"""Tensorflow backend implementation"""
from __future__ import absolute_import

from distutils.version import LooseVersion

import tensorflow as tf
from tensorflow.python.eager import context
import builtins
import numbers
import numpy as np
import os

from ... import ndarray as nd
from ..._deprecate import kernel as K
from ...function.base import TargetCode

if not os.getenv("USE_TFDLPACK", False):
    if LooseVersion(tf.__version__) < LooseVersion("2.2.0"):
        raise RuntimeError("DGL requires tensorflow>=2.2.0 for the official DLPack support.")

    def zerocopy_to_dlpack(data):
        return tf.experimental.dlpack.to_dlpack(data)

    def zerocopy_from_dlpack(dlpack_tensor):
        # TODO(Jinjing): Tensorflow requires memory to be 64-bytes aligned. We check the
        #   alignment and make a copy if needed. The functionality is better in TF's main repo.
        aligned = nd.from_dlpack(dlpack_tensor).to_dlpack(64)
        return tf.experimental.dlpack.from_dlpack(aligned)
else:
    # Use our own DLPack solution
    try:
        import tfdlpack
    except ImportError:
        raise ImportError('Cannot find tfdlpack, which is required by the Tensorflow backend. '
                          'Please follow https://github.com/VoVAllen/tf-dlpack for installation.')

    if LooseVersion(tf.__version__) < LooseVersion("2.1.0"):
        raise RuntimeError("DGL requires tensorflow>=2.1.0.")

    def zerocopy_to_dlpack(input):
        return tfdlpack.to_dlpack(input)

    def zerocopy_from_dlpack(input):
        return tfdlpack.from_dlpack(input)


def data_type_dict():
    return {'float16': tf.float16,
            'float32': tf.float32,
            'float64': tf.float64,
            'uint8': tf.uint8,
            'int8': tf.int8,
            'int16': tf.int16,
            'int32': tf.int32,
            'int64': tf.int64,
            'bool' : tf.bool}


def cpu():
    return "/cpu:0"


def tensor(data, dtype=None):
    if isinstance(data, tf.Tensor):
        if dtype is None or data.dtype == dtype:
            return data
        else:
            return tf.cast(data, dtype=dtype)
    else:
        if isinstance(data, numbers.Number):
            data = [data]
        return tf.convert_to_tensor(data, dtype=dtype)


def initialize_context():
    tf.zeros(1)


def as_scalar(data):
    data = data.numpy()
    return data if np.isscalar(data) else data.item()


def get_preferred_sparse_format():
    """Get the preferred sparse matrix format supported by the backend.

    Different backends have their preferred backend. This info is useful when
    constructing a sparse matrix.
    """
    return "coo"


def sparse_matrix(data, index, shape, force_format=False):
    fmt = index[0]
    if fmt != 'coo':
        raise TypeError(
            'Tensorflow backend only supports COO format. But got %s.' % fmt)
    # tf.SparseTensor only supports int64 indexing,
    # therefore manually casting to int64 when input in int32
    spmat = tf.SparseTensor(indices=tf.cast(tf.transpose(
        index[1], (1, 0)), tf.int64), values=data, dense_shape=shape)
    return spmat, None


def sparse_matrix_indices(spmat):
    return ('coo', spmat.indices)


def is_tensor(obj):
    return isinstance(obj, tf.Tensor)


def shape(input):
    return input.shape


def dtype(input):
    return input.dtype


def ndim(input):
    return input.ndim


def context(input):
    spec = tf.DeviceSpec.from_string(input.device)
    return "/{}:{}".format(spec.device_type.lower(), spec.device_index)

def device_type(ctx):
    return tf.DeviceSpec.from_string(ctx).device_type.lower()


def device_id(ctx):
    return tf.DeviceSpec.from_string(ctx).device_index


def to_backend_ctx(dglctx):
    dev_type = dglctx.device_type
    if dev_type == 1:
        return "/cpu:0"
    elif dev_type == 2:
        return "/gpu:%d" % (dglctx.device_id)
    else:
        raise ValueError('Unsupported DGL device context:', dglctx)


def astype(input, ty):
    with tf.device(input.device):
        return tf.cast(input, dtype=ty)


def asnumpy(input):
    if isinstance(input, tf.SparseTensor):
        # tf.sparse.to_dense assume sorted indices, need to turn off validate_indices in our cases
        return tf.sparse.to_dense(input, validate_indices=False).numpy()
    else:
        return input.numpy()


def copy_to(input, ctx, **kwargs):
    with tf.device(ctx):
        new_tensor = tf.identity(input)
    return new_tensor


def sum(input, dim, keepdims=False):
    if input.dtype == tf.bool:
        input = tf.cast(input, tf.int32)
    return tf.reduce_sum(input, axis=dim, keepdims=keepdims)


def reduce_sum(input):
    if input.dtype == tf.bool:
        input = tf.cast(input, tf.int32)
    return tf.reduce_sum(input)


def mean(input, dim):
    return tf.reduce_mean(input, axis=dim)


def reduce_mean(input):
    return th.reduce_mean(input)


def max(input, dim):
    return tf.reduce_max(input, axis=dim)


def reduce_max(input):
    return tf.reduce_max(input)


def min(input, dim):
    return tf.reduce_min(input, axis=dim)


def reduce_min(input):
    return tf.reduce_min(input)


def argsort(input, dim, descending):
    if descending:
        return tf.cast(tf.argsort(input, axis=dim, direction="DESCENDING"), dtype=tf.int64)
    else:
        return tf.cast(tf.argsort(input, axis=dim, direction="ASCENDING"), dtype=tf.int64)


def topk(input, k, dim, descending=True):
    if not descending:
        input = -input
    shape = np.arange(input.ndim)
    shape[dim], shape[-1] = shape[-1], shape[dim]
    out1 = tf.transpose(input, perm=shape)
    out2 = tf.math.top_k(out1, k=k, sorted=True)
    out = tf.transpose(out2[0], shape)
    if not descending:
        out = -out
    return out


def argtopk(input, k, dim, descending=True):
    if not descending:
        input = -input
    shape = np.arange(input.ndim)
    shape[dim], shape[-1] = shape[-1], shape[dim]
    out1 = tf.transpose(input, perm=shape)
    out2 = tf.math.top_k(out1, k=k, sorted=True)
    out = tf.transpose(out2[1], shape)
    if not descending:
        out = -out
    return out


def exp(input):
    return tf.exp(input)


def sqrt(input):
    return tf.sqrt(input)


def softmax(input, dim=-1):
    return tf.math.softmax(input, axis=dim)


def cat(seq, dim):
    return tf.concat(seq, axis=dim)


def stack(seq, dim):
    return tf.stack(seq, axis=dim)


def split(input, sizes_or_sections, dim):
    return tf.split(input, sizes_or_sections, axis=dim)


def repeat(input, repeats, dim):
    return tf.repeat(input, repeats, dim)


def gather_row(data, row_index):
    return tf.gather(data, row_index)


def slice_axis(data, axis, begin, end):
    # assert axis == 0
    # tf doesn't behave well with negative
    s = [slice(None) for i in range(data.ndim)]
    if end == 0:
        end = data.shape[axis]
    s[axis] = slice(begin, end, None)
    return data[tuple(s)]


def take(data, indices, dim):
    return tf.gather_nd(data, indices, dim)


def narrow_row(x, start, stop):
    return x[start:stop]


def scatter_row(data, row_index, value):
    row_index = tf.expand_dims(row_index, 1)
    # XXX(minjie): Normally, the copy_to here is unnecessary. However, TF has this
    #   notorious legacy issue that int32 type data is always on CPU, which will
    #   crash the program since DGL requires feature data to be on the same device
    #   as graph structure.
    return copy_to(tf.tensor_scatter_nd_update(data, row_index, value), data.device)


def index_add_inplace(data, row_idx, value):
    raise NotImplementedError("Tensorflow doesn't support inplace index_add")


def scatter_row_inplace(data, row_index, value):
    raise NotImplementedError("Tensorflow doesn't support inplace update")


def squeeze(input, dim):
    return tf.squeeze(input, axis=dim)


def unsqueeze(input, dim):
    return tf.expand_dims(input, axis=dim)


def reshape(input, shape):
    return tf.reshape(input, shape)


def swapaxes(input, axis1, axis2):
    return tf.transpose(input, perm=[axis1, axis2])


def zeros(shape, dtype, ctx):
    with tf.device(ctx):
        t = tf.zeros(shape, dtype=dtype)
    return t


def zeros_like(input):
    return tf.zeros_like(input)


def ones(shape, dtype, ctx):
    with tf.device(ctx):
        t = tf.ones(shape, dtype=dtype)
    return t


def uniform(shape, dtype, ctx, low, high):
    with tf.device(ctx):
        t = tf.random.uniform(shape, dtype=dtype, minval=low, maxval=high)
    return t


def randint(shape, dtype, ctx, low, high):
    with tf.device(ctx):
        t = tf.random.uniform(shape, dtype=dtype, minval=low, maxval=high)
    return t


def pad_packed_tensor(input, lengths, value, l_min=None):
    old_shape = input.shape
    if isinstance(lengths, tf.Tensor):
        max_len = as_scalar(tf.reduce_max(lengths))
    else:
        max_len = builtins.max(lengths)

    if l_min is not None:
        max_len = builtins.max(max_len, l_min)

    batch_size = len(lengths)
    ndim = input.ndim
    tensor_list = []
    cum_row = 0
    pad_nparray = np.zeros((ndim, 2), dtype=np.int32)
    for l in lengths:
        t = input[cum_row:cum_row+l]
        pad_nparray[0, 1] = max_len - l
        t = tf.pad(t, tf.constant(pad_nparray),
                   mode='CONSTANT', constant_values=value)
        tensor_list.append(t)
        cum_row += l
    return tf.stack(tensor_list, axis=0)


def pack_padded_tensor(input, lengths):
    out_list = []
    for i, l in enumerate(lengths):
        t = input[i]
        out = t[:l]
        out_list.append(out)
    return tf.concat(out_list, axis=0)


def boolean_mask(input, mask):
    return tf.boolean_mask(input, mask)


def equal(x, y):
    return x == y


def logical_not(input):
    return ~input

def logical_and(input1, input2):
    return tf.math.logical_and(input1, input2)

def clone(input):
    # TF tensor is always immutable so returning the input is safe.
    return input

def clamp(data, min_val, max_val):
    return tf.clip_by_value(data, min_val, max_val)

def replace_inf_with_zero(x):
    return tf.where(tf.abs(x) == np.inf, 0, x)

def unique(input):
    return tf.unique(input).y


def full_1d(length, fill_value, dtype, ctx):
    with tf.device(ctx):
        t = tf.fill([length], value=fill_value)
        t = tf.cast(t, dtype=dtype)
    return t


def nonzero_1d(input):
    nonzero_bool = tf.cast(input, tf.bool)
    return tf.reshape(tf.where(nonzero_bool), (-1, ))


def sort_1d(input):
    return tf.sort(input), tf.cast(tf.argsort(input), dtype=tf.int64)


def arange(start, stop, dtype=tf.int64):
    with tf.device("/cpu:0"):
        t = tf.range(start, stop, dtype=dtype)
    return t


def rand_shuffle(arr):
    return tf.random.shuffle(arr)


def zerocopy_to_numpy(input):
    return np.asarray(memoryview(input))


def zerocopy_from_numpy(np_array):
    # NOTE: not zerocopy
    # This assumes tensor should be on cpu
    with tf.device("/cpu:0"):
        t = tf.convert_to_tensor(np_array)
    return t


def zerocopy_to_dgl_ndarray(data):
    if data.dtype == tf.int32 and device_type(data.device) == 'gpu':
        # NOTE: TF doesn't keep int32 tensors on GPU due to legacy issues with
        #   shape inference. Convert it to uint32 and cast it back afterwards.
        data = tf.cast(data, tf.uint32)
        return nd.cast_to_signed(nd.from_dlpack(zerocopy_to_dlpack(data)))
    else:
        return nd.from_dlpack(zerocopy_to_dlpack(data))


def zerocopy_to_dgl_ndarray_for_write(input):
    return zerocopy_to_dgl_ndarray(input)


def zerocopy_from_dgl_ndarray(input):
    return zerocopy_from_dlpack(input.to_dlpack())


def binary_reduce(reducer, binary_op, graph, lhs, rhs, lhs_data, rhs_data,
                  out_size, lhs_map=(None, None), rhs_map=(None, None), out_map=(None, None)):

    @tf.custom_gradient
    def _lambda(lhs_data, rhs_data):
        return binary_reduce_real(reducer, binary_op, graph, lhs, rhs, lhs_data, rhs_data,
                                  out_size, lhs_map, rhs_map, out_map)
    return _lambda(lhs_data, rhs_data)


def binary_reduce_real(reducer, binary_op, graph, lhs, rhs, lhs_data, rhs_data,
                       out_size, lhs_map, rhs_map, out_map):
    with tf.device(lhs_data.device):
        lhs_data_nd = zerocopy_to_dgl_ndarray(lhs_data)
        rhs_data_nd = zerocopy_to_dgl_ndarray(rhs_data)
        feat_shape = K.infer_binary_feature_shape(
            binary_op, lhs_data_nd, rhs_data_nd)
        out_shape = feat_shape
        if binary_op == 'dot':
            out_shape = feat_shape[:-1]
        out_data = tf.zeros((out_size,) + out_shape, dtype=lhs_data.dtype)
        out_data_nd = zerocopy_to_dgl_ndarray(out_data)
        K.binary_op_reduce(
            reducer if reducer != 'mean' else 'sum',
            binary_op, graph, lhs, rhs, lhs_data_nd, rhs_data_nd,
            out_data_nd, lhs_map[0], rhs_map[0], out_map[0])
        # normalize if mean reducer
        # NOTE(zihao): this is a temporary hack and we should have better solution in the future.
        if reducer == 'mean':
            degs = tf.zeros((out_data.shape[0],), dtype=lhs_data.dtype)
            degs_nd = zerocopy_to_dgl_ndarray(degs)
            if lhs != TargetCode.DST:  # src or edge
                target = lhs
                n = lhs_data.shape[0]
                in_map = lhs_map[0]
            else:  # rhs != TargetCode.DST
                target = rhs
                n = rhs_data.shape[0]
                in_map = rhs_map[0]
            in_ones = tf.ones((n,), dtype=lhs_data.dtype)
            in_ones_nd = zerocopy_to_dgl_ndarray(in_ones)
            K.copy_reduce(
                'sum', graph, target, in_ones_nd, degs_nd, in_map, out_map[0])
            # reshape
            degs = tf.reshape(degs,
                              (out_data.shape[0],) + (1,) * (out_data.ndim - 1))
            degs = tf.clip_by_value(degs, clip_value_min=1,
                                    clip_value_max=np.inf)  # ???
            out_data = out_data / degs
        else:
            degs = None

    def grad(grad_out):
        with tf.device(grad_out.device):
            grad_lhs = None
            grad_rhs = None
            if reducer == 'mean':
                grad_out = grad_out / degs
            grad_out_nd = zerocopy_to_dgl_ndarray(grad_out)

            # comptue gradient for lhs
            grad_lhs = tf.zeros((lhs_data_nd.shape[0],) + feat_shape)
            K.backward_lhs_binary_op_reduce(
                reducer if reducer != 'mean' else 'sum',
                binary_op, graph, lhs, rhs, lhs_data_nd, rhs_data_nd,
                out_data_nd, grad_out_nd, zerocopy_to_dgl_ndarray(grad_lhs),
                lhs_map[1], rhs_map[1], out_map[1])
            grad_lhs = _reduce_grad(grad_lhs, lhs_data_nd.shape)

            # compute gradient for rhs
            grad_rhs = tf.zeros((rhs_data_nd.shape[0],) + feat_shape)
            K.backward_rhs_binary_op_reduce(
                reducer if reducer != 'mean' else 'sum',
                binary_op, graph, lhs, rhs, lhs_data_nd, rhs_data_nd,
                out_data_nd, grad_out_nd, zerocopy_to_dgl_ndarray(grad_rhs),
                lhs_map[1], rhs_map[1], out_map[1])
            grad_rhs = _reduce_grad(grad_rhs, rhs_data_nd.shape)

            return grad_lhs, grad_rhs
    return out_data, grad


def copy_reduce(reducer, graph, target, in_data, out_size, in_map=(None, None),
                out_map=(None, None)):
    @tf.custom_gradient
    def _lambda(in_data):
        return copy_reduce_real(reducer, graph, target, in_data, out_size, in_map,
                                out_map)
    return _lambda(in_data)


def copy_reduce_real(reducer, graph, target, in_data, out_size, in_map,
                     out_map):
    with tf.device(in_data.device):
        out_data = tf.zeros(
            (out_size,) + tuple(in_data.shape[1:]), dtype=in_data.dtype)
        in_data_nd = zerocopy_to_dgl_ndarray(in_data)
        out_data_nd = zerocopy_to_dgl_ndarray(out_data)
        K.copy_reduce(
            reducer if reducer != 'mean' else 'sum',
            graph, target, in_data_nd, out_data_nd, in_map[0], out_map[0])
        # normalize if mean reducer
        # NOTE(zihao): this is a temporary hack and we should have better solution in the future.
        if reducer == 'mean':
            in_ones = tf.ones(in_data.shape[0], dtype=in_data.dtype)
            degs = tf.zeros(out_data.shape[0], dtype=in_data.dtype)
            in_ones_nd = zerocopy_to_dgl_ndarray(in_ones)
            degs_nd = zerocopy_to_dgl_ndarray(degs)
            K.copy_reduce(
                'sum', graph, target, in_ones_nd, degs_nd, in_map[0], out_map[0])
            # reshape
            degs = tf.reshape(degs,
                              (out_data.shape[0],) + (1,) * (out_data.ndim - 1))
            degs = tf.clip_by_value(degs, clip_value_min=1,
                                    clip_value_max=np.inf)  # TODO: ???
            out_data = out_data / degs
        else:
            degs = None

    def grad(grad_out):
        with tf.device(grad_out.device):
            if reducer == 'mean':
                grad_out = grad_out / degs
            grad_out_nd = zerocopy_to_dgl_ndarray(grad_out)
            grad_in = tf.zeros(in_data_nd.shape)
            K.backward_copy_reduce(
                reducer if reducer != 'mean' else 'sum',
                graph, target, in_data_nd, out_data_nd, grad_out_nd,
                zerocopy_to_dgl_ndarray(grad_in), in_map[1], out_map[1])
            return grad_in
    return out_data, grad


def _reduce_grad(grad, shape):
    """Reduce gradient on the broadcast dimension

    If there is broadcast in forward pass, gradients need to be reduced on
    broadcast dimension. This function checks the input tensor shape and
    gradient shape and perform the reduction.

    Parameters
    ----------
    grad: Tensor
        Gradient tensor
    shape: tuple
        Shape of input tensor

    Returns
    -------
    Tensor
    """
    grad_shape = grad.shape[1:]
    in_shape = shape[1:]
    if in_shape == grad_shape:
        # no need to reduce
        return grad
    num_to_squeeze = len(grad_shape) - len(in_shape)
    # pad inshape
    in_shape = (1,) * num_to_squeeze + in_shape
    reduce_idx = np.asarray(np.nonzero(np.asarray(grad_shape) - np.asarray(in_shape)))
    reduce_idx += 1  # skip batch dim
    reduce_idx_tensor = tf.constant(tuple(
        reduce_idx.flatten().tolist()))
    grad = tf.reduce_sum(grad, axis=reduce_idx_tensor, keepdims=True)
    return tf.reshape(grad, shape)


def sync():
    context = context().context()
    context.async_wait()


class GradContext:
    def __init__(self):
        self.tensor_for_grad = []
        self.grad_list = []
        self.tape = None

    def set_tape(self, tape):
        self.tape = tape

    def add_tensor(self, x):
        idx_pop = []
        for idx, ele in enumerate(self.tensor_for_grad):
            if ele._id == x._id:
                idx_pop.append(idx)
        if len(idx_pop) > 0:
            self.tensor_for_grad.pop(idx_pop[0])
        if self.tape is not None:
            self.tape.watch(x)
        self.tensor_for_grad.append(x)

    def backward(self, x, head_gradient=None):
        if head_gradient is not None:
            x = x * head_gradient
        self.grad_list = self.tape.gradient(x, self.tensor_for_grad)

    def is_no_grad(self, x):
        idx_pop = []
        for idx, ele in enumerate(self.tensor_for_grad):
            if ele._id == x._id:
                idx_pop.append(idx)
        if len(idx_pop) == 0:
            return True
        else:
            return self.grad_list[idx_pop[0]] is None

    def grad(self, x):
        idx_pop = []
        for idx, ele in enumerate(self.tensor_for_grad):
            if ele._id == x._id:
                idx_pop.append(idx)
        assert len(idx_pop) == 1
        t = self.grad_list[idx_pop[0]]
        return tf.convert_to_tensor(t)


cgrad = GradContext()


def get_cgrad():
    return cgrad


class record_grad:
    def __init__(self):
        self.tape = tf.GradientTape()

    def __enter__(self):
        cgrad.set_tape(self.tape)
        self.tape.__enter__()
        for x in cgrad.tensor_for_grad:
            self.tape.watch(x)

    def __exit__(self, exc_type, exc_value, exc_traceback):
        # pass
        self.tape.__exit__(exc_type, exc_value, exc_traceback)
        cgrad.tape = None


def attach_grad(x):
    cgrad.add_tensor(x)
    return x


def backward(x, head_gradient=None):
    cgrad.backward(x, head_gradient)


def grad(x):
    return cgrad.grad(x)


def is_no_grad(x):
    return cgrad.is_no_grad(x)


def is_recording():
    raise NotImplementedError("Tensorflow doesn't support is_recording")

no_grad = None

initialize_context()
