import logging

import tensorflow as tf
from io import StringIO
import matplotlib.pyplot as plt
import numpy as np

def get_logger(name, level=logging.INFO):
    logging.basicConfig()
    logger = logging.getLogger(name)
    logger.handlers = [] # This is the key thing for the question!

    # Start defining and assigning your handlers here
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter("[%(levelname)s] %(asctime)s %(name)s: %(message)s"))
    handler.setLevel(level)
    logger.handlers = [handler]
    logger.propagate = False
    logger.setLevel(level)

    return logger

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

class TensorBoardLogger(object):
    """Logging in tensorboard without tensorflow ops."""

    def __init__(self, log_dir):
        """Creates a summary writer logging to log_dir."""
        self.writer = tf.compat.v1.summary.FileWriter(log_dir)

    def log_scalar(self, tag, value, step):
        """Log a scalar variable.

        Parameter
        ----------
        tag : basestring
            Name of the scalar
        value
        step : int
            training iteration
        """
        summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag,
                                                     simple_value=value)])
        self.writer.add_summary(summary, step)

    def log_images(self, tag, images, step):
        """Logs a list of images."""

        im_summaries = []
        for nr, img in enumerate(images):
            # Write the image to a string
            s = StringIO()
            plt.imsave(s, img, format='png')

            # Create an Image object
            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                                       height=img.shape[0],
                                       width=img.shape[1])
            # Create a Summary value
            im_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, nr),
                                                 image=img_sum))

        # Create and write Summary
        summary = tf.Summary(value=im_summaries)
        self.writer.add_summary(summary, step)
        

    def log_histogram(self, tag, values, step, bins=1000):
        """Logs the histogram of a list/vector of values."""
        # Convert to a numpy array
        values = np.array(values)
        
        # Create histogram using numpy        
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill fields of histogram proto
        hist = tf.HistogramProto()
        hist.min = float(np.min(values))
        hist.max = float(np.max(values))
        hist.num = int(np.prod(values.shape))
        hist.sum = float(np.sum(values))
        hist.sum_squares = float(np.sum(values**2))

        # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
        # Thus, we drop the start of the first bin
        bin_edges = bin_edges[1:]

        # Add bin edges and counts
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
