#
# 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
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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.
# 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/qwen2_moe/modeling_qwen2_moe.py

# coding=utf-8
# 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.

""" PyTorch Qwen2MoE model."""
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from typing import Optional, Tuple, Union, List
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache

from transformers.models.qwen2_moe.modeling_qwen2_moe import (
    _prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask,
    Qwen2MoeAttention,
)
from transformers.modeling_outputs import MoeModelOutputWithPast
from transformers.cache_utils import Cache, DynamicCache
from transformers import logging


logger = logging.get_logger(__name__)


def qwen2moe_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,
    output_router_logits: 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.shared_expert.up_proj, 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 qwen2_moe_model_forward_internal(
        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,
        output_router_logits=output_router_logits,
        return_dict=return_dict,
    )


def qwen2_moe_model_forward_internal(
        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,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
) -> Union[Tuple, MoeModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None \
            else self.config.output_attentions
        output_router_logits = (
            output_router_logits if output_router_logits is not None else
            self.config.output_router_logits
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else
            self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            invalidInputError(False, "You cannot specify both decoder_input_ids and "
                              "decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            invalidInputError(False,
                              "You have to specify decoder_input_ids or decoder_inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing."
                    " Setting `use_cache=False`..."
                )
                use_cache = False

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length,
                dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if attention_mask is not None and self._attn_implementation == "flash_attention_2" \
                and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                invalidInputError(
                    False,
                    "You are attempting to perform batched generation with padding_side='right'"
                    " this may lead to unexpected behaviour for Flash Attention version of"
                    " Qwen2MoE. Make sure to call `tokenizer.padding_side='left'`"
                    " before tokenizing the input."
                )

        if self._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and
                                                0 in attention_mask) else None
        elif self._attn_implementation == "sdpa" and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    output_router_logits,
                    use_cache,
                )
            else:
                # ipex-llm changes
                curr_device = decoder_layer.input_layernorm.weight.device
                if attention_mask is not None:
                    attention_mask = attention_mask.to(curr_device)
                if position_ids is not None:
                    position_ids = position_ids.to(curr_device)
                # ipex-llm changes end
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

            if output_router_logits and layer_outputs[-1] is not None:
                all_router_logits += (layer_outputs[-1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache \
                else next_decoder_cache

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns,
                          all_router_logits] if v is not None
            )
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            router_logits=all_router_logits,
        )


def merge_qkv(module: torch.nn.Module):
    if isinstance(module, Qwen2MoeAttention):
        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 qwen2moe_moeblock_forward(self, hidden_states: torch.Tensor):
    batch_size, sequence_length, hidden_dim = hidden_states.shape
    hidden_states = hidden_states.view(-1, hidden_dim)
    bs = hidden_states.shape[0]
    # router_logits: (batch * sequence_length, n_experts)
    router_logits = self.gate(hidden_states)

    routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
    routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
    if self.norm_topk_prob:
        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
    # we cast back to the input dtype
    routing_weights = routing_weights.to(hidden_states.dtype)

    if bs == 1:
        selected_experts = selected_experts[0].cpu().tolist()
        for idx in range(self.top_k):
            exp_id = selected_experts[idx]
            expert_layer = self.experts[exp_id]
            weight = routing_weights[:, idx]
            if idx == 0:
                final_hidden_states = expert_layer(hidden_states) * weight
            else:
                final_hidden_states = final_hidden_states + expert_layer(hidden_states) * weight
    elif bs < 256 and hidden_states.device.type == 'xpu':
        final_hidden_states = torch.zeros((batch_size * sequence_length, hidden_dim),
                                          dtype=hidden_states.dtype, device=hidden_states.device)
        import xe_linear
        indexes = xe_linear.get_moe_indexes(selected_experts.to(torch.int32).cpu(), 60)
        for expert_idx in range(self.num_experts):
            expert_layer = self.experts[expert_idx]
            idx_list = indexes[0][expert_idx]
            top_x_list = indexes[1][expert_idx]
            if len(idx_list) == 0:
                continue

            top_x = torch.tensor(top_x_list, dtype=torch.long, device=hidden_states.device)
            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
            current_hidden_states = expert_layer(current_state) * \
                routing_weights[top_x_list, idx_list, None]
            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
    else:
        final_hidden_states = torch.zeros(
            (batch_size * sequence_length, hidden_dim),
            dtype=hidden_states.dtype,
            device=hidden_states.device
        )

        # One hot encode the selected experts to create an expert mask
        # this will be used to easily index which expert is going to be sollicitated
        expert_mask = torch.nn.functional.one_hot(selected_experts,
                                                  num_classes=self.num_experts).permute(2, 1, 0)

        # Loop over all available experts in the model and perform the computation on each expert
        for expert_idx in range(self.num_experts):
            expert_layer = self.experts[expert_idx]
            idx, top_x = torch.where(expert_mask[expert_idx])

            if top_x.shape[0] == 0:
                continue

            # Index the correct hidden states and compute the expert hidden state for
            # the current expert. We need to make sure to multiply the output hidden
            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]

            # However `index_add_` only support torch tensors for indexing so we'll use
            # the `top_x` tensor here.
            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
    shared_expert_output = self.shared_expert(hidden_states)
    shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output

    final_hidden_states = final_hidden_states + shared_expert_output

    final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
    return final_hidden_states, router_logits
