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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical, Distribution
from typing import Optional, List

from gluonts.core.component import validated
from gluonts.torch.distributions import DistributionOutput

class MixtureDistribution(Distribution):

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import pandas as pd
import numpy as np
import mxnet as mx
from gluonts.dataset.pandas import PandasDataset
from gluonts.dataset.split import split
from gluonts.mx import DeepAREstimator, Trainer
from gluonts.model.predictor import Predictor
from pathlib import Path

def train_model():

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from gluonts.dataset.repository.datasets import get_dataset
from gluonts.transform import ExpectedNumInstanceSampler
from gluonts.torch.model.deepar import DeepAREstimator
import torch

dataset = get_dataset("electricity")
context_length = 2 * 7 * 24
prediction_length = dataset.metadata.prediction_length

model = DeepAREstimator(

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from gluonts.dataset.repository.datasets import get_dataset
from gluonts.torch.model.deepar import DeepARModel, DeepARLightningModule
from gluonts.transform import (
    AddObservedValuesIndicator,
    InstanceSplitter,
    ExpectedNumInstanceSampler,
)
from gluonts.dataset.field_names import FieldName
from gluonts.dataset.loader import TrainDataLoader
from gluonts.itertools import Cached

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from datetime import datetime
import pandas as pd
import cudf
import numpy as np

start = pd.Timestamp(datetime.strptime('2021-03-12 00:00+0000',  '%Y-%m-%d %H:%M%z'))
end = pd.Timestamp(datetime.strptime('2021-03-12 11:00+0000',  '%Y-%m-%d %H:%M%z'))
timestamps = pd.date_range(start, end, freq='1H')
value = np.random.normal(size=12)
df = pd.DataFrame(value, index=timestamps, columns=['value'])

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from gluonts.dataset.repository.datasets import get_dataset
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx.distribution.gaussian import GaussianOutput
from gluonts.mx import Trainer
from gluonts.mx.trainer.callback import TrainingHistory
from gluonts.evaluation import Evaluator
from gluonts.dataset.common import Dataset
from gluonts.mx import copy_parameters
from gluonts.model.predictor import Predictor

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from gluonts.dataset.repository.datasets import get_dataset
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx.distribution.gaussian import GaussianOutput
from gluonts.mx import Trainer
from gluonts.mx.trainer.callback import TrainingHistory
from gluonts.evaluation import Evaluator
from gluonts.dataset.common import Dataset
from gluonts.mx import copy_parameters
from gluonts.model.predictor import Predictor

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import glob
import numpy as np
import cupy as cp
import imageio
from random import shuffle
from nvidia.dali import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.plugin.tf as dali_tf
import tensorflow as tf

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import glob
import numpy as np
import cupy as cp
import imageio
from random import shuffle
from nvidia.dali import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.plugin.tf as dali_tf
import tensorflow as tf

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import glob
import numpy as np
import cupy as cp
import imageio
from random import shuffle
from nvidia.dali import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.plugin.tf as dali_tf
import tensorflow as tf