import sys
_module = sys.modules[__name__]
del sys
agents = _module
agent = _module
ppo = _module
random_agent = _module
curiosity = _module
base = _module
icm = _module
no_curiosity = _module
envs = _module
converters = _module
multi_env = _module
runner = _module
utils = _module
models = _module
datasets = _module
mlp = _module
model = _module
normalizers = _module
no_normalizer = _module
normalizer = _module
standard_normalizer = _module
reporters = _module
log_reporter = _module
no_reporter = _module
reporter = _module
tensorboard_reporter = _module
rewards = _module
advantage = _module
gae = _module
gae_reward = _module
n_step_advantage = _module
n_step_reward = _module
reward = _module
run_cartpole = _module
run_mountain_car = _module
run_pendulum = _module
test_ppo = _module
test_random_agent = _module
test_icm = _module
test_no_curiosity = _module
test_converters = _module
test_multi_env = _module
test_runner = _module
test_datasets = _module
test_mlp = _module
test_no_normalizer = _module
test_standard_normalizer = _module
test_log_reporter = _module
test_reporter = _module
test_tensorboard_reporter = _module
test_gae = _module
test_gae_reward = _module
test_n_step_advantage = _module
test_n_step_reward = _module
test_utils = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


from typing import List


from typing import Union


import numpy as np


import torch


from itertools import chain


from torch import Tensor


from torch.distributions import Distribution


from torch.nn.modules.loss import _Loss


from torch.optim import Adam


from torch.utils.data import DataLoader


from abc import abstractmethod


from abc import ABCMeta


from typing import Generator


from torch import nn


from typing import Tuple


from torch.distributions import Categorical


from torch.distributions import Normal


from torch.nn import CrossEntropyLoss


from torch.nn import MSELoss


from torch.utils.data import Dataset


from torch import nn as nn


from collections import Counter


import torch.nn as nn


class Reporter(metaclass=ABCMeta):

    def __init__(self, report_interval: int=1):
        self.counter = Counter()
        self.graph_initialized = False
        self.report_interval = report_interval
        self.t = 0

    def will_report(self, tag: str) ->bool:
        return self.counter[tag] % (self.report_interval + 1) == 0

    def scalar(self, tag: str, value: float):
        if self.will_report(tag):
            self._scalar(tag, value, self.counter[tag])
        self.counter[tag] += 1

    def graph(self, model: nn.Module, input_to_model: Tensor):
        if not self.graph_initialized:
            self._graph(model, input_to_model)
            self.graph_initialized = True

    @abstractmethod
    def _scalar(self, tag: str, value: float, step: int):
        raise NotImplementedError('Implement me')

    def _graph(self, model: nn.Module, input_to_model: Tensor):
        raise NotImplementedError('Implement me')


class PPOLoss(_Loss):
    """
    Calculates the PPO loss given by equation:

    .. math:: L_t^{CLIP+VF+S}(\\theta) = \\mathbb{E} \\left [L_t^{CLIP}(\\theta) - c_v * L_t^{VF}(\\theta)
                                        + c_e S[\\pi_\\theta](s_t) \\right ]

    where:

    .. math:: L_t^{CLIP}(\\theta) = \\hat{\\mathbb{E}}_t \\left [\\text{min}(r_t(\\theta)\\hat{A}_t,
                                  \\text{clip}(r_t(\\theta), 1 - \\epsilon, 1 + \\epsilon)\\hat{A}_t )\\right ]

    .. math:: r_t(\\theta) = \\frac{\\pi_\\theta(a_t|s_t)}{\\pi_{\\theta_{old}}(a_t|s_t)}\\hat{A}_t

    .. math:: \\L_t^{VF}(\\theta) = (V_\\theta(s_t) - V_t^{targ})^2

    and :math:`S[\\pi_\\theta](s_t)` is an entropy

    """

    def __init__(self, clip_range: float, v_clip_range: float, c_entropy: float, c_value: float, reporter: Reporter):
        """

        :param clip_range: clip range for surrogate function clipping
        :param v_clip_range: clip range for value function clipping
        :param c_entropy: entropy coefficient constant
        :param c_value: value coefficient constant
        :param reporter: reporter to be used to report loss scalars
        """
        super().__init__()
        self.clip_range = clip_range
        self.v_clip_range = v_clip_range
        self.c_entropy = c_entropy
        self.c_value = c_value
        self.reporter = reporter

    def forward(self, distribution_old: Distribution, value_old: Tensor, distribution: Distribution, value: Tensor, action: Tensor, reward: Tensor, advantage: Tensor):
        value_old_clipped = value_old + (value - value_old).clamp(-self.v_clip_range, self.v_clip_range)
        v_old_loss_clipped = (reward - value_old_clipped).pow(2)
        v_loss = (reward - value).pow(2)
        value_loss = torch.min(v_old_loss_clipped, v_loss).mean()
        advantage = (advantage - advantage.mean()) / (advantage.std(unbiased=False) + 1e-08)
        advantage.detach_()
        log_prob = distribution.log_prob(action)
        log_prob_old = distribution_old.log_prob(action)
        ratio = (log_prob - log_prob_old).exp().view(-1)
        surrogate = advantage * ratio
        surrogate_clipped = advantage * ratio.clamp(1 - self.clip_range, 1 + self.clip_range)
        policy_loss = torch.min(surrogate, surrogate_clipped).mean()
        entropy = distribution.entropy().mean()
        losses = policy_loss + self.c_entropy * entropy - self.c_value * value_loss
        total_loss = -losses
        self.reporter.scalar('ppo_loss/policy', -policy_loss.item())
        self.reporter.scalar('ppo_loss/entropy', -entropy.item())
        self.reporter.scalar('ppo_loss/value_loss', value_loss.item())
        self.reporter.scalar('ppo_loss/total', total_loss)
        return total_loss


class NormalDistributionModule(nn.Module):

    def __init__(self, in_features: int, n_action_values: int):
        super().__init__()
        self.policy_mean = nn.Linear(in_features, n_action_values)
        self.policy_std = nn.Parameter(torch.zeros(1, n_action_values))

    def forward(self, x):
        policy = self.policy_mean(x)
        policy_std = self.policy_std.expand_as(policy).exp()
        return torch.cat((policy, policy_std), dim=-1)


class Normalizer(metaclass=ABCMeta):
    """
    Base class for normalizers used to normalize the sate/reward during training before inputing it to the model
    or a curiosity module
    """

    @abstractmethod
    def partial_fit(self, array: np.ndarray) ->None:
        """
        Incrementally fit the normalizer eg. incrementally calculate mean
        :param array: array of shape ``N*T`` or ``N*T*any`` to calculate statistics on
        """
        raise NotImplementedError('Implement me')

    @abstractmethod
    def transform(self, array: np.ndarray) ->np.ndarray:
        """
        Normalizes the array using insights gathered with ``partial_fit``
        :param array: array of shape ``N*T`` or ``N*T*any`` to be normalized
        :return:  normalized array of same shape as input
        """
        raise NotImplementedError('Implement me')

    def partial_fit_transform(self, array: np.ndarray) ->np.ndarray:
        """
        Handy method to run ``partial_fit`` and ``transform`` at once
        :param array: array of shape ``N*T`` or ``N*T*any`` to be normalized
        :return: normalized array of same shape as input
        """
        self.partial_fit(array)
        return self.transform(array)


class StandardNormalizer(Normalizer):
    """
    Normalizes the input by subtracting the mean and dividing by standard deviation.
    Uses ``sklearn.preprocessing.StandardScaler`` under the hood.
    """

    def __init__(self):
        self.scaler = StandardScaler()

    def partial_fit(self, array: np.ndarray) ->None:
        self.scaler.partial_fit(self._reshape_for_scaler(array))

    def transform(self, array: np.ndarray) ->np.ndarray:
        return self.scaler.transform(self._reshape_for_scaler(array)).reshape(array.shape)

    @staticmethod
    def _reshape_for_scaler(array: np.ndarray):
        new_shape = (-1, *array.shape[2:]) if array.ndim > 2 else (-1, 1)
        return array.reshape(new_shape)


class NoNormalizer(Normalizer):
    """
    Does no normalization on the array. Handy for observation spaces like ``gym.Discrete``
    """

    def partial_fit(self, array: np.ndarray) ->None:
        pass

    def transform(self, array: np.ndarray) ->np.ndarray:
        return array


import torch
from torch.nn import MSELoss, ReLU
from _paritybench_helpers import _mock_config, _mock_layer, _paritybench_base, _fails_compile


TESTCASES = [
    # (nn.Module, init_args, forward_args, jit_compiles)
    (NormalDistributionModule,
     lambda: ([], {'in_features': 4, 'n_action_values': 4}),
     lambda: ([torch.rand([4, 4, 4, 4])], {}),
     True),
]

class Test_adik993_ppo_pytorch(_paritybench_base):
    def test_000(self):
        self._check(*TESTCASES[0])

