# coding=utf-8
# Copyright 2024 weak-kajuma and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on Llama implementations in this library and Microsoft's
# Differential Transformer implementations.

# 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.
import math
from typing import Optional

import torch
from torch import nn

from ...cache_utils import Cache, StaticCache
from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
from ...utils import logging
from ..gemma.modeling_gemma import GemmaForCausalLM
from ..llama.modeling_llama import (
    LlamaDecoderLayer,
    LlamaForQuestionAnswering,
    LlamaForSequenceClassification,
    LlamaForTokenClassification,
    LlamaModel,
    LlamaPreTrainedModel,
    apply_rotary_pos_emb,
    repeat_kv,
)
from ..mistral.modeling_mistral import MistralMLP
from .configuration_diffllama import DiffLlamaConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "kajuma/DiffLlama-0.3B-handcut"
_CONFIG_FOR_DOC = "DiffLlamaConfig"


class DiffLlamaMLP(MistralMLP):
    pass


def lambda_init_fn(layer_idx):
    return 0.8 - 0.6 * math.exp(-0.3 * layer_idx)


class DiffLlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        # under this are not used
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)

        self.lambda_init = lambda_init_fn(layer_idx)
        self.lambda_q1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
        self.lambda_k1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
        self.lambda_q2 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
        self.lambda_k2 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
        self.groupnorm = nn.RMSNorm(2 * self.head_dim, eps=config.rms_norm_eps, elementwise_affine=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        bsz, target_len, _ = hidden_states.size()
        q_len = target_len

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
        value_states = value_states.repeat(1, 2, 1, 1)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_full = lambda_1 - lambda_2 + self.lambda_init

        attn_output = torch.matmul(attn_weights, value_states)
        attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=1)

        attn_output = attn_output1 - lambda_full * attn_output2
        attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class DiffLlamaFlashAttention2(DiffLlamaAttention):
    """
    DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

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

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        if isinstance(past_key_value, StaticCache):
            raise ValueError(
                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
            )

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        if position_embeddings is None:
            logger.warning_once(
                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
                "removed and `position_embeddings` will be mandatory."
            )
            cos, sin = self.rotary_emb(value_states, position_ids)
        else:
            cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
        # to be able to avoid many of these transpose/reshape/view.
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        dropout_rate = self.attention_dropout if self.training else 0.0

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (DiffLlamaRMSNorm handles it correctly)

        input_dtype = query_states.dtype
        device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = (
                    torch.get_autocast_dtype(device_type)
                    if hasattr(torch, "get_autocast_dtype")
                    else torch.get_autocast_gpu_dtype()
                )
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        value_states1, value_states2 = torch.chunk(value_states, 2, dim=2)
        value_states1 = value_states1.repeat(1, 1, 2, 1)
        value_states2 = value_states2.repeat(1, 1, 2, 1)

        attn_output1 = _flash_attention_forward(
            query_states,
            key_states,
            value_states1,
            attention_mask,
            q_len,
            position_ids=position_ids,
            dropout=dropout_rate,
            sliding_window=getattr(self, "sliding_window", None),
            use_top_left_mask=self._flash_attn_uses_top_left_mask,
            is_causal=self.is_causal,
        )

        attn_output2 = _flash_attention_forward(
            query_states,
            key_states,
            value_states2,
            attention_mask,
            q_len,
            position_ids=position_ids,
            dropout=dropout_rate,
            sliding_window=getattr(self, "sliding_window", None),
            use_top_left_mask=self._flash_attn_uses_top_left_mask,
            is_causal=self.is_causal,
        )

        attn_output = torch.cat([attn_output1, attn_output2], dim=-1)
        attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=2)

        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_full = lambda_1 - lambda_2 + self.lambda_init

        attn_output = attn_output1 - lambda_full * attn_output2
        attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, None


class DiffLlamaSdpaAttention(DiffLlamaAttention):
    """
    DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from DiffLlamaAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
        value_states = value_states.repeat(1, 2, 1, 1)

        causal_mask = attention_mask
        if attention_mask is not None:
            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and causal_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
        is_causal = True if causal_mask is None and q_len > 1 else False

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=causal_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            is_causal=is_causal,
        )

        attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=1)

        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
            query_states.dtype
        )
        lambda_full = lambda_1 - lambda_2 + self.lambda_init

        attn_output = attn_output1 - lambda_full * attn_output2
        attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)
        return attn_output, None


DIFFLLAMA_ATTENTION_CLASSES = {
    "eager": DiffLlamaAttention,
    "flash_attention_2": DiffLlamaFlashAttention2,
    "sdpa": DiffLlamaSdpaAttention,
}


class DiffLlamaDecoderLayer(LlamaDecoderLayer):
    def __init__(self, config: DiffLlamaConfig, layer_idx: int):
        super().__init__(config, layer_idx)

        self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)


class DiffLlamaPreTrainedModel(LlamaPreTrainedModel):
    _supports_flex_attn = False
    _supports_attention_backend = False

    def _init_weights(self, module):
        LlamaPreTrainedModel._init_weights(module)
        if isinstance(module, DiffLlamaAttention):
            module.lambda_q1.data.normal_(0, self.config.lambda_std_dev)
            module.lambda_k1.data.normal_(0, self.config.lambda_std_dev)
            module.lambda_q2.data.normal_(0, self.config.lambda_std_dev)
            module.lambda_k2.data.normal_(0, self.config.lambda_std_dev)


class DiffLlamaModel(LlamaModel):
    pass


class DiffLlamaForCausalLM(GemmaForCausalLM):
    pass


class DiffLlamaForSequenceClassification(LlamaForSequenceClassification):
    pass


class DiffLlamaForQuestionAnswering(LlamaForQuestionAnswering):
    pass


class DiffLlamaForTokenClassification(LlamaForTokenClassification):
    pass


__all__ = [
    "DiffLlamaPreTrainedModel",
    "DiffLlamaModel",  # noqa: F822
    "DiffLlamaForCausalLM",
    "DiffLlamaForSequenceClassification",
    "DiffLlamaForQuestionAnswering",
    "DiffLlamaForTokenClassification",
]