# SPDX-License-Identifier: Apache-2.0

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np  # type: ignore
from typing import Any, Tuple

import onnx
from ..base import Base
from . import expect


class RNN_Helper():
    def __init__(self, **params):  # type: (**Any) -> None
        # RNN Input Names
        X = str('X')
        W = str('W')
        R = str('R')
        B = str('B')
        H_0 = str('initial_h')
        LAYOUT = str('layout')

        required_inputs = [X, W, R]
        for i in required_inputs:
            assert i in params, "Missing Required Input: {0}".format(i)

        self.num_directions = params[str(W)].shape[0]

        if self.num_directions == 1:
            for k in params.keys():
                if k != X:
                    params[k] = np.squeeze(params[k], axis=0)

            hidden_size = params[R].shape[-1]
            batch_size = params[X].shape[1]

            layout = params[LAYOUT] if LAYOUT in params else 0
            x = params[X]
            x = x if layout == 0 else np.swapaxes(x, 0, 1)
            b = params[B] if B in params else np.zeros(2 * hidden_size, dtype=np.float32)
            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32)

            self.X = x
            self.W = params[W]
            self.R = params[R]
            self.B = b
            self.H_0 = h_0
            self.LAYOUT = layout

        else:
            raise NotImplementedError()

    def f(self, x):  # type: (np.ndarray) -> np.ndarray
        return np.tanh(x)

    def step(self):  # type: () -> Tuple[np.ndarray, np.ndarray]
        seq_length = self.X.shape[0]
        hidden_size = self.H_0.shape[-1]
        batch_size = self.X.shape[1]

        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
        h_list = []

        H_t = self.H_0
        for x in np.split(self.X, self.X.shape[0], axis=0):
            H = self.f(np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(
                *np.split(self.B, 2)))
            h_list.append(H)
            H_t = H

        concatenated = np.concatenate(h_list)
        if self.num_directions == 1:
            Y[:, 0, :, :] = concatenated

        if self.LAYOUT == 0:
            Y_h = Y[-1]
        else:
            Y = np.transpose(Y, [2, 0, 1, 3])
            Y_h = Y[:, :, -1, :]

        return Y, Y_h


class RNN(Base):

    @staticmethod
    def export_defaults():  # type: () -> None
        input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)

        input_size = 2
        hidden_size = 4
        weight_scale = 0.1

        node = onnx.helper.make_node(
            'RNN',
            inputs=['X', 'W', 'R'],
            outputs=['', 'Y_h'],
            hidden_size=hidden_size
        )

        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)

        rnn = RNN_Helper(X=input, W=W, R=R)
        _, Y_h = rnn.step()
        expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_simple_rnn_defaults')

    @staticmethod
    def export_initial_bias():  # type: () -> None
        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)

        input_size = 3
        hidden_size = 5
        custom_bias = 0.1
        weight_scale = 0.1

        node = onnx.helper.make_node(
            'RNN',
            inputs=['X', 'W', 'R', 'B'],
            outputs=['', 'Y_h'],
            hidden_size=hidden_size
        )

        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)

        # Adding custom bias
        W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
        R_B = np.zeros((1, hidden_size)).astype(np.float32)
        B = np.concatenate((W_B, R_B), axis=1)

        rnn = RNN_Helper(X=input, W=W, R=R, B=B)
        _, Y_h = rnn.step()
        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)],
               name='test_simple_rnn_with_initial_bias')

    @staticmethod
    def export_seq_length():  # type: () -> None
        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
                          [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)

        input_size = 3
        hidden_size = 5

        node = onnx.helper.make_node(
            'RNN',
            inputs=['X', 'W', 'R', 'B'],
            outputs=['', 'Y_h'],
            hidden_size=hidden_size
        )

        W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
        R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)

        # Adding custom bias
        W_B = np.random.randn(1, hidden_size).astype(np.float32)
        R_B = np.random.randn(1, hidden_size).astype(np.float32)
        B = np.concatenate((W_B, R_B), axis=1)

        rnn = RNN_Helper(X=input, W=W, R=R, B=B)
        _, Y_h = rnn.step()
        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_rnn_seq_length')

    @staticmethod
    def export_batchwise():  # type: () -> None
        input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)

        input_size = 2
        hidden_size = 4
        weight_scale = 0.5
        layout = 1

        node = onnx.helper.make_node(
            'RNN',
            inputs=['X', 'W', 'R'],
            outputs=['Y', 'Y_h'],
            hidden_size=hidden_size,
            layout=layout
        )

        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)

        rnn = RNN_Helper(X=input, W=W, R=R, layout=layout)
        Y, Y_h = rnn.step()
        expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_simple_rnn_batchwise')
