# Path: sacred/metrics_logger.py
# class MetricsLogger:
# class ScalarMetricLogEntry:
#     def __init__(self):
#     def log_scalar_metric(self, metric_name, value, step=None):
#     def get_last_metrics(self):
#     def __init__(self, name, step, timestamp, value):
# def linearize_metrics(logged_metrics):
# 
# Path: sacred/metrics_logger.py
# def linearize_metrics(logged_metrics):
#     """
#     Group metrics by name.
# 
#     Takes a list of individual measurements, possibly belonging
#     to different metrics and groups them by name.
# 
#     :param logged_metrics: A list of ScalarMetricLogEntries
#     :return: Measured values grouped by the metric name:
#     {"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6],
#     "timestamps": [datetime, datetime, datetime]},
#     "metric_name2": {...}}
#     """
#     metrics_by_name = {}
#     for metric_entry in logged_metrics:
#         if metric_entry.name not in metrics_by_name:
#             metrics_by_name[metric_entry.name] = {
#                 "steps": [],
#                 "values": [],
#                 "timestamps": [],
#                 "name": metric_entry.name,
#             }
#         metrics_by_name[metric_entry.name]["steps"].append(metric_entry.step)
#         metrics_by_name[metric_entry.name]["values"].append(metric_entry.value)
#         metrics_by_name[metric_entry.name]["timestamps"].append(metric_entry.timestamp)
#     return metrics_by_name
# 
# Path: sacred/randomness.py
# def set_global_seed(seed):
#     random.seed(seed)
#     if opt.has_numpy:
#         opt.np.random.seed(seed)
#     if module_is_in_cache("tensorflow"):
#         tf = opt.get_tensorflow()
#         tf.set_random_seed(seed)
#     if module_is_in_cache("torch"):
#         import torch
# 
#         torch.manual_seed(seed)
#         if torch.cuda.is_available():
#             torch.cuda.manual_seed_all(seed)
# 
# Path: sacred/utils.py
# class SacredInterrupt(Exception):
#     """Base-Class for all custom interrupts.
# 
#     For more information see :ref:`custom_interrupts`.
#     """
# 
#     STATUS = "INTERRUPTED"
# 
# def join_paths(*parts):
#     """Join different parts together to a valid dotted path."""
#     return ".".join(str(p).strip(".") for p in parts if p)
# 
# class IntervalTimer(threading.Thread):
#     @classmethod
#     def create(cls, func, interval=10):
#         stop_event = threading.Event()
#         timer_thread = cls(stop_event, func, interval)
#         return stop_event, timer_thread
# 
#     def __init__(self, event, func, interval=10.0):
#         super().__init__()
#         self.stopped = event
#         self.func = func
#         self.interval = interval
# 
#     def run(self):
#         while not self.stopped.wait(self.interval):
#             self.func()
#         self.func()
# 
# Path: sacred/stdout_capturing.py
# def get_stdcapturer(mode=None):
#     mode = mode if mode is not None else SETTINGS.CAPTURE_MODE
#     capture_options = {"no": no_tee, "fd": tee_output_fd, "sys": tee_output_python}
#     if mode not in capture_options:
#         raise KeyError(
#             "Unknown capture mode '{}'. Available options are {}".format(
#                 mode, sorted(capture_options.keys())
#             )
#         )
#     return mode, capture_options[mode]


import datetime
import os.path
import sys
import traceback as tb
from sacred import metrics_logger
from sacred.metrics_logger import linearize_metrics
from sacred.randomness import set_global_seed
from sacred.utils import SacredInterrupt, join_paths, IntervalTimer
from sacred.stdout_capturing import get_stdcapturer
        """
        filename = os.path.abspath(filename)
        name = os.path.basename(filename) if name is None else name
        self._emit_artifact_added(name, filename, metadata, content_type)

    def __call__(self, *args):
        r"""Start this run.

        Parameters
        ----------
        \*args
            parameters passed to the main function

        Returns
        -------
            the return value of the main function

        """
        if self.start_time is not None:
            raise RuntimeError(
                "A run can only be started once. "
                "(Last start was {})".format(self.start_time)
            )

        if self.unobserved:
            self.observers = []
        else:
            self.observers = sorted(self.observers, key=lambda x: -x.priority)

        self.warn_if_unobserved()

# Path: sacred/metrics_logger.py
# class MetricsLogger:
# class ScalarMetricLogEntry:
#     def __init__(self):
#     def log_scalar_metric(self, metric_name, value, step=None):
#     def get_last_metrics(self):
#     def __init__(self, name, step, timestamp, value):
# def linearize_metrics(logged_metrics):
# 
# Path: sacred/metrics_logger.py
# def linearize_metrics(logged_metrics):
#     """
#     Group metrics by name.
# 
#     Takes a list of individual measurements, possibly belonging
#     to different metrics and groups them by name.
# 
#     :param logged_metrics: A list of ScalarMetricLogEntries
#     :return: Measured values grouped by the metric name:
#     {"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6],
#     "timestamps": [datetime, datetime, datetime]},
#     "metric_name2": {...}}
#     """
#     metrics_by_name = {}
#     for metric_entry in logged_metrics:
#         if metric_entry.name not in metrics_by_name:
#             metrics_by_name[metric_entry.name] = {
#                 "steps": [],
#                 "values": [],
#                 "timestamps": [],
#                 "name": metric_entry.name,
#             }
#         metrics_by_name[metric_entry.name]["steps"].append(metric_entry.step)
#         metrics_by_name[metric_entry.name]["values"].append(metric_entry.value)
#         metrics_by_name[metric_entry.name]["timestamps"].append(metric_entry.timestamp)
#     return metrics_by_name
# 
# Path: sacred/randomness.py
# def set_global_seed(seed):
#     random.seed(seed)
#     if opt.has_numpy:
#         opt.np.random.seed(seed)
#     if module_is_in_cache("tensorflow"):
#         tf = opt.get_tensorflow()
#         tf.set_random_seed(seed)
#     if module_is_in_cache("torch"):
#         import torch
# 
#         torch.manual_seed(seed)
#         if torch.cuda.is_available():
#             torch.cuda.manual_seed_all(seed)
# 
# Path: sacred/utils.py
# class SacredInterrupt(Exception):
#     """Base-Class for all custom interrupts.
# 
#     For more information see :ref:`custom_interrupts`.
#     """
# 
#     STATUS = "INTERRUPTED"
# 
# def join_paths(*parts):
#     """Join different parts together to a valid dotted path."""
#     return ".".join(str(p).strip(".") for p in parts if p)
# 
# class IntervalTimer(threading.Thread):
#     @classmethod
#     def create(cls, func, interval=10):
#         stop_event = threading.Event()
#         timer_thread = cls(stop_event, func, interval)
#         return stop_event, timer_thread
# 
#     def __init__(self, event, func, interval=10.0):
#         super().__init__()
#         self.stopped = event
#         self.func = func
#         self.interval = interval
# 
#     def run(self):
#         while not self.stopped.wait(self.interval):
#             self.func()
#         self.func()
# 
# Path: sacred/stdout_capturing.py
# def get_stdcapturer(mode=None):
#     mode = mode if mode is not None else SETTINGS.CAPTURE_MODE
#     capture_options = {"no": no_tee, "fd": tee_output_fd, "sys": tee_output_python}
#     if mode not in capture_options:
#         raise KeyError(
#             "Unknown capture mode '{}'. Available options are {}".format(
#                 mode, sorted(capture_options.keys())
#             )
#         )
#     return mode, capture_options[mode]


# Path: sacred/run.py
import datetime
import os.path
import sys
import traceback as tb
from sacred import metrics_logger
from sacred.metrics_logger import linearize_metrics
from sacred.randomness import set_global_seed
from sacred.utils import SacredInterrupt, join_paths, IntervalTimer
from sacred.stdout_capturing import get_stdcapturer

        """
        filename = os.path.abspath(filename)
        name = os.path.basename(filename) if name is None else name
        self._emit_artifact_added(name, filename, metadata, content_type)

    def __call__(self, *args):
        r"""Start this run.

        Parameters
        ----------
        \*args
            parameters passed to the main function

        Returns
        -------
            the return value of the main function

        """
        if self.start_time is not None:
            raise RuntimeError(
                "A run can only be started once. "
                "(Last start was {})".format(self.start_time)
            )

        if self.unobserved:
            self.observers = []
        else:
            self.observers = sorted(self.observers, key=lambda x: -x.priority)

        self.warn_if_unobserved()

        set_global_seed(self.config["seed"])