#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import re
import numpy as np
from collections import namedtuple

import paddle.fluid as F
import paddle.fluid.layers as L
import paddle.fluid.dygraph as D


def gen_bias(encoder_inputs, decoder_inputs, step):
    decoder_bsz, decoder_seqlen = decoder_inputs.shape[:2]
    attn_bias = L.reshape(L.range(0, decoder_seqlen, 1, dtype='float32') + 1, [1, -1, 1])
    decoder_bias = L.cast((L.matmul(attn_bias, 1. / attn_bias, transpose_y=True) >= 1.),
                          'float32')  #[1, 1, decoderlen, decoderlen]
    encoder_bias = L.unsqueeze(L.cast(L.ones_like(encoder_inputs), 'float32'), [1])  #[bsz, 1, encoderlen]
    encoder_bias = L.expand(encoder_bias, [1, decoder_seqlen, 1])  #[bsz,decoderlen, encoderlen]
    decoder_bias = L.expand(decoder_bias, [decoder_bsz, 1, 1])  #[bsz, decoderlen, decoderlen]
    if step > 0:
        bias = L.concat([encoder_bias, L.ones([decoder_bsz, decoder_seqlen, step], 'float32'), decoder_bias], -1)
    else:
        bias = L.concat([encoder_bias, decoder_bias], -1)
    return bias


@D.no_grad
def greedy_search_infilling(model,
                            q_ids,
                            q_sids,
                            sos_id,
                            eos_id,
                            attn_id,
                            max_encode_len=640,
                            max_decode_len=100,
                            tgt_type_id=3):
    model.eval()
    _, logits, info = model(q_ids, q_sids)
    gen_ids = L.argmax(logits, -1)
    d_batch, d_seqlen = q_ids.shape
    seqlen = L.reduce_sum(L.cast(q_ids != 0, 'int64'), 1, keep_dim=True)
    has_stopped = np.zeros([d_batch], dtype=np.bool)
    gen_seq_len = np.zeros([d_batch], dtype=np.int64)
    output_ids = []

    past_cache = info['caches']

    cls_ids = L.ones([d_batch], dtype='int64') * sos_id
    attn_ids = L.ones([d_batch], dtype='int64') * attn_id
    ids = L.stack([cls_ids, attn_ids], -1)
    for step in range(max_decode_len):
        bias = gen_bias(q_ids, ids, step)
        pos_ids = D.to_variable(np.tile(np.array([[step, step + 1]], dtype=np.int64), [d_batch, 1]))
        pos_ids += seqlen
        _, logits, info = model(
            ids, L.ones_like(ids) * tgt_type_id, pos_ids=pos_ids, attn_bias=bias, past_cache=past_cache)
        gen_ids = L.argmax(logits, -1)

        past_cached_k, past_cached_v = past_cache
        cached_k, cached_v = info['caches']
        cached_k = [L.concat([pk, k[:, :1, :]], 1) for pk, k in zip(past_cached_k, cached_k)]  # concat cached
        cached_v = [L.concat([pv, v[:, :1, :]], 1) for pv, v in zip(past_cached_v, cached_v)]
        past_cache = (cached_k, cached_v)

        gen_ids = gen_ids[:, 1]
        ids = L.stack([gen_ids, attn_ids], 1)

        gen_ids = gen_ids.numpy()
        has_stopped |= (gen_ids == eos_id).astype(np.bool)
        gen_seq_len += (1 - has_stopped.astype(np.int64))
        output_ids.append(gen_ids.tolist())
        if has_stopped.all():
            break
    output_ids = np.array(output_ids).transpose([1, 0])
    return output_ids


BeamSearchState = namedtuple('BeamSearchState', ['log_probs', 'lengths', 'finished'])
BeamSearchOutput = namedtuple('BeamSearchOutput', ['scores', 'predicted_ids', 'beam_parent_ids'])


def log_softmax(x):
    e_x = np.exp(x - np.max(x))
    return np.log(e_x / e_x.sum())


def mask_prob(p, onehot_eos, finished):
    is_finished = L.cast(L.reshape(finished, [-1, 1]) != 0, 'float32')
    p = is_finished * (1. - L.cast(onehot_eos, 'float32')) * -9999. + (1. - is_finished) * p
    return p


def hyp_score(log_probs, length, length_penalty):
    lp = L.pow((5. + L.cast(length, 'float32')) / 6., length_penalty)
    return log_probs / lp


def beam_search_step(state, logits, eos_id, beam_width, is_first_step, length_penalty):
    """logits.shape == [B*W, V]"""
    beam_size, vocab_size = logits.shape  # as batch size=1 in this hub module. the first dim means bsz * beam_size equals beam_size
    logits_np = logits.numpy()
    for i in range(beam_size):
        logits_np[i][17963] = 0  # make [UNK] prob = 0
    logits = D.to_variable(logits_np)

    bsz, beam_width = state.log_probs.shape
    onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size), 'int64')  #[1, V]

    probs = L.log(L.softmax(logits))  #[B*W, V]
    probs = mask_prob(probs, onehot_eos, state.finished)  #[B*W, V]
    allprobs = L.reshape(state.log_probs, [-1, 1]) + probs  #[B*W, V]

    not_finished = 1 - L.reshape(state.finished, [-1, 1])  #[B*W,1]
    not_eos = 1 - onehot_eos
    length_to_add = not_finished * not_eos  #[B*W,V]
    alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add

    allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size])
    alllen = L.reshape(alllen, [-1, beam_width * vocab_size])
    allscore = hyp_score(allprobs, alllen, length_penalty)
    if is_first_step:
        allscore = L.reshape(allscore, [bsz, beam_width, -1])[:, 0, :]  # first step only consiter beam 0
    scores, idx = L.topk(allscore, k=beam_width)  #[B, W]
    next_beam_id = idx // vocab_size  #[B, W]
    next_word_id = idx % vocab_size

    gather_idx = L.concat([L.where(idx != -1)[:, :1], L.reshape(idx, [-1, 1])], 1)
    next_probs = L.reshape(L.gather_nd(allprobs, gather_idx), idx.shape)
    next_len = L.reshape(L.gather_nd(alllen, gather_idx), idx.shape)

    gather_idx = L.concat([L.where(next_beam_id != -1)[:, :1], L.reshape(next_beam_id, [-1, 1])], 1)
    next_finished = L.reshape(L.gather_nd(state.finished, gather_idx),
                              state.finished.shape)  #[gather new beam state according to new beam id]

    next_finished += L.cast(next_word_id == eos_id, 'int64')
    next_finished = L.cast(next_finished > 0, 'int64')

    next_state = BeamSearchState(log_probs=next_probs, lengths=next_len, finished=next_finished)
    output = BeamSearchOutput(scores=scores, predicted_ids=next_word_id, beam_parent_ids=next_beam_id)

    return output, next_state


@D.no_grad
def beam_search_infilling(model,
                          q_ids,
                          q_sids,
                          sos_id,
                          eos_id,
                          attn_id,
                          max_encode_len=640,
                          max_decode_len=100,
                          beam_width=5,
                          tgt_type_id=3,
                          length_penalty=1.0):
    model.eval()
    _, __, info = model(q_ids, q_sids)
    d_batch, d_seqlen = q_ids.shape

    state = BeamSearchState(
        log_probs=L.zeros([d_batch, beam_width], 'float32'),
        lengths=L.zeros([d_batch, beam_width], 'int64'),
        finished=L.zeros([d_batch, beam_width], 'int64'))
    outputs = []

    def reorder_(t, parent_id):
        """reorder cache according to parent beam id"""
        gather_idx = L.where(parent_id != -1)[:, 0] * beam_width + L.reshape(parent_id, [-1])
        t = L.gather(t, gather_idx)
        return t

    def tile_(t, times):
        _shapes = list(t.shape[1:])
        ret = L.reshape(L.expand(L.unsqueeze(t, [1]), [
            1,
            times,
        ] + [
            1,
        ] * len(_shapes)), [
            -1,
        ] + _shapes)
        return ret

    cached_k, cached_v = info['caches']
    cached_k = [tile_(k, beam_width) for k in cached_k]
    cached_v = [tile_(v, beam_width) for v in cached_v]
    past_cache = (cached_k, cached_v)

    q_ids = tile_(q_ids, beam_width)
    seqlen = L.reduce_sum(L.cast(q_ids != 0, 'int64'), 1, keep_dim=True)

    cls_ids = L.ones([d_batch * beam_width], dtype='int64') * sos_id
    attn_ids = L.ones([d_batch * beam_width], dtype='int64') * attn_id  # SOS
    ids = L.stack([cls_ids, attn_ids], -1)
    for step in range(max_decode_len):
        bias = gen_bias(q_ids, ids, step)
        pos_ids = D.to_variable(np.tile(np.array([[step, step + 1]], dtype=np.int64), [d_batch * beam_width, 1]))
        pos_ids += seqlen

        _, logits, info = model(
            ids, L.ones_like(ids) * tgt_type_id, pos_ids=pos_ids, attn_bias=bias, past_cache=past_cache)

        output, state = beam_search_step(
            state,
            logits[:, 1],
            eos_id=eos_id,
            beam_width=beam_width,
            is_first_step=(step == 0),
            length_penalty=length_penalty)
        outputs.append(output)

        past_cached_k, past_cached_v = past_cache
        cached_k, cached_v = info['caches']
        cached_k = [
            reorder_(L.concat([pk, k[:, :1, :]], 1), output.beam_parent_ids) for pk, k in zip(past_cached_k, cached_k)
        ]  # concat cached
        cached_v = [
            reorder_(L.concat([pv, v[:, :1, :]], 1), output.beam_parent_ids) for pv, v in zip(past_cached_v, cached_v)
        ]
        past_cache = (cached_k, cached_v)

        pred_ids_flatten = L.reshape(output.predicted_ids, [d_batch * beam_width])
        ids = L.stack([pred_ids_flatten, attn_ids], 1)

        if state.finished.numpy().all():
            break

    final_ids = L.stack([o.predicted_ids for o in outputs], 0)
    final_parent_ids = L.stack([o.beam_parent_ids for o in outputs], 0)
    final_ids = L.gather_tree(final_ids, final_parent_ids)  #[:, :,
    #0]  #pick best beam
    final_ids = L.transpose(L.reshape(final_ids, [-1, d_batch * 1, beam_width]), [1, 2, 0])
    return final_ids


en_patten = re.compile(r'^[a-zA-Z0-9]*$')


def post_process(token):
    if token.startswith('##'):
        ret = token[2:]
    else:
        if en_patten.match(token):
            ret = ' ' + token
        else:
            ret = token
    return ret
