#
# 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.

# This file is adapted from
# https://huggingface.co/baichuan-inc/Baichuan-7B/blob/c1a5c7d5b7f50ecc51bb0e08150a9f12e5656756/modeling_baichuan.py
# and
# https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/a4a558127068f2ce965aa56aeb826bf501a68970/modeling_baichuan.py


import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
import torch.nn.functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \
    append_kv_cache, is_enough_kv_cache_room_4_31
from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
    restore_fp8_kv_cache, use_quantize_kv_cache
from ipex_llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu

import os

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


def baichuan_attention_forward_7b(
    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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    if use_quantize_kv_cache(self.W_pack, hidden_states):
        forward_function = baichuan_attention_forward_7b_quantized
    else:
        forward_function = baichuan_attention_forward_7b_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
    )


def baichuan_attention_forward_7b_quantized(
    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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()
    device = hidden_states.device

    proj = self.W_pack(hidden_states)
    proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
    # batch_size x source_len x hidden_size
    query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # batch_size x target_len x head_size
    key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # batch_size x source_len x hidden_size
    value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]
    if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
        query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
                                                                     key_states,
                                                                     position_ids,
                                                                     "baichuan")
    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, "baichuan")
    # [bsz, nh, t, hd]

    if past_key_value is None:
        attn_weights = torch.matmul(query_states,
                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            invalidInputError(
                False,
                f"Attention weights should be of size "
                f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
                f", 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
            attn_weights = torch.max(attn_weights,
                                     torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1,
                                             dtype=torch.float32).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)
        kv_seq_len = key_states.shape[-2]
        if use_cache:
            k_cache, v_cache = init_fp8_kv_cache(
                bsz, self.num_heads, kv_seq_len, self.head_dim,
                device=device, new_layout=True
            )
            key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, key_states,
                                                           value_states, new_layout=True)
            past_key_value = (key_states, value_states)
    else:
        k_cache, v_cache = past_key_value
        key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
                                                       key_states, value_states, new_layout=True)
        kv_seq_len = key_states.shape[-2]
        past_key_value = (key_states, value_states)
        if query_states.size(2) != 1 or query_states.device.type != 'xpu':
            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
                                                            query_states.dtype)
            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
            attn_weights = attn_weights / math.sqrt(self.head_dim)

            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
                invalidInputError(
                    False,
                    f"Attention weights should be of size "
                    f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
                    f", 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
                attn_weights = torch.max(attn_weights,
                                         torch.tensor(torch.finfo(attn_weights.dtype).min))

            # upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1,
                                                 dtype=torch.float32).to(query_states.dtype)
            attn_output = torch.matmul(attn_weights, value_states)
        else:
            import linear_q4_0
            attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
                                              attention_mask)
            attn_weights = None

    invalidInputError(
        attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
        f"`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)
    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.to(hidden_states.dtype), attn_weights, past_key_value


def baichuan_attention_forward_7b_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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()
    device = hidden_states.device

    proj = self.W_pack(hidden_states)
    proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
    # batch_size x source_len x hidden_size
    query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # batch_size x target_len x head_size
    key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # batch_size x source_len x hidden_size
    value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

    kv_seq_len = key_states.shape[-2]
    enough_kv_room = True
    if past_key_value is not None:
        enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
        kv_seq_len += past_key_value[0].shape[-2]
    if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
        query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
                                                                     key_states,
                                                                     position_ids,
                                                                     "baichuan")
    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, "baichuan")
    # [bsz, nh, t, hd]

    # if past_key_value is not None:
    #     # reuse k, v, self_attention
    #     key_states = torch.cat([past_key_value[0], key_states], dim=2)
    #     value_states = torch.cat([past_key_value[1], value_states], dim=2)
    if past_key_value is not None:
        # reuse k, v, self_attention
        cache_k = past_key_value[0]
        cache_v = past_key_value[1]
        if not enough_kv_room:
            # allocate new
            new_cache_k, new_cache_v = extend_kv_cache(bsz,
                                                       self.num_heads,
                                                       self.head_dim,
                                                       cache_k.size(2),
                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                                                       dtype=cache_k.dtype,
                                                       device=device)
            new_cache_k[:] = cache_k
            new_cache_v[:] = cache_v
            cache_k = new_cache_k
            cache_v = new_cache_v

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

    elif use_cache:
        max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
        new_key_states, new_value_states = init_kv_cache(bsz,
                                                         self.num_heads,
                                                         self.head_dim,
                                                         kv_seq_len,
                                                         max_cache_length,
                                                         dtype=key_states.dtype,
                                                         device=device)
        new_key_states[:] = key_states
        new_value_states[:] = value_states
        key_states = new_key_states
        value_states = new_value_states

    past_key_value = (key_states, value_states) if use_cache else None

    if not self.training and not hidden_states.requires_grad and \
            use_flash_attention(query_states, key_states, attention_mask):
        attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
                                                     key_states.to(device, dtype=torch.float16),
                                                     value_states.to(device, dtype=torch.float16),
                                                     is_causal=True)
        attn_weights = None
    elif not self.training and not hidden_states.requires_grad and \
            use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
        import linear_fp16_esimd
        attn_output = linear_fp16_esimd.sdp_forward(query_states,
                                                    key_states,
                                                    value_states)
        attn_output = attn_output.view(query_states.shape)
        attn_weights = None
    else:
        attn_weights = torch.matmul(query_states,
                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            invalidInputError(False,
                              f"Attention weights should be of size "
                              f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
                              f", 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
            attn_weights = torch.max(attn_weights,
                                     torch.tensor(torch.finfo(attn_weights.dtype).min))

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

    invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
                      f"`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)
    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.to(hidden_states.dtype), attn_weights, past_key_value


def baichuan_attention_forward_13b(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    if use_quantize_kv_cache(self.W_pack, hidden_states):
        forward_function = baichuan_attention_forward_13b_quantized
    else:
        forward_function = baichuan_attention_forward_13b_origin
    return forward_function(
        self=self,
        hidden_states=hidden_states,
        attention_mask=attention_mask,
        past_key_value=past_key_value,
        output_attentions=output_attentions,
        use_cache=use_cache
    )


def baichuan_attention_forward_13b_quantized(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

    bsz, q_len, _ = hidden_states.size()
    device = hidden_states.device

    proj = self.W_pack(hidden_states)
    proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
    query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    if past_key_value is None:
        attn_weights = torch.matmul(query_states,
                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
        if attention_mask is not None:
            if q_len == 1:  # inference with cache
                if len(attention_mask.size()) == 4:
                    attention_mask = attention_mask[:, :, -1:, :]
                else:
                    attention_mask = attention_mask[:, -1:, :]
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights,
                                     torch.tensor(torch.finfo(attn_weights.dtype).min))

        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)

        attn_output = torch.matmul(attn_weights, value_states)
        kv_seq_len = key_states.shape[-2]
        if use_cache:
            k_cache, v_cache = init_fp8_kv_cache(
                bsz, self.num_heads, kv_seq_len, self.head_dim,
                device=device
            )
            key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
                                                           key_states, value_states)
            past_key_value = (key_states, value_states)
    else:
        k_cache, v_cache = past_key_value
        key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
                                                       key_states, value_states)
        kv_seq_len = key_states.shape[-2]
        past_key_value = (key_states, value_states)
        if query_states.size(2) != 1 or query_states.device.type != 'xpu':
            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
                                                            query_states.dtype)
            attn_weights = torch.matmul(query_states, key_states.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)

        if attention_mask is not None:
            if q_len == 1:  # inference with cache
                if len(attention_mask.size()) == 4:
                    attention_mask = attention_mask[:, :, -1:, :]
                else:
                    attention_mask = attention_mask[:, -1:, :]
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights,
                                     torch.tensor(torch.finfo(attn_weights.dtype).min))

        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
        if query_states.size(2) != 1 or query_states.device.type != 'xpu':
            attn_output = torch.matmul(attn_weights, value_states)
        else:
            import linear_q4_0
            attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights,
                                                            value_states.transpose(-1, -2))

    attn_output = attn_output.transpose(1, 2)
    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 baichuan_attention_forward_13b_origin(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

    bsz, q_len, _ = hidden_states.size()
    device = hidden_states.device

    proj = self.W_pack(hidden_states)
    proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
    query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

    kv_seq_len = key_states.shape[-2]
    enough_kv_room = True
    if past_key_value is not None:
        enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
        kv_seq_len += past_key_value[0].shape[-2]

    # if past_key_value is not None:
    #     # reuse k, v, self_attention
    #     key_states = torch.cat([past_key_value[0], key_states], dim=2)
    #     value_states = torch.cat([past_key_value[1], value_states], dim=2)
    if past_key_value is not None:
        # reuse k, v, self_attention
        cache_k = past_key_value[0]
        cache_v = past_key_value[1]
        if not enough_kv_room:
            # allocate new
            new_cache_k, new_cache_v = extend_kv_cache(bsz,
                                                       self.num_heads,
                                                       self.head_dim,
                                                       cache_k.size(2),
                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                                                       dtype=cache_k.dtype,
                                                       device=device)
            new_cache_k[:] = cache_k
            new_cache_v[:] = cache_v
            cache_k = new_cache_k
            cache_v = new_cache_v

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

    elif use_cache:
        max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
        new_key_states, new_value_states = init_kv_cache(bsz,
                                                         self.num_heads,
                                                         self.head_dim,
                                                         kv_seq_len,
                                                         max_cache_length,
                                                         dtype=key_states.dtype,
                                                         device=device)
        new_key_states[:] = key_states
        new_value_states[:] = value_states
        key_states = new_key_states
        value_states = new_value_states

    past_key_value = (key_states, value_states) if use_cache else None

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

    if attention_mask is not None:
        if q_len == 1:  # inference with cache
            if len(attention_mask.size()) == 4:
                attention_mask = attention_mask[:, :, -1:, :]
            else:
                attention_mask = attention_mask[:, -1:, :]
        attn_weights = attn_weights + attention_mask
        attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

    attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)

    attn_output = torch.matmul(attn_weights, value_states)

    attn_output = attn_output.transpose(1, 2)
    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.to(hidden_states.dtype), attn_weights, past_key_value