# -*- coding: utf-8 -*-

from __future__ import annotations

import math
import warnings
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint
from transformers.activations import ACT2FN
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from transformers.utils.deprecation import deprecate_kwarg

from fla.layers.attn import Attention
from fla.layers.gated_deltaproduct import GatedDeltaProduct
from fla.models.gated_deltaproduct.configuration_gated_deltaproduct import GatedDeltaProductConfig
from fla.models.utils import Cache
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm
from fla.modules.activations import swiglu_linear
from fla.modules.layernorm import rms_norm_linear

if TYPE_CHECKING:
    from transformers.processing_utils import Unpack

logger = logging.get_logger(__name__)


class GatedDeltaNetMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        hidden_ratio: Optional[int] = None,
        intermediate_size: Optional[int] = None,
        hidden_act: str = "swish",
        norm_first: bool = True,
        norm_eps: float = 1e-5,
    ) -> GatedDeltaNetMLP:
        super().__init__()

        self.hidden_size = hidden_size
        # the final number of params is `hidden_ratio * hidden_size^2`
        # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
        if hidden_ratio is None:
            hidden_ratio = 4
        if intermediate_size is None:
            intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
            intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
        self.hidden_ratio = hidden_ratio
        self.intermediate_size = intermediate_size
        self.norm_first = norm_first

        if norm_first:
            self.norm = RMSNorm(hidden_size=hidden_size, eps=norm_eps)

        self.gate_proj = nn.Linear(
            self.hidden_size, self.intermediate_size * 2, bias=False
        )
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(
        self,
        x: torch.Tensor,
        **kwargs: Unpack[Dict],
    ) -> torch.Tensor:
        if self.norm_first:
            x = rms_norm_linear(
                x,
                self.norm.weight,
                self.norm.bias,
                self.gate_proj.weight,
                self.gate_proj.bias,
            )
        else:
            x = self.gate_proj(x)
        gate, y = x.chunk(2, -1)
        return swiglu_linear(gate, y, self.down_proj.weight, self.down_proj.bias)


class GatedDeltaProductBlock(nn.Module):
    def __init__(self, config: GatedDeltaProductConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        if not config.norm_first:
            self.attn_norm = RMSNorm(
                hidden_size=config.hidden_size, eps=config.norm_eps
            )
        if config.attn is not None and layer_idx in config.attn["layers"]:
            self.attn = Attention(
                hidden_size=config.hidden_size,
                num_heads=config.attn["num_heads"],
                num_kv_heads=config.attn["num_kv_heads"],
                window_size=config.attn["window_size"],
                max_position_embeddings=config.max_position_embeddings,
                layer_idx=layer_idx,
            )
        else:
            self.attn = GatedDeltaProduct(
                mode=config.attn_mode,
                hidden_size=config.hidden_size,
                expand_v=config.expand_v,
                head_dim=config.head_dim,
                num_heads=config.num_heads,
                use_gate=config.use_gate,
                use_forget_gate=config.use_forget_gate,
                use_short_conv=config.use_short_conv,
                conv_size=config.conv_size,
                norm_first=config.norm_first,
                norm_eps=config.norm_eps,
                allow_neg_eigval=config.allow_neg_eigval,
                num_householder=config.num_householder,
                layer_idx=layer_idx,
                use_beta_conv=config.use_beta_conv
            )
        if not config.norm_first:
            self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
        self.mlp = GatedDeltaNetMLP(
            hidden_size=config.hidden_size,
            hidden_ratio=config.hidden_ratio,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            norm_first=config.norm_first,
            norm_eps=config.norm_eps,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        **kwargs: Unpack[Dict],
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states
        if hasattr(self, "attn_norm"):
            hidden_states = self.attn_norm(hidden_states)
        hidden_states, attentions, past_key_values = self.attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            **kwargs,
        )
        if hasattr(self, "mlp_norm"):
            hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
        else:
            hidden_states = residual + hidden_states
            residual = hidden_states
        hidden_states = self.mlp(hidden_states, **kwargs)
        hidden_states = residual + hidden_states

        outputs = (hidden_states, attentions, past_key_values)

        return outputs


class GatedDeltaProductPreTrainedModel(PreTrainedModel):
    config_class = GatedDeltaProductConfig
    supports_gradient_checkpointing = True
    _no_split_modules = ["GatedDeltaNetBlock"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(
        self,
        module: nn.Module,
        rescale_prenorm_residual: bool = True,
        num_residuals_per_layer: int = 2,
    ):
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

        if rescale_prenorm_residual:
            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
            #
            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
            for name, p in module.named_parameters():
                if name in ["o_proj.weight", "down_proj.weight"]:
                    # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
                    # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
                    # We need to reinit p since this code could be called multiple times
                    # Having just p *= scale would repeatedly scale it down
                    with torch.no_grad():
                        p /= math.sqrt(
                            num_residuals_per_layer * self.config.num_hidden_layers
                        )


class GatedDeltaProductModel(GatedDeltaProductPreTrainedModel):
    def __init__(self, config: GatedDeltaProductConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = nn.ModuleList(
            [
                GatedDeltaProductBlock(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)

        self.gradient_checkpointing = False

        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings

    def set_input_embeddings(self, value):
        self.embeddings = value

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Union[Cache, 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,
        **kwargs: Unpack[Dict],
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        if output_attentions:
            warnings.warn(
                "`GatedDeltaNetModel` does not `output_attentions` now, setting it to `False`.",
                stacklevel=2,
            )
            output_attentions = False
        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
        )
        use_cache = (
            use_cache
            if use_cache is not None
            else (self.config.use_cache if not self.training else False)
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        # retrieve input_ids and inputs_embeds
        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"
            )
        if input_ids is None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embeddings(input_ids)
        hidden_states = inputs_embeds

        if use_cache and not isinstance(past_key_values, Cache):
            past_key_values = Cache.from_legacy_cache(past_key_values)

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
            )
            use_cache = False

        all_hidden_states = () if output_hidden_states else None
        all_attns = () if output_attentions else None
        for layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                hidden_states, attentions, past_key_values = (
                    self._gradient_checkpointing_func(
                        layer.__call__,
                        hidden_states,
                        attention_mask,
                        past_key_values,
                        use_cache,
                        output_attentions,
                        **kwargs,
                    )
                )
            else:
                hidden_states, attentions, past_key_values = layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    past_key_values=past_key_values,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    **kwargs,
                )

            if output_attentions:
                all_attns += (attentions,)

        hidden_states = self.norm(hidden_states)
        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(
                i
                for i in [
                    hidden_states,
                    past_key_values,
                    all_hidden_states,
                    all_attns,
                ]
                if i is not None
            )
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_attns,
        )


class GatedDeltaProductForCausalLM(GatedDeltaProductPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = GatedDeltaProductModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def get_input_embeddings(self):
        return self.model.embeddings

    def set_input_embeddings(self, value):
        self.model.embeddings = value

    def get_output_embeddings(self):
        return self.lm_head

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

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def generate(self, *args, **kwargs):
        try:
            return super().generate(*args, **kwargs)
        except AttributeError as exception:
            if "past_key_values" in str(exception):
                raise AttributeError(
                    f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
                    f"which is not supported for {self.__class__.__name__}. "
                    f"Try another generation strategy instead. "
                    f"For the available generation strategies, check this doc: "
                    f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
                )
            else:
                raise exception

    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor = None,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: bool = True,
        num_logits_to_keep: Optional[int] = None,
        logits_to_keep: Optional[int] = None,
        **kwargs,
    ):
        # only last token for `inputs_ids` if the `past_key_values` is passed along is not empty.
        if past_key_values is not None and len(past_key_values) > 0:
            input_ids = input_ids[:, -1:]
        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
            # recompiles graphs as the stride of the inputs is a guard.
            # Ref: https://github.com/huggingface/transformers/pull/29114
            # TODO: use `next_tokens` directly instead.
            model_inputs = {"input_ids": input_ids.contiguous()}

        if logits_to_keep is not None:
            model_inputs['logits_to_keep'] = logits_to_keep

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
                "num_logits_to_keep": num_logits_to_keep,
            }
        )
        return model_inputs

    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        num_logits_to_keep: Optional[int] = 0,
        logits_to_keep: Optional[int] = 0,
        **kwargs: Unpack[Dict],
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        num_logits_to_keep = 0 if num_logits_to_keep is None else num_logits_to_keep
        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
        )
        kwargs.pop("num_items_in_batch", None)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        hidden_states = outputs[0]
        fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training

        loss, logits = None, None
        if not fuse_linear_and_cross_entropy or labels is None:
            logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:])
        if labels is not None:
            if self.config.fuse_cross_entropy:
                if fuse_linear_and_cross_entropy:
                    loss_fct = FusedLinearCrossEntropyLoss()
                else:
                    loss_fct = FusedCrossEntropyLoss(inplace_backward=True)
            else:
                loss_fct = nn.CrossEntropyLoss()
            # Enable model parallelism
            labels = labels.to(hidden_states.device)
            labels = torch.cat(
                (
                    labels[..., 1:],
                    torch.full_like(labels[:, :1], loss_fct.ignore_index),
                ),
                1,
            )
            if fuse_linear_and_cross_entropy:
                loss = loss_fct(
                    hidden_states.view(-1, self.config.hidden_size),
                    labels.view(-1),
                    self.lm_head.weight,
                    self.lm_head.bias,
                )
            else:
                loss = loss_fct(
                    logits.view(-1, self.config.vocab_size), labels.view(-1)
                )

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss, *output) if loss is not None else output
        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
