#
# 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/v4.37.0/src/transformers/models/qwen2/modeling_qwen2.py
# which is licensed under Apache License 2.0:
#
# Copyright 2024 The Qwen team, Alibaba Group 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 warnings
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List

import torch
import torch.nn as nn

from bigdl.llm.transformers.models.llama import repeat_kv
from bigdl.llm.transformers.models.utils import extend_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_36
from bigdl.llm.transformers.kv import DynamicFp8Cache
from bigdl.llm.utils.common import invalidInputError
from transformers.models.qwen2.modeling_qwen2 import Qwen2Model, apply_rotary_pos_emb


KV_CACHE_ALLOC_BLOCK_LENGTH = 256


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


def qwen2_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
    if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
        if not isinstance(past_key_values, DynamicFp8Cache):
            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
    return Qwen2Model.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,
    )


def qwen2_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,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    if use_quantize_kv_cache(self.q_proj, hidden_states):
        forward_function = qwen2_attention_forward_quantized
    else:
        forward_function = qwen2_attention_forward_origin
    return forward_function(
        self=self,
        hidden_states=hidden_states,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_value=past_key_value,
        output_attentions=output_attentions,
        use_cache=use_cache,
        **kwargs,
    )


def qwen2_attention_forward_quantized(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[DynamicFp8Cache] = 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()

    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:
        invalidInputError(self.layer_idx is not None,
                          "The cache structure has changed since version v4.36. "
                          f"If you are using {self.__class__.__name__} "
                          "for auto-regressive decoding with 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)
    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)

    if past_key_value is not None:
        cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
        key_states, value_states = past_key_value.update(key_states, value_states,
                                                         self.layer_idx, cache_kwargs)

    if q_len != 1:
        key, value = restore_fp8_kv_cache(key_states, value_states, query_states.dtype)
        key = repeat_kv(key, self.num_key_value_groups)
        value = repeat_kv(value, self.num_key_value_groups)
        attn_weights = torch.matmul(query_states, key.transpose(2, 3))
    else:
        import linear_q4_0
        attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)

    attn_weights = attn_weights / math.sqrt(self.head_dim)

    invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
                      ("Attention weights should be of size "
                       f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
                       "but is {attn_weights.size()}"))

    if attention_mask is not None:
        invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
                          (f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
                           f" but is {attention_mask.size()}"))

        attn_weights = attn_weights + attention_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)

    if q_len != 1:
        attn_output = torch.matmul(attn_weights, value)
    else:
        import linear_q4_0
        attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights,
                                                        value_states.transpose(-1, -2))

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

    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)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def qwen2_attention_forward_origin(
    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,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)

    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()
    device = hidden_states.device

    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)

    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 decoding with 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)
    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)

    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]

        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)

    invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
                      ("Attention weights should be of size "
                       f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
                       "but is {attn_weights.size()}"))

    if attention_mask is not None:
        invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
                          (f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
                           f" but is {attention_mask.size()}"))

        attn_weights = attn_weights + attention_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)

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

    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)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value
