# This file taken from
#     https://github.com/deepmind/scalable_agent/blob/
#         d24bd74bd53d454b7222b7f0bea57a358e4ca33e/vtrace_test.py
# and modified.

# Copyright 2018 Google LLC
#
# 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
#
#     https://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.

"""Tests for V-trace.

For details and theory see:

"IMPALA: Scalable Distributed Deep-RL with
Importance Weighted Actor-Learner Architectures"
by Espeholt, Soyer, Munos et al.
"""

import unittest

import numpy as np
import torch

import vtrace


def _shaped_arange(*shape):
    """Runs np.arange, converts to float and reshapes."""
    return np.arange(np.prod(shape), dtype=np.float32).reshape(*shape)


def _softmax(logits):
    """Applies softmax non-linearity on inputs."""
    return np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)


def _ground_truth_calculation(discounts, log_rhos, rewards, values,
                              bootstrap_value, clip_rho_threshold,
                              clip_pg_rho_threshold):
    """Calculates the ground truth for V-trace in Python/Numpy."""
    vs = []
    seq_len = len(discounts)
    rhos = np.exp(log_rhos)
    cs = np.minimum(rhos, 1.0)
    clipped_rhos = rhos
    if clip_rho_threshold:
        clipped_rhos = np.minimum(rhos, clip_rho_threshold)
    clipped_pg_rhos = rhos
    if clip_pg_rho_threshold:
        clipped_pg_rhos = np.minimum(rhos, clip_pg_rho_threshold)

    # This is a very inefficient way to calculate the V-trace ground truth.
    # We calculate it this way because it is close to the mathematical notation
    # of V-trace.
    # v_s = V(x_s)
    #             + \sum^{T-1}_{t=s} \gamma^{t-s}
    #                 * \prod_{i=s}^{t-1} c_i
    #                 * \rho_t (r_t + \gamma V(x_{t+1}) - V(x_t))
    # Note that when we take the product over c_i, we write `s:t` as the
    # notation of the paper is inclusive of the `t-1`, but Python is exclusive.
    # Also note that np.prod([]) == 1.
    values_t_plus_1 = np.concatenate([values, bootstrap_value[None, :]], axis=0)
    for s in range(seq_len):
        v_s = np.copy(values[s])  # Very important copy.
        for t in range(s, seq_len):
            v_s += (
                np.prod(discounts[s:t], axis=0) * np.prod(
                    cs[s:t], axis=0) * clipped_rhos[t] *
                (rewards[t] + discounts[t] * values_t_plus_1[t + 1] - values[t]
                ))
        vs.append(v_s)
    vs = np.stack(vs, axis=0)
    pg_advantages = (clipped_pg_rhos * (rewards + discounts * np.concatenate(
        [vs[1:], bootstrap_value[None, :]], axis=0) - values))

    return vtrace.VTraceReturns(vs=vs, pg_advantages=pg_advantages)


def assert_allclose(actual, desired):
    return np.testing.assert_allclose(actual, desired, rtol=1e-06, atol=1e-05)


class ActionLogProbsTest(unittest.TestCase):

    def test_action_log_probs(self, batch_size=2):
        seq_len = 7
        num_actions = 3

        policy_logits = _shaped_arange(seq_len, batch_size, num_actions) + 10
        actions = np.random.randint(
            0, num_actions, size=(seq_len, batch_size), dtype=np.int64)

        action_log_probs_tensor = vtrace.action_log_probs(
            torch.from_numpy(policy_logits), torch.from_numpy(actions))

        # Ground Truth
        # Using broadcasting to create a mask that indexes action logits
        action_index_mask = actions[..., None] == np.arange(num_actions)

        def index_with_mask(array, mask):
            return array[mask].reshape(*array.shape[:-1])

        # Note: Normally log(softmax) is not a good idea because it's not
        # numerically stable. However, in this test we have well-behaved values.
        ground_truth_v = index_with_mask(
            np.log(_softmax(policy_logits)), action_index_mask)

        assert_allclose(ground_truth_v, action_log_probs_tensor)

    def test_action_log_probs_batch_1(self):
        self.test_action_log_probs(1)


class VtraceTest(unittest.TestCase):

    def test_vtrace(self, batch_size=5):
        """Tests V-trace against ground truth data calculated in python."""
        seq_len = 5

        # Create log_rhos such that rho will span from near-zero to above the
        # clipping thresholds. In particular, calculate log_rhos in [-2.5, 2.5),
        # so that rho is in approx [0.08, 12.2).
        log_rhos = _shaped_arange(seq_len, batch_size) / (batch_size * seq_len)
        log_rhos = 5 * (log_rhos - 0.5)  # [0.0, 1.0) -> [-2.5, 2.5).
        values = {
            'log_rhos':
            log_rhos,
            # T, B where B_i: [0.9 / (i+1)] * T
            'discounts':
            np.array(
                [[0.9 / (b + 1)
                  for b in range(batch_size)]
                 for _ in range(seq_len)],
                dtype=np.float32),
            'rewards':
            _shaped_arange(seq_len, batch_size),
            'values':
            _shaped_arange(seq_len, batch_size) / batch_size,
            'bootstrap_value':
            _shaped_arange(batch_size) + 1.0,
            'clip_rho_threshold':
            3.7,
            'clip_pg_rho_threshold':
            2.2,
        }

        ground_truth = _ground_truth_calculation(**values)

        values = {key: torch.tensor(value) for key, value in values.items()}
        output = vtrace.from_importance_weights(**values)

        for a, b in zip(ground_truth, output):
            assert_allclose(a, b)

    def test_vtrace_batch_1(self):
        self.test_vtrace(1)

    def test_vtrace_from_logits(self, batch_size=2):
        """Tests V-trace calculated from logits."""
        seq_len = 5
        num_actions = 3
        clip_rho_threshold = None  # No clipping.
        clip_pg_rho_threshold = None  # No clipping.

        values = {
            'behavior_policy_logits':
            _shaped_arange(seq_len, batch_size, num_actions),
            'target_policy_logits':
            _shaped_arange(seq_len, batch_size, num_actions),
            'actions':
            np.random.randint(0, num_actions - 1, size=(seq_len, batch_size)),
            'discounts':
            np.array(  # T, B where B_i: [0.9 / (i+1)] * T
                [[0.9 / (b + 1)
                  for b in range(batch_size)]
                 for _ in range(seq_len)],
                dtype=np.float32),
            'rewards':
            _shaped_arange(seq_len, batch_size),
            'values':
            _shaped_arange(seq_len, batch_size) / batch_size,
            'bootstrap_value':
            _shaped_arange(batch_size) + 1.0,  # B
        }
        values = {k: torch.from_numpy(v) for k, v in values.items()}

        from_logits_output = vtrace.from_logits(
            clip_rho_threshold=clip_rho_threshold,
            clip_pg_rho_threshold=clip_pg_rho_threshold,
            **values)

        target_log_probs = vtrace.action_log_probs(
            values['target_policy_logits'], values['actions'])
        behavior_log_probs = vtrace.action_log_probs(
            values['behavior_policy_logits'], values['actions'])
        log_rhos = target_log_probs - behavior_log_probs

        # Calculate V-trace using the ground truth logits.
        from_iw = vtrace.from_importance_weights(
            log_rhos=log_rhos,
            discounts=values['discounts'],
            rewards=values['rewards'],
            values=values['values'],
            bootstrap_value=values['bootstrap_value'],
            clip_rho_threshold=clip_rho_threshold,
            clip_pg_rho_threshold=clip_pg_rho_threshold)

        assert_allclose(from_iw.vs, from_logits_output.vs)
        assert_allclose(from_iw.pg_advantages, from_logits_output.pg_advantages)
        assert_allclose(behavior_log_probs,
                        from_logits_output.behavior_action_log_probs)
        assert_allclose(target_log_probs,
                        from_logits_output.target_action_log_probs)
        assert_allclose(log_rhos, from_logits_output.log_rhos)

    def test_vtrace_from_logits_batch_1(self):
        self.test_vtrace_from_logits(1)

    def test_higher_rank_inputs_for_importance_weights(self):
        """Checks support for additional dimensions in inputs."""
        T = 3  # pylint: disable=invalid-name
        B = 2  # pylint: disable=invalid-name
        values = {
            'log_rhos': torch.zeros(T, B, 1),
            'discounts': torch.zeros(T, B, 1),
            'rewards': torch.zeros(T, B, 42),
            'values': torch.zeros(T, B, 42),
            'bootstrap_value': torch.zeros(B, 42),
        }
        output = vtrace.from_importance_weights(**values)
        self.assertSequenceEqual(output.vs.shape, (T, B, 42))

    def test_inconsistent_rank_inputs_for_importance_weights(self):
        """Test one of many possible errors in shape of inputs."""
        T = 3  # pylint: disable=invalid-name
        B = 2  # pylint: disable=invalid-name

        values = {
            'log_rhos': torch.zeros(T, B, 1),
            'discounts': torch.zeros(T, B, 1),
            'rewards': torch.zeros(T, B, 42),
            'values': torch.zeros(T, B, 42),
            # Should be [B, 42].
            'bootstrap_value': torch.zeros(B),
        }

        with self.assertRaisesRegex(RuntimeError,
                                    'same number of dimensions: got 3 and 2'):
            vtrace.from_importance_weights(**values)


if __name__ == '__main__':
    unittest.main()
