import json
import torch
import pandas as pd
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
import numpy as np
from moviepy.editor import ImageSequenceClip

def ensure_dir(dirname):
    dirname = Path(dirname)
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=False)

def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)

def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)

def inf_loop(data_loader):
    ''' wrapper function for endless data loader. '''
    for loader in repeat(data_loader):
        yield from loader

def prepare_device(n_gpu_use):
    """
    setup GPU device if available. get gpu device indices which are used for DataParallel
    """
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        print("Warning: There\'s no GPU available on this machine,"
              "training will be performed on CPU.")
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        print(f"Warning: The number of GPU\'s configured to use is {n_gpu_use}, but only {n_gpu} are "
              "available on this machine.")
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids

class MetricTracker:
    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts', 'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)

def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    # ensure that the file has the .gif extension
    filename = filename + '.gif'

    # copy into the color dimension if the images are black and white
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)

    # make the moviepy clip
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True
0fc1eeeb461e9d7a7e8ffea31b9b418ae500f59a
<mask token>


def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)


def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)


<mask token>


class MetricTracker:

    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts',
            'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key
            ]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)


def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    filename = filename + '.gif'
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True

<mask token>


def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)


def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)


def inf_loop(data_loader):
    """ wrapper function for endless data loader. """
    for loader in repeat(data_loader):
        yield from loader


def prepare_device(n_gpu_use):
    """
    setup GPU device if available. get gpu device indices which are used for DataParallel
    """
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        print(
            "Warning: There's no GPU available on this machine,training will be performed on CPU."
            )
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        print(
            f"Warning: The number of GPU's configured to use is {n_gpu_use}, but only {n_gpu} are available on this machine."
            )
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids


class MetricTracker:

    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts',
            'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key
            ]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)


def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    filename = filename + '.gif'
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True

<mask token>


def ensure_dir(dirname):
    dirname = Path(dirname)
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=False)


def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)


def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)


def inf_loop(data_loader):
    """ wrapper function for endless data loader. """
    for loader in repeat(data_loader):
        yield from loader


def prepare_device(n_gpu_use):
    """
    setup GPU device if available. get gpu device indices which are used for DataParallel
    """
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        print(
            "Warning: There's no GPU available on this machine,training will be performed on CPU."
            )
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        print(
            f"Warning: The number of GPU's configured to use is {n_gpu_use}, but only {n_gpu} are available on this machine."
            )
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids


class MetricTracker:

    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts',
            'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key
            ]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)


def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    filename = filename + '.gif'
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True

import json
import torch
import pandas as pd
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
import numpy as np
from moviepy.editor import ImageSequenceClip


def ensure_dir(dirname):
    dirname = Path(dirname)
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=False)


def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)


def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)


def inf_loop(data_loader):
    """ wrapper function for endless data loader. """
    for loader in repeat(data_loader):
        yield from loader


def prepare_device(n_gpu_use):
    """
    setup GPU device if available. get gpu device indices which are used for DataParallel
    """
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        print(
            "Warning: There's no GPU available on this machine,training will be performed on CPU."
            )
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        print(
            f"Warning: The number of GPU's configured to use is {n_gpu_use}, but only {n_gpu} are available on this machine."
            )
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids


class MetricTracker:

    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts',
            'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key
            ]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)


def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    filename = filename + '.gif'
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True

import json
import torch
import pandas as pd
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
import numpy as np
from moviepy.editor import ImageSequenceClip

def ensure_dir(dirname):
    dirname = Path(dirname)
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=False)

def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)

def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)

def inf_loop(data_loader):
    ''' wrapper function for endless data loader. '''
    for loader in repeat(data_loader):
        yield from loader

def prepare_device(n_gpu_use):
    """
    setup GPU device if available. get gpu device indices which are used for DataParallel
    """
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        print("Warning: There\'s no GPU available on this machine,"
              "training will be performed on CPU.")
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        print(f"Warning: The number of GPU\'s configured to use is {n_gpu_use}, but only {n_gpu} are "
              "available on this machine.")
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids

class MetricTracker:
    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts', 'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)

def gif(filename, array, fps=10, scale=1.0):
    """Creates a gif given a stack of images using moviepy
    >>> X = randn(100, 64, 64)
    >>> gif('test.gif', X)
    """
    # ensure that the file has the .gif extension
    filename = filename + '.gif'

    # copy into the color dimension if the images are black and white
    if array.ndim == 3:
        array = array[..., np.newaxis] * np.ones(3)

    # make the moviepy clip
    clip = ImageSequenceClip(list(array), fps=fps).resize(scale)
    clip.write_gif(filename, fps=fps)
    return True