# Path: deep_qa/common/params.py
# PARAMETER = 60
#     DEFAULT = object()
# def __param(self, message, *args, **kws):
#     def __init__(self, params: Dict[str, Any], history: str=""):
#     def pop(self, key: str, default: Any=DEFAULT):
#     def get(self, key: str, default: Any=DEFAULT):
#     def pop_choice(self, key: str, choices: List[Any], default_to_first_choice: bool=False):
#     def as_dict(self, quiet=False):
#         def log_recursively(parameters, history):
#     def assert_empty(self, class_name: str):
#     def __getitem__(self, key):
#     def __setitem__(self, key, value):
#     def __delitem__(self, key):
#     def __iter__(self):
#     def __len__(self):
#     def __check_is_dict(self, new_history, value):
# def pop_choice(params: Dict[str, Any],
#                key: str,
#                choices: List[Any],
#                default_to_first_choice: bool=False,
#                history: str="?.") -> Any:
# def replace_none(dictionary: Dict[str, Any]) -> Dict[str, Any]:
# class Params(MutableMapping):
# 
# Path: deep_qa/common/tee_logger.py
# class TeeLogger:
#     """
#     This class is an attempt to maintain logs of both stdout and stderr for when models are run.
#     To use this class, at the beginning of your script insert these lines::
# 
#         sys.stdout = TeeLogger("stdout.log", sys.stdout)
#         sys.stderr = TeeLogger("stdout.log", sys.stderr)
#     """
#     def __init__(self, filename: str, terminal: io.TextIOWrapper):
#         self.terminal = terminal
#         parent_directory = os.path.dirname(filename)
#         os.makedirs(parent_directory, exist_ok=True)
#         self.log = open(filename, 'a')
# 
#     def write(self, message):
#         self.terminal.write(message)
#         # We'll special case a particular thing that keras does, to make the log file more
#         # readable.  Keras uses ^H characters to get the training line to update for each batch
#         # without adding more lines to the terminal output.  Displaying those in a file won't work
#         # correctly, so we'll just make sure that each batch shows up on its own line.
#         if '\x08' in message:
#             message = message.replace('\x08', '')
#             if len(message) == 0 or message[-1] != '\n':
#                 message += '\n'
#         self.log.write(message)
# 
#     def flush(self):
#         self.terminal.flush()
#         self.log.flush()


from typing import Dict, List, Tuple, Union
from copy import deepcopy
from .common.params import Params, replace_none, ConfigurationError
from .common.tee_logger import TeeLogger
    from deep_qa.common.checks import log_keras_version_info
    from deep_qa.models import concrete_models
    from keras import backend as K
    from deep_qa.models import concrete_models
import sys
import logging
import os
import json
import random
import pyhocon
import numpy
    import tensorflow
    else:
        raise ConfigurationError("The supplied model does not have enough training inputs.")


def load_model(param_path: str, model_class=None):
    """
    Loads and returns a model.

    Parameters
    ----------
    param_path: str, required
        A json file specifying a DeepQaModel.
    model_class: DeepQaModel, optional (default=None)
        This option is useful if you have implemented a new model
        class which is not one of the ones implemented in this library.

    Returns
    -------
    A ``DeepQaModel`` instance.
    """
    logger.info("Loading model from parameter file: %s", param_path)
    param_dict = pyhocon.ConfigFactory.parse_file(param_path)
    params = Params(replace_none(param_dict))
    prepare_environment(params)

    if model_class is None:
        model_type = params.pop_choice('model_class', concrete_models.keys())
        model_class = concrete_models[model_type]
    else:
        if params.pop('model_class', None) is not None:

# Path: deep_qa/common/params.py
# PARAMETER = 60
#     DEFAULT = object()
# def __param(self, message, *args, **kws):
#     def __init__(self, params: Dict[str, Any], history: str=""):
#     def pop(self, key: str, default: Any=DEFAULT):
#     def get(self, key: str, default: Any=DEFAULT):
#     def pop_choice(self, key: str, choices: List[Any], default_to_first_choice: bool=False):
#     def as_dict(self, quiet=False):
#         def log_recursively(parameters, history):
#     def assert_empty(self, class_name: str):
#     def __getitem__(self, key):
#     def __setitem__(self, key, value):
#     def __delitem__(self, key):
#     def __iter__(self):
#     def __len__(self):
#     def __check_is_dict(self, new_history, value):
# def pop_choice(params: Dict[str, Any],
#                key: str,
#                choices: List[Any],
#                default_to_first_choice: bool=False,
#                history: str="?.") -> Any:
# def replace_none(dictionary: Dict[str, Any]) -> Dict[str, Any]:
# class Params(MutableMapping):
# 
# Path: deep_qa/common/tee_logger.py
# class TeeLogger:
#     """
#     This class is an attempt to maintain logs of both stdout and stderr for when models are run.
#     To use this class, at the beginning of your script insert these lines::
# 
#         sys.stdout = TeeLogger("stdout.log", sys.stdout)
#         sys.stderr = TeeLogger("stdout.log", sys.stderr)
#     """
#     def __init__(self, filename: str, terminal: io.TextIOWrapper):
#         self.terminal = terminal
#         parent_directory = os.path.dirname(filename)
#         os.makedirs(parent_directory, exist_ok=True)
#         self.log = open(filename, 'a')
# 
#     def write(self, message):
#         self.terminal.write(message)
#         # We'll special case a particular thing that keras does, to make the log file more
#         # readable.  Keras uses ^H characters to get the training line to update for each batch
#         # without adding more lines to the terminal output.  Displaying those in a file won't work
#         # correctly, so we'll just make sure that each batch shows up on its own line.
#         if '\x08' in message:
#             message = message.replace('\x08', '')
#             if len(message) == 0 or message[-1] != '\n':
#                 message += '\n'
#         self.log.write(message)
# 
#     def flush(self):
#         self.terminal.flush()
#         self.log.flush()


# Path: deep_qa/run.py
from typing import Dict, List, Tuple, Union
from copy import deepcopy
from .common.params import Params, replace_none, ConfigurationError
from .common.tee_logger import TeeLogger
    from deep_qa.common.checks import log_keras_version_info
    from deep_qa.models import concrete_models
    from keras import backend as K
    from deep_qa.models import concrete_models
import sys
import logging
import os
import json
import random
import pyhocon
import numpy
    import tensorflow

    else:
        raise ConfigurationError("The supplied model does not have enough training inputs.")


def load_model(param_path: str, model_class=None):
    """
    Loads and returns a model.

    Parameters
    ----------
    param_path: str, required
        A json file specifying a DeepQaModel.
    model_class: DeepQaModel, optional (default=None)
        This option is useful if you have implemented a new model
        class which is not one of the ones implemented in this library.

    Returns
    -------
    A ``DeepQaModel`` instance.
    """
    logger.info("Loading model from parameter file: %s", param_path)
    param_dict = pyhocon.ConfigFactory.parse_file(param_path)
    params = Params(replace_none(param_dict))
    prepare_environment(params)

    if model_class is None:
        model_type = params.pop_choice('model_class', concrete_models.keys())
        model_class = concrete_models[model_type]
    else:
        if params.pop('model_class', None) is not None:

            raise ConfigurationError("You have specified a local model class and passed a model_class argument"