# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
import timeit
import itertools
import argparse
import os

class OpArgMngr(object):
    """Operator argument manager for storing operator workloads."""
    args = {}

    @staticmethod
    def add_workload(funcname, *args, **kwargs):
        if "_specifier" not in kwargs:
            _specifier = funcname
        else:
            _specifier = kwargs["_specififer"]
            del kwargs["_specififer"]
        if _specifier in OpArgMngr.args:
            raise ValueError("duplicate {}".format(_specifier))
        OpArgMngr.args[_specifier] = {'args': args, 'kwargs': kwargs, 'funcname': funcname}


def generate_workloads():
    array_pool = {}
    shapes = []
    for ndim in range(4):
        shapes.extend(list(itertools.product(range(4), repeat=ndim)))
    for shape in shapes:
        name = 'x'.join(str(i) for i in shape)
        if name in array_pool:
            raise ValueError("duplicate array {}".format(name))
        array_pool[name] = dnp.ones(shape)
    return array_pool


def prepare_workloads():
    pool = generate_workloads()
    OpArgMngr.add_workload("zeros", (2, 2))
    OpArgMngr.add_workload("full", (2, 2), 10)
    OpArgMngr.add_workload("identity", 3)
    OpArgMngr.add_workload("ones", (2, 2))
    OpArgMngr.add_workload("einsum", "ii", pool['2x2'], optimize=False)
    OpArgMngr.add_workload("unique", pool['1'], return_index=True, return_inverse=True, return_counts=True, axis=-1)
    OpArgMngr.add_workload("dstack", (pool['2x1'], pool['2x1'], pool['2x1'], pool['2x1']))
    OpArgMngr.add_workload("polyval", dnp.arange(10), pool['2x2'])
    OpArgMngr.add_workload("ediff1d", pool['2x2'], pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("nan_to_num", pool['2x2'])
    OpArgMngr.add_workload("tri", 2, 3, 4)
    OpArgMngr.add_workload("tensordot", pool['2x2'], pool['2x2'], ((1, 0), (0, 1)))
    OpArgMngr.add_workload("cumsum", pool['3x2'], axis=0, out=pool['3x2'])
    OpArgMngr.add_workload("random.shuffle", pool['3'])
    OpArgMngr.add_workload("equal", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("not_equal", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("less", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("greater_equal", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("less_equal", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("maximum", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("minimum", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("sum", pool['2x2'], axis=0, keepdims=True, out=pool['1x2'])
    OpArgMngr.add_workload("std", pool['2x2'], axis=0, ddof=0, keepdims=True, out=pool['1x2'])
    OpArgMngr.add_workload("var", pool['2x2'], axis=0, ddof=1, keepdims=True, out=pool['1x2'])
    OpArgMngr.add_workload("average", pool['2x2'], weights=pool['2'], axis=1, returned=True)
    OpArgMngr.add_workload("histogram", pool['2x2'], bins=10, range=(0.0, 10.0))
    OpArgMngr.add_workload("add", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("cross", pool['2'], pool['2'])
    OpArgMngr.add_workload("linalg.eig", pool['3x3'])
    OpArgMngr.add_workload("linalg.eigh", pool['3x3'])
    OpArgMngr.add_workload("linalg.det", pool['3x3'])
    OpArgMngr.add_workload("linalg.slogdet", pool['3x3'])
    OpArgMngr.add_workload("linalg.matrix_rank", pool['3x3'], pool['1'], hermitian=False)
    OpArgMngr.add_workload("linalg.svd", pool['3x3'])
    OpArgMngr.add_workload("linalg.cholesky", pool['1x1'])
    OpArgMngr.add_workload("linalg.qr", pool['3x3'])
    OpArgMngr.add_workload("linalg.lstsq", pool['2x1'], pool['2'], rcond=None)
    OpArgMngr.add_workload("linalg.eigvals", pool['1x1'])
    OpArgMngr.add_workload("linalg.eigvalsh", pool['1x1'], UPLO='L')
    OpArgMngr.add_workload("linalg.inv", pool['1x1'])
    OpArgMngr.add_workload("linalg.pinv", pool['2x3x3'], pool['1'], hermitian=False)
    OpArgMngr.add_workload("linalg.solve", pool['1x1'], pool['1'])
    OpArgMngr.add_workload("linalg.tensorinv", pool['1x1'], ind=2)
    OpArgMngr.add_workload("linalg.norm", pool['3x3'])
    OpArgMngr.add_workload("linalg.tensorsolve", pool['1x1x1'], pool['1x1x1'], (2, 0, 1))
    OpArgMngr.add_workload("tile", pool['2x2'], 1)
    OpArgMngr.add_workload("trace", pool['2x2'])
    OpArgMngr.add_workload("transpose", pool['2x2'])
    OpArgMngr.add_workload("split", pool['3x3'], (0, 1, 2), axis=1)
    OpArgMngr.add_workload("vstack", (pool['3x3'], pool['3x3'], pool['3x3']))
    OpArgMngr.add_workload("argmax", pool['3x2'], axis=-1)
    OpArgMngr.add_workload("argmin", pool['3x2'], axis=-1)
    OpArgMngr.add_workload("atleast_1d", pool['2'], pool['2x2'])
    OpArgMngr.add_workload("atleast_2d", pool['2'], pool['2x2'])
    OpArgMngr.add_workload("atleast_3d", pool['2'], pool['2x2'])
    OpArgMngr.add_workload("argsort", pool['3x2'], axis=-1)
    OpArgMngr.add_workload("sort", pool['3x2'], axis=-1)
    OpArgMngr.add_workload("indices", dimensions=(1, 2, 3))
    OpArgMngr.add_workload("subtract", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("multiply", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("mod", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("remainder", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("divide", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("true_divide", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("power", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("lcm", pool['2x2'].astype('int32'), pool['2x2'].astype('int32'))
    OpArgMngr.add_workload("diff", pool['2x2'], n=1, axis=-1)
    OpArgMngr.add_workload("inner", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("random.multinomial", n=2, pvals=[1/6.]*6, size=(2,2))
    OpArgMngr.add_workload("random.rand", 3, 2)
    OpArgMngr.add_workload("random.randn", 2, 2)
    OpArgMngr.add_workload("nonzero", pool['2x2'])
    OpArgMngr.add_workload("tril", pool['2x2'], k=0)
    OpArgMngr.add_workload("random.choice", pool['2'], size=(2, 2))
    OpArgMngr.add_workload("take", pool['2'], dnp.array([1,0], dtype='int64'))
    OpArgMngr.add_workload("clip", pool['2x2'], 0, 1)
    OpArgMngr.add_workload("expand_dims", pool['2x2'], axis=0)
    OpArgMngr.add_workload("broadcast_to", pool['2x2'], (2, 2, 2))
    OpArgMngr.add_workload("full_like", pool['2x2'], 2)
    OpArgMngr.add_workload("zeros_like", pool['2x2'])
    OpArgMngr.add_workload("ones_like", pool['2x2'])
    OpArgMngr.add_workload("bitwise_and", pool['2x2'].astype(int), pool['2x2'].astype(int))
    OpArgMngr.add_workload("bitwise_xor", pool['2x2'].astype(int), pool['2x2'].astype(int))
    OpArgMngr.add_workload("bitwise_or", pool['2x2'].astype(int), pool['2x2'].astype(int))
    OpArgMngr.add_workload("copysign", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("arctan2", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("hypot", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("ldexp", pool['2x2'].astype(int), pool['2x2'].astype(int))
    OpArgMngr.add_workload("logical_and", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("logical_or", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("logical_xor", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("random.uniform", low=0, high=1, size=1)
    OpArgMngr.add_workload("random.exponential", scale=2, size=(2,2))
    OpArgMngr.add_workload("random.rayleigh", scale=2, size=(2,2))
    OpArgMngr.add_workload("random.weibull", a=2, size=(2,2))
    OpArgMngr.add_workload("random.pareto", a=2, size=(2,2))
    OpArgMngr.add_workload("random.power", a=2, size=(2,2))
    OpArgMngr.add_workload("random.logistic", loc=2, scale=2, size=(2,2))
    OpArgMngr.add_workload("random.gumbel", loc=2, scale=2, size=(2,2))
    OpArgMngr.add_workload("where", pool['2x3'], pool['2x3'], pool['2x1'])
    OpArgMngr.add_workload("may_share_memory", pool['2x3'][:0], pool['2x3'][:1])
    OpArgMngr.add_workload('squeeze', pool['2x2'], axis=None)
    OpArgMngr.add_workload("pad", pool['2x2'], pad_width=((1,2),(1,2)), mode="constant")
    OpArgMngr.add_workload("prod", pool['2x2'], axis=1, dtype="float64", keepdims=False)
    OpArgMngr.add_workload("around", pool['2x2'], decimals=0)
    OpArgMngr.add_workload("round", pool['2x2'], decimals=1)
    OpArgMngr.add_workload("repeat", pool['2x2'], repeats=1, axis=None)
    OpArgMngr.add_workload("diagflat", pool['2x2'], k=1)
    OpArgMngr.add_workload("diag", pool['2x2'], k=1)
    OpArgMngr.add_workload("diagonal", pool['2x2x2'], offset=-1, axis1=0, axis2=1)
    OpArgMngr.add_workload("diag_indices_from", pool['2x2'])
    OpArgMngr.add_workload("bincount", dnp.arange(3, dtype=int), pool['3'], minlength=4)
    OpArgMngr.add_workload("percentile", pool['2x2x2'], 80, axis=0, out=pool['2x2'],\
                           interpolation='midpoint')
    OpArgMngr.add_workload("quantile", pool['2x2x2'], 0.8, axis=0, out=pool['2x2'],\
                           interpolation='midpoint')
    OpArgMngr.add_workload("all", pool['2x2x2'], axis=(0, 1),\
                           out=dnp.array([False, False], dtype=bool), keepdims=False)
    OpArgMngr.add_workload("any", pool['2x2x2'], axis=(0, 1),\
                           out=dnp.array([False, False], dtype=bool), keepdims=False)
    OpArgMngr.add_workload("roll", pool["2x2"], 1, axis=0)
    OpArgMngr.add_workload("rot90", pool["2x2"], 2)
    OpArgMngr.add_workload("column_stack", (pool['3x3'], pool['3x3'], pool['3x3']))
    OpArgMngr.add_workload("hstack", (pool['3x3'], pool['3x3'], pool['3x3']))
    OpArgMngr.add_workload("triu", pool['3x3'])
    OpArgMngr.add_workload("array_split", pool['2x2'], 2, axis=1)
    OpArgMngr.add_workload("vsplit", pool['2x2'], 2)
    OpArgMngr.add_workload("hsplit", pool['2x2'], 2)
    OpArgMngr.add_workload("dsplit", pool['2x2x2'], 2)
    OpArgMngr.add_workload("arange", 10)
    OpArgMngr.add_workload("concatenate", (pool['1x2'], pool['1x2'], pool['1x2']), axis=0)
    OpArgMngr.add_workload("append", pool['2x2'], pool['1x2'], axis=0)
    OpArgMngr.add_workload("insert", pool['3x2'], 1, pool['1x1'], axis=0)
    OpArgMngr.add_workload("delete", pool['3x2'], 1, axis=0)
    OpArgMngr.add_workload("blackman", 12)
    OpArgMngr.add_workload("eye", 5)
    OpArgMngr.add_workload("hamming", 12)
    OpArgMngr.add_workload("hanning", 12)
    OpArgMngr.add_workload("linspace", 0, 10, 8, endpoint=False)
    OpArgMngr.add_workload("logspace", 2.0, 3.0, num=4, base=2.0, dtype=onp.float32)
    OpArgMngr.add_workload("matmul", pool['2x2'], pool['2x2'])
    OpArgMngr.add_workload("mean", pool['2x2'], axis=0, keepdims=True)
    OpArgMngr.add_workload("random.gamma", 1, size=(2, 3))
    OpArgMngr.add_workload("random.normal", 1, size=(2, 3))
    OpArgMngr.add_workload("max", pool["2x2"], axis=0, out=pool['2'], keepdims=False)
    OpArgMngr.add_workload("min", pool["2x2"], axis=0, out=pool['2'], keepdims=False)
    OpArgMngr.add_workload("amax", pool["2x2"], axis=1, out=pool['2'], keepdims=False)
    OpArgMngr.add_workload("amin", pool["2x2"], axis=1, out=pool['2'], keepdims=False)

    unary_ops = ['negative', 'reciprocal', 'abs', 'sign', 'rint', 'ceil', 'floor',
                 'bitwise_not', 'trunc', 'fix', 'square', 'sqrt', 'cbrt', 'exp',
                 'log', 'log10', 'log2', 'log1p', 'expm1', 'logical_not', 'isnan',
                 'isinf', 'isposinf', 'isneginf', 'isfinite', 'sin', 'cos', 'tan',
                 'arcsin', 'arccos', 'arctan', 'degrees', 'radians', 'sinh', 'cosh',
                 'tanh', 'arcsinh', 'arccosh', 'arctanh']  # 'rad2deg', 'deg2rad' cannot run without tvm
    for unary_op in unary_ops:
        if unary_op == "bitwise_not":
            OpArgMngr.add_workload(unary_op, dnp.ones((2, 2), dtype=int))
        else:
            OpArgMngr.add_workload(unary_op, pool['2x2'])


def benchmark_helper(f, *args, **kwargs):
    number = 10000
    return timeit.timeit(lambda: f(*args, **kwargs), number=number) / number


def get_op(module, funcname):
    funcname = funcname.split(".")
    for fname in funcname:
        module = getattr(module, fname)
    return module


def run_benchmark(packages):
    results = {}
    for (k, v) in OpArgMngr.args.items():
        result = {}
        for (name, package) in packages.items():
            print('{}.{} running...'.format(name, k))
            op = get_op(package["module"], v["funcname"])
            args = [package["data"](arg) for arg in v["args"]]
            kwargs = {k: package["data"](v) for (k, v) in v["kwargs"].items()}
            benchmark = benchmark_helper(op, *args, **kwargs)
            result[name] = benchmark
        results[k] = result
    return results


def show_results(results):
    print("{:>24}{:>24}{:>24}".format("name", "package", "time(us)"))
    for (specifier, d) in results.items():
        for (k, v) in d.items():
            print("{:>24}{:>24}{:>24}".format(specifier, k, v * 10 ** 6))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('ffi_type')
    parsed = parser.parse_args()
    if parsed.ffi_type == "cython":
        os.environ['MXNET_ENABLE_CYTHON'] = '1'
        os.environ['MXNET_ENFORCE_CYTHON'] = '1'
    elif parsed.ffi_type == "ctypes":
        os.environ['MXNET_ENABLE_CYTHON'] = '0'
    else:
        raise ValueError("unknown ffi_type {}",format(parsed.ffi_type))
    os.environ["MXNET_ENGINE_TYPE"] = "NaiveEngine"
    import mxnet as mx
    import numpy as onp
    from mxnet import np as dnp

    mx.npx.set_np(dtype=False)
    packages = {
        "onp": {
            "module": onp,
            "data": lambda arr: arr.asnumpy() if isinstance(arr, dnp.ndarray) else arr
        },
        "dnp": {
            "module": dnp,
            "data": lambda arr: arr
        }
    }
    prepare_workloads()
    results = run_benchmark(packages)
    show_results(results)

[((250, 13, 250, 38), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ({}, {}), '()', False, 'import argparse\n'), ((265, 4, 265, 30), 'mxnet.npx.set_np', 'mx.npx.set_np', (), '', True, 'import mxnet as mx\n'), ((47, 27, 47, 42), 'mxnet.np.ones', 'dnp.ones', ({(47, 36, 47, 41): 'shape'}, {}), '(shape)', True, 'from mxnet import np as dnp\n'), ((60, 38, 60, 52), 'mxnet.np.arange', 'dnp.arange', ({(60, 49, 60, 51): '(10)'}, {}), '(10)', True, 'from mxnet import np as dnp\n'), ((127, 46, 127, 77), 'mxnet.np.array', 'dnp.array', (), '', True, 'from mxnet import np as dnp\n'), ((164, 39, 164, 63), 'mxnet.np.arange', 'dnp.arange', (), '', True, 'from mxnet import np as dnp\n'), ((170, 31, 170, 68), 'mxnet.np.array', 'dnp.array', (), '', True, 'from mxnet import np as dnp\n'), ((172, 31, 172, 68), 'mxnet.np.array', 'dnp.array', (), '', True, 'from mxnet import np as dnp\n'), ((210, 45, 210, 72), 'mxnet.np.ones', 'dnp.ones', (), '', True, 'from mxnet import np as dnp\n')]