# 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


def run_model(param_dict: Dict[str, any], model_class=None):
    """
    This function is the normal entry point to DeepQA. Use this to run a DeepQA model in
    your project. Note that if you care about exactly reproducible experiments,
    you should avoid importing Keras before you import and use this function, as
    Keras relies on random seeds which can be set in this function via a
    JSON specification file.

    Note that this function performs training and will also evaluate the trained
    model on development and test sets if provided in the parameter json.

    Parameters
    ----------
    param_dict: Dict[str, any], required.
        A parameter 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.
    """
    params = Params(replace_none(param_dict))
    prepare_environment(params)

    # These have to be imported _after_ we set the random seed,
    # because keras uses the numpy random seed.

    log_dir = params.get("model_serialization_prefix", None)  # pylint: disable=no-member
    if log_dir is not None:
        sys.stdout = TeeLogger(log_dir + "_stdout.log", sys.stdout)

# 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



def run_model(param_dict: Dict[str, any], model_class=None):
    """
    This function is the normal entry point to DeepQA. Use this to run a DeepQA model in
    your project. Note that if you care about exactly reproducible experiments,
    you should avoid importing Keras before you import and use this function, as
    Keras relies on random seeds which can be set in this function via a
    JSON specification file.

    Note that this function performs training and will also evaluate the trained
    model on development and test sets if provided in the parameter json.

    Parameters
    ----------
    param_dict: Dict[str, any], required.
        A parameter 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.
    """
    params = Params(replace_none(param_dict))
    prepare_environment(params)

    # These have to be imported _after_ we set the random seed,
    # because keras uses the numpy random seed.

    log_dir = params.get("model_serialization_prefix", None)  # pylint: disable=no-member
    if log_dir is not None:
        sys.stdout = TeeLogger(log_dir + "_stdout.log", sys.stdout)

        sys.stderr = TeeLogger(log_dir + "_stderr.log", sys.stderr)