# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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 __future__ import annotations

import numpy as np
import paddle
import paddle.nn as nn

from paddlenlp.experimental.transformers.fused_transformer_layers import (
    FusedMultiTransformerBase,
    FusedMultiTransformerConfig,
)
from paddlenlp.experimental.transformers.generation_utils import (
    GenerationInferenceModel,
)
from paddlenlp.experimental.transformers.utils import (
    infererence_model_from_config,
    infererence_model_from_pretrained,
)
from paddlenlp.transformers import OPTPretrainedModel
from paddlenlp.transformers.model_utils import (
    dy2st_nocheck_guard_context,
    register_base_model,
)
from paddlenlp.transformers.opt.configuration import OPTConfig
from paddlenlp.transformers.opt.modeling import OPTEmbeddings, OPTLMHead

__all__ = ["OPTForCausalLMInferenceModel", "OPTForBlip2InferenceModel"]


@register_base_model
class OPTInferenceModel(OPTPretrainedModel):
    def __init__(self, config: OPTConfig):
        super(OPTInferenceModel, self).__init__(config)
        self.pad_token_id = config.pad_token_id
        self.initializer_range = config.initializer_range
        self.vocab_size = config.vocab_size
        self.embeddings = OPTEmbeddings(config)

        if config.normalize_before:
            self.final_layer_norm = nn.LayerNorm(config.hidden_size)
        else:
            self.final_layer_norm = None

        self.num_layers = config.num_hidden_layers
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_size = self.hidden_size // self.num_heads

        self.epsilon = 1e-5

        ln_scale_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.norm1.weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        ln_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.norm1.bias".format(i))
            for i in range(config.num_hidden_layers)
        ]

        qkv_weight_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.qkv_weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        qkv_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.qkv_bias".format(i)) for i in range(config.num_hidden_layers)
        ]

        out_proj_weight_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.self_attn.out_proj.weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        out_proj_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.self_attn.out_proj.bias".format(i))
            for i in range(config.num_hidden_layers)
        ]

        ffn_ln_scale_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.norm2.weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        ffn_ln_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.norm2.bias".format(i))
            for i in range(config.num_hidden_layers)
        ]

        ffn1_weight_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.linear1.weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        ffn1_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.linear1.bias".format(i))
            for i in range(config.num_hidden_layers)
        ]
        ffn2_weight_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.linear2.weight".format(i))
            for i in range(config.num_hidden_layers)
        ]
        ffn2_bias_attrs = [
            paddle.ParamAttr(name="opt.decoder.layers.{}.linear2.bias".format(i))
            for i in range(config.num_hidden_layers)
        ]

        transformer_config = FusedMultiTransformerConfig(
            config.hidden_size,
            config.num_attention_heads,
            config.intermediate_size,
            dropout_rate=0.0,
            activation="relu",
            normalize_before=True,
            num_layers=config.num_hidden_layers,
            tp_degree=1,
            ring_id=-1,
            ln_scale_attrs=ln_scale_attrs,
            ln_bias_attrs=ln_bias_attrs,
            qkv_weight_attrs=qkv_weight_attrs,
            qkv_bias_attrs=qkv_bias_attrs,
            linear_weight_attrs=out_proj_weight_attrs,
            linear_bias_attrs=out_proj_bias_attrs,
            ffn_ln_scale_attrs=ffn_ln_scale_attrs,
            ffn_ln_bias_attrs=ffn_ln_bias_attrs,
            ffn1_weight_attrs=ffn1_weight_attrs,
            ffn1_bias_attrs=ffn1_bias_attrs,
            ffn2_weight_attrs=ffn2_weight_attrs,
            ffn2_bias_attrs=ffn2_bias_attrs,
            epsilon=self.epsilon,
        )

        self.transformer_block = FusedMultiTransformerBase(transformer_config)

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def remove_padding(self, input_ids, seq_lens_this_time):
        cum_offsets_now = paddle.cumsum(paddle.max(seq_lens_this_time) - seq_lens_this_time)
        token_num = paddle.sum(seq_lens_this_time)
        from paddlenlp_ops import get_padding_offset

        ids_remove_padding, cum_offsets, padding_offset = get_padding_offset(
            input_ids, cum_offsets_now, token_num, seq_lens_this_time
        )
        return ids_remove_padding, padding_offset, cum_offsets

    # This function is a little different from prepare_input_ids_for_generation in paddlenlp/transformers/generation/utils.py
    @staticmethod
    def prepare_input_ids_for_generation(bos_token_id, encoder_output=None):
        batch_size = 1
        seq_len = 1
        if bos_token_id is None:
            raise ValueError("`bos_token_id` should be defined when no " "`input_ids` are provided.")
        if encoder_output is not None:
            batch_size = encoder_output.shape[0]
            seq_len = encoder_output.shape[1]
        return paddle.ones([batch_size, seq_len], dtype="int64") * bos_token_id

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        use_cache=None,
        cache_kvs=None,
        seq_len_encoder=None,
        seq_len_decoder=None,
        past_key_values=None,
        output_attentions=False,
        output_hidden_states=None,
        return_dict=False,
        **kwargs,
    ):
        # kwargs["cache"] is used used to distinguish between encoder and decoder phase.
        past_key_values = kwargs.get("cache", None)
        is_decoder = past_key_values is not None

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        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

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        # generate a fake input_ids according to inputs_embeds
        # this is usually occurred in img2txt multimodal model when first enter into this forward function.
        if input_ids is None and inputs_embeds is not None:
            input_ids = self.prepare_input_ids_for_generation(self.config.bos_token_id, inputs_embeds)

        batch, seq_len = input_ids.shape

        past_kv_length = paddle.max(seq_len_decoder) if is_decoder else 0
        now_len = past_kv_length + seq_len
        embedding_output = self.embeddings(
            input_ids=input_ids,
            attention_mask=paddle.ones([batch, now_len], dtype="int64"),
            input_embeddings=inputs_embeds,
            past_key_values_length=past_kv_length,
        )

        var_embedding_output = None
        if not is_decoder:
            # support variable sequence length embeddings
            var_embedding_output = embedding_output[0, 0 : seq_len_encoder[0][0], :]
            for b in range(1, batch):
                var_embedding_output = paddle.concat(
                    [var_embedding_output, embedding_output[b, 0 : seq_len_encoder[b][0], :]], axis=0
                )
        else:
            # merge batch and seq_len dimension.
            var_embedding_output = embedding_output.reshape([batch * seq_len, self.hidden_size])
        embedding_output = var_embedding_output

        if not is_decoder:
            # ids_remove_padding
            _, padding_offset, cum_offsets = self.remove_padding(input_ids, seq_len_encoder)
        else:
            _ = input_ids
            padding_offset = None
            cum_offsets = None

        seq_lens = seq_len_decoder if is_decoder else seq_len_encoder
        with dy2st_nocheck_guard_context():

            hidden_states, _ = self.transformer_block(
                input_ids,
                embedding_output,
                cum_offsets=cum_offsets,
                padding_offset=padding_offset,
                attn_mask=paddle.cast(attention_mask, dtype=embedding_output.dtype),
                caches=cache_kvs,
                seq_lens=seq_lens,
                rotary_embs=None,
                rotary_emb_dims=0,
                time_step=paddle.increment(paddle.shape(attention_mask)[-1], -1) if is_decoder else None,
            )

        output = hidden_states

        if self.final_layer_norm:
            output = self.final_layer_norm(output)
        return output

    @paddle.no_grad()
    def set_state_dict(self, state_dict):
        self.transformer_block.init_weight()

        self.embeddings.position_embeddings.weight.set_value(
            state_dict.pop("opt.embeddings.position_embeddings.weight")
        )
        self.embeddings.word_embeddings.weight.set_value(state_dict.pop("opt.embeddings.word_embeddings.weight"))
        self.final_layer_norm.weight.set_value(state_dict.pop("opt.decoder.final_layer_norm.weight"))
        self.final_layer_norm.bias.set_value(state_dict.pop("opt.decoder.final_layer_norm.bias"))

        for i in range(self.num_layers):
            ln_scale = state_dict.pop("opt.decoder.layers.{}.norm1.weight".format(i))
            ln_bias = state_dict.pop("opt.decoder.layers.{}.norm1.bias".format(i))
            ln_scale = paddle.cast(ln_scale, "float32")
            ln_bias = paddle.cast(ln_bias, "float32")

            q_weight = state_dict.pop("opt.decoder.layers.{}.self_attn.q_proj.weight".format(i))
            k_weight = state_dict.pop("opt.decoder.layers.{}.self_attn.k_proj.weight".format(i))
            v_weight = state_dict.pop("opt.decoder.layers.{}.self_attn.v_proj.weight".format(i))
            q_bias = state_dict["opt.decoder.layers.{}.self_attn.q_proj.bias".format(i)]
            k_bias = state_dict["opt.decoder.layers.{}.self_attn.k_proj.bias".format(i)]
            v_bias = state_dict["opt.decoder.layers.{}.self_attn.v_proj.bias".format(i)]

            concated_qkv_weight = np.concatenate([q_weight, k_weight, v_weight], axis=-1)
            concated_qkv_weight = concated_qkv_weight.transpose(1, 0)
            concated_qkv_weight = concated_qkv_weight.reshape(3 * self.num_heads * self.head_size, self.hidden_size)
            concated_qkv_weight = paddle.to_tensor(concated_qkv_weight)

            concated_qkv_bias = np.concatenate([q_bias, k_bias, v_bias], axis=-1)
            concated_qkv_bias = concated_qkv_bias.reshape(3 * self.num_heads * self.head_size)
            concated_qkv_bias = paddle.to_tensor(concated_qkv_bias)

            out_proj_weight = state_dict.pop("opt.decoder.layers.{}.self_attn.out_proj.weight".format(i))
            out_proj_bias = state_dict.pop("opt.decoder.layers.{}.self_attn.out_proj.bias".format(i))

            ffn_ln_scale = state_dict.pop("opt.decoder.layers.{}.norm2.weight".format(i))
            ffn_ln_bias = state_dict.pop("opt.decoder.layers.{}.norm2.bias".format(i))
            ffn_ln_scale = paddle.cast(ffn_ln_scale, "float32")
            ffn_ln_bias = paddle.cast(ffn_ln_bias, "float32")

            ffn1_weight = state_dict.pop("opt.decoder.layers.{}.linear1.weight".format(i))
            ffn1_bias = state_dict.pop("opt.decoder.layers.{}.linear1.bias".format(i))
            ffn2_weight = state_dict.pop("opt.decoder.layers.{}.linear2.weight".format(i))
            ffn2_bias = state_dict.pop("opt.decoder.layers.{}.linear2.bias".format(i))

            self.transformer_block.ln_scales[i].set_value(ln_scale)
            self.transformer_block.ln_biases[i].set_value(ln_bias)

            self.transformer_block.qkv_weights[i].set_value(concated_qkv_weight)
            self.transformer_block.qkv_biases[i].set_value(concated_qkv_bias)

            self.transformer_block.linear_weights[i].set_value(out_proj_weight)
            self.transformer_block.linear_biases[i].set_value(out_proj_bias)

            self.transformer_block.ffn_ln_scales[i].set_value(ffn_ln_scale)
            self.transformer_block.ffn_ln_biases[i].set_value(ffn_ln_bias)

            self.transformer_block.ffn1_weights[i].set_value(ffn1_weight)
            self.transformer_block.ffn1_biases[i].set_value(ffn1_bias)

            self.transformer_block.ffn2_weights[i].set_value(ffn2_weight)
            self.transformer_block.ffn2_biases[i].set_value(ffn2_bias)


class OPTForCausalLMInferenceModel(GenerationInferenceModel, OPTPretrainedModel):
    def __init__(self, config: OPTConfig, **kwargs):
        super(OPTForCausalLMInferenceModel, self).__init__(config)
        self.opt = OPTInferenceModel(config)
        self.lm_head = OPTLMHead(config)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
        return infererence_model_from_pretrained(cls, pretrained_model_name_or_path, args, kwargs)

    @classmethod
    def from_config(cls, config, *args, **kwargs):
        return infererence_model_from_config(cls, config, args, kwargs)

    @classmethod
    def get_cache_kvs_shape(
        cls, config: OPTConfig, max_batch_size: int = None, max_length: int = None
    ) -> list[list[int]]:
        """get cache_kvs tensor for opt model

        Args:
            max_batch_size (int): the max batch size
            max_length (int | None, optional): the max_length of cache_kvs. Defaults to None.

        Returns:
            list[paddle.Tensor]: the list tensor shape for cache
        """
        if max_length is None:
            max_length = config.max_position_embeddings

        cache_kvs = []
        for _ in range(config.num_hidden_layers):
            cache_kvs.append(
                [
                    2,
                    max_batch_size,
                    config.num_attention_heads // max(config.tensor_parallel_degree, 1),
                    max_length,
                    config.hidden_size // config.num_attention_heads,
                ]
            )
        return cache_kvs

    def prepare_inputs_for_generation(
        self,
        input_ids,
        cache_kvs,
        seq_len_encoder,
        seq_len_decoder,
        tgt_ids,
        tgt_pos,
        tgt_generation_mask,
        **kwargs,
    ):
        position_ids = kwargs.get("position_ids", None)
        attention_mask = kwargs.get("attention_mask", None)
        cache = kwargs.get("cache", None)
        inputs_embeds = kwargs.get("inputs_embeds", None)
        if cache is not None:
            input_ids = tgt_ids
            position_ids = tgt_pos
            attention_mask = (tgt_generation_mask - 1) * 1e4
            # make inputs_embeds be none in decoder phase.
            # in forward function, it will be assigned according to input_ids.
            inputs_embeds = None
        else:
            attention_mask = (attention_mask - 1) * 1e4
        model_inputs = {
            "input_ids": input_ids,
            "inputs_embeds": inputs_embeds,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "cache_kvs": cache_kvs,
            "seq_len_encoder": seq_len_encoder,
            "seq_len_decoder": seq_len_decoder,
            "cache": cache,
        }
        return model_inputs

    def forward(
        self,
        input_ids,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        use_cache=False,
        cache=None,
        cache_kvs=None,
        seq_len_encoder=None,
        seq_len_decoder=None,
        past_key_values=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.opt(
            input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache=cache,
            cache_kvs=cache_kvs,
            seq_len_encoder=seq_len_encoder,
            seq_len_decoder=seq_len_decoder,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs
        logits = self.lm_head(hidden_states)
        return logits

    @paddle.no_grad()
    def set_state_dict(self, state_dict):
        if "lm_head.decoder_weight" in state_dict:
            self.lm_head.decoder_weight.set_value(state_dict["lm_head.decoder_weight"])
        self.opt.set_state_dict({k: state_dict[k] for k in state_dict.keys()})


class OPTForBlip2InferenceModel(OPTForCausalLMInferenceModel):
    """
    This class is 99% like OPTForCausalLMInferenceModel.
    Used only for blip2's second part.
    """

    # This function corresponds to miniGPT4's second part, only used in miniGPT4.
    @paddle.no_grad()
    def generate_text_with_image_features(
        self,
        image_features: paddle.Tensor,
        second_input_ids: paddle.Tensor,
        attention_mask: paddle.Tensor,
        position_ids=None,
        penalty_score=None,
        frequency_score=None,
        presence_score=None,
        min_length=None,
        max_length=None,
        temperature=None,
        top_p=None,
        eos_token_id=None,
        seq_len_encoder=None,
        seq_len_decoder=None,
        step_idx=None,
        stop_flags=None,
        tgt_ids=None,
        tgt_pos=None,
        tgt_generation_mask=None,
        pre_ids=None,
        stop_nums=None,
        cache_kvs=[],
        inputs_embeds=None,
        **generate_kwargs
    ) -> paddle.Tensor:

        second_embeds = self.opt.get_input_embeddings()(second_input_ids)
        image_features = paddle.cast(image_features, dtype=second_embeds.dtype)
        inputs_embeds = paddle.concat([image_features, second_embeds], axis=1)

        outputs = self.generate(
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            penalty_score=penalty_score,
            frequency_score=frequency_score,
            presence_score=presence_score,
            min_length=min_length,
            max_length=max_length,
            temperature=temperature,
            top_p=top_p,
            eos_token_id=eos_token_id,
            seq_len_encoder=seq_len_encoder,
            seq_len_decoder=seq_len_decoder,
            step_idx=step_idx,
            stop_flags=stop_flags,
            tgt_ids=tgt_ids,
            tgt_pos=tgt_pos,
            tgt_generation_mask=tgt_generation_mask,
            pre_ids=pre_ids,
            stop_nums=stop_nums,
            cache_kvs=cache_kvs,
        )
        return outputs

    # rewrite to_static function in generation_utils.py
    def to_static(self, output_path: str, config: dict):
        dtype = config.get("dtype", paddle.get_default_dtype())
        cache_kvs_shapes = self.get_cache_kvs_shape(self.config, max_length=config.get("max_length", None))
        input_spec = [
            paddle.static.InputSpec(
                shape=[None, None, None], dtype="float32", name="image_features"
            ),  # image_features
            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="second_input_ids"),  # second_input_ids
            paddle.static.InputSpec(shape=[None, None], dtype=dtype, name="attention_mask"),  # attention_mask
            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="position_ids"),  # position_ids
            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="penalty_score"),  # penalty_score
            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="frequency_score"),  # frequency_score
            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="presence_score"),  # presence_score
            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="min_length"),  # min_decode_length
            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="max_length"),  # max_decode_length
            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="temperature"),  # temperature
            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="top_p"),  # top_p
            paddle.static.InputSpec(shape=[None], dtype="int64", name="eos_token_id"),  # eos_token_id
            paddle.static.InputSpec(shape=[None, 1], dtype="int32", name="seq_len_encoder"),  # seq_len_encoder
            paddle.static.InputSpec(shape=[None, 1], dtype="int32", name="seq_len_decoder"),  # seq_len_decoder
            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="step_idx"),  # step_idx
            paddle.static.InputSpec(shape=[None, 1], dtype="bool", name="stop_flags"),  # stop_flags
            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="tgt_ids"),  # tgt_ids
            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="tgt_pos"),  # tgt_pos
            paddle.static.InputSpec(
                shape=[None, 1, 1, None], dtype=dtype, name="tgt_generation_mask"
            ),  # tgt_generation_mask
            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="pre_ids"),  # pre_ids
            paddle.static.InputSpec(shape=[1], dtype="int64", name="stop_nums"),  # stop_nums
            [
                paddle.static.InputSpec(
                    shape=shape,
                    dtype=dtype,
                    name="cache_kvs_{}".format(i),
                )
                for i, shape in enumerate(cache_kvs_shapes)
            ],  # cache_kvs
        ]

        model = paddle.jit.to_static(self.generate_text_with_image_features, input_spec=input_spec)
        paddle.jit.save(model, output_path, skip_prune_program=True)
