from typing import Any
import random
random.seed(0)
import numpy as np
np.random.seed(0)
import tensorflow as tf
import onnx2tf.gs as gs
from onnx2tf.utils.common_functions import (
    get_constant_or_variable,
    print_node_info,
    inverted_operation_enable_disable,
    convert_axis,
    make_tf_node_info,
    get_replacement_parameter,
    pre_process_transpose,
    post_process_transpose,
)


@print_node_info
@inverted_operation_enable_disable
@get_replacement_parameter
def make_node(
    *,
    graph_node: gs.Node,
    tf_layers_dict: dict,
    **kwargs: Any,
):
    """MeanVarianceNormalization

    Parameters
    ----------
    graph_node: gs.Node
        graph_surgeon Node

    tf_layers_dict: dict
        optype, shape, dtype, tensorflow graph
    """
    before_op_output_shape_trans_1 = \
        tf_layers_dict.get(graph_node.inputs[0].name, {}).get('before_op_output_shape_trans', True)
    before_op_output_shape_trans = \
        before_op_output_shape_trans_1

    graph_node_input = get_constant_or_variable(
        graph_node.inputs[0],
        before_op_output_shape_trans,
    )
    input_tensor = tf_layers_dict[graph_node_input.name]['tf_node'] \
        if isinstance(graph_node_input, gs.Variable) else graph_node_input

    # Pre-process transpose
    input_tensor = pre_process_transpose(
        value_before_transpose=input_tensor,
        param_target='inputs',
        param_name=graph_node.inputs[0].name,
        **kwargs,
    )

    input_tensor_rank = len(input_tensor.shape)


    graph_node_output: gs.Variable = graph_node.outputs[0]

    shape = graph_node_output.shape
    dtype = graph_node_output.dtype

    axes = graph_node.attrs.get('axes', [0,2,3])
    if isinstance(axes, list) or (isinstance(axes, np.ndarray) and len(axes.shape) > 0):
        axes = [
            convert_axis(
                axis=idx,
                tensor_rank=input_tensor_rank,
                before_op_output_shape_trans=before_op_output_shape_trans,
            ) for idx in axes
        ]
    elif axes is not None and isinstance(axes, np.ndarray) and len(axes.shape) == 0:
        axes = convert_axis(
            axis=axes,
            tensor_rank=input_tensor_rank,
            before_op_output_shape_trans=before_op_output_shape_trans,
        )

    mvn_epsilon = kwargs.get('mvn_epsilon', 0.0000000001)
    mvn_epsilon = tf.constant(
        value=mvn_epsilon,
        dtype=tf.float32,
    )

    # Preserving Graph Structure (Dict)
    tf_layers_dict[graph_node_output.name] = {
        'optype': graph_node.op,
        'shape': shape,
        'dtype': dtype,
    }

    # Generation of TF OP
    moments_axes = [0] if input_tensor_rank < 3 else axes
    mean, variance = tf.nn.moments(
        x=input_tensor,
        axes=moments_axes,
        keepdims=True,
    )
    tf_layers_dict[graph_node_output.name]['tf_node'] = \
        tf.math.divide(
            tf.math.subtract(input_tensor, mean),
            tf.sqrt(tf.math.add(variance, mvn_epsilon)),
        )

    # Post-process transpose
    tf_layers_dict[graph_node_output.name]['tf_node'] = post_process_transpose(
        value_before_transpose=tf_layers_dict[graph_node_output.name]['tf_node'],
        param_target='outputs',
        param_name=graph_node.outputs[0].name,
        **kwargs,
    )

    # Generation of Debug Info
    tf_layers_dict[graph_node_output.name]['tf_node_info'] = \
        make_tf_node_info(
            node_info={
                'tf_op_type': 'MeanVarianceNormalization',
                'tf_inputs': {
                    'x': input_tensor,
                    'axes': axes,
                    'epsilon': mvn_epsilon,
                },
                'tf_outputs': {
                    'output': tf_layers_dict[graph_node_output.name]['tf_node'],
                },
            }
        )
