import argparse
import logging
import os
import tempfile

from rasa_core import config, cli
from rasa_core import utils
from rasa_core.agent import Agent
from rasa_core.broker import PikaProducer
from rasa_core.domain import TemplateDomain
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.run import AvailableEndpoints
from rasa_core.tracker_store import TrackerStore
from rasa_core.training import interactive
from rasa_core.training.dsl import StoryFileReader
from rasa_core.utils import set_default_subparser

logger = logging.getLogger(__name__)


def create_argument_parser():
    """Parse all the command line arguments for the training script."""

    parser = argparse.ArgumentParser(
        description='Train a dialogue model for Rasa Core. '
                    'The training will use your conversations '
                    'in the story training data format and '
                    'your domain definition to train a dialogue '
                    'model to predict a bots actions.')
    parent_parser = argparse.ArgumentParser(add_help=False)
    add_general_args(parent_parser)

    subparsers = parser.add_subparsers(
        help='Training mode of core.',
        dest='mode')
    subparsers.required = True

    train_parser = subparsers.add_parser(
        'default',
        help='train a dialogue model',
        parents=[parent_parser])
    compare_parser = subparsers.add_parser(
        'compare',
        help='train multiple dialogue models to compare '
             'policies',
        parents=[parent_parser])
    interactive_parser = subparsers.add_parser(
        'interactive',
        help='teach the bot with interactive learning',
        parents=[parent_parser])

    add_compare_args(compare_parser)
    add_interactive_args(interactive_parser)
    add_train_args(train_parser)

    return parser


def add_compare_args(parser):
    parser.add_argument(
        '--percentages',
        nargs="*",
        type=int,
        default=[0, 5, 25, 50, 70, 90, 95],
        help="Range of exclusion percentages")
    parser.add_argument(
        '--runs',
        type=int,
        default=3,
        help="Number of runs for experiments")

    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=True)
    cli.arguments.add_config_arg(
        parser,
        nargs="*")
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=False)
    cli.arguments.add_domain_arg(
        parser,
        required=True)


def add_interactive_args(parser):
    parser.add_argument(
        '-u', '--nlu',
        type=str,
        default=None,
        help="trained nlu model")
    parser.add_argument(
        '--endpoints',
        default=None,
        help="Configuration file for the connectors as a yml file")
    parser.add_argument(
        '--skip_visualization',
        default=False,
        action='store_true',
        help="disables plotting the visualization during "
             "interactive learning")
    parser.add_argument(
        '--finetune',
        default=False,
        action='store_true',
        help="retrain the model immediately based on feedback.")

    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=False)
    cli.arguments.add_config_arg(
        parser,
        nargs=1)
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=True)
    cli.arguments.add_domain_arg(
        parser,
        required=False)


def add_train_args(parser):
    cli.arguments.add_config_arg(
        parser,
        nargs=1)
    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=True)
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=False)
    cli.arguments.add_domain_arg(
        parser,
        required=True)


def add_general_args(parser):
    parser.add_argument(
        '--augmentation',
        type=int,
        default=50,
        help="how much data augmentation to use during training")
    parser.add_argument(
        '--dump_stories',
        default=False,
        action='store_true',
        help="If enabled, save flattened stories to a file")
    parser.add_argument(
        '--debug_plots',
        default=False,
        action='store_true',
        help="If enabled, will create plots showing checkpoints "
             "and their connections between story blocks in a  "
             "file called `story_blocks_connections.html`.")

    utils.add_logging_option_arguments(parser)


def train_dialogue_model(domain_file, stories_file, output_path,
                         interpreter=None,
                         endpoints=AvailableEndpoints(),
                         dump_stories=False,
                         policy_config=None,
                         exclusion_percentage=None,
                         kwargs=None):
    if not kwargs:
        kwargs = {}

    policies = config.load(policy_config)

    agent = Agent(domain_file,
                  generator=endpoints.nlg,
                  action_endpoint=endpoints.action,
                  interpreter=interpreter,
                  policies=policies)

    data_load_args, kwargs = utils.extract_args(kwargs,
                                                {"use_story_concatenation",
                                                 "unique_last_num_states",
                                                 "augmentation_factor",
                                                 "remove_duplicates",
                                                 "debug_plots"})

    training_data = agent.load_data(stories_file,
                                    exclusion_percentage=exclusion_percentage,
                                    **data_load_args)
    agent.train(training_data, **kwargs)
    agent.persist(output_path, dump_stories)

    return agent


def _additional_arguments(args):
    additional = {
        "augmentation_factor": args.augmentation,
        "debug_plots": args.debug_plots
    }

    # remove None values
    return {k: v for k, v in additional.items() if v is not None}


def train_comparison_models(stories,
                            domain,
                            output_path="",
                            exclusion_percentages=None,
                            policy_configs=None,
                            runs=1,
                            dump_stories=False,
                            kwargs=None):
    """Train multiple models for comparison of policies"""

    exclusion_percentages = exclusion_percentages or []
    policy_configs = policy_configs or []

    for r in range(runs):
        logging.info("Starting run {}/{}".format(r + 1, runs))

        for i in exclusion_percentages:
            current_round = exclusion_percentages.index(i) + 1

            for policy_config in policy_configs:
                policies = config.load(policy_config)

                if len(policies) > 1:
                    raise ValueError("You can only specify one policy per "
                                     "model for comparison")

                policy_name = type(policies[0]).__name__
                output = os.path.join(output_path, 'run_' + str(r + 1),
                                      policy_name +
                                      str(current_round))

                logging.info("Starting to train {} round {}/{}"
                             " with {}% exclusion"
                             "".format(policy_name, current_round,
                                       len(exclusion_percentages), i))

                train_dialogue_model(
                    domain, stories, output,
                    policy_config=policy_config,
                    exclusion_percentage=i,
                    kwargs=kwargs,
                    dump_stories=dump_stories)


def get_no_of_stories(story_file, domain):
    """Get number of stories in a file."""

    stories = StoryFileReader.read_from_folder(story_file,
                                               TemplateDomain.load(domain))
    return len(stories)


def do_default_training(cmdline_args, stories, additional_arguments):
    """Train a model."""

    train_dialogue_model(domain_file=cmdline_args.domain,
                         stories_file=stories,
                         output_path=cmdline_args.out,
                         dump_stories=cmdline_args.dump_stories,
                         policy_config=cmdline_args.config[0],
                         kwargs=additional_arguments)


def do_compare_training(cmdline_args, stories, additional_arguments):
    train_comparison_models(stories,
                            cmdline_args.domain,
                            cmdline_args.out,
                            cmdline_args.percentages,
                            cmdline_args.config,
                            cmdline_args.runs,
                            cmdline_args.dump_stories,
                            additional_arguments)

    no_stories = get_no_of_stories(cmdline_args.stories,
                                   cmdline_args.domain)

    # store the list of the number of stories present at each exclusion
    # percentage
    story_range = [no_stories - round((x / 100.0) * no_stories)
                   for x in cmdline_args.percentages]

    story_n_path = os.path.join(cmdline_args.out, 'num_stories.json')
    utils.dump_obj_as_json_to_file(story_n_path, story_range)


def do_interactive_learning(cmdline_args, stories, additional_arguments):
    _endpoints = AvailableEndpoints.read_endpoints(cmdline_args.endpoints)
    _interpreter = NaturalLanguageInterpreter.create(cmdline_args.nlu,
                                                     _endpoints.nlu)

    if cmdline_args.core:
        if cmdline_args.finetune:
            raise ValueError("--core can only be used without --finetune flag.")

        logger.info("Loading a pre-trained model. This means that "
                    "all training-related parameters will be ignored.")

        _broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
        _tracker_store = TrackerStore.find_tracker_store(
            None,
            _endpoints.tracker_store,
            _broker)

        _agent = Agent.load(cmdline_args.core,
                            interpreter=_interpreter,
                            generator=_endpoints.nlg,
                            tracker_store=_tracker_store,
                            action_endpoint=_endpoints.action)
    else:
        if cmdline_args.out:
            model_directory = cmdline_args.out
        else:
            model_directory = tempfile.mkdtemp(suffix="_core_model")

        _agent = train_dialogue_model(cmdline_args.domain,
                                      stories,
                                      model_directory,
                                      _interpreter,
                                      _endpoints,
                                      cmdline_args.dump_stories,
                                      cmdline_args.config[0],
                                      None,
                                      additional_arguments)

    interactive.run_interactive_learning(
        _agent, stories,
        finetune=cmdline_args.finetune,
        skip_visualization=cmdline_args.skip_visualization)


if __name__ == '__main__':

    # Running as standalone python application
    arg_parser = create_argument_parser()
    set_default_subparser(arg_parser, 'default')
    cmdline_arguments = arg_parser.parse_args()
    additional_args = _additional_arguments(cmdline_arguments)

    utils.configure_colored_logging(cmdline_arguments.loglevel)

    training_stories = cli.stories_from_cli_args(cmdline_arguments)

    if cmdline_arguments.mode == 'default':
        do_default_training(cmdline_arguments,
                            training_stories,
                            additional_args)

    elif cmdline_arguments.mode == 'interactive':
        do_interactive_learning(cmdline_arguments,
                                training_stories,
                                additional_args)

    elif cmdline_arguments.mode == 'compare':
        do_compare_training(cmdline_arguments,
                            training_stories,
                            additional_args)

import argparse
import logging
import os
import tempfile

from rasa_core import config, cli
from rasa_core import utils
from rasa_core.agent import Agent
from rasa_core.broker import PikaProducer
from rasa_core.domain import TemplateDomain
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.run import AvailableEndpoints
from rasa_core.tracker_store import TrackerStore
from rasa_core.training import interactive
from rasa_core.training.dsl import StoryFileReader
from rasa_core.utils import set_default_subparser

logger = logging.getLogger(__name__)


def create_argument_parser():
    """Parse all the command line arguments for the training script."""

    parser = argparse.ArgumentParser(
        description='Train a dialogue model for Rasa Core. '
                    'The training will use your conversations '
                    'in the story training data format and '
                    'your domain definition to train a dialogue '
                    'model to predict a bots actions.')
    parent_parser = argparse.ArgumentParser(add_help=False)
    add_general_args(parent_parser)

    subparsers = parser.add_subparsers(
        help='Training mode of core.',
        dest='mode')
    subparsers.required = True

    train_parser = subparsers.add_parser(
        'default',
        help='train a dialogue model',
        parents=[parent_parser])
    compare_parser = subparsers.add_parser(
        'compare',
        help='train multiple dialogue models to compare '
             'policies',
        parents=[parent_parser])
    interactive_parser = subparsers.add_parser(
        'interactive',
        help='teach the bot with interactive learning',
        parents=[parent_parser])

    add_compare_args(compare_parser)
    add_interactive_args(interactive_parser)
    add_train_args(train_parser)

    return parser


def add_compare_args(parser):
    parser.add_argument(
        '--percentages',
        nargs="*",
        type=int,
        default=[0, 5, 25, 50, 70, 90, 95],
        help="Range of exclusion percentages")
    parser.add_argument(
        '--runs',
        type=int,
        default=3,
        help="Number of runs for experiments")

    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=True)
    cli.arguments.add_config_arg(
        parser,
        nargs="*")
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=False)
    cli.arguments.add_domain_arg(
        parser,
        required=True)


def add_interactive_args(parser):
    parser.add_argument(
        '-u', '--nlu',
        type=str,
        default=None,
        help="trained nlu model")
    parser.add_argument(
        '--endpoints',
        default=None,
        help="Configuration file for the connectors as a yml file")
    parser.add_argument(
        '--skip_visualization',
        default=False,
        action='store_true',
        help="disables plotting the visualization during "
             "interactive learning")
    parser.add_argument(
        '--finetune',
        default=False,
        action='store_true',
        help="retrain the model immediately based on feedback.")

    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=False)
    cli.arguments.add_config_arg(
        parser,
        nargs=1)
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=True)
    cli.arguments.add_domain_arg(
        parser,
        required=False)


def add_train_args(parser):
    cli.arguments.add_config_arg(
        parser,
        nargs=1)
    cli.arguments.add_output_arg(
        parser,
        help_text="directory to persist the trained model in",
        required=True)
    cli.arguments.add_model_and_story_group(
        parser,
        allow_pretrained_model=False)
    cli.arguments.add_domain_arg(
        parser,
        required=True)


def add_general_args(parser):
    parser.add_argument(
        '--augmentation',
        type=int,
        default=50,
        help="how much data augmentation to use during training")
    parser.add_argument(
        '--dump_stories',
        default=False,
        action='store_true',
        help="If enabled, save flattened stories to a file")
    parser.add_argument(
        '--debug_plots',
        default=False,
        action='store_true',
        help="If enabled, will create plots showing checkpoints "
             "and their connections between story blocks in a  "
             "file called `story_blocks_connections.html`.")

    utils.add_logging_option_arguments(parser)


def train_dialogue_model(domain_file, stories_file, output_path,
                         interpreter=None,
                         endpoints=AvailableEndpoints(),
                         dump_stories=False,
                         policy_config=None,
                         exclusion_percentage=None,
                         kwargs=None):
    if not kwargs:
        kwargs = {}

    policies = config.load(policy_config)

    agent = Agent(domain_file,
                  generator=endpoints.nlg,
                  action_endpoint=endpoints.action,
                  interpreter=interpreter,
                  policies=policies)

    data_load_args, kwargs = utils.extract_args(kwargs,
                                                {"use_story_concatenation",
                                                 "unique_last_num_states",
                                                 "augmentation_factor",
                                                 "remove_duplicates",
                                                 "debug_plots"})

    training_data = agent.load_data(stories_file,
                                    exclusion_percentage=exclusion_percentage,
                                    **data_load_args)
    agent.train(training_data, **kwargs)
    agent.persist(output_path, dump_stories)

    return agent


def _additional_arguments(args):
    additional = {
        "augmentation_factor": args.augmentation,
        "debug_plots": args.debug_plots
    }

    # remove None values
    return {k: v for k, v in additional.items() if v is not None}


def train_comparison_models(stories,
                            domain,
                            output_path="",
                            exclusion_percentages=None,
                            policy_configs=None,
                            runs=1,
                            dump_stories=False,
                            kwargs=None):
    """Train multiple models for comparison of policies"""

    exclusion_percentages = exclusion_percentages or []
    policy_configs = policy_configs or []

    for r in range(runs):
        logging.info("Starting run {}/{}".format(r + 1, runs))

        for i in exclusion_percentages:
            current_round = exclusion_percentages.index(i) + 1

            for policy_config in policy_configs:
                policies = config.load(policy_config)

                if len(policies) > 1:
                    raise ValueError("You can only specify one policy per "
                                     "model for comparison")

                policy_name = type(policies[0]).__name__
                output = os.path.join(output_path, 'run_' + str(r + 1),
                                      policy_name +
                                      str(current_round))

                logging.info("Starting to train {} round {}/{}"
                             " with {}% exclusion"
                             "".format(policy_name, current_round,
                                       len(exclusion_percentages), i))

                train_dialogue_model(
                    domain, stories, output,
                    policy_config=policy_config,
                    exclusion_percentage=i,
                    kwargs=kwargs,
                    dump_stories=dump_stories)


def get_no_of_stories(story_file, domain):
    """Get number of stories in a file."""

    stories = StoryFileReader.read_from_folder(story_file,
                                               TemplateDomain.load(domain))
    return len(stories)


def do_default_training(cmdline_args, stories, additional_arguments):
    """Train a model."""

    train_dialogue_model(domain_file=cmdline_args.domain,
                         stories_file=stories,
                         output_path=cmdline_args.out,
                         dump_stories=cmdline_args.dump_stories,
                         policy_config=cmdline_args.config[0],
                         kwargs=additional_arguments)


def do_compare_training(cmdline_args, stories, additional_arguments):
    train_comparison_models(stories,
                            cmdline_args.domain,
                            cmdline_args.out,
                            cmdline_args.percentages,
                            cmdline_args.config,
                            cmdline_args.runs,
                            cmdline_args.dump_stories,
                            additional_arguments)

    no_stories = get_no_of_stories(cmdline_args.stories,
                                   cmdline_args.domain)

    # store the list of the number of stories present at each exclusion
    # percentage
    story_range = [no_stories - round((x / 100.0) * no_stories)
                   for x in cmdline_args.percentages]

    story_n_path = os.path.join(cmdline_args.out, 'num_stories.json')
    utils.dump_obj_as_json_to_file(story_n_path, story_range)


def do_interactive_learning(cmdline_args, stories, additional_arguments):
    _endpoints = AvailableEndpoints.read_endpoints(cmdline_args.endpoints)
    _interpreter = NaturalLanguageInterpreter.create(cmdline_args.nlu,
                                                     _endpoints.nlu)

    if cmdline_args.core:
        if cmdline_args.finetune:
            raise ValueError("--core can only be used without --finetune flag.")

        logger.info("Loading a pre-trained model. This means that "
                    "all training-related parameters will be ignored.")

        _broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
        _tracker_store = TrackerStore.find_tracker_store(
            None,
            _endpoints.tracker_store,
            _broker)

        _agent = Agent.load(cmdline_args.core,
                            interpreter=_interpreter,
                            generator=_endpoints.nlg,
                            tracker_store=_tracker_store,
                            action_endpoint=_endpoints.action)
    else:
        if cmdline_args.out:
            model_directory = cmdline_args.out
        else:
            model_directory = tempfile.mkdtemp(suffix="_core_model")

        _agent = train_dialogue_model(cmdline_args.domain,
                                      stories,
                                      model_directory,
                                      _interpreter,
                                      _endpoints,
                                      cmdline_args.dump_stories,
                                      cmdline_args.config[0],
                                      None,
                                      additional_arguments)

    interactive.run_interactive_learning(
        _agent, stories,
        finetune=cmdline_args.finetune,
        skip_visualization=cmdline_args.skip_visualization)


if __name__ == '__main__':

    # Running as standalone python application
    arg_parser = create_argument_parser()
    set_default_subparser(arg_parser, 'default')
    cmdline_arguments = arg_parser.parse_args()
    additional_args = _additional_arguments(cmdline_arguments)

    utils.configure_colored_logging(cmdline_arguments.loglevel)

    training_stories = cli.stories_from_cli_args(cmdline_arguments)

    if cmdline_arguments.mode == 'default':
        do_default_training(cmdline_arguments,
                            training_stories,
                            additional_args)

    elif cmdline_arguments.mode == 'interactive':
        do_interactive_learning(cmdline_arguments,
                                training_stories,
                                additional_args)

    elif cmdline_arguments.mode == 'compare':
        do_compare_training(cmdline_arguments,
                            training_stories,
                            additional_args)

Parse all the command line arguments for the training script. # remove None values Train multiple models for comparison of policies Get number of stories in a file. Train a model. # store the list of the number of stories present at each exclusion # percentage # Running as standalone python application