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

from __future__ import annotations

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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from dataclasses import dataclass
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

import triton
import triton.language as tl

from fla.layers.attn import Attention
from fla.models.transformer.configuration_transformer import TransformerConfig
from fla.models.utils import Cache
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, FusedLinearListNetLoss
from fla.modules import GatedMLP as TransformerMLP
from fla.modules import RMSNorm
from fla.modules.seq_to_myopic import seq_to_myopic

if TYPE_CHECKING:
    from transformers.processing_utils import Unpack


logger = logging.get_logger(__name__)

@dataclass
class TOPLMOutputWithPast(CausalLMOutputWithPast):
    ntp_loss: Optional[torch.FloatTensor] = None
    top_loss: Optional[torch.FloatTensor] = None

class TransformerBlock(nn.Module):

    def __init__(self, config: TransformerConfig, layer_idx: int):
        super().__init__()

        self.config = config
        self.layer_idx = layer_idx

        self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
        self.attn = Attention(
            hidden_size=config.hidden_size,
            num_heads=config.num_heads,
            num_kv_heads=config.num_kv_heads,
            qkv_bias=config.qkv_bias,
            qk_norm=config.qk_norm,
            window_size=config.window_size,
            rope_theta=config.rope_theta,
            max_position_embeddings=config.max_position_embeddings,
            layer_idx=layer_idx
        )

        self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
        self.mlp = TransformerMLP(
            hidden_size=config.hidden_size,
            hidden_ratio=config.hidden_ratio,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            fuse_swiglu=config.fuse_swiglu
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs: Unpack[Any]
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:

        residual = hidden_states
        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 self.config.fuse_norm:
            hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
        else:
            hidden_states = residual + hidden_states
            residual = hidden_states
            hidden_states = self.mlp_norm(hidden_states)
        hidden_states = self.mlp(hidden_states, **kwargs)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attentions,)

        if use_cache:
            outputs += (past_key_values,)

        return outputs


class TransformerPreTrainedModel(PreTrainedModel):

    config_class = TransformerConfig
    base_model_prefix = 'model'
    supports_gradient_checkpointing = True
    _no_split_modules = ['TransformerBlock']
    _supports_cache_class = True

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

    def _init_weights(
        self,
        module: nn.Module,
        rescale_prenorm_residual: bool = False,
        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)
        elif hasattr(module, 'reset_parameters'):
            module.reset_parameters()

        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
            p = None
            if hasattr(module, 'o_proj'):
                p = module.o_proj.weight
            elif hasattr(module, 'down_proj'):
                p = module.down_proj.weight
            if p is not None:
                # 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
                nn.init.kaiming_uniform_(p, a=math.sqrt(5))
                with torch.no_grad():
                    p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers)


class TransformerModel(TransformerPreTrainedModel):

    def __init__(
        self,
        config: TransformerConfig
    ) -> TransformerModel:
        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([TransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
        self.norm = (RMSNorm if config.fuse_norm else nn.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,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[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[Any]
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if output_attentions:
            warnings.warn(
                "`TransformerModel` does not support output attention weights now, so `output_attentions` is set to `False`."
            )
            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")
        elif input_ids is None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

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

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

        # embed positions
        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if 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
        next_cache = None

        for layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

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

            hidden_states = layer_outputs[0]

            if use_cache:
                next_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_attns += (layer_outputs[1],)

        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(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_attns
        )


class TransformerForCausalLM(TransformerPreTrainedModel, GenerationMixin):

    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = TransformerModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        if config.use_myopic_loss:
            self.myopic_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
            self.myopic_criterion = FusedLinearListNetLoss()
        self.criterion = None
        self.pad_token_id = config.pad_token_id

        # 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

    @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,
        logits_to_keep: Optional[int] = None,
        **kwargs
    ):
        # only last token for `inputs_ids` if the `past_key_values` 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 len(past_key_values) == 0:
            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,
        })
        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,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[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,
        logits_to_keep: Optional[int] = 0,
        **kwargs: Unpack[Any]
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            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
        logits = None if fuse_linear_and_cross_entropy else self.lm_head(hidden_states[:, -logits_to_keep:])

        loss = None
        ntp_loss = None
        myopic_loss = None
        if labels is not None:
            if getattr(self, 'criterion', None) is None:
                if fuse_linear_and_cross_entropy:
                    criterion = FusedLinearCrossEntropyLoss()
                elif self.config.fuse_cross_entropy:
                    criterion = FusedCrossEntropyLoss(inplace_backward=True)
                else:
                    criterion = nn.CrossEntropyLoss()
            else:
                criterion = self.criterion
            # Enable model parallelism
            labels = labels.to(hidden_states.device)
            labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1)
            ntp_labels = labels[..., :hidden_states.shape[1]].contiguous()
            if fuse_linear_and_cross_entropy:
                ntp_loss = criterion(hidden_states, ntp_labels, self.lm_head.weight, self.lm_head.bias)
            else:
                ntp_loss = criterion(logits.view(ntp_labels.numel(), -1), ntp_labels.reshape(-1))

            if self.config.use_myopic_loss:
                myopic_labels = seq_to_myopic(labels, self.vocab_size, hidden_states.shape[1], pad_token_id=self.pad_token_id).contiguous()
                myopic_loss = self.myopic_criterion(hidden_states, myopic_labels, self.myopic_head.weight, self.myopic_head.bias)
                # print(f"NTP Loss: {ntp_loss.item()}, Myopic Loss: {myopic_loss.item()}")
                # For debugging, get the index where the myopic label is the highest and print the corresponding logits
                # idx_max = torch.argmax(myopic_labels.view(-1, self.vocab_size), dim=1)
                # # Print the labels and logits at that index
                # print(f"Labels: {myopic_labels.view(-1, self.vocab_size)[0, idx_max[0]-3:idx_max[0]+3]}")
                # print(f"Logits: {F.sigmoid(myopic_logits).view(-1, self.vocab_size)[0, idx_max[0]-3:idx_max[0]+3]}")
                loss = ntp_loss + myopic_loss
            else:
                loss = ntp_loss

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return TOPLMOutputWithPast(
            loss=loss,
            ntp_loss=ntp_loss,
            top_loss=myopic_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )