#
# 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/v4.31.0/src/transformers/models/llama/modeling_llama.py
# which is licensed under Apache License 2.0:
#
# Copyright 2021 The 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.

import torch
from typing import Optional, Tuple
import torch.nn.functional as F
from ipex_llm.transformers.models.utils import repeat_kv
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import should_use_fuse_rope
from ipex_llm.transformers.models.utils import update_past_key_value
from ipex_llm.utils.common import invalidInputError

import os

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


def decilm_attention_forward_4_35_2(
    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,
    padding_mask: Optional[torch.LongTensor] = None,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()
    is_decode = past_key_value is not None
    device = hidden_states.device

    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:
        kv_seq_len += past_key_value[0].shape[-2]

    if should_use_fuse_rope(hidden_states, position_ids, self.training):
        import xe_addons
        xe_addons.rotary_half_inplaced(self.maybe_rotary.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, "llama")

    key_states, value_states = update_past_key_value(
        past_key_value, key_states, value_states,
        kv_seq_len, False, device
    )

    past_key_value = (key_states, value_states) if use_cache else None

    # 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)

    if is_decode:
        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
                                                     is_causal=False,
                                                     attn_mask=attention_mask)
        attn_output = attn_output.contiguous().view(bsz, q_len, self.hidden_size)

    else:
        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
                                                     is_causal=attention_mask is None,
                                                     attn_mask=attention_mask)

        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)}, but is"
                          f" {attn_output.size()}")

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

    attn_output = attn_output.to(hidden_states.dtype)
    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value