from functools import partial

import tensorflow as tf

from onnx_tf.common import get_unique_suffix
from onnx_tf.common import exception
from onnx_tf.handlers.backend_handler import BackendHandler
from onnx_tf.handlers.handler import onnx_op
from onnx_tf.handlers.handler import partial_support
from onnx_tf.handlers.handler import ps_description
from .rnn_mixin import RNNMixin


@onnx_op("RNN")
@partial_support(True)
@ps_description("RNN with clip is not supported in Tensorflow.")
class RNN(RNNMixin, BackendHandler):

  @classmethod
  def args_check(cls, node, **kwargs):
    if "clip" in node.attrs:
      exception.OP_UNSUPPORTED_EXCEPT("RNN with clip", "Tensorflow")

  @classmethod
  def _custom_getter(cls,
                     getter,
                     name,
                     node=None,
                     tensor_dict=None,
                     is_bidirectional=None,
                     *args,
                     **kwargs):
    names = name.split("/")
    if is_bidirectional:
      if "fw" in names:
        index = 0
      elif "bw" in names:
        index = 1
      else:
        raise RuntimeError("Can not get {} for bidirectional. "
                           "Either fw and bw is not in name scope.".format(
                               names[-1]))
    if names[-1] == "kernel":
      if is_bidirectional:
        w = tf.split(tensor_dict[node.inputs[1]], 2)[index]
        r = tf.split(tensor_dict[node.inputs[2]], 2)[index]
      else:
        w = tensor_dict[node.inputs[1]]
        r = tensor_dict[node.inputs[2]]
      new_w = tf.transpose(tf.squeeze(w))
      new_r = tf.transpose(tf.squeeze(r))
      kernel = tf.concat([new_w, new_r], 0)
      return kernel
    if names[-1] == "bias":
      if len(node.inputs) >= 4:
        if is_bidirectional:
          b = tf.split(tensor_dict[node.inputs[3]], 2)[index]
        else:
          b = tensor_dict[node.inputs[3]]
        w_b, r_b = tf.split(tf.squeeze(b), 2)
        w_b = tf.transpose(w_b)
        r_b = tf.transpose(r_b)
        return tf.add(w_b, r_b)
      return getter(name, *args, **kwargs)
    return getter(name, *args, **kwargs)

  @classmethod
  def _common(cls, node, **kwargs):
    tensor_dict = kwargs["tensor_dict"]
    x = tensor_dict[node.inputs[0]]
    input_shape = x.get_shape().as_list()
    input_size = len(node.inputs)
    hidden_size = node.attrs["hidden_size"]
    direction = node.attrs.get("direction", "forward")
    num_directions = 2 if direction == "bidirectional" else 1

    output_sequence = node.attrs.get("output_sequence", 0)

    # TODO(fumihwh): check if prev node is one of RNN
    # process input if it comes from other previous cell
    # which has shape [seq_length, num_directions, batch_size, hidden_size]
    if len(input_shape) == 4 and input_shape[1] == 1:
      x = tf.squeeze(x)

    sequence_length = None
    if input_size >= 5 and node.inputs[4] in tensor_dict:
      sequence_length = tensor_dict[node.inputs[4]]

    cell_kwargs = {}

    tf_activations = [tf.nn.tanh]
    if "activations" in node.attrs:
      activations = list(map(lambda x: x.lower(), node.attrs["activations"]))
      activation_alpha = node.attrs.get("activation_alpha", [None] * 2)
      activation_beta = node.attrs.get("activation_beta", [None] * 2)
      tf_activations = [
          cls.rnn_get_activation(activations[0], activation_alpha[0],
                                 activation_beta[0])
      ]
      if num_directions == 2:
        tf_activations.append(
            cls.rnn_get_activation(activations[1], activation_alpha[1],
                                   activation_beta[1]))

    # TODO(fumihwh): check if reverse and bidirectional works
    with tf.variable_scope(
        "RNN_" + get_unique_suffix(),
        custom_getter=partial(
            cls._custom_getter,
            node=node,
            tensor_dict=tensor_dict,
            is_bidirectional=num_directions == 2)):

      cell_kwargs["num_units"] = hidden_size
      initial_state = None
      initial_state_bw = None
      if input_size == 6:
        initial_h = tensor_dict.get(node.inputs[5], None)
        if initial_h is not None:
          initial_state = (initial_h[0],)
          if num_directions == 2:
            initial_state_bw = (initial_h[1],)

      rnn_kwargs = {}
      if num_directions == 1:
        rnn_kwargs["initial_state"] = initial_state
      elif num_directions == 2:
        rnn_kwargs["initial_state_fw"] = initial_state
        rnn_kwargs["initial_state_bw"] = initial_state_bw
      rnn_kwargs["sequence_length"] = sequence_length
      rnn_kwargs["time_major"] = True
      rnn_kwargs["dtype"] = tf.float32

      outputs, states = cls.rnn(x, tf.nn.rnn_cell.BasicRNNCell, cell_kwargs,
                                rnn_kwargs, tf_activations, direction)

    if num_directions == 1:
      state = states[0]
      h = tf.expand_dims(state, 0)
      output = tf.expand_dims(outputs, 1)
    else:
      state_fw = states[0][0]
      state_bw = states[1][0]
      output_fw = outputs[0]
      output_bw = outputs[1]
      h_fw = tf.expand_dims(state_fw, 0)
      h_bw = tf.expand_dims(state_bw, 0)
      h = tf.concat((h_fw, h_bw), axis=0)
      output_fw = tf.expand_dims(output_fw, 1)
      output_bw = tf.expand_dims(output_bw, 1)
      output = tf.concat((output_fw, output_bw), axis=1)

    return [output, h] if output_sequence == 0 else [h]

  @classmethod
  def version_1(cls, node, **kwargs):
    return cls._common(node, **kwargs)

  @classmethod
  def version_7(cls, node, **kwargs):
    return cls._common(node, **kwargs)