#===============================================================================
# Copyright 2014-2021 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================

# daal4py low order moments example for streaming on shared memory systems

import daal4py as d4p
import numpy as np
import os
from daal4py.oneapi import sycl_buffer

# let's use a generator for getting stream from file (defined in stream.py)
import sys
sys.path.insert(0, '..')
from stream import read_next

try:
    from dpctx import device_context, device_type
    with device_context(device_type.gpu, 0):
        gpu_available = True
except:
    try:
        from daal4py.oneapi import sycl_context
        with sycl_context('gpu'):
            gpu_available = True
    except:
        gpu_available = False


# At this moment with sycl we are working only with numpy arrays
def to_numpy(data):
    try:
        from pandas import DataFrame
        if isinstance(data, DataFrame):
            return np.ascontiguousarray(data.values)
    except ImportError:
        pass
    try:
        from scipy.sparse import csr_matrix
        if isinstance(data, csr_matrix):
            return data.toarray()
    except ImportError:
        pass
    return data


def main(readcsv=None, method='defaultDense'):
    # read data from file
    infile = os.path.join('..', 'data', 'batch', 'covcormoments_dense.csv')

    # Using of the classic way (computations on CPU)
    # Configure a low order moments object for streaming
    algo = d4p.low_order_moments(streaming=True, fptype='float')
    # get the generator (defined in stream.py)...
    rn = read_next(infile, 55, readcsv)
    # ... and iterate through chunks/stream
    for chunk in rn:
        algo.compute(chunk)
    # finalize computation
    result_classic = algo.finalize()

    try:
        from dpctx import device_context, device_type

        def gpu_context():
            return device_context(device_type.gpu, 0)

        def cpu_context():
            return device_context(device_type.cpu, 0)
    except:
        from daal4py.oneapi import sycl_context

        def gpu_context():
            return sycl_context('gpu')

        def cpu_context():
            return sycl_context('cpu')

    # It is possible to specify to make the computations on GPU
    try:
        with gpu_context():
            # Configure a low order moments object for streaming
            algo = d4p.low_order_moments(streaming=True, fptype='float')
            # get the generator (defined in stream.py)...
            rn = read_next(infile, 55, readcsv)
            # ... and iterate through chunks/stream
            for chunk in rn:
                sycl_chunk = sycl_buffer(to_numpy(chunk))
                algo.compute(sycl_chunk)
            # finalize computation
            result_gpu = algo.finalize()
        for name in ['minimum', 'maximum', 'sum', 'sumSquares', 'sumSquaresCentered',
                     'mean', 'secondOrderRawMoment', 'variance', 'standardDeviation',
                     'variation']:
            assert np.allclose(getattr(result_classic, name), getattr(result_gpu, name))
    except RuntimeError:
        pass
    # It is possible to specify to make the computations on CPU
    with cpu_context():
        # Configure a low order moments object for streaming
        algo = d4p.low_order_moments(streaming=True, fptype='float')
        # get the generator (defined in stream.py)...
        rn = read_next(infile, 55, readcsv)
        # ... and iterate through chunks/stream
        for chunk in rn:
            sycl_chunk = sycl_buffer(to_numpy(chunk))
            algo.compute(sycl_chunk)
        # finalize computation
        result_cpu = algo.finalize()

    # result provides minimum, maximum, sum, sumSquares, sumSquaresCentered,
    # mean, secondOrderRawMoment, variance, standardDeviation, variation
    for name in ['minimum', 'maximum', 'sum', 'sumSquares', 'sumSquaresCentered', 'mean',
                 'secondOrderRawMoment', 'variance', 'standardDeviation', 'variation']:
        assert np.allclose(getattr(result_classic, name), getattr(result_cpu, name))

    return result_classic


if __name__ == "__main__":
    res = main()
    # print results
    print("\nMinimum:\n", res.minimum)
    print("\nMaximum:\n", res.maximum)
    print("\nSum:\n", res.sum)
    print("\nSum of squares:\n", res.sumSquares)
    print("\nSum of squared difference from the means:\n", res.sumSquaresCentered)
    print("\nMean:\n", res.mean)
    print("\nSecond order raw moment:\n", res.secondOrderRawMoment)
    print("\nVariance:\n", res.variance)
    print("\nStandard deviation:\n", res.standardDeviation)
    print("\nVariation:\n", res.variation)
    print('All looks good!')