#
# Copyright 2016 The BigDL Authors.
#
# 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.
#
# Some parts of this file is adapted from
# https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/starcoder2/modeling_starcoder2.py
# which is licensed under Apache License 2.0:
#
# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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
import torch
import warnings

from ipex_llm.transformers.models.utils import (
    use_quantize_kv_cache, restore_fp8_kv_cache,
    should_use_fuse_rope, use_sdp, use_sdp_causal
)
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
from ipex_llm.utils.common.log4Error import invalidInputError

from typing import Optional, Tuple, List
from transformers.cache_utils import Cache
from transformers.models.starcoder2.modeling_starcoder2 import repeat_kv, apply_rotary_pos_emb
from transformers.models.starcoder2.modeling_starcoder2 import Starcoder2Model, Starcoder2Attention


def merge_qkv(module: torch.nn.Module):
    if isinstance(module, Starcoder2Attention):
        new_weight = torch.cat([
            module.q_proj.weight.data,
            module.k_proj.weight.data,
            module.v_proj.weight.data,
        ], dim=0)
        new_bias = torch.cat([
            module.q_proj.bias.data,
            module.k_proj.bias.data,
            module.v_proj.bias.data,
        ], dim=-1)

        qkv_proj = torch.nn.Linear(0, 0, bias=True)
        qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
        qkv_proj.bias = torch.nn.Parameter(new_bias, requires_grad=False)
        qkv_proj.in_features = new_weight.size(1)
        qkv_proj.out_features = new_weight.size(0)
        module.qkv_proj = qkv_proj

        del module.q_proj, module.k_proj, module.v_proj


def attention_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Cache] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    if "padding_mask" in kwargs:
        warnings.warn(
            "Passing `padding_mask` is deprecated and will be removed in v4.37. "
            "Please make sure use `attention_mask` instead.`"
        )
    bsz, q_len, _ = hidden_states.size()

    qkv = self.qkv_proj(hidden_states)
    qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
    qkv = qkv.transpose(1, 2)
    query_states, key_states, value_states = qkv.split([self.num_heads,
                                                        self.num_key_value_heads,
                                                        self.num_key_value_heads], dim=1)

    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

    # IPEX-LLM OPT: fuse rope
    if should_use_fuse_rope(hidden_states, position_ids, self.training):
        import xe_addons
        xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
                                       query_states, key_states)
    else:
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, position_ids)

    # IPEX-LLM OPT: kv cache and quantize kv cache
    invalidInputError(past_key_value is not None,
                      "`past_key_value` cannot be None")
    key_states, value_states = past_key_value.update(key_states, value_states,
                                                     self.layer_idx, None)

    # IPEX-LLM OPT: sdp
    if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
        import xe_addons
        if isinstance(past_key_value, DynamicFp8Cache):
            attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
                                            attention_mask)
        else:
            attn_output = xe_addons.sdp(query_states, key_states, value_states,
                                        attention_mask)
    elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
        import xe_addons
        if isinstance(past_key_value, DynamicFp8Cache):
            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
                                                   value_states, attention_mask)
        else:
            attn_output = xe_addons.sdp_causal(query_states, key_states,
                                               value_states, attention_mask)
    else:
        if isinstance(past_key_value, DynamicFp8Cache):
            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
                                                            query_states.dtype)
        # 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:
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
                                                   dtype=torch.float32).to(query_states.dtype)
        attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
                                                   training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

    attn_output = attn_output.transpose(1, 2).contiguous()
    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

    attn_output = self.o_proj(attn_output)
    attn_output = torch.nn.functional.dropout(attn_output, p=self.residual_dropout,
                                              training=self.training)
    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def model_forward(
    self,
    input_ids: torch.LongTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = 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,
):
    use_cache = use_cache if use_cache is not None else self.config.use_cache
    use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.c_fc, input_ids)
    if use_cache:
        if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
        if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
            past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
    return Starcoder2Model.forward(
        self=self,
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        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,
    )
