#
# 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/main/src/transformers/models/mistral/modeling_mistral.py
#
# Copyright 2023 Mistral AI 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.

from typing import Optional, Tuple, Union, List

import os
import torch
from transformers.cache_utils import Cache
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.models.mistral.modeling_mistral import MistralModel, MistralAttention

from ipex_llm.transformers.models.common import merge_qkv_base
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))


def mistral_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,
) -> Union[Tuple, BaseModelOutputWithPast]:
    # ipex-llm changes start
    # IPEX-LLM OPT: kv cache and quantize kv cache
    inputs = input_ids if input_ids is not None else inputs_embeds
    use_cache = use_cache if use_cache is not None else self.config.use_cache
    use_cache = use_cache or inputs.device.type == 'xpu'
    use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
                                            self.config.num_attention_heads,
                                            self.config.num_key_value_heads)
    use_compress_kv = should_use_compresskv(inputs, inputs.size(1)) or \
        isinstance(past_key_values, DynamicCompressCache)

    if use_cache:
        if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
            if use_quantize_kv:
                past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
            else:
                past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
        elif use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
        elif (
            not use_quantize_kv
            and not use_compress_kv
            and not isinstance(past_key_values, DynamicNormalCache)
        ):
            past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
    # ipex-llm changes end

    return MistralModel.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 merge_qkv(module: torch.nn.Module):
    merge_qkv_base(module, MistralAttention)


def mistral_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,
):
    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]
    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

    # IPEX 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, "mistral")

    if isinstance(past_key_value, DynamicCompressCache):
        enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, q_len)
        key_states, value_states = past_key_value.update(
            key_states, value_states, self.layer_idx,
            query_states, attention_mask, self.num_key_value_groups,
            self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH
        )
    else:
        key_states, value_states = past_key_value.update(key_states, value_states,
                                                         self.layer_idx, None)

    # IPEX-LLM OPT: sdpa
    attn_weights = None
    attn_output = scaled_dot_product_attention(
        query_states, key_states, value_states,
        attention_mask, q_len == kv_seq_len
    )

    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