import random
from collections import deque, namedtuple

import numpy as np
import torch

from ..tools import seq2unique_transition
from . import Experience
from .sumtree import SumTree

transition = {
    "FFTransition": [
        "observation",
        "action",
        "reward",
        "next_observation",
        "done_flag",
    ],
    "RNNTransition": [
        "observation",
        "h0",
        "action",
        "reward",
        "next_observation",
        "done_flag",
    ],
}


class ReplayMemory(Experience):
    def __init__(self, capacity, transition_type="FFTransition"):
        self.capacity = capacity
        self.memory = []
        self.position = 0
        self.transition_type = transition_type
        self.transition = namedtuple(
            "Transition", field_names=transition[transition_type]
        )

    def push(self, *transition):
        assert len(self.transition._fields) == len(
            transition
        ), "Invalid number of arguments"
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = self.transition(*transition)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size=1):
        assert batch_size <= len(self), "Batch size > Memory length"
        _sample = random.sample(self.memory, batch_size)
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        # sample_arr = [torch.from_numpy(np.asarray(s)).float() for s in _sample]
        return self.transition(*sample_arr)

    def __len__(self):
        return len(self.memory)

    def __repr__(self):
        return "ReplayMemory<{}>({}/{})".format(
            self.transition_type, len(self), self.capacity
        )

    def get_transition(self, index):
        _sample = []
        for ind in index:
            _sample.append(self.memory[ind])
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        # sample_arr = [torch.from_numpy(np.asarray(s)).float() for s in _sample]
        return self.transition(*sample_arr)

    def sample_index(self, batch_size):
        assert batch_size <= len(self)
        return np.random.randint(len(self), size=batch_size)


class PrioritizedReplayMemory(Experience):
    beta_increment_per_sampling = 0.001

    def __init__(
        self, capacity, alpha=0.6, beta=0.4, eps=1e-6, transition_type="FFTransition"
    ):

        self.tree = SumTree(capacity)

        # self.seed = seed
        self.alpha = alpha
        self.beta = beta
        self.eps = eps

        self.transition_type = transition_type
        self.transition = namedtuple(
            "Transition", field_names=transition[transition_type]
        )

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        # print("Err : ", error)
        return (np.abs(error) + self.eps) ** self.alpha

    # def push(self, error, *transition):
    #     assert len(self.transition._fields)==len(transition) , "Invalid number of transition values : {} given instead of {} required".format(len(transition), len(self.transition._fields))

    #     p = self._get_priority(error)
    #     self.tree.add(p, transition)

    #     self.current_transition = self.none_transition_dict()

    def push(self, error, transition):
        assert len(self.transition._fields) == len(
            transition._fields
        ), "Invalid number of transition values : {} given instead of {} required".format(
            len(transition._fields), len(self.transition._fields)
        )

        p = self._get_priority(error)
        self.tree.add(p, transition)

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []

        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])

        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            (idx, p, data) = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)

        batch = seq2unique_transition(sample)

        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, -self.beta)
        is_weight /= is_weight.max()

        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return "PrioritizedReplayMemory<{}>({}/{})".format(
            self.transition_type, len(self.tree), self.tree.capacity
        )

    def __len__(self):
        return len(self.tree)

    # def push_error(self, error):
    #     # assert len(self.transition._fields)==len(transition) , "Invalid number of transition values : {} given instead of {} required".format(len(transition), len(self.transition._fields))

    #     p = self._get_priority(error)
    #     tr = self.get_current_transition()
    #     self.tree.add(p, tr)

    #     self.current_transition = self.none_transition_dict()

    # def push_transition(self, observation, action, reward, next_observation, done_flag):
    #     self.push_to_current_transition('observation', observation)
    #     self.push_to_current_transition('action', action)
    #     self.push_to_current_transition('reward', reward)
    #     self.push_to_current_transition('done_flag', done_flag)
    #     self.push_to_current_transition('next_observation', next_observation)

    # def push_to_current_transition(self, key, val):
    #     assert key in self.transition._fields, "'{}' is not a transition's field"
    #     self.current_transition[key] = val

198c871cdda4a3b1277799d71eb38d40a51cada4
<mask token>


class PrioritizedReplayMemory(Experience):
    <mask token>

    def __init__(self, capacity, alpha=0.6, beta=0.4, eps=1e-06,
        transition_type='FFTransition'):
        self.tree = SumTree(capacity)
        self.alpha = alpha
        self.beta = beta
        self.eps = eps
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        return (np.abs(error) + self.eps) ** self.alpha
    <mask token>

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []
        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])
        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            idx, p, data = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)
        batch = seq2unique_transition(sample)
        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, 
            -self.beta)
        is_weight /= is_weight.max()
        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return 'PrioritizedReplayMemory<{}>({}/{})'.format(self.
            transition_type, len(self.tree), self.tree.capacity)

    def __len__(self):
        return len(self.tree)

<mask token>


class PrioritizedReplayMemory(Experience):
    <mask token>

    def __init__(self, capacity, alpha=0.6, beta=0.4, eps=1e-06,
        transition_type='FFTransition'):
        self.tree = SumTree(capacity)
        self.alpha = alpha
        self.beta = beta
        self.eps = eps
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        return (np.abs(error) + self.eps) ** self.alpha

    def push(self, error, transition):
        assert len(self.transition._fields) == len(transition._fields
            ), 'Invalid number of transition values : {} given instead of {} required'.format(
            len(transition._fields), len(self.transition._fields))
        p = self._get_priority(error)
        self.tree.add(p, transition)

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []
        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])
        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            idx, p, data = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)
        batch = seq2unique_transition(sample)
        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, 
            -self.beta)
        is_weight /= is_weight.max()
        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return 'PrioritizedReplayMemory<{}>({}/{})'.format(self.
            transition_type, len(self.tree), self.tree.capacity)

    def __len__(self):
        return len(self.tree)

<mask token>


class ReplayMemory(Experience):

    def __init__(self, capacity, transition_type='FFTransition'):
        self.capacity = capacity
        self.memory = []
        self.position = 0
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])
    <mask token>

    def sample(self, batch_size=1):
        assert batch_size <= len(self), 'Batch size > Memory length'
        _sample = random.sample(self.memory, batch_size)
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        return self.transition(*sample_arr)
    <mask token>
    <mask token>

    def get_transition(self, index):
        _sample = []
        for ind in index:
            _sample.append(self.memory[ind])
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        return self.transition(*sample_arr)
    <mask token>


class PrioritizedReplayMemory(Experience):
    beta_increment_per_sampling = 0.001

    def __init__(self, capacity, alpha=0.6, beta=0.4, eps=1e-06,
        transition_type='FFTransition'):
        self.tree = SumTree(capacity)
        self.alpha = alpha
        self.beta = beta
        self.eps = eps
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        return (np.abs(error) + self.eps) ** self.alpha

    def push(self, error, transition):
        assert len(self.transition._fields) == len(transition._fields
            ), 'Invalid number of transition values : {} given instead of {} required'.format(
            len(transition._fields), len(self.transition._fields))
        p = self._get_priority(error)
        self.tree.add(p, transition)

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []
        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])
        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            idx, p, data = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)
        batch = seq2unique_transition(sample)
        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, 
            -self.beta)
        is_weight /= is_weight.max()
        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return 'PrioritizedReplayMemory<{}>({}/{})'.format(self.
            transition_type, len(self.tree), self.tree.capacity)

    def __len__(self):
        return len(self.tree)

<mask token>


class ReplayMemory(Experience):

    def __init__(self, capacity, transition_type='FFTransition'):
        self.capacity = capacity
        self.memory = []
        self.position = 0
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])

    def push(self, *transition):
        assert len(self.transition._fields) == len(transition
            ), 'Invalid number of arguments'
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = self.transition(*transition)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size=1):
        assert batch_size <= len(self), 'Batch size > Memory length'
        _sample = random.sample(self.memory, batch_size)
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        return self.transition(*sample_arr)
    <mask token>
    <mask token>

    def get_transition(self, index):
        _sample = []
        for ind in index:
            _sample.append(self.memory[ind])
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        return self.transition(*sample_arr)

    def sample_index(self, batch_size):
        assert batch_size <= len(self)
        return np.random.randint(len(self), size=batch_size)


class PrioritizedReplayMemory(Experience):
    beta_increment_per_sampling = 0.001

    def __init__(self, capacity, alpha=0.6, beta=0.4, eps=1e-06,
        transition_type='FFTransition'):
        self.tree = SumTree(capacity)
        self.alpha = alpha
        self.beta = beta
        self.eps = eps
        self.transition_type = transition_type
        self.transition = namedtuple('Transition', field_names=transition[
            transition_type])

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        return (np.abs(error) + self.eps) ** self.alpha

    def push(self, error, transition):
        assert len(self.transition._fields) == len(transition._fields
            ), 'Invalid number of transition values : {} given instead of {} required'.format(
            len(transition._fields), len(self.transition._fields))
        p = self._get_priority(error)
        self.tree.add(p, transition)

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []
        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])
        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            idx, p, data = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)
        batch = seq2unique_transition(sample)
        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, 
            -self.beta)
        is_weight /= is_weight.max()
        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return 'PrioritizedReplayMemory<{}>({}/{})'.format(self.
            transition_type, len(self.tree), self.tree.capacity)

    def __len__(self):
        return len(self.tree)

import random
from collections import deque, namedtuple

import numpy as np
import torch

from ..tools import seq2unique_transition
from . import Experience
from .sumtree import SumTree

transition = {
    "FFTransition": [
        "observation",
        "action",
        "reward",
        "next_observation",
        "done_flag",
    ],
    "RNNTransition": [
        "observation",
        "h0",
        "action",
        "reward",
        "next_observation",
        "done_flag",
    ],
}


class ReplayMemory(Experience):
    def __init__(self, capacity, transition_type="FFTransition"):
        self.capacity = capacity
        self.memory = []
        self.position = 0
        self.transition_type = transition_type
        self.transition = namedtuple(
            "Transition", field_names=transition[transition_type]
        )

    def push(self, *transition):
        assert len(self.transition._fields) == len(
            transition
        ), "Invalid number of arguments"
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = self.transition(*transition)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size=1):
        assert batch_size <= len(self), "Batch size > Memory length"
        _sample = random.sample(self.memory, batch_size)
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        # sample_arr = [torch.from_numpy(np.asarray(s)).float() for s in _sample]
        return self.transition(*sample_arr)

    def __len__(self):
        return len(self.memory)

    def __repr__(self):
        return "ReplayMemory<{}>({}/{})".format(
            self.transition_type, len(self), self.capacity
        )

    def get_transition(self, index):
        _sample = []
        for ind in index:
            _sample.append(self.memory[ind])
        _sample = list(zip(*_sample))
        sample_arr = [np.asarray(s) for s in _sample]
        # sample_arr = [torch.from_numpy(np.asarray(s)).float() for s in _sample]
        return self.transition(*sample_arr)

    def sample_index(self, batch_size):
        assert batch_size <= len(self)
        return np.random.randint(len(self), size=batch_size)


class PrioritizedReplayMemory(Experience):
    beta_increment_per_sampling = 0.001

    def __init__(
        self, capacity, alpha=0.6, beta=0.4, eps=1e-6, transition_type="FFTransition"
    ):

        self.tree = SumTree(capacity)

        # self.seed = seed
        self.alpha = alpha
        self.beta = beta
        self.eps = eps

        self.transition_type = transition_type
        self.transition = namedtuple(
            "Transition", field_names=transition[transition_type]
        )

    @property
    def capacity(self):
        return self.tree.capacity

    def _get_priority(self, error):
        # print("Err : ", error)
        return (np.abs(error) + self.eps) ** self.alpha

    # def push(self, error, *transition):
    #     assert len(self.transition._fields)==len(transition) , "Invalid number of transition values : {} given instead of {} required".format(len(transition), len(self.transition._fields))

    #     p = self._get_priority(error)
    #     self.tree.add(p, transition)

    #     self.current_transition = self.none_transition_dict()

    def push(self, error, transition):
        assert len(self.transition._fields) == len(
            transition._fields
        ), "Invalid number of transition values : {} given instead of {} required".format(
            len(transition._fields), len(self.transition._fields)
        )

        p = self._get_priority(error)
        self.tree.add(p, transition)

    def sample(self, batch_size=1):
        sample = []
        idxs = []
        segment = self.tree.total() / batch_size
        priorities = []

        self.beta = np.min([1.0, self.beta + self.beta_increment_per_sampling])

        for i in range(batch_size):
            a = segment * i
            b = segment * (i + 1)
            s = random.uniform(a, b)
            (idx, p, data) = self.tree.get(s)
            priorities.append(p)
            sample.append(data)
            idxs.append(idx)

        batch = seq2unique_transition(sample)

        sampling_probabilities = priorities / self.tree.total()
        is_weight = np.power(self.tree.n_entries * sampling_probabilities, -self.beta)
        is_weight /= is_weight.max()

        return batch, idxs, is_weight

    def get_current_transition(self):
        return self.transition(**self.current_transition)

    def update(self, index, error):
        priority = self._get_priority(error)
        priority = np.array(priority)
        index = np.array(index)
        for idx, p in list(zip(index, priority)):
            self.tree.update(idx, p)

    def __repr__(self):
        return "PrioritizedReplayMemory<{}>({}/{})".format(
            self.transition_type, len(self.tree), self.tree.capacity
        )

    def __len__(self):
        return len(self.tree)

    # def push_error(self, error):
    #     # assert len(self.transition._fields)==len(transition) , "Invalid number of transition values : {} given instead of {} required".format(len(transition), len(self.transition._fields))

    #     p = self._get_priority(error)
    #     tr = self.get_current_transition()
    #     self.tree.add(p, tr)

    #     self.current_transition = self.none_transition_dict()

    # def push_transition(self, observation, action, reward, next_observation, done_flag):
    #     self.push_to_current_transition('observation', observation)
    #     self.push_to_current_transition('action', action)
    #     self.push_to_current_transition('reward', reward)
    #     self.push_to_current_transition('done_flag', done_flag)
    #     self.push_to_current_transition('next_observation', next_observation)

    # def push_to_current_transition(self, key, val):
    #     assert key in self.transition._fields, "'{}' is not a transition's field"
    #     self.current_transition[key] = val
