from typing import List

import logging

from smiles_lstm_ppo.ppo_trainer import PPOTrainer, OptResult
from smiles_lstm_ppo.rnn_model import SmilesRnnActorCritic
from smiles_lstm_hc.rnn_sampler import SmilesRnnSampler
from guacamol.scoring_function import ScoringFunction

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class PPOMoleculeGenerator:
    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device) -> None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list, **kwargs) -> List[OptResult]:
        if start_population:
            logger.warning("PPO algorithm does not support (yet) a starting population")
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']

        trainer = PPOTrainer(self.model, objective, device=self.device,
                             max_seq_length=self.max_seq_length, batch_size=batch_size,
                             num_epochs=num_epochs, clip_param=clip_param,
                             episode_size=episode_size, entropy_weight=entropy_weight,
                             kl_div_weight=kl_div_weight)
        trainer.train()

        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) -> List[str]:
        return self.sampler.sample(self.model.smiles_rnn,
                                   num_to_sample=num_mols,
                                   max_seq_len=self.max_seq_length)



86e5b696a0798d732974423f04281acc4212ae48
<mask token>


class PPOMoleculeGenerator:

    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device
        ) ->None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list,
        **kwargs) ->List[OptResult]:
        if start_population:
            logger.warning(
                'PPO algorithm does not support (yet) a starting population')
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']
        trainer = PPOTrainer(self.model, objective, device=self.device,
            max_seq_length=self.max_seq_length, batch_size=batch_size,
            num_epochs=num_epochs, clip_param=clip_param, episode_size=
            episode_size, entropy_weight=entropy_weight, kl_div_weight=
            kl_div_weight)
        trainer.train()
        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) ->List[str]:
        return self.sampler.sample(self.model.smiles_rnn, num_to_sample=
            num_mols, max_seq_len=self.max_seq_length)

<mask token>
logger.addHandler(logging.NullHandler())


class PPOMoleculeGenerator:

    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device
        ) ->None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list,
        **kwargs) ->List[OptResult]:
        if start_population:
            logger.warning(
                'PPO algorithm does not support (yet) a starting population')
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']
        trainer = PPOTrainer(self.model, objective, device=self.device,
            max_seq_length=self.max_seq_length, batch_size=batch_size,
            num_epochs=num_epochs, clip_param=clip_param, episode_size=
            episode_size, entropy_weight=entropy_weight, kl_div_weight=
            kl_div_weight)
        trainer.train()
        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) ->List[str]:
        return self.sampler.sample(self.model.smiles_rnn, num_to_sample=
            num_mols, max_seq_len=self.max_seq_length)

<mask token>
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class PPOMoleculeGenerator:

    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device
        ) ->None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list,
        **kwargs) ->List[OptResult]:
        if start_population:
            logger.warning(
                'PPO algorithm does not support (yet) a starting population')
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']
        trainer = PPOTrainer(self.model, objective, device=self.device,
            max_seq_length=self.max_seq_length, batch_size=batch_size,
            num_epochs=num_epochs, clip_param=clip_param, episode_size=
            episode_size, entropy_weight=entropy_weight, kl_div_weight=
            kl_div_weight)
        trainer.train()
        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) ->List[str]:
        return self.sampler.sample(self.model.smiles_rnn, num_to_sample=
            num_mols, max_seq_len=self.max_seq_length)

from typing import List
import logging
from smiles_lstm_ppo.ppo_trainer import PPOTrainer, OptResult
from smiles_lstm_ppo.rnn_model import SmilesRnnActorCritic
from smiles_lstm_hc.rnn_sampler import SmilesRnnSampler
from guacamol.scoring_function import ScoringFunction
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class PPOMoleculeGenerator:

    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device
        ) ->None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list,
        **kwargs) ->List[OptResult]:
        if start_population:
            logger.warning(
                'PPO algorithm does not support (yet) a starting population')
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']
        trainer = PPOTrainer(self.model, objective, device=self.device,
            max_seq_length=self.max_seq_length, batch_size=batch_size,
            num_epochs=num_epochs, clip_param=clip_param, episode_size=
            episode_size, entropy_weight=entropy_weight, kl_div_weight=
            kl_div_weight)
        trainer.train()
        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) ->List[str]:
        return self.sampler.sample(self.model.smiles_rnn, num_to_sample=
            num_mols, max_seq_len=self.max_seq_length)

from typing import List

import logging

from smiles_lstm_ppo.ppo_trainer import PPOTrainer, OptResult
from smiles_lstm_ppo.rnn_model import SmilesRnnActorCritic
from smiles_lstm_hc.rnn_sampler import SmilesRnnSampler
from guacamol.scoring_function import ScoringFunction

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class PPOMoleculeGenerator:
    def __init__(self, model: SmilesRnnActorCritic, max_seq_length, device) -> None:
        self.model = model
        self.max_seq_length = max_seq_length
        self.device = device
        self.sampler = SmilesRnnSampler(device=device, batch_size=512)

    def optimise(self, objective: ScoringFunction, start_population: list, **kwargs) -> List[OptResult]:
        if start_population:
            logger.warning("PPO algorithm does not support (yet) a starting population")
        num_epochs = kwargs['num_epochs']
        episode_size = kwargs['optimize_episode_size']
        batch_size = kwargs['optimize_batch_size']
        entropy_weight = kwargs['entropy_weight']
        kl_div_weight = kwargs['kl_div_weight']
        clip_param = kwargs['clip_param']

        trainer = PPOTrainer(self.model, objective, device=self.device,
                             max_seq_length=self.max_seq_length, batch_size=batch_size,
                             num_epochs=num_epochs, clip_param=clip_param,
                             episode_size=episode_size, entropy_weight=entropy_weight,
                             kl_div_weight=kl_div_weight)
        trainer.train()

        return sorted(trainer.smiles_history, reverse=True)

    def sample(self, num_mols) -> List[str]:
        return self.sampler.sample(self.model.smiles_rnn,
                                   num_to_sample=num_mols,
                                   max_seq_len=self.max_seq_length)


