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import os
import sys
import mxnet as mx
import numpy as np
from random import randint
import warnings
import collections
import ctypes
from mxnet import amp
import pytest
from mxnet.test_utils import set_default_device, same_symbol_structure
from mxnet.gluon.model_zoo.vision import get_model
from mxnet.gluon import SymbolBlock, nn, rnn
from mxnet.operator import get_all_registered_operators_grouped
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
sys.path.insert(0, os.path.join(curr_path, '../unittest'))
from common import assert_raises_cudnn_not_satisfied
sys.path.insert(0, os.path.join(curr_path, '../train'))
set_default_device(mx.gpu(0))

@pytest.fixture()
def amp_tests(request):
    def teardown():
        mx.nd.waitall()

    request.addfinalizer(teardown)

def test_amp_coverage(amp_tests):
    conditional = [item[0] for item in amp.lists.symbol_fp16.CONDITIONAL_FP32_FUNCS]

    # Check for duplicates
    for a in [amp.lists.symbol_fp16.FP16_FUNCS,
          amp.lists.symbol_fp16.FP16_FP32_FUNCS,
          amp.lists.symbol_fp16.FP32_FUNCS,
          amp.lists.symbol_fp16.WIDEST_TYPE_CASTS,
          conditional]:
        ret = [item for item, count in collections.Counter(a).items() if count > 1]
        assert ret == [], "Elements " + str(ret) + " are duplicated in the AMP lists."

    t = []
    for a in [amp.lists.symbol_fp16.FP16_FUNCS,
              amp.lists.symbol_fp16.FP16_FP32_FUNCS,
              amp.lists.symbol_fp16.FP32_FUNCS,
              amp.lists.symbol_fp16.WIDEST_TYPE_CASTS,
              conditional]:
        t += a
    ret = [item for item, count in collections.Counter(t).items() if count > 1]
    assert ret == [], "Elements " + str(ret) + " exist in more than 1 AMP list."

    # Check the coverage
    covered = set(t)
    ops = get_all_registered_operators_grouped()
    required = set(k for k in ops
                   if not k.startswith(("_backward", "_contrib_backward", "_npi_backward")) and
                   not k.endswith("_backward"))

    extra = covered - required
    assert not extra, f"{len(extra)} operators are not needed in the AMP lists: {sorted(extra)}"

    guidelines = """Please follow these guidelines for choosing a proper list:
    - if your operator is not to be used in a computational graph
      (e.g. image manipulation operators, optimizers) or does not have
      inputs, put it in FP16_FP32_FUNCS list,
    - if your operator requires FP32 inputs or is not safe to use with lower
      precision, put it in FP32_FUNCS list,
    - if your operator supports both FP32 and lower precision, has
      multiple inputs and expects all inputs to be of the same
      type, put it in WIDEST_TYPE_CASTS list,
    - if your operator supports both FP32 and lower precision and has
      either a single input or supports inputs of different type,
      put it in FP16_FP32_FUNCS list,
    - if your operator is both safe to use in lower precision and
      it is highly beneficial to use it in lower precision, then
      put it in FP16_FUNCS (this is unlikely for new operators)
    - If you are not sure which list to choose, FP32_FUNCS is the
                     safest option"""
    diff = required - covered
    assert not diff, f"{len(diff)} operators {sorted(diff)} do not exist in AMP lists (in " \
        f"python/mxnet/amp/lists/symbol_fp16.py) - please add them. " \
        f"\n{guidelines}"

@pytest.mark.skip(reason='Error during waitall(). Tracked in #18099')
@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
def test_amp_conversion_rnn(amp_tests):
    with mx.Device(mx.gpu(0)):
        model = nn.HybridSequential()
        model.add(rnn.LSTM(hidden_size=10, num_layers=2, bidirectional=True))
        model.add(nn.Dense(2))
        model.initialize()
        model.hybridize()
        out = model(mx.nd.ones((2, 3, 4)))
        new_model = amp.convert_hybrid_block(model)
        out2 = new_model(mx.nd.ones((2, 3, 4)))
        mx.test_utils.assert_almost_equal(out.asnumpy(), out2.asnumpy(), atol=1e-2, rtol=1e-2)


def test_fp16_casting(amp_tests):
    data = mx.sym.var("data")
    out1 = mx.sym.amp_cast(data, dtype="float16")
    out2 = mx.sym.amp_cast(data, dtype="float32")
    out3 = mx.sym.amp_cast(data, dtype="float16")
    # When two ops from data, with different dtypes,
    # data should be float32
    res = mx.sym.Group([out1, out2])
    final_res = amp.convert_symbol(res, data_names=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float32

    # When two ops from data, both casted to float16,
    # data should be float16
    res = mx.sym.Group([out1, out3])
    final_res = amp.convert_symbol(res, data_names=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float16

    # AMP Multicast test where one node is float32, another is float16
    data = mx.sym.var("data", dtype=np.float32)
    data2 = mx.sym.var("data2", dtype=np.float16)
    out4 = mx.sym.amp_multicast(data, data2, num_outputs=2)
    final_res = amp.convert_symbol(out4, cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data2=(1, 2), data=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float16

    # AMP Multicast test where two non input nodes are float16,
    # and one input node is float32
    data = mx.sym.var("data", dtype=np.float32)
    data2 = mx.sym.var("data2", dtype=np.float16)
    data3 = mx.sym.var("data3", dtype=np.float16)
    out5 = mx.sym.amp_multicast(data,
                                mx.sym.elemwise_add(data2, data3),
                                num_outputs=2)
    final_res = amp.convert_symbol(out5, target_dtype_ops=[],
                                   fp32_ops=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2), data3=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float32

    # AMP Multicast test where three input nodes one fp16, one fp32
    # one unknown
    data = mx.sym.var("data", dtype=np.float16)
    data2 = mx.sym.var("data2", dtype=np.float32)
    data3 = mx.sym.var("data3")
    out6 = mx.sym.amp_multicast(data, data2, data3, num_outputs=3)
    final_res = amp.convert_symbol(out6, target_dtype_ops=[],
                                   fp32_ops=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2),
                                data3=(1, 2))
    assert exe.arg_arrays[2].dtype == np.float32

    # Input node to amp_multicast and amp_cast, if dtypes conflict
    # and input node is already fp16, it should still be fp16
    data = mx.sym.var("data", dtype=np.float16)
    data2 = mx.sym.var("data2", dtype=np.float32)
    out7 = mx.sym.Group([mx.sym.amp_multicast(data, data2, num_outputs=2), mx.sym.amp_cast(data, dtype="float16")])
    final_res = amp.convert_symbol(out7, target_dtype_ops=[],
                                   fp32_ops=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float16

    # Input node to amp_multicast and amp_cast, if dtypes conflict
    # and input node is already fp32, it should be changed to fp16
    data = mx.sym.var("data", dtype=np.float32)
    data2 = mx.sym.var("data2", dtype=np.float16)
    out8 = mx.sym.Group([mx.sym.amp_multicast(data, data2, num_outputs=2), mx.sym.amp_cast(data, dtype="float16")])
    final_res = amp.convert_symbol(out8, target_dtype_ops=[],
                                   fp32_ops=[], cast_optional_params=True)
    exe = final_res._simple_bind(ctx=mx.gpu(), data=(1, 2), data2=(1, 2))
    assert exe.arg_arrays[0].dtype == np.float16

    # Check for symbol which has slice channel
    data = mx.sym.var("data")
    data2 = mx.sym.var("data2")
    data._set_attr(__dtype__="-1")
    data2._set_attr(__dtype__="-1")
    concat_res = mx.sym.concat(data, data2)
    out = mx.sym.split(concat_res, axis=1, num_outputs=2)
    final_res = amp.convert_symbol(out)