# Copyright 2022 The HuggingFace Team
# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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
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"""
Core functions to implement PPO algorithms.
The function implemented in this file should be used by trainer with different distributed strategies to
implement PPO
"""

from abc import ABC, abstractmethod
from collections import defaultdict
from typing import TYPE_CHECKING, Tuple

import numpy as np
import torch
import torch.nn.functional as F

from ..utils import torch_functional as VF


if TYPE_CHECKING:
    from .config import AlgorithmConfig


class KLController(ABC):
    @abstractmethod
    def update(self, current_kl: float, n_steps: int) -> None: ...


class AdaptiveKLController(KLController):
    """Adaptive KL controller described in: https://arxiv.org/pdf/1909.08593.pdf"""

    def __init__(self, init_kl_coef: float, target_kl: float, horizon: float):
        self.value = init_kl_coef
        self.target = target_kl
        self.horizon = horizon

    def update(self, current_kl: float, n_steps: int) -> None:
        target = self.target
        proportional_error = np.clip(current_kl / target - 1, -0.2, 0.2)
        mult = 1 + proportional_error * n_steps / self.horizon
        self.value *= mult


class FixedKLController(KLController):
    """Fixed KL controller."""

    def __init__(self, init_kl_coef: float):
        self.value = init_kl_coef

    def update(self, current_kl: float, n_steps: int) -> None:
        pass


def get_kl_controller(algorithm_config: "AlgorithmConfig") -> KLController:
    """Adapted from https://github.com/huggingface/trl/blob/v0.11.0/trl/trainer/ppo_trainer.py#L319"""
    if algorithm_config.kl_type == "fixed":
        kl_ctrl = FixedKLController(init_kl_coef=algorithm_config.kl_coef)
    elif algorithm_config.kl_type == "adaptive":
        assert algorithm_config.kl_horizon > 0, f"horizon must be larger than 0. Got {algorithm_config.kl_horizon}."
        kl_ctrl = AdaptiveKLController(
            init_kl_coef=algorithm_config.kl_coef,
            target_kl=algorithm_config.kl_target,
            horizon=algorithm_config.kl_horizon,
        )
    else:
        raise ValueError(f"Unknown kl type: {algorithm_config.kl_type}.")

    return kl_ctrl


@torch.no_grad()
def compute_gae_advantage_return(
    token_level_rewards: torch.Tensor,
    values: torch.Tensor,
    eos_mask: torch.Tensor,
    gamma: torch.Tensor,
    lam: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Adapted from https://github.com/huggingface/trl/blob/v0.16.0/trl/trainer/ppo_trainer.py#L513

    Args:
        token_level_rewards: `(torch.Tensor)`
            shape: (bs, response_length)
        values: `(torch.Tensor)`
            shape: (bs, response_length)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length). [EOS] mask. The token after [EOS] have mask zero.
        gamma: `(float)`
            discounted factor used in RL
        lam: `(float)`
            lambda value when computing Generalized Advantage Estimation (https://arxiv.org/abs/1506.02438)

    Returns:
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        Returns: `(torch.Tensor)`
            shape: (bs, response_length)

    """
    lastgaelam = 0
    advantages_reversed = []
    gen_len = token_level_rewards.shape[-1]
    for t in reversed(range(gen_len)):
        nextvalues = values[:, t + 1] if t < gen_len - 1 else 0.0
        delta = token_level_rewards[:, t] + gamma * nextvalues - values[:, t]
        lastgaelam = delta + gamma * lam * lastgaelam
        advantages_reversed.append(lastgaelam)

    advantages = torch.stack(advantages_reversed[::-1], dim=1)
    returns = (advantages + values) * eos_mask
    advantages = VF.masked_whiten(advantages, eos_mask) * eos_mask
    return advantages, returns


# NOTE(sgm): this implementation only consider outcome supervision, where the reward is a scalar.
@torch.no_grad()
def compute_grpo_outcome_advantage(
    token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, index: torch.Tensor, eps: float = 1e-6
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Compute advantage for GRPO, operating only on Outcome reward
    (with only one scalar reward for each response).
    Args:
        token_level_rewards: `(torch.Tensor)`
            shape: (bs, response_length)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)

    Returns:
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        Returns: `(torch.Tensor)`
            shape: (bs, response_length)
    """
    response_length = token_level_rewards.shape[-1]
    scores = token_level_rewards.sum(dim=-1)
    id2score = defaultdict(list)
    id2mean, id2std = {}, {}

    bsz = scores.shape[0]
    for i in range(bsz):
        id2score[index[i]].append(scores[i])

    for idx in id2score:
        if len(id2score[idx]) == 1:
            id2mean[idx] = torch.tensor(0.0)
            id2std[idx] = torch.tensor(1.0)
        elif len(id2score[idx]) > 1:
            id2mean[idx] = torch.mean(torch.tensor(id2score[idx]))
            id2std[idx] = torch.std(torch.tensor([id2score[idx]]))
        else:
            raise ValueError(f"no score in prompt index: {idx}")

    for i in range(bsz):
        scores[i] = (scores[i] - id2mean[index[i]]) / (id2std[index[i]] + eps)

    scores = scores.unsqueeze(-1).tile([1, response_length]) * eos_mask
    return scores, scores


@torch.no_grad()
def compute_rloo_outcome_advantage(
    token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, index: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740
    Args:
        token_level_rewards: `(torch.Tensor)`
            shape: (bs, response_length)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)

    Returns:
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        Returns: `(torch.Tensor)`
            shape: (bs, response_length)
    """
    response_length = token_level_rewards.shape[-1]
    scores = token_level_rewards.sum(dim=-1)

    id2score = defaultdict(list)
    id2mean = {}
    bsz = scores.shape[0]
    for i in range(bsz):
        id2score[index[i]].append(scores[i])

    for idx in id2score:
        if len(id2score[idx]) == 1:
            id2mean[idx] = torch.tensor(0.0)
        elif len(id2score[idx]) > 1:
            id2mean[idx] = torch.mean(torch.tensor(id2score[idx]))
        else:
            raise ValueError(f"no score in prompt index: {idx}.")

    for i in range(bsz):
        response_num = len(id2score[index[i]])
        if response_num > 1:
            scores[i] = scores[i] * response_num / (response_num - 1) - id2mean[index[i]] * response_num / (
                response_num - 1
            )

    scores = scores.unsqueeze(-1).tile([1, response_length]) * eos_mask
    return scores, scores


@torch.no_grad()
def compute_reinforce_plus_plus_outcome_advantage(
    token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, gamma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Compute advantage for REINFORCE++.
    This implementation is based on the paper: https://arxiv.org/abs/2501.03262
    Args:
        token_level_rewards: `(torch.Tensor)`
            shape: (bs, response_length)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)

    Returns:
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        Returns: `(torch.Tensor)`
            shape: (bs, response_length)
    """
    returns = torch.zeros_like(token_level_rewards)
    running_return = 0
    for t in reversed(range(token_level_rewards.shape[1])):
        running_return = token_level_rewards[:, t] + gamma * running_return
        returns[:, t] = running_return
        # Reset after EOS
        running_return = running_return * eos_mask[:, t]

    advantages = VF.masked_whiten(returns, eos_mask)
    advantages *= eos_mask
    returns *= eos_mask
    return advantages, returns


@torch.no_grad()
def compute_remax_outcome_advantage(
    token_level_rewards: torch.Tensor, reward_baselines: torch.Tensor, eos_mask: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Compute advantage for ReMax, operating only on Outcome reward
    This implementation is based on the paper: https://arxiv.org/abs/2310.10505

    (with only one scalar reward for each response).
    Args:
        token_level_rewards: `(torch.Tensor)`
            shape: (bs, response_length)
        reward_baselines: `(torch.Tensor)`
            shape: (bs,)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)

    Returns:
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        Returns: `(torch.Tensor)`
            shape: (bs, response_length)
    """
    response_length = token_level_rewards.shape[-1]
    # scores = token_level_rewards.sum(dim=-1)
    returns = (token_level_rewards * eos_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) * eos_mask
    advantages = returns - reward_baselines.unsqueeze(-1).tile([1, response_length]) * eos_mask
    return advantages, returns


def compute_rewards(
    token_level_scores: torch.Tensor,
    log_probs: torch.Tensor,
    ref_log_probs: torch.Tensor,
    kl_ratio: float,
) -> torch.Tensor:
    kl = log_probs - ref_log_probs
    return token_level_scores - kl * kl_ratio


def compute_policy_loss(
    old_log_probs: torch.Tensor,
    log_probs: torch.Tensor,
    advantages: torch.Tensor,
    eos_mask: torch.Tensor,
    cliprange: float,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Compute the policy loss.

    Adapted from https://github.com/huggingface/trl/blob/v0.15.0/trl/trainer/ppo_trainer.py#L568

    Args:
        old_log_prob: `(torch.Tensor)`
            shape: (bs, response_length)
        log_prob: `(torch.Tensor)`
            shape: (bs, response_length)
        advantages: `(torch.Tensor)`
            shape: (bs, response_length)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)
        cliprange: (float)
            The clip range used in PPO. See https://arxiv.org/abs/1707.06347

    Returns:
        pg_loss: `a scalar torch.Tensor`
            policy gradient loss computed via PPO
        pg_clipfrac: (float)
            a float number indicating the fraction of policy gradient loss being clipped
    """
    negative_approx_kl = log_probs - old_log_probs
    # clamp the ratio before exp to avoid nan
    # see: https://github.com/pytorch/pytorch/issues/10729
    ratio = torch.exp(negative_approx_kl)
    clipped_ratio = torch.exp(torch.clamp(negative_approx_kl, np.log(1.0 - cliprange), np.log(1.0 + cliprange)))
    ppo_kl = VF.masked_mean(-negative_approx_kl, eos_mask)

    pg_losses = -advantages * ratio
    pg_losses2 = -advantages * clipped_ratio

    pg_loss = VF.masked_mean(torch.max(pg_losses, pg_losses2), eos_mask)
    pg_clipfrac = VF.masked_mean(torch.gt(pg_losses2, pg_losses).float(), eos_mask)
    return pg_loss, pg_clipfrac, ppo_kl


def compute_value_loss(
    vpreds: torch.Tensor,
    returns: torch.Tensor,
    values: torch.Tensor,
    eos_mask: torch.Tensor,
    cliprange_value: float,
) -> Tuple[torch.Tensor, float]:
    """Compute the value loss.

    Copied from https://github.com/huggingface/trl/blob/v0.15.0/trl/trainer/ppo_trainer.py#L556

    Args:
        vpreds (`torch.FloatTensor`):
            Predicted values of the value head, shape (`batch_size`, `response_length`)
        returns: (`torch.FloatTensor`):
            Ground truth returns, shape (`batch_size`, `response_length`)
        values (`torch.FloatTensor`):
            Old values of value head, shape (`batch_size`, `response_length`)
        eos_mask: `(torch.Tensor)`
            shape: (bs, response_length)
        cliprange_value: (float)
            The clip range for value net used in PPO. See https://arxiv.org/abs/1707.06347

    Returns:
        vf_loss: a scalar (`torch.FloatTensor`):
            value function loss
        vf_clipfrac: a float
            The ratio of vf being clipped
    """
    vpredclipped = torch.clamp(vpreds, values - cliprange_value, values + cliprange_value)
    vf_losses1 = torch.square(vpreds - returns)
    vf_losses2 = torch.square(vpredclipped - returns)
    vf_loss = 0.5 * VF.masked_mean(torch.max(vf_losses1, vf_losses2), eos_mask)
    vf_clipfrac = VF.masked_mean(torch.gt(vf_losses2, vf_losses1).float(), eos_mask)
    return vf_loss, vf_clipfrac


def kl_penalty(log_probs: torch.FloatTensor, ref_log_probs: torch.FloatTensor, kl_penalty: str) -> torch.Tensor:
    """Compute KL divergence given log_probs and ref_log_probs.
    Copied from https://github.com/huggingface/trl/blob/v0.11.0/trl/trainer/ppo_trainer.py#L1150

    Args:
        log_probs: torch.Tensor
        ref_log_probs: torch.Tensor

    Returns:
        kl_div: torch.Tensor
    """
    log_probs, ref_log_probs = log_probs.float(), ref_log_probs.float()
    if kl_penalty == "kl":
        return log_probs - ref_log_probs

    if kl_penalty == "abs":
        return (log_probs - ref_log_probs).abs()

    if kl_penalty == "mse":
        return 0.5 * (log_probs - ref_log_probs).square()

    # J. Schulman. Approximating kl divergence, 2020.
    # URL http://joschu.net/blog/kl-approx.html
    if kl_penalty == "low_var_kl":
        kl = ref_log_probs - log_probs
        kld = (kl.exp() - kl - 1).contiguous()
        return torch.clamp(kld, min=-10, max=10)

    if kl_penalty == "full":
        return F.kl_div(ref_log_probs, log_probs, log_target=True, reduction="none").sum(-1)

    raise NotImplementedError(f"Unknown KL penalty: {kl_penalty}.")
