# coding=utf-8
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI 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 Any, Dict, Iterable, List, Optional, Tuple, Union

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import (get_pp_group, get_pp_indices,
                              get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              split_tensor_along_last_dim,
                              tensor_model_parallel_all_gather,
                              tensor_model_parallel_all_reduce)

from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader, kv_cache_scales_loader)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.utils import is_hip, print_warning_once

from .interfaces import SupportsLoRA


class TeleChatMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
    ) -> None:
        super().__init__()
        self.gate_proj = ColumnParallelLinear(
            input_size=hidden_size,
            output_size=intermediate_size,
            bias=False,
            quant_config=quant_config)
        
        self.up_proj = ColumnParallelLinear(
            input_size=hidden_size,
            output_size=intermediate_size,
            bias=False,
            quant_config=quant_config)
        
        self.down_proj = RowParallelLinear(input_size=intermediate_size,
                                           output_size=hidden_size,
                                        #    bias=bias,
                                           bias=True,
                                           quant_config=quant_config,
                                           input_is_parallel=True)
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_output, _ = self.gate_proj(x)
        up_output, _ = self.up_proj(x)

        gate_output = self.act_fn(torch.cat([gate_output, up_output], dim=-1))

        output, _ = self.down_proj(gate_output)
        return output
    
class TeleChatAttention(nn.Module):

    def __init__(
        self,
        config,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        cache_config: Optional[CacheConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config 
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.tp_size = tp_size
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        self.query = ColumnParallelLinear(
            input_size=hidden_size,
            output_size=hidden_size,
            bias=False,
            quant_config=quant_config,
            gather_output=False)
        
        kv_projection_size = self.head_dim * self.total_num_heads
        self.key_value = MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[kv_projection_size] * 2,
            bias=False,
            quant_config=quant_config,
            gather_output=False,)
        
        self.dense = RowParallelLinear(
            input_size=hidden_size,
            output_size=hidden_size,
            bias=True,
            quant_config=quant_config,
            input_is_parallel=True,
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )

        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config)
    
    def split_tensor_along_last_dim(self,
                                    tensor: torch.Tensor,
                                    num_partitions: int,
                                    contiguous_split_chunks: bool = False,
                                    ):

        # Get the size and dimension.
        last_dim = tensor.dim() - 1
        last_dim_size = tensor.size()[last_dim] // num_partitions
        # Split.
        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)

        return tensor_list
    
    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:

        query_layer, _ = self.query(hidden_states)
        mixed_kv_layer, _ = self.key_value(hidden_states)
        (key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_kv_layer, 2)
        query_layer, key_layer = self.rotary_emb(positions, query_layer, key_layer)
        attn_output = self.attn(query_layer, key_layer, value_layer, kv_cache, attn_metadata)
        output, _ = self.dense(attn_output)

        return output


class TeleChatDecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        if rope_scaling is not None and getattr(
                config, "original_max_position_embeddings", None):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        attention_bias = getattr(config, "attention_bias", False) or getattr(
            config, "bias", False)
        self.self_attention = TeleChatAttention(
            config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=getattr(config, "num_key_value_heads",
                                 config.num_attention_heads),
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=attention_bias,
            cache_config=cache_config,
        )
        self.mlp = TeleChatMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            bias=getattr(config, "mlp_bias", False),
        )
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)
        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm


    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        residual = hidden_states
        layernorm_output = self.input_layernorm(hidden_states)
        attn_outputs = self.self_attention(
            positions=positions,
            hidden_states=layernorm_output,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )
        attn_outputs = residual + attn_outputs
        residual = attn_outputs
        layernorm_output = self.post_attention_layernorm(attn_outputs)
        output = residual + self.mlp(layernorm_output)
        return output

class TeleChatModel(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.word_embeddings = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )
        self.h = nn.ModuleList(
           [
                TeleChatDecoderLayer(config=config,
                                  cache_config=cache_config,
                                  quant_config=quant_config)
                for _ in range(self.config.num_hidden_layers)
            ] 
            )
        
        self.ln_f = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)


    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.word_embeddings(input_ids)

    def forward(
        self,
        input_ids: Optional[torch.Tensor],
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        
        hidden_states = self.get_input_embeddings(input_ids)
       
        for i in range(self.config.num_hidden_layers):
            layer = self.h[i]
            hidden_states = layer(
                positions,
                hidden_states,
                kv_caches[i],
                attn_metadata,
            )

        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class TeleChat12BForCausalLM(nn.Module, SupportsLoRA):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    # LoRA specific attributes
    supported_lora_modules = [
        "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
        "lm_head"
    ]
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]
    bitsandbytes_stacked_params_mapping = {
        # shard_name, weight_name, index
        "q_proj": ("qkv_proj", 0),
        "k_proj": ("qkv_proj", 1),
        "v_proj": ("qkv_proj", 2),
        "gate_proj": ("gate_up_proj", 0),
        "up_proj": ("gate_up_proj", 1),
    }

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        super().__init__()
        config.intermediate_size = config.ffn_hidden_size
        config.hidden_act = "silu"
        config.rms_norm_eps = config.layer_norm_epsilon
        config.tie_word_embeddings = False

        self.config = config
        self.lora_config = lora_config

        self.transformer = TeleChatModel(config,
                                cache_config,
                                quant_config,
                                lora_config=lora_config)
  
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = Sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        model_output = self.transformer(input_ids, positions, kv_caches,
                                  attn_metadata)
        return model_output

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def make_empty_intermediate_tensors(
            self, batch_size: int, dtype: torch.dtype,
            device: torch.device) -> IntermediateTensors:
        return IntermediateTensors({
            "hidden_states":
            torch.zeros((batch_size, self.config.hidden_size),
                        dtype=dtype,
                        device=device),
            "residual":
            torch.zeros((batch_size, self.config.hidden_size),
                        dtype=dtype,
                        device=device),
        })

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):


        params_dict = dict(self.named_parameters())

        num_key_value_heads = self.config.num_attention_heads
        head_dim = self.config.hidden_size // num_key_value_heads
        for name, loaded_weight in weights:
            if "self_attention.key_value" in name:
                k_weight = []
                v_weight = []
                for i in range(num_key_value_heads):
                    start =i * head_dim * 2
                    k_weight.append(loaded_weight[start:start+head_dim,:])
                    v_weight.append(loaded_weight[start+head_dim:start+2*head_dim:])
                
                k_weight = torch.cat(k_weight,dim=0)
                v_weight = torch.cat(v_weight,dim=0)

                loaded_weight = torch.cat([k_weight, v_weight], dim=0)

            try:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            except KeyError:
                print("key error")
                pass

    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
        tp_size = get_tensor_model_parallel_world_size()
        tp_rank = get_tensor_model_parallel_rank()
        for layer_idx, scaling_factor in kv_cache_scales_loader(
                quantization_param_path, tp_rank, tp_size,
                self.config.num_hidden_layers,
                self.config.__class__.model_type):
            if not isinstance(self.model.layers[layer_idx], nn.Identity):
                layer_self_attn = self.model.layers[layer_idx].self_attn

            if is_hip():
                # The scaling factor convention we are assuming is
                # quantized_value * scaling_factor ~= true_value
                # which is consistent with the practice of setting
                # scaling_factor = tensor_amax / FPtype_max
                scaling_factor *= 2
            if hasattr(layer_self_attn, "kv_scale"):
                layer_self_attn.attn._kv_scale = scaling_factor
            else:
                raise RuntimeError("Self attention has no KV cache scaling "
                                   "factor attribute!")