# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0


import torch
from torch import Tensor, nn


def compute_returns(self, rewards, values, dones, last_values, gamma, lam):
    advantage = 0
    returns = torch.zeros_like(values)
    for step in reversed(range(self.num_transitions_per_env)):
        if step == self.num_transitions_per_env - 1:
            next_values = last_values
        else:
            next_values = values[step + 1]
        next_is_not_terminal = 1.0 - dones[step].float()
        delta = rewards[step] + next_is_not_terminal * gamma * next_values - values[step]
        advantage = delta + next_is_not_terminal * gamma * lam * advantage
        returns[step] = advantage + values[step]

    # Compute and normalize the advantages
    advantages = returns - values
    advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)


class RolloutStorage(nn.Module):

    def __init__(self, num_envs, num_transitions_per_env, device="cpu"):

        super().__init__()

        self.device = device

        self.num_transitions_per_env = num_transitions_per_env
        self.num_envs = num_envs

        # rnn - for storing hidden states during rollout
        self.saved_hidden_states_a = None
        self.saved_hidden_states_c = None

        self.step = 0
        self.stored_keys = list()

    def register_key(self, key: str, shape=(), dtype=torch.float):
        # This class was partially copied from https://github.com/NVlabs/ProtoMotions/blob/94059259ba2b596bf908828cc04e8fc6ff901114/phys_anim/agents/utils/data_utils.py
        assert not hasattr(self, key), key
        assert isinstance(shape, (list, tuple)), f"shape must be a list or tuple, got {type(shape)}"
        buffer = torch.zeros(
            (self.num_transitions_per_env, self.num_envs) + shape, dtype=dtype, device=self.device
        )
        self.register_buffer(key, buffer, persistent=False)
        self.stored_keys.append(key)

    def increment_step(self):
        self.step += 1

    def update_key(self, key: str, data: Tensor):
        # This class was partially copied from https://github.com/NVlabs/ProtoMotions/blob/94059259ba2b596bf908828cc04e8fc6ff901114/phys_anim/agents/utils/data_utils.py
        assert not data.requires_grad
        assert self.step < self.num_transitions_per_env, "Rollout buffer overflow"
        try:
            getattr(self, key)[self.step].copy_(data)
        except:
            import ipdb

            ipdb.set_trace()

    def batch_update_data(self, key: str, data: Tensor):
        # This class was partially copied from https://github.com/NVlabs/ProtoMotions/blob/94059259ba2b596bf908828cc04e8fc6ff901114/phys_anim/agents/utils/data_utils.py
        assert not data.requires_grad
        getattr(self, key)[:] = data
        # self.store_dict[key] += self.total_sum()

    def _save_hidden_states(self, hidden_states):
        """Save hidden states for recurrent policies.

        Args:
            hidden_states: Tuple of (actor_hidden_states, critic_hidden_states)
                          Each can be None, a tensor (GRU), or a tuple of tensors (LSTM)
        """
        if hidden_states is None or hidden_states == (None, None):
            return

        # make a tuple out of GRU hidden state to match the LSTM format
        hid_a = (
            hidden_states[0]
            if isinstance(hidden_states[0], tuple)
            else (hidden_states[0],) if hidden_states[0] is not None else None
        )
        hid_c = (
            hidden_states[1]
            if isinstance(hidden_states[1], tuple)
            else (hidden_states[1],) if hidden_states[1] is not None else None
        )

        # initialize if needed
        if hid_a is not None and self.saved_hidden_states_a is None:
            self.saved_hidden_states_a = [
                torch.zeros(self.num_transitions_per_env, *hid_a[i].shape, device=self.device)
                for i in range(len(hid_a))
            ]
        if hid_c is not None and self.saved_hidden_states_c is None:
            self.saved_hidden_states_c = [
                torch.zeros(self.num_transitions_per_env, *hid_c[i].shape, device=self.device)
                for i in range(len(hid_c))
            ]

        # copy the states - CRITICAL: must detach for proper TBPTT
        # We save detached hidden states from rollout to use as initial states during training
        # This prevents backprop through the entire rollout history
        if hid_a is not None:
            for i in range(len(hid_a)):
                self.saved_hidden_states_a[i][self.step].copy_(hid_a[i].detach())
        if hid_c is not None:
            for i in range(len(hid_c)):
                self.saved_hidden_states_c[i][self.step].copy_(hid_c[i].detach())

    def clear(self):
        self.step = 0

    def get_statistics(self):
        raise NotImplementedError
        done = self.dones
        done[-1] = 1
        flat_dones = done.permute(1, 0, 2).reshape(-1, 1)
        done_indices = torch.cat(
            (
                flat_dones.new_tensor([-1], dtype=torch.int64),
                flat_dones.nonzero(as_tuple=False)[:, 0],
            )
        )
        trajectory_lengths = done_indices[1:] - done_indices[:-1]
        return trajectory_lengths.float().mean(), self.rewards.mean()

    def query_key(self, key: str):
        assert hasattr(self, key), key
        return getattr(self, key)

    def mini_batch_generator(self, num_mini_batches, num_epochs=8):
        batch_size = self.num_envs * self.num_transitions_per_env
        mini_batch_size = batch_size // num_mini_batches
        indices = torch.randperm(
            num_mini_batches * mini_batch_size, requires_grad=False, device=self.device
        )

        _buffer_dict = {key: getattr(self, key)[:].flatten(0, 1) for key in self.stored_keys}

        for epoch in range(num_epochs):
            for i in range(num_mini_batches):

                start = i * mini_batch_size
                end = (i + 1) * mini_batch_size
                batch_idx = indices[start:end]

                _batch_buffer_dict = {key: _buffer_dict[key][batch_idx] for key in self.stored_keys}
                yield _batch_buffer_dict