch.rsqrt(mean_square + self.eps)
        return input


class MegaScaleNorm(nn.Module):
    """
    Scale normalization introduced in MEGA which is similar to RMSNorm, but uses a single parameter for scalar
    multiplication instead of a vector, and applies over a specified dimension
    """

    def __init__(self, dim, eps=1e-6, affine=True):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.affine = affine
        if affine:
            self.scalar = nn.Parameter(torch.Tensor(1))
        else:
            self.register_parameter("scalar", None)

    def forward(self, input):
        mean_square = torch.mean(torch.square(input), dim=self.dim, keepdim=True)
        if self.scalar is not None:
            input = self.scalar * input

        output = input * torch.rsqrt(mean_square + self.eps)
        return output


class MegaSequenceNorm(nn.Module):
    """
    A wrapper class for various layer normalization options used in Mega. Used to handle differences in expectations on
    input axis locations for different normalization methods.
    """

    def __init__(self, norm_type, embedding_dim, eps=1e-5, affine=True, export=False):
        super().__init__()
        if norm_type == "layernorm":
            self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine=affine)
        elif norm_type == "scalenorm":
            self.norm = MegaScaleNorm(dim=-1, eps=eps, affine=affine)
        elif norm_type == "rmsnorm":
            self.norm = MegaRMSNorm(embedding_dim, eps=eps, affine=affine)
        elif norm_type == "batchnorm":
            self.norm = nn.BatchNorm1d(embedding_dim, eps=eps, affine=affine)
        elif norm_type == "syncbatchnorm":
            self.norm = nn.SyncBatchNorm(embedding_dim, eps=eps, affine=affine)
        else:
            raise ValueError("Unknown norm type: {}".format(norm_type))

    def forward(self, input):
        if isinstance(self.norm, nn.modules.batchnorm._BatchNorm):
            if input.dim() != 3:
                raise ValueError("BatchNorm inputs must be exactly 3-dimensional")
            input = input.permute(1, 2, 0)
            input = self.norm(input)
            return input.permute(2, 0, 1)
        else:
            return self.norm(input)


# add this layernorm class to ALL_LAYERNORM_LAYERS
ALL_LAYERNORM_LAYERS.append(MegaSequenceNorm)


class MegaMultiDimensionDampedEma(nn.Module):
    """
    Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
    variable names and moving away from the stateful representation of incremental decoding state. See
    "https://arxiv.org/abs/2209.10655" for more details.
    """

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

        self.config = config

        self.embed_dim = config.hidden_size
        self.ndim = config.ema_projection_size
        self.bidirectional = config.bidirectional
        self.truncation = config.truncation
        self.scale = math.sqrt(1.0 / self.ndim)

        kernel_dim = 2 * config.hidden_size if self.bidirectional else config.hidden_size
        # renamed delta (damping_factor) and alpha (decay_factor) to be more descriptive of what the parameters are doing
        self.damping_factor = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1))
        self.decay_factor = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1))
        # renamed gamma (kernel_projection_matrix) and beta (ema_expansion_matrix) respectively to avoid HF renaming
        # things and align with the paper's description of these params' behavior
        self.ema_expansion_matrix = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1))
        self.kernel_projection_matrix = nn.Parameter(torch.Tensor(kernel_dim, self.ndim))
        # renamed omega to residual_weight to describe what it's doing
        self.residual_weight = nn.Parameter(torch.Tensor(config.hidden_size))
        self._kernel = None
        self._coeffs = None

    def _compute_ema_coefficients(self):
        self._coeffs = None
        # convert the alpha and delta parameters (kernel_dim x EMA projection size x 1) to [0, 1] with sigmoid
        damping_factor = torch.sigmoid(self.damping_factor)
        decay_factor = torch.sigmoid(self.decay_factor)
        previous_timestep_weight = 1.0 - damping_factor * decay_factor
        return damping_factor, previous_timestep_weight

    def _compute_efficient_ema_kernel(self, length: int):
        # computes the kernel used for efficient damped EMA applied via FFT convolution
        self._kernel = None
        # p and q have shape (kernel_dim x ema_projection_size x 1)
        damping_factor, previous_timestep_weight = self._compute_ema_coefficients()
        # extend the kernel to (kernel_dim X ema_projection_size X sequence_length) and
        # multiply q by sequential ints up to the sequence length
        vander = torch.arange(length).to(damping_factor).view(1, 1, length) * torch.log(previous_timestep_weight)
        kernel = (damping_factor * self.ema_expansion_matrix) * torch.exp(vander)
        # (kernel_dim X ema_projection_size X sequence_length) -> (kernel_dim, sequence_length)
        return torch.einsum("dnl,dn->dl", kernel, self.kernel_projection_matrix * self.scale)

    def get_ema_coefficients(self):
        if self.training:
            return self._compute_ema_coefficients()
        else:
            if self._coeffs is None:
                self._coeffs = self._compute_ema_coefficients()
            return self._coeffs

    def get_ema_kernel(self, length: int):
        kernel_size = length if self.truncation is None else min(self.truncation, length)
        if self.training:
            return self._compute_efficient_ema_kernel(kernel_size)
        else:
            if self._kernel is None or self._kernel.size(-1) < kernel_size:
                self._kernel = self._compute_efficient_ema_kernel(kernel_size)
            return self._kernel[..., :kernel_size]

    def fft_convolution(self, inputs, kernel, length):
        # this is a wrapper for repeated use of EMA calculation via FFT (fast Fourier transform) convolution
        inputs_fft = torch.fft.rfft(inputs.float(), n=2 * length)
        kernel_fft = torch.fft.rfft(kernel.float(), n=2 * length)
        convolved_sequence = torch.fft.irfft(inputs_fft * kernel_fft, n=2 * length)
        return convolved_sequence

    def ema_step(self, inputs, length, past_state=None):
        if length == 1:
            return self.one_ema_step(inputs, past_state=past_state)

        # (kernel_dim X ema_projection_size X 1)
        damping_factor, previous_timestep_weight = self.get_ema_coefficients()
        # (kernel_dim X ema_projection_size X 1+sequence_length)
        vander = torch.arange(length + 1).to(damping_factor).view(1, 1, length + 1) * torch.log(
            previous_timestep_weight
        )
        vander = torch.exp(vander)
        if past_state is not None:
            # (kernel_dim X ema_projection_size X sequence_length) * (kernel_dim X ema_projection_size X 1)
            # -> (kernel_dim X ema_projection_size X sequence_length)
            past_ema_proj = vander[:, :, 1:] * (self.kernel_projection_matrix * self.scale).unsqueeze(-1)
            # past_state will be (batch_size, kernel_dim, ema_projection_size)
            past_ema_state = torch.einsum("bdn,dnl->bdl", past_state, past_ema_proj)
            # (kernel_dim X ema_projection_size) * (batch_size X kernel_dim X ema_projection_size)
            # -> (batch_size X kernel_dim X ema_projection_size)
            past_vandermonde = vander[:, :, -1] * past_state
        else:
            past_ema_state = None
            past_vandermonde = None

        # (kernel_dim X ema_projection_size X sequence_length)
        vander = vander[:, :, :-1]
        kernel = (damping_factor * self.ema_expansion_matrix) * vander
        kernel_proj = torch.einsum("dnl,dn->dl", kernel, self.kernel_projection_matrix * self.scale)

        ema_output = self.fft_convolution(inputs, kernel_proj, length=length)[..., 0:length]
        ema_output = ema_output.type_as(inputs)
        if past_ema_state is not None:
            ema_output = ema_output + past_ema_state

        updated_hidden_state = torch.einsum("bdl,dnl->bdn", inputs, torch.flip(kernel, dims=[2]))
        if past_vandermonde is not None:
            updated_hidden_state = updated_hidden_state + past_vandermonde
        # return a tuple:
        # (sequence_length, batch_size, kernel_dim)
        # (batch_size, kernel_dim, ema_projection_size)
        return ema_output.permute(2, 0, 1), updated_hidden_state

    def one_ema_step(self, inputs, past_state=None):
        damping_factor, previous_timestep_weight = self.get_ema_coefficients()
        # (kernel_dim X ema_projection_size) x (batch_size X kernel_dim X 1)
        # -> (batch_size X kernel_dim X ema_projection_size)
        updated_state = (damping_factor * self.ema_expansion_matrix).squeeze(-1) * inputs
        if past_state is not None:
            updated_state = updated_state + previous_timestep_weight.squeeze(-1) * past_state
        # (batch_size X kernel_dim)
        out = torch.einsum("bdn,dn->bd", updated_state, self.kernel_projection_matrix * self.scale)
        # (1 X batch_size X kernel_dim), (batch_size X kernel_dim X ema_projection_size)
        return out.unsqueeze(0), updated_state

    def forward(
        self,
        inputs,
        attention_mask: Optional[torch.Tensor] = None,
        prev_state: Optional[torch.Tensor] = None,
        use_cache: bool = False,
    ) -> torch.Tensor:
        """
        Mega's exponential moving average (EMA) sub-layer applied prior to single-headed (traditional) self-attention

        Args:
            inputs (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`):
                Hidden state / embedding input to update via EMA based on FFT convolution
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Indicates which inputs are to be ignored (mostly due to padding), where elements are either 1 for *not
                masked* or 0 for *masked*
            prev_state (`torch.Tensor` of shape `(batch_size, config.ndim)`, *optional*):
                The hidden state returned from the previous timestep during incremental decoding.
            use_cache (`bool`, default `False`):
                Whether to perfom incremental decoding; uses `prev_state` as the prior timestep, and returns the
                updated EMA hidden state for use in the next step

        Returns:
            `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
            inputs:
            - **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden
              states updated by EMA, with same shapes as inputs
            - **updated_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor of shape `(batch_size,
              config.ndim)` -- The incremental EMA state for use in the next step of incremental decoding
        """

        seq_len, bsz, embed_dim = inputs.size()
        if embed_dim != self.embed_dim:
            raise ValueError(
                f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
            )

        # sequence_length X batch_size X hidden_size
        residual = inputs * self.residual_weight

        # (sequence_length x batch_size x hidden_size) -> (batch_size x hidden_size x sequence_length)
        inputs = inputs.permute(1, 2, 0)
        # mask the input: output is a tensor with 0 in the masked positions
        if attention_mask is not None:
            inputs = inputs * (attention_mask.unsqueeze(1).type_as(inputs))

        if self.bidirectional and use_cache:
            raise RuntimeError("Bidirectional EMA does not support incremental state")

        if use_cache:
            out, updated_state = self.ema_step(inputs, seq_len, past_state=prev_state)

            # (batch_size X hidden_size) -> (1 x batch_size x hidden_size)
            out = F.silu(out + residual)

            # if incremental decoding, return the new state along with the output
            return out, updated_state
        else:
            # (hidden_size x sequence_length)
            kernel = self.get_ema_kernel(seq_len)
            fft_len = seq_len
            s_index = 0
            kernel_size = kernel.size(1)
            if self.bidirectional:
                # split the kernel for each direction of EMA
                k1, k2 = torch.split(kernel, [self.embed_dim, self.embed_dim], dim=0)
                # (hidden_size X 2*sequence_length - 1)
                kernel = F.pad(k1, (kernel_size - 1, 0)) + F.pad(k2.flip(-1), (0, kernel_size - 1))
                inputs = F.pad(inputs, (kernel_size - 1, 0))
                fft_len = fft_len + kernel_size - 1
                s_index = 2 * kernel_size - 2

            ema_output = self.fft_convolution(inputs, kernel, length=fft_len)[..., s_index : s_index + seq_len]
            ema_output = ema_output.type_as(inputs)
            # (batch_size X hidden_size X sequence_length) -> (sequence_length X batch_size X hidden_size)
            gated_ema_output = F.silu(ema_output.permute(2, 0, 1) + residual)

            return gated_ema_output, None


class MegaGatedCrossAttention(nn.Module):
    """
    Gated Structured State Attention for use in encoder-decoder model. See Mega paper for more details. Only
    modifications from original implementation are variable names, removing the unnecessary `before_attn_fn` and
    `static_kv` arguments, and the stateful representation of incremental decoder state.
    """

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

        self.config = config
        self.activation = ACT2FN[self.config.activation]
        self.attention_activation = self.config.attention_activation
        self.scaling = (
            self.config.shared_representation_size**-0.5 if self.attention_activation == "softmax" else None
        )

        self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout)
        self.hidden_dropout = MegaDropout(
            self.config.hidden_dropout_prob, is_featurewise=self.config.use_feature_dropout
        )
        # Attention dropout is standard dropout
        self.attention_dropout = MegaDropout(self.config.attention_probs_dropout_prob, is_featurewise=False)

        self.prenorm = self.config.normalize_before_mega
        self.norm = MegaSequenceNorm(
            self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine
        )

        self.k_proj = nn.Linear(self.config.hidden_size, self.config.shared_representation_size)
        self.v_proj = nn.Linear(self.config.hidden_size, self.config.hidden_size)
        self.q_proj = nn.Linear(
            self.config.hidden_size, 2 * self.config.hidden_size + self.config.shared_representation_size
        )
        self.h_proj = nn.Linear(self.config.hidden_size, self.config.hidden_size)

        if self.config.relative_positional_bias == "simple":
            self.rel_pos_bias = MegaSimpleRelativePositionalBias(config)
        elif self.config.relative_positional_bias == "rotary":
            self.rel_pos_bias = MegaRotaryRelativePositionalBias(config)
        else:
            raise ValueError("unknown relative position bias: {}".format(self.config.relative_positional_bias))

        self.softmax = nn.Softmax(dim=-1)

    def element_attention(self, query, key, key_padding_mask, pidx):
        bsz, src_len, _ = key.size()
        tgt_len = query.size(1) if pidx is None else pidx + 1
        if key_padding_mask is not None:
            # (batch_size X source_sequence_length) --> (batch_size X 1 X 1)
            lengths = key_padding_mask.sum(dim=-1).view(bsz, 1, 1)
        else:
            lengths = src_len

        # (target_sequence_length X source_sequence_length)
        bias = self.rel_pos_bias(max(tgt_len, src_len))[:, :src_len]
        if pidx is not None:
            if query.size(1) != 1:
                raise ValueError("Position offset provided with queries longer than 1 token")
            # source_sequence_length
            bias = bias[pidx]
        else:
            # (target_sequence_length X source_sequence_length)
            bias = bias[:tgt_len]

        # (batch_size X target_sequence_length X source_sequence_length)
        qk = torch.bmm(query, key.transpose(1, 2)) / lengths + bias

        attn_weights = ACT2FN[self.attention_activation](qk).type_as(qk)

        if key_padding_mask is not None:
            attn_weights = attn_weights * key_padding_mask.unsqueeze(1)

        return attn_weights

    def softmax_attention(self, query, key, key_padding_mask, pidx):
        bsz, src_len, _ = key.size()
        tgt_len = query.size(1) if pidx is None else pidx + 1

        # (target_sequence_length X source_sequence_length)
        bias = self.rel_pos_bias(max(tgt_len, src_len))[:, :src_len]
        if pidx is not None:
            if query.size(1) != 1:
                raise ValueError("Position offset provided with queries longer than 1 token")
            # source_sequence_length
            bias = bias[pidx]
        else:
            # (target_sequence_length X source_sequence_length)
            bias = bias[:tgt_len]

        # scaled attention
        query = query * self.scaling
        # (batch_size X target_sequence_length X source_sequence_length)
        qk = torch.bmm(query, key.transpose(1, 2)) + bias

        if key_padding_mask is not None:
            qk = qk.masked_fill((1 - key_padding_mask).unsqueeze(1).to(torch.bool), float("-inf"))

        attn_weights = self.softmax(qk).type_as(qk)
        return attn_weights

    def forward(
        self,
        query,
        key: Optional[torch.Tensor],
        value: Optional[torch.Tensor],
        key_padding_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        """
        Gated cross-attention used in Mega

        Args:
            query (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`):
                The self (or target) sequence input used as query inputs for cross-attention
            key (`torch.Tensor` of shape `(source_sequence_length, batch_size, hidden_size)`):
                The cross (or source) sequence input with shape used as keys in cross-attention
            value (`torch.Tensor` of shape `(source_sequence_length, batch_size, hidden_size)`):
                The cross (or source) sequence input with shape used as values in cross-attention
            key_padding_mask (`torch.LongTensor` of shape `(batch_size, source_sequence_length)`, *optional*):
                Padding mask corresponding to the source sequence, where entries are 1 for *not masked* and 0 for
                *masked* tokens
            past_key_values (`tuple(torch.FloatTensor)`, *optional*):
                If provided, the hidden state returned from the previous timestep during incremental decoding; expects
                that prior cross-attention keys and values will be the last two items in the tuple
            output_attentions (`bool`, defaults to `False`):
                Whether or not to return the cross-attention weights.
            use_cache (`bool`, defaults to `False`):
                Whether to perfom incremental decoding; uses `prev_state` as the prior timestep, and returns the
                updated EMA hidden state for use in the next step

        Returns:
            `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
            inputs:
            - **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) --
              Hidden states from target sequence updated by gated cross-attention
            - **attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape
              `(batch_size, source_sequence_length, target_sequence_length)` -- The pairwise cross-attention weights
              corresponding to each token in the source and target sequences
            - **cross_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              source_sequence_length, config.shared_representation_size)` -- The cross-attention key state for use in
              the next step of incremental decoding
            - **cross_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              source_sequence_length, config.hidden_size)` -- The cross-attention value state for use in the next step
              of incremental decoding
        """

        seq_len, bsz, embed_dim = query.size()
        if embed_dim != self.config.hidden_size:
            raise ValueError(
                f"Unexpected embedding dimension received: input is {embed_dim} but expected {self.config.hidden_size}"
            )

        if past_key_values is not None:
            # make sure the inputs only have a sequence length of 1 if we're doing incremental decoding
            if seq_len != 1:
                raise ValueError(f"Incremental decoding requested with self-sequence length > 1: {seq_len}")
            # expect past_key_values to have (self_key, self_value, self_ema, cross_key, cross_value)
            prev_cross_key, prev_cross_value = past_key_values[-2:]
            key = value = None

            # use the self-attention cache to get the position id of the current step
            prev_self_key = past_key_values[0]
            num_incremental_steps = prev_self_key.size(1) + 1
        else:
            prev_cross_key = prev_cross_value = None
            # we still need the position id if we're doing incremental decoding (past_key_values will be None for the first step)
            num_incremental_steps = 0 if use_cache and (seq_len == 1) else None

        full_query = query
        if self.prenorm:
            full_query = self.norm(full_query)

        # (target_sequence_length X batch_size X 2*hidden_size + shared_representation_size)
        query_projected = self.q_proj(full_query)
        # split the query projections into separate components
        # - residual_weight is passed through sigmoid and sent through elementwise multiplication to the gated/weighted targets prior to being added to the query directly
        # - target_gate is a silu-gated tensor that is multiplied by the attention-weighted target below prior to residual connection
        # - attention_query is the part that is passed to the attention function
        residual_weight, target_gate, attention_query = torch.split(
            query_projected,
            [self.config.hidden_size, self.config.hidden_size, self.config.shared_representation_size],
            dim=-1,
        )

        # (target_sequence_length X batch_size X hidden_size)
        residual_weight = torch.sigmoid(residual_weight)
        target_gate = F.silu(target_gate)

        if key is None:
            if value is not None:
                raise ValueError("Key and value must be `None` simultaneously")
            projected_key = projected_value = None
        else:
            # (source_sequence_length X batch_size X shared_representation_size)
            projected_key = self.k_proj(key)
            # (source_sequence_length X batch_size X hidden_size)
            projected_value = self.activation(self.v_proj(key))

        # (target_sequence_length X batch_size X shared_representation_size)
        # -> (batch_size X target_sequence_length X shared_representation_size)
        attention_query = attention_query.transpose(0, 1)
        if projected_key is not None:
            projected_key = projected_key.transpose(0, 1)
        if projected_value is not None:
            projected_value = projected_value.transpose(0, 1)

        # if we're doing incremental decoding, k and v are None and need to be overwritten with past values
        if past_key_values is not None:
            projected_key = prev_cross_key
            projected_value = prev_cross_value

        # if we're returning the cache for later use, store these now for later return (can be done without having past_key_values provided)
        if use_cache:
            updated_cross_key = projected_key
            updated_cross_value = projected_value

        ctx_len = projected_key.size(1)
        # This is part of a workaround to get around fork/join parallelism
        # not supporting Optional types.
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None

        if key_padding_mask is not None:
            if key_padding_mask.size(0) != bsz:
                raise ValueError("Key padding mask does not align on the batch dimension")
            if key_padding_mask.size(1) != ctx_len:
                raise ValueError("Key padding mask does not align on the sequence length dimension")

        if self.attention_activation == "softmax":
            attn_weights = self.softmax_attention(
                attention_query, projected_key, key_padding_mask, num_incremental_steps
            )
        else:
            attn_weights = self.element_attention(
                attention_query, projected_key, key_padding_mask, num_incremental_steps
            )

        projected_value = self.hidden_dropout(projected_value, batch_first=True)
        kernel = self.attention_dropout(attn_weights)
        # (batch_size X target_sequence_length X hidden_size)
        # -> (target_sequence_length X batch_size X hidden_size)
        weighted_targets = torch.bmm(kernel, projected_value).transpose(0, 1)
        # (target_sequence_length X batch_size X hidden_size)
        weighted_targets = self.activation(self.h_proj(weighted_targets * target_gate))
        weighted_targets = self.dropout(weighted_targets)
        out = torch.addcmul(query, residual_weight, weighted_targets - query)

        if not self.prenorm:
            out = self.norm(out)

        outputs = (out, attn_weights) if output_attentions else (out,)
        if use_cache:
            outputs = outputs + (updated_cross_key, updated_cross_value)

        return outputs


class MegaMovingAverageGatedAttention(nn.Module):
    """
    Pure PyTorch implementation of Mega block; see https://arxiv.org/abs/2209.10655 and original fairseq implementation
    at https://github.com/facebookresearch/mega (copyright Meta Research, licensed under MIT License)

    Differences from original implementation include hidden state refactor and fixed inconsistency with additive /
    multiplicative attention masks
    """

    def __init__(self, config: MegaConfig):
        super().__init__()
        self.config = config
        self.activation = ACT2FN[self.config.activation]
        self.scaling = (
            self.config.shared_representation_size**-0.5 if self.config.attention_activation == "softmax" else None
        )
        self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout)
        self.hidden_dropout = MegaDropout(
            self.config.hidden_dropout_prob, is_featurewise=self.config.use_feature_dropout
        )
        # attention dropout is standard dropout
        self.attention_dropout = MegaDropout(self.config.attention_probs_dropout_prob, is_featurewise=False)

        self.norm = MegaSequenceNorm(
            self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine
        )
        self.ema_gate = MegaMultiDimensionDampedEma(config)

        self.v_proj = nn.Linear(self.config.hidden_size, self.config.intermediate_size)
        self.mx_proj = nn.Linear(
            self.config.hidden_size,
            self.config.shared_representation_size + self.config.intermediate_size + 2 * self.config.hidden_size,
        )
        self.h_proj = nn.Linear(self.config.intermediate_size, self.config.hidden_size)

        self.qk_weight = nn.Parameter(torch.Tensor(2, self.config.shared_representation_size))
        self.qk_bias = nn.Parameter(torch.Tensor(2, self.config.shared_representation_size))

        if self.config.relative_positional_bias == "simple":
            self.rel_pos_bias = MegaSimpleRelativePositionalBias(config)
        elif self.config.relative_positional_bias == "rotary":
            self.rel_pos_bias = MegaRotaryRelativePositionalBias(config)
        else:
            raise ValueError(f"Unknown relative positional bias: {self.config.relative_positional_bias}")

        self.softmax = nn.Softmax(dim=-1)
        self.attention_function = (
            self.softmax_attention if self.config.attention_activation == "softmax" else self.element_attention
        )

    def element_attention(self, query, key, padding_mask, causal_mask):
        """
        Apply element-wise attention via relu^2 or laplace. Same as original implementation but with standardized
        causal attention mask. Expects the Hugging Face standard attention mask paradigm: 1 for not masked, and 0 for
        masked.
        """
        seq_len = key.size(2)
        if padding_mask is not None:
            # (batch_size X number of chunks X 1)
            lengths = padding_mask.sum(-1, keepdim=True)
            # (batch_size X number of chunks X 1 X 1)
            lengths = lengths.clamp(min=1.0).unsqueeze(-1)
        else:
            lengths = seq_len

        if causal_mask is not None:
            lengths = causal_mask.sum(dim=-1, keepdim=True)

        # (sequence_length X sequence_length)
        bias = self.rel_pos_bias(seq_len)
        if seq_len != query.size(2):
            if query.size(2) != 1:
                raise ValueError("Size mismatch between Q and K in element attention")
            # (1 X sequence_length)
            bias = bias[-1:]

        # (batch_size X number of chunks X sequence_length X sequence_length)
        qk = torch.matmul(query, key.transpose(2, 3)) / lengths + bias

        attn_weights = ACT2FN[self.config.attention_activation](qk).type_as(qk)

        if padding_mask is not None:
            attn_weights = attn_weights * padding_mask.unsqueeze(2)

        if causal_mask is not None:
            attn_weights = attn_weights * causal_mask

        return attn_weights

    def softmax_attention(self, query, key, padding_mask, causal_mask):
        "Standard softmax self-attention, as in the original Transformer paper"
        seq_len = key.size(2)
        # (sequence_length X sequence_length)
        bias = self.rel_pos_bias(seq_len)
        if seq_len != query.size(2):
            if query.size(2) != 1:
                raise ValueError("Size mismatch between Q and K in softmax attention")
            # (1 X sequence_length)
            bias = bias[-1:]

        # scaled attention
        query = query * self.scaling

        # (batch_size x number of chunks x chunk_size x chunk_size) if chunking
        # (batch_size x 1 x sequence_length x sequence_length) otherwise
        qk = torch.matmul(query, key.transpose(2, 3)) + bias

        # apply causal mask (presumed to be 1/0 for not masked / masked)
        # additive, but convert to 0/-inf (which is not explicitly in the Mega source code)
        if causal_mask is not None:
            additive_causal_mask = torch.zeros_like(causal_mask, dtype=qk.dtype)
            additive_causal_mask = additive_causal_mask.masked_fill((1 - causal_mask).bool(), float("-inf"))
            qk = qk + additive_causal_mask

        if padding_mask is not None:
            # 1 for tokens which are *not masked*
            # 0 for tokens which are *masked*
            # replace masked tokens with -inf to make softmax ignore them
            # need to invert the padding mask to match what mega original did
            padding_mask = 1 - padding_mask
            padding_mask_all = padding_mask.all(dim=-1, keepdim=True)
            padding_mask = torch.logical_and(padding_mask, ~padding_mask_all)
            qk = qk.masked_fill(padding_mask.unsqueeze(2).to(torch.bool), float("-inf"))

        attn_weights = self.softmax(qk).type_as(qk)
        return attn_weights

    def forward(
        self,
        input,
        padding_mask: Optional[torch.Tensor] = None,
        causal_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        output_attentions=False,
        use_cache=False,
    ):
        """
        Mega's self-attention block, which combines multi-headed EMA with traditional self-attention

        Args:
            input (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`):
                Hidden states to be updated by Mega's self-attention
            padding_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked*
                or 0 for *masked*
            causal_mask (`torch.LongTensor` of shape `(sequence_length, sequence_length)`, *optional*):
                Indicates which inputs are to be ignored due to causal attention, where elements are either 1 for *not
                masked* or 0 for *masked*
            past_key_values (`tuple(torch.Tensor)`, *optional*):
                The hidden states returned from the previous timestep during incremental decoding; expects that
                self-attention key, value, and EMA states are the first 3 entries in the tuple
            output_attentions (`bool`, default `False`):
                Whether to return self-attention weights
            use_cache (`bool`, default `False`):
                Whether to perfom incremental decoding; uses `past_key_values` as prior state, and returns the updated
                states for use in the next step

        Returns:
            `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
            inputs:
            - **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden
              states from target sequence updated by Mega's self-attention
            - **attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape
              `(batch_size, 1, sequence_length, sequence_length)` -- The self-attention weights corresponding to how
              each token in the input sequence attends to every other token
            - **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next
              step of incremental decoding
            - **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of
              incremental decoding
            - **self_ema_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape
              `(batch_size, config.ndim)` The incremental EMA state for use in the next step of incremental decoding.
        """

        seq_len, bsz, embed_dim = input.size()
        if embed_dim != self.config.hidden_size:
            raise ValueError(f"Input embedding dimension should be {self.config.hidden_size}; received {embed_dim}")

        # store inputs for residual connection and handle pre-norm if requested
        residual = input
        if self.config.normalize_before_mega:
            input = self.norm(input)

        # (sequence_length X batch_size X hidden_size) -> (sequence_length X batch_size X intermediate_size)
        value = self.activation(self.v_proj(input))

        # unpack the incremental state if provided
        # assumed to be (self K, self V, self EMA state, cross K, cross V)
        # also assumes that incremental decoding is working one token at a time, so input sequence length must be 1
        if self.config.is_decoder and (past_key_values is not None):
            if seq_len > 1:
                raise ValueError(f"Incremental decoding only supports self sequence length of 1; received {seq_len}")
            # the first 3 items in the saved states will be these regardless of whether cross-attention is present
            prev_self_key, prev_self_value, prev_ema_state = past_key_values[0:3]
        else:
            prev_self_key = prev_self_value = prev_ema_state = None

        # ema output is (sequence_length x batch_size x hidden_size)
        # updated_ema_state will be None if use_cache=False; otherwise (batch_size, config.ndim)
        ema_out, updated_ema_state = self.ema_gate(
            input, attention_mask=padding_mask, prev_state=prev_ema_state, use_cache=use_cache
        )
        ema_out = self.dropout(ema_out)

        # (sequence_length X batch_size X hidden_size)
        # -> (sequence_length X batch_size X 2*hidden_size + config.shared_representation_size + config.intermediate_size)
        # - residual_weight -> sigmoid -> applied to residual connection in torch.addcmul
        # - query_key_gates -> split into two components: query_key becomes query and key for attention input, gates becomes gating for self-attention output
        # - intermediate_state -> added to weighted attention output, sent through activation, and has inputs subtracted during
        #   torch.addcmul to create the final layer output
        base = self.mx_proj(ema_out)
        residual_weight, query_key_gates, intermediate_state = torch.split(
            base,
            [
                self.config.hidden_size,
                self.config.shared_representation_size + self.config.intermediate_size,
                self.config.hidden_size,
            ],
            dim=-1,
        )

        # (sequence_length X batch_size X hidden_size)
        residual_weight = torch.sigmoid(residual_weight)

        # (sequence_length X batch_size X shared_representation_size + intermediate_size)
        query_key_gates = F.silu(query_key_gates)

        # split into two different tensors: one for Q/K usage and the other for gating self-attention
        query_key, attention_gate = torch.split(
            query_key_gates, [self.config.shared_representation_size, self.config.intermediate_size], dim=-1
        )

        # (sequence_length X batch_size X shared_representation_size)
        # -> (sequence_length X batch_size X 1 X shared_representation_size)
        # -> (sequence_length X batch_size X 2 X shared_representation_size)
        query_key = query_key.unsqueeze(2) * self.qk_weight + self.qk_bias

        # (sequence_length X batch_size X 2 X shared_representation_size)
        # -> 2 tensors of (sequence_length X batch_size X shared_representation_size)
        query, key = torch.unbind(query_key, dim=2)

        # (sequence_length X batch_size X dimension)
        # -> (batch_size X sequence_length X dimension)
        # where `dimension` is either shared_representation_size (queries and keys) or intermediate_size (values)
        query = query.transpose(0, 1)
        key = key.transpose(0, 1)
        value = value.transpose(0, 1)

        if self.config.is_decoder:
            # combine history and current to save updated state (if history is provided)
            # when chunking is applied, the past states will be None at the end of the chunk, in
            # which case, proceed as if no K/V history had been provided
            # saved states are stored with shape (batch_size X sequence_length X dimension)
            if prev_self_key is not None:
                key = torch.cat([prev_self_key, key], dim=1)
            if prev_self_value is not None:
                value = torch.cat([prev_self_value, value], dim=1)

            # if not chunking, store as-is
            if not self.config.use_chunking:
                updated_self_key = key
                updated_self_value = value
            else:
                curr_len = key.size(1) % self.config.chunk_size
                if curr_len == 0:
                    # if we're chunking and have reached the end of a chunk, wipe out the saved state
                    updated_self_key = None
                    updated_self_value = None
                else:
                    updated_self_key = key
                    updated_self_value = value

        ctx_len = key.size(1)  # potentially differs from seq_len because of incremental decoding
        if not self.config.use_chunking:
            # if we're not chunking, treat the entire sequence as one long chunk
            # (batch_size X sequence_length X dimension) -> (batch_size X 1 X sequence_length X dimension)
            query = query.unsqueeze(1)
            key = key.unsqueeze(1)
            value = value.unsqueeze(1)
            if padding_mask is not None:
                # (batch_size X sequence_length) -> (batch_size X 1 X sequence_length)
                padding_mask = padding_mask.unsqueeze(1)
        else:
            # otherwise, split the sequences in the batch into `n_chunks` chunks of size `chunk_size`
            if seq_len < self.config.chunk_size:
                query = query.unsqueeze(1)
            else:
                # (batch_size X sequence_length X dimension) -> (batch_size X n_chunks X chunk_size X dimension)
                n_chunks = seq_len // self.config.chunk_size
                query = query.reshape(bsz, n_chunks, self.config.chunk_size, self.config.shared_representation_size)

            if ctx_len < self.config.chunk_size:
                key = key.unsqueeze(1)
                value = value.unsqueeze(1)
                if padding_mask is not None:
                    padding_mask = padding_mask.unsqueeze(1)
            else:
                # (batch_size X sequence_length X dimension) -> (batch_size X n_chunks X chunk_size X dimension)
                n_chunks = ctx_len // self.config.chunk_size
                key = key.reshape(bsz, n_chunks, self.config.chunk_size, self.config.shared_representation_size)
                value = value.reshape(bsz, n_chunks, self.config.chunk_size, self.config.intermediate_size)
                if padding_mask is not None:
                    padding_mask = padding_mask.view(bsz, n_chunks, self.config.chunk_size)

        # this is in the original Mega implementation to work around fork/join parallelism not supporting optional types
        if padding_mask is not None and padding_mask.dim() == 0:
            padding_mask = None

        attn_weights = self.attention_function(query, key, padding_mask=padding_mask, causal_mask=causal_mask)

        value = self.hidden_dropout(value, batch_first=True)
        kernel = self.attention_dropout(attn_weights)

        # (batch_size x n_chunks x chunk_size x intermediate_size) -> (sequence_length X batch_size X intermediate_size)
        weighted_self_output = (
            torch.matmul(kernel, value).view(bsz, seq_len, self.config.intermediate_size).transpose(0, 1)
        )

        # (sequence_length X batch_size X intermediate_size) -> (sequence_length X batch_size X hidden_size)
        weighted_self_output = self.activation(intermediate_state + self.h_proj(weighted_self_output * attention_gate))
        weighted_self_output = self.dropout(weighted_self_output)
        # (sequence_length X batch_size X hidden_size)
        out = torch.addcmul(residual, residual_weight, weighted_self_output - residual)

        if not self.config.normalize_before_mega:
            out = self.norm(out)

        return_values = (out, attn_weights) if output_attentions else (out,)

        if self.config.is_decoder:
            return_values = return_values + (updated_self_key, updated_self_value, updated_ema_state)

        return return_values


class MegaNormalizedFeedForwardNetwork(nn.Module):
    """
    Normalized feed-forward network used in Mega blocks. Left as-is from original Mega repo aside from retrieving args
    from Hugging Face config
    """

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

        self.config = config
        self.hidden_dim = config.nffn_hidden_size
        self.act_fn = config.activation
        self.activation = ACT2FN[config.activation]

        self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout)
        self.hidden_dropout = MegaDropout(
            self.config.nffn_activation_dropout_prob, is_featurewise=self.config.use_feature_dropout
        )

        self.prenorm = self.config.normalize_before_ffn
        self.norm = MegaSequenceNorm(
            self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine
        )

        self.fc1 = nn.Linear(self.config.hidden_size, self.config.nffn_hidden_size)
        self.fc2 = nn.Linear(self.config.nffn_hidden_size, self.config.hidden_size)

    def forward(self, inputs):
        residual = inputs

        if self.prenorm:
            inputs = self.norm(inputs)

        hidden = self.activation(self.fc1(inputs))
        hidden = self.hidden_dropout(hidden)
        output = self.fc2(hidden)
        output = self.dropout(output)
        output = output + residual

        if not self.prenorm:
            output = self.norm(output)

        return output


class MegaBlock(nn.Module):
    def __init__(self, config: MegaConfig):
        super().__init__()
        self.seq_len_dim = 1
        self.mega_layer = MegaMovingAverageGatedAttention(config)
        self.nffn = MegaNormalizedFeedForwardNetwork(config) if config.use_normalized_ffn else None
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.cross_attn = MegaGatedCrossAttention(config)
        else:
            self.cross_attn = None

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        causal_mask: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[torch.FloatTensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor]:
        """
        A single Mega layer: either encoder or decoder, with optional cross-attention and optional normalized
        feed-forward layer

        Args:
            hidden_states (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`):
                Hidden states to be updated by the Mega block
            attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
                Indicates which entries in the self/target sequence are to be ignored (mostly due to padding), where
                elements are either 1 for *not masked* or 0 for *masked*. Causal attention is enforced internally.
            causal_mask (`torch.LongTensor` of shape `(sequence_length, sequence_length)`, *optional*):
                Indicates which inputs are to be ignored due to causal attention, where elements are either 1 for *not
                masked* or 0 for *masked*
            encoder_hidden_states (`torch.Tensor`, of shape `(source_sequence_length, batch_size, hidden_size)`, *optional*):
                Encoder hidden states to be used for cross-attention (and required for encoder-decoder model setup)
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, source_sequence_length)`, *optional*):
                Indicates which entries in the cross/source sequence are to be ignored (mostly due to padding), where
                elements are either 1 for *not masked* or 0 for *masked*.
            past_key_value (`tuple(torch.Tensor)`, *optional*):
                The hidden states returned from the previous timestep during incremental decoding; expects that
                self-attention key, value, and EMA states are the first 3 entries in the tuple, and (if doing
                cross-attention) cross-attention key and value are the last 2 entries in the tuple
            output_attentions (`bool`, default `False`):
                Whether to return self-attention weights
            use_cache (`bool`, default `False`):
                Whether to perfom incremental decoding; uses `past_key_value` as prior state, and returns the updated
                states for use in the next step

        Returns:
            `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
            inputs:
            - **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) --
              Hidden states from target sequence updated by Mega
            - **self_attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape
              `(batch_size, 1, target_sequence_length, target_sequence_length)` -- The self-attention weights
              corresponding to how each token in the input sequence attends to every other token
            - **cross_attn_weights** (*optional*, returned when `output_attentions=True` and
              `config.add_cross_attention=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length,
              target_sequence_length)` -- Pairwise cross-attention weights between every entry in the source sequence
              and target sequence
            - **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next
              step of incremental decoding
            - **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of
              incremental decoding
            - **self_ema_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape
              `(batch_size, config.ndim)` The incremental EMA state for use in the next step of incremental decoding.
            - **cross_key** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`)
              `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.shared_representation_size)` --
              The cross-attention key state for use in the next step of incremental decoding
            - **cross_value** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`)
              `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.hidden_size)` -- The
              cross-attention value state for use in the next step of incremental decoding
        """

        # incremental decoding in the MegaMultiDimensionDampedEma module requires that the attention mask has the same
        # sequence length as the input tensor; if we're caching incremental states, we assume the input
        # sequence length is 1 (Mega will break otherwise), so we take the padding mask for the final
        # token in the input (mask is received as [batch X sequence length])
        if use_cache and (past_key_value is not None) and (attention_mask is not None):
            mega_padding_mask = attention_mask[:, -1].unsqueeze(-1)
        else:
            mega_padding_mask = attention_mask

        mega_outputs = self.mega_layer(
            input=hidden_states,
            padding_mask=mega_padding_mask,
            causal_mask=causal_mask,
            past_key_values=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )

        new_hidden_states = mega_outputs[0]
        self_key, self_value, self_ema_state = mega_outputs[-3:] if use_cache else (None, None, None)
        self_attention_weights = mega_outputs[1] if output_attentions else None

        # optional cross attention
        if self.cross_attn is not None:
            if encoder_hidden_states is None:
                raise ValueError("Requested cross-attention without providing encoder hidden states")

            cross_attn_outputs = self.cross_attn(
                query=new_hidden_states,
                key=encoder_hidden_states,
                value=encoder_hidden_states,
                key_padding_mask=encoder_attention_mask,
                past_key_values=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

            # update the hidden state from cross attention
            new_hidden_states = cross_attn_outputs[0]
            # store cross-attention k/v if caching
            cross_key, cross_value = cross_attn_outputs[-2:] if use_cache else (None, None)
            cross_attention_weights = cross_attn_outputs[1] if output_attentions else None

        # optional NFFN follows cross attention
        if self.nffn is not None:
            new_hidden_states = self.nffn(new_hidden_states)

        outs = (new_hidden_states,)
        if output_attentions:
            outs = outs + (self_attention_weights,)
            if self.cross_attn is not None:
                outs = outs + (cross_attention_weights,)

        if use_cache:
            new_key_values = (
                self_key,
                self_value,
                self_ema_state,
            )
            if self.cross_attn is not None:
                new_key_values = new_key_values + (cross_key, cross_value)

            outs = outs + (new_key_values,)

        return outs


# copied from transformers.models.roberta.modeling_roberta.RobertaPooler with Roberta->Mega
class MegaPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

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


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

    config_class = MegaConfig
    base_model_prefix = "mega"
    supports_gradient_checkpointing = False
    _no_split_modules = []

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, MegaMultiDimensionDampedEma):
            with torch.no_grad():
                # delta & alpha
                nn.init.normal_(module.damping_factor, mean=0.0, std=self.config.ema_delta_alpha_range)
                nn.init.normal_(module.decay_factor, mean=0.0, std=self.config.ema_delta_alpha_range)
                # beta [1, -1, 1, -1, ...] seems more stable.
                val = torch.ones(self.config.ema_projection_size, 1)
                if self.config.ema_projection_size > 1:
                    idx = torch.tensor(list(range(1, self.config.ema_projection_size, 2)))
                    val.index_fill_(0, idx, -1.0)
                module.ema_expansion_matrix.normal_(mean=0.0, std=self.config.ema_beta_range).add_(val)
                # gamma & omega
                nn.init.normal_(module.kernel_projection_matrix, mean=0.0, std=self.config.ema_gamma_omega_range)
                nn.init.normal_(module.residual_weight, mean=0.0, std=self.config.ema_gamma_omega_range)
        elif isinstance(module, MegaSimpleRelativePositionalBias):
            nn.init.normal_(module.rel_pos_bias, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, MegaRotaryRelativePositionalBias):
            nn.init.normal_(module.alpha, mean=0.0, std=self.config.initializer_range)
            nn.init.normal_(module.b_param, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, MegaScaleNorm):
            if self.config.norm_affine:
                nn.init.constant_(module.scalar, 1.0)
        elif isinstance(module, MegaRMSNorm):
            if self.config.norm_affine:
                nn.init.constant_(module.weight, 1.0)
        elif isinstance(module, MegaMovingAverageGatedAttention):
            # linear layers covered separately by the generic nn.Linear init below
            nn.init.normal_(module.qk_weight, mean=0.0, std=self.config.initializer_range)
            nn.init.constant_(module.qk_bias, 0.0)
        elif isinstance(module, nn.Linear):
            # initializes all linear layers in the entire network
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


MEGA_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MegaConfig`]): Model configuration class with all the parameters of the
            model. Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MEGA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            This parameter can only be used when the model is initialized with `add_token_type_embeddings` parameter
            set to `True`. All the value in this tensor should be always < config.type_vocab_size.

            [What are token type IDs?](../glossary#token-type-ids)
        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare MEGA Model transformer outputting raw hidden-states without any specific head on top.",
    MEGA_START_DOCSTRING,
)
class MegaModel(MegaPreTrainedModel):
    """

    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 after self-attention, following the architecture described in *Mega: Moving Average
    Equipped Gated Attention*_ by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig,
    Jonathan May, and Luke Zettlemoyer

    To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to
    `True` and `bidirectional` set to `False`. To be used in a Seq2Seq model, the model needs to initialized with both
    `is_decoder=True` and `bidirectional=False` argument as well as `add_cross_attention` set to `True`; an
    `encoder_hidden_states` is then expected as an input to the forward pass.

    .. _*Mega: Moving Average Equipped Gated Attention*: https://arxiv.org/abs/2209.10655

    """

    def __init__(self, config: MegaConfig, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embedding_layer = MegaEmbeddings(config)
        self.layers = nn.ModuleList([MegaBlock(config) for _ in range(config.num_hidden_layers)])

        self.pooler = MegaPooler(config) if add_pooling_layer else None

        # Initialize weights and apply final processing (retained from RoBERTa code)
        self.post_init()

    def get_input_embeddings(self):
        return self.embedding_layer.word_embeddings

    def set_input_embeddings(self, value):
        self.embedding_layer.word_embeddings = value

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        r"""
        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        """

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

        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
            device = input_ids.device
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            device = inputs_embeds.device
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if self.config.use_chunking:
            input_shape = torch.tensor([input_shape[0], self.config.chunk_size])

        batch_size, sequence_length = input_shape

        if self.config.use_chunking and (sequence_length > self.config.chunk_size):
            if sequence_length % self.config.chunk_size != 0:
                raise ValueError(
                    f"config.use_chunking is activated; input sequence length must be shorter than or a multiple of config.chunk_size\nreceived sequence length of {sequence_length} with chunk size {self.config.chunk_size}"
                )

        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache

            # Mega expects the causal mask to be a 2D square matrix of (from) x (to) over the input sequence length
            # the HF utility function generates a 3D causal mask which includes batch size, so we'll create a dummy
            # mask with the correct device and all ones
            temp_mask_for_extension = torch.ones((1, sequence_length), dtype=torch.long, device=device)
            causal_mask = self.create_extended_attention_mask_for_decoder(input_shape, temp_mask_for_extension)

            # get rid of batch dimension in the generated mask; result is (sequence_length X sequence_length)
            causal_mask = causal_mask.squeeze(0)
        else:
            use_cache = False
            causal_mask = None

        # if using cache, make sure we have a tuple of tuples which matches the length of our hidden layers
        if (past_key_values is not None) and (len(past_key_values) != self.config.num_hidden_layers):
            raise ValueError(
                f"Received past key/value cache with size mismatch; expected {self.config.num_hidden_layers}, received {len(past_key_values)}"
            )

        # get embeddings (batch X sequence length X embed dim)
        embedding_output = self.embedding_layer(
            input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )

        # transpose for Mega --> (seq len X batch X embed dim)
        hidden_states = embedding_output.transpose(0, 1)

        # we expect encoder hidden states to also have batch first in line
        # with typical Hugging Face behavior (which is also how we return them)
        # Mega expects sequence length first, so do the same transpose here
        if encoder_hidden_states is not None:
            encoder_hidden_states = encoder_hidden_states.transpose(0, 1)

        # pass through mega layers
        all_hidden_states = (embedding_output,) if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        next_decoder_cache = () if use_cache else None
        for i, mega_layer in enumerate(self.layers):
            current_decoder_cache = past_key_values[i] if past_key_values is not None else None
            mega_outputs = mega_layer(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                causal_mask=causal_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                past_key_value=current_decoder_cache,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

            hidden_states = mega_outputs[0]
            if output_hidden_states:
                # store layer-wise hidden states in the way that the user expects
                # (seq len X batch X embed dim) --> (batch X seq len X embed dim)
                all_hidden_states += (hidden_states.transpose(0, 1),)
            if output_attentions:
                self_attn_weights = mega_outputs[1]
                all_self_attentions += (self_attn_weights,)
                if self.config.add_cross_attention:
                    cross_attn_weights = mega_outputs[2]
                    all_cross_attentions += (cross_attn_weights,)
            if use_cache:
                updated_cache = mega_outputs[-1]
                next_decoder_cache += (updated_cache,)

        # transpose final hidden states
        hidden_states = hidden_states.transpose(0, 1)

        # optional pooling layer
        pooled_output = self.pooler(hidden_states) if self.pooler is not None else None

        if not return_dict:
            return (hidden_states, pooled_output) + (
                all_hidden_states,
                next_decoder_cache,
                all_self_attentions,
                all_cross_attentions,
            )

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled_output,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


@add_start_docstrings(
    """MEGA Model with a `language modeling` head on top for CLM fine-tuning.""", MEGA_START_DOCSTRING
)
class MegaForCausalLM(MegaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

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

        if not config.is_decoder:
            logger.warning("If you want to use `MegaForCausalLM` as a standalone, add `is_decoder=True.`")

        self.mega = MegaModel(config, add_pooling_layer=False)

        if config.add_lm_hidden_dense_layer:
            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
            self.hidden_activation = nn.Tanh()
        else:
            self.dense = None
            self.hidden_activation = None

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)

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

    def get_output_embeddings(self):
        return self.lm_head

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

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = 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,
        past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
        r"""
        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the left-to-right language modeling loss (next word prediction). 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]`
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, MegaForCausalLM, AutoConfig
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
        >>> config = AutoConfig.from_pretrained("mnaylor/mega-base-wikitext")
        >>> config.is_decoder = True
        >>> config.bidirectional = False
        >>> model = MegaForCausalLM.from_pretrained(
        ...     "mnaylor/mega-base-wikitext", config=config, ignore_mismatched_sizes=True
        ... )

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> prediction_logits = outputs.logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False

        outputs = self.mega(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        if self.dense is not None:
            sequence_output = self.dense(sequence_output)
            sequence_output = self.hidden_activation(sequence_output)

        prediction_scores = self.lm_head(sequence_output)

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

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

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]

        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}

    def _reorder_cache(self, past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past


@add_start_docstrings("""MEGA Model with a `language modeling` head on top.""", MEGA_START_DOCSTRING)
class MegaForMaskedLM(MegaPreTrainedModel):
    _tied_weights_keys = ["mlm_head.weight"]

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

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

        self.mega = MegaModel(config, add_pooling_layer=False)
        if config.add_lm_hidden_dense_layer:
            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
            self.hidden_activation = nn.Tanh()
        else:
            self.dense = None
            self.hidden_activation = None
        self.mlm_head = nn.Linear(config.hidden_size, config.vocab_size)
        self.dropout = nn.Dropout(config.dropout_prob)

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

    def get_output_embeddings(self):
        return self.mlm_head

    def set_output_embeddings(self, new_embeddings):
        self.mlm_head = new_embeddings

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
        mask="<mask>",
        expected_output="' Paris'",
        expected_loss=0.1,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = 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.mega(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            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]
        if self.dense is not None:
            sequence_output = self.dense(sequence_output)
            sequence_output = self.hidden_activation(sequence_output)
        prediction_scores = self.mlm_head(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            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,
        )


@add_start_docstrings(
    """
    MEGA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """,
    MEGA_START_DOCSTRING,
)
class MegaForSequenceClassification(MegaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.mega = MegaModel(config, add_pooling_layer=False)
        self.classifier = MegaClassificationHead(config)

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

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = 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.mega(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            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:
            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,
        )


@add_start_docstrings(
    """
    MEGA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    MEGA_START_DOCSTRING,
)
class MegaForMultipleChoice(MegaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.mega = MegaModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)

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

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        flat_inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.mega(
            flat_input_ids,
            token_type_ids=flat_token_type_ids,
            attention_mask=flat_attention_mask,
            inputs_embeds=flat_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

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

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    MEGA Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    MEGA_START_DOCSTRING,
)
class MegaForTokenClassification(MegaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.mega = MegaModel(config, add_pooling_layer=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = 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], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mega(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


# copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Mega
class MegaClassificationHead(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


@add_start_docstrings(
    """
    MEGA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    MEGA_START_DOCSTRING,
)
class MegaForQuestionAnswering(MegaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.mega = MegaModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

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

    @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mega(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
