#
# Copyright 2016 The BigDL Authors.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
#
# Some parts of this file is adapted from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma/modeling_gemma.py
# coding=utf-8
# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# 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, Tuple

import torch
from torch import nn
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
from ipex_llm.transformers.models.utils import mlp_fusion_check, GELU
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36, rotate_half
from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5
from ipex_llm.transformers.models.utils import use_decoding_fast_path

import os

KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
    The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
    to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
                                                           n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def should_use_fuse_rope(self, hidden_states, position_ids):
    use_fuse_rope = hidden_states.device.type == "xpu"
    use_fuse_rope = use_fuse_rope and not (self.training and hidden_states.requires_grad)
    use_fuse_rope = use_fuse_rope and position_ids is not None
    return use_fuse_rope


def gemma_rms_norm_forward(self, hidden_states):
    if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad):
        import linear_q4_0
        x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
        output = linear_q4_0.rms_norm(self.weight + 1, x_2d, self.eps)
        return output.reshape(hidden_states.shape)

    input_dtype = hidden_states.dtype
    hidden_states = hidden_states.to(torch.float32)
    variance = hidden_states.pow(2).mean(-1, keepdim=True)
    hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
    return (1 + self.weight) * hidden_states.to(input_dtype)


def gemma_mlp_forward(
    self,
    x: torch.Tensor,
    residual=None
) -> torch.Tensor:
    x_2d = x.view(-1, x.shape[-1])
    bsz, hidden_size = x_2d.shape
    qtype = getattr(self.gate_proj, "qtype", None)
    if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
        import linear_q4_0
        if not x_2d.is_contiguous():
            x_2d = x_2d.contiguous()
        out = self.down_proj(linear_q4_0.mlp_forward_xpu(
            x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
            x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
            GELU, qtype
        ))
    else:
        out = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
    if residual is not None:
        return out + residual
    else:
        return out


def gemma_attention_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor]=None,
    position_ids: Optional[torch.LongTensor]=None,
    past_key_value: Optional[Tuple[torch.Tensor]]=None,
    output_attentions: bool=False,
    use_cache: bool=False,
    cache_position: Optional[torch.Tensor]=None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, hidden_size = hidden_states.size()
    device = hidden_states.device
    # for flash attention
    original_dtype = hidden_states.dtype

    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
    decoding_fast_path = use_decoding_fast_path(self.q_proj,
                                                use_fuse_rope,
                                                enough_kv_room,
                                                bsz * q_len)

    if decoding_fast_path:
        hidden_states = hidden_states.view(1, -1)

        cache_k = past_key_value.key_cache[self.layer_idx]
        cache_v = past_key_value.value_cache[self.layer_idx]

        kv_seq_len = cache_k.shape[-2]

        import linear_q4_0
        query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
                                                                         self.q_proj.weight,
                                                                         self.k_proj.weight,
                                                                         self.v_proj.weight,
                                                                         position_ids,
                                                                         cache_k, cache_v,
                                                                         self.q_proj.weight.qtype,
                                                                         self.v_proj.weight.qtype,
                                                                         kv_seq_len,
                                                                         self.head_dim)
        kv_seq_len += 1

        # update past_key_value's seem_tokens and kv caches.
        if self.layer_idx == 0:
            past_key_value.seen_tokens = kv_seq_len
        past_key_value.key_cache[self.layer_idx] = key_states
        past_key_value.value_cache[self.layer_idx] = value_states

    else:
        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)

        kv_seq_len = key_states.shape[-2]

        if past_key_value is not None:
            if self.layer_idx is None:
                invalidInputError(False,
                                  "The cache structure has changed since version v4.36. "
                                  f"If you are using {self.__class__.__name__} for "
                                  "auto-regressive decodingwith k/v caching, please make sure "
                                  "to initialize the attention class with a layer index.")
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        if use_fuse_rope:
            cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
            query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
                                                                           sin, cos, "gemma")
        else:
            cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
                                                            cos, sin, None)

        if past_key_value is not None:
            # update the number of seen tokens
            if self.layer_idx == 0:
                past_key_value.seen_tokens += key_states.shape[-2]

            # reuse k, v, self_attention
            # update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
            if len(past_key_value.key_cache) <= self.layer_idx:
                past_key_value.key_cache.append(key_states)
                past_key_value.value_cache.append(value_states)
            else:
                cache_k = past_key_value.key_cache[self.layer_idx]
                cache_v = past_key_value.value_cache[self.layer_idx]

                if not enough_kv_room:
                    # allocate new
                    new_c_k, new_c_v = extend_kv_cache(bsz,
                                                       self.num_key_value_heads,  # Support GQA
                                                       self.head_dim,
                                                       cache_k.size(2),
                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                                                       dtype=cache_k.dtype,
                                                       device=device)

                    new_c_k[:] = cache_k
                    new_c_v[:] = cache_v
                    cache_k = new_c_k
                    cache_v = new_c_v

                key_states, value_states = append_kv_cache(cache_k, cache_v,
                                                           key_states, value_states)

                # update past_key_value
                past_key_value.key_cache[self.layer_idx] = key_states
                past_key_value.value_cache[self.layer_idx] = value_states

    # repeat k/v heads if n_kv_heads < n_heads
    key_states = repeat_kv(key_states, self.num_key_value_groups)
    value_states = repeat_kv(value_states, self.num_key_value_groups)
    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
        if cache_position is not None:
            causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
        else:
            causal_mask = attention_mask
        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)
    attn_output = torch.matmul(attn_weights, value_states)

    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
        invalidInputError(
            False,
            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
            f" {attn_output.size()}"
        )

    attn_output = attn_output.transpose(1, 2).contiguous()

    attn_output = attn_output.view(bsz, q_len, -1)
    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output.to(original_dtype), attn_weights, past_key_value