import functools
import hashlib
import operator
import os
import os.path
import sys
import numpy as np
import pytest

# Bamboo utilities
current_file = os.path.realpath(__file__)
current_dir = os.path.dirname(current_file)
sys.path.insert(0, os.path.join(os.path.dirname(current_dir), 'common_python'))
import tools

# ==============================================
# Objects for Python data reader
# ==============================================
# Note: The Python data reader imports this file as a module and calls
# the functions below to ingest data.

# Data
np.random.seed(20200717)
_num_samples = 17
_sample_size = 5
_samples = np.random.normal(size=(_num_samples,_sample_size)).astype(np.float32)

# Sample access functions
def get_sample(index):
    return _samples[index,:]
def num_samples():
    return _num_samples
def sample_dims():
    return (_sample_size,)

# ==============================================
# Hash function
# ==============================================

def uniform_hash(x):
    hashes = [hashlib.md5(val) for val in x]
    hashes = [int.from_bytes(h.digest(), 'little') for h in hashes]
    hashes = [h / 2**128 for h in hashes]
    return np.array(hashes, dtype=np.float64)

# ==============================================
# Setup LBANN experiment
# ==============================================

def setup_experiment(lbann):
    """Construct LBANN experiment.

    Args:
        lbann (module): Module for LBANN Python frontend

    """

    # Skip test on non-GPU systems
    if not tools.gpus_per_node(lbann):
        message = f'{os.path.basename(__file__)} requires GPUs'
        print('Skip - ' + message)
        pytest.skip(message)

    mini_batch_size = num_samples() // 2
    trainer = lbann.Trainer(mini_batch_size)
    model = construct_model(lbann)
    data_reader = construct_data_reader(lbann)
    optimizer = lbann.NoOptimizer()
    return trainer, model, data_reader, optimizer

def construct_model(lbann):
    """Construct LBANN model.

    Args:
        lbann (module): Module for LBANN Python frontend

    """

    # Input data
    x_lbann = lbann.Input(data_field='samples')

    # Objects for LBANN model
    obj = []
    metrics = []
    callbacks = []

    # ------------------------------------------
    # GPU
    # ------------------------------------------

    # LBANN implementation
    x = x_lbann
    y = lbann.UniformHash(x, device='GPU')
    z = lbann.L2Norm2(y)
    obj.append(z)
    metrics.append(lbann.Metric(z, name='GPU'))

    # NumPy implementation
    vals = []
    for i in range(num_samples()):
        x = get_sample(i).astype(np.float32)
        y = uniform_hash(x)
        z = tools.numpy_l2norm2(y)
        vals.append(z)
    val = np.mean(vals)
    tol = 8 * val * np.finfo(np.float32).eps
    callbacks.append(lbann.CallbackCheckMetric(
        metric=metrics[-1].name,
        lower_bound=val-tol,
        upper_bound=val+tol,
        error_on_failure=True,
        execution_modes='test'))

    # ------------------------------------------
    # Construct model
    # ------------------------------------------

    num_epochs = 0
    return lbann.Model(num_epochs,
                       layers=lbann.traverse_layer_graph(x_lbann),
                       objective_function=obj,
                       metrics=metrics,
                       callbacks=callbacks)

def construct_data_reader(lbann):
    """Construct Protobuf message for Python data reader.

    The Python data reader will import the current Python file to
    access the sample access functions.

    Args:
        lbann (module): Module for LBANN Python frontend

    """

    # Note: The training data reader should be removed when
    # https://github.com/LLNL/lbann/issues/1098 is resolved.
    message = lbann.reader_pb2.DataReader()
    message.reader.extend([
        tools.create_python_data_reader(
            lbann,
            current_file,
            'get_sample',
            'num_samples',
            'sample_dims',
            'train'
        )
    ])
    message.reader.extend([
        tools.create_python_data_reader(
            lbann,
            current_file,
            'get_sample',
            'num_samples',
            'sample_dims',
            'test'
        )
    ])
    return message

# ==============================================
# Setup PyTest
# ==============================================

# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment, __file__):
    globals()[_test_func.__name__] = _test_func
