# Copyright (c) 2021 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.
"""Contains U2 model."""
import json
import os
import time
from collections import defaultdict
from collections import OrderedDict
from contextlib import nullcontext

import jsonlines
import numpy as np
import paddle
from paddle import distributed as dist
from paddle.nn.utils import clip_grad_norm_

from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
from paddlespeech.s2t.io.dataloader import DataLoaderFactory
from paddlespeech.s2t.models.u2 import U2Model
from paddlespeech.s2t.training.optimizer import OptimizerFactory
from paddlespeech.s2t.training.reporter import ObsScope
from paddlespeech.s2t.training.reporter import report
from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
from paddlespeech.s2t.training.timer import Timer
from paddlespeech.s2t.training.trainer import Trainer
from paddlespeech.s2t.utils import ctc_utils
from paddlespeech.s2t.utils import error_rate
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import UpdateConfig

logger = Log(__name__).getlog()


class U2Trainer(Trainer):
    def __init__(self, config, args):
        super().__init__(config, args)

    def train_batch(self, batch_index, batch_data, scaler, msg):
        train_conf = self.config
        start = time.time()

        # forward
        utt, audio, audio_len, text, text_len = batch_data
        with paddle.amp.auto_cast(
                level=self.amp_level, enable=True if scaler else False):
            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
                                                        text_len)

        # loss div by `batch_size * accum_grad`
        loss /= train_conf.accum_grad
        losses_np = {'loss': float(loss) * train_conf.accum_grad}
        if attention_loss:
            losses_np['att_loss'] = float(attention_loss)
        if ctc_loss:
            losses_np['ctc_loss'] = float(ctc_loss)

        # loss backward
        if (batch_index + 1) % train_conf.accum_grad != 0:
            # Disable gradient synchronizations across DDP processes.
            # Within this context, gradients will be accumulated on module
            # variables, which will later be synchronized.
            # When using cpu w/o DDP, model does not have `no_sync`
            context = self.model.no_sync if (hasattr(self.model, "no_sync") and
                                             self.parallel) else nullcontext
        else:
            # Used for single gpu training and DDP gradient synchronization
            # processes.
            context = nullcontext
        with context():
            if scaler:
                scaler.scale(loss).backward()
            else:
                loss.backward()
            layer_tools.print_grads(self.model, print_func=None)

        # optimizer step
        if (batch_index + 1) % train_conf.accum_grad == 0:
            # do global grad clip
            if train_conf.global_grad_clip != 0:
                if scaler:
                    scaler.unscale_(self.optimizer)
                # need paddlepaddle==develop or paddlepaddle>=2.5
                clip_grad_norm_(self.model.parameters(),
                                train_conf.global_grad_clip)
            if scaler:
                scaler.step(self.optimizer)
                scaler.update()
            else:
                self.optimizer.step()
            self.optimizer.clear_grad()
            self.lr_scheduler.step()
            self.iteration += 1

        iteration_time = time.time() - start

        for k, v in losses_np.items():
            report(k, v)
        report("batch_size", self.config.batch_size)
        report("accum", train_conf.accum_grad)
        report("step_cost", iteration_time)

        if (batch_index + 1) % train_conf.accum_grad == 0:
            if dist.get_rank() == 0 and self.visualizer:
                losses_np_v = losses_np.copy()
                losses_np_v.update({"lr": self.lr_scheduler()})
                for key, val in losses_np_v.items():
                    self.visualizer.add_scalar(
                        tag='train/' + key, value=val, step=self.iteration - 1)

    @paddle.no_grad()
    def valid(self):
        self.model.eval()
        if not self.use_streamdata:
            logger.info(
                f"Valid Total Examples: {len(self.valid_loader.dataset)}")
        valid_losses = defaultdict(list)
        num_seen_utts = 1
        total_loss = 0.0
        for i, batch in enumerate(self.valid_loader):
            utt, audio, audio_len, text, text_len = batch
            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
                                                        text_len)
            if paddle.isfinite(loss):
                num_utts = batch[1].shape[0]
                num_seen_utts += num_utts
                total_loss += float(loss) * num_utts
                valid_losses['val_loss'].append(float(loss))
                if attention_loss:
                    valid_losses['val_att_loss'].append(float(attention_loss))
                if ctc_loss:
                    valid_losses['val_ctc_loss'].append(float(ctc_loss))

            if (i + 1) % self.config.log_interval == 0:
                valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                valid_dump['val_history_loss'] = total_loss / num_seen_utts

                # logging
                msg = f"Valid: Rank: {dist.get_rank()}, "
                msg += "epoch: {}, ".format(self.epoch)
                msg += "step: {}, ".format(self.iteration)
                if not self.use_streamdata:
                    msg += "batch: {}/{}, ".format(i + 1,
                                                   len(self.valid_loader))
                msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                 for k, v in valid_dump.items())
                logger.info(msg)

        logger.info('Rank {} Val info val_loss {}'.format(
            dist.get_rank(), total_loss / num_seen_utts))
        return total_loss, num_seen_utts

    def do_train(self):
        """The training process control by step."""
        # !!!IMPORTANT!!!
        # Try to export the model by script, if fails, we should refine
        # the code to satisfy the script export requirements
        # script_model = paddle.jit.to_static(self.model)
        # script_model_path = str(self.checkpoint_dir / 'init')
        # paddle.jit.save(script_model, script_model_path)

        self.before_train()

        if not self.use_streamdata:
            logger.info(
                f"Train Total Examples: {len(self.train_loader.dataset)}")
        while self.epoch < self.config.n_epoch:
            with Timer("Epoch-Train Time Cost: {}"):
                self.model.train()
                try:
                    data_start_time = time.time()
                    for batch_index, batch in enumerate(self.train_loader):
                        dataload_time = time.time() - data_start_time
                        msg = "Train:"
                        observation = OrderedDict()
                        with ObsScope(observation):
                            report("Rank", dist.get_rank())
                            report("epoch", self.epoch)
                            report('step', self.iteration)
                            report("lr", self.lr_scheduler())
                            self.train_batch(batch_index, batch, self.scaler,
                                             msg)
                            self.after_train_batch()
                            report('iter', batch_index + 1)
                            if not self.use_streamdata:
                                report('total', len(self.train_loader))
                            report('reader_cost', dataload_time)
                        observation['batch_cost'] = observation[
                            'reader_cost'] + observation['step_cost']
                        observation['samples'] = observation['batch_size']
                        observation['ips,samples/s'] = observation[
                            'batch_size'] / observation['batch_cost']
                        for k, v in observation.items():
                            msg += f" {k.split(',')[0]}: "
                            msg += f"{v:>.8f}" if isinstance(v,
                                                             float) else f"{v}"
                            msg += f" {k.split(',')[1]}" if len(
                                k.split(',')) == 2 else ""
                            msg += ","
                        msg = msg[:-1]  # remove the last ","
                        if (batch_index + 1) % self.config.log_interval == 0:
                            logger.info(msg)
                        data_start_time = time.time()
                except Exception as e:
                    logger.error(e)
                    raise e
            with Timer("Eval Time Cost: {}"):
                total_loss, num_seen_utts = self.valid()
                if dist.get_world_size() > 1:
                    num_seen_utts = paddle.to_tensor(num_seen_utts)
                    # the default operator in all_reduce function is sum.
                    dist.all_reduce(num_seen_utts)
                    total_loss = paddle.to_tensor(total_loss)
                    dist.all_reduce(total_loss)
                    cv_loss = total_loss / num_seen_utts
                    cv_loss = float(cv_loss)
                else:
                    cv_loss = total_loss / num_seen_utts

            logger.info(
                'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
            if self.visualizer:
                self.visualizer.add_scalar(
                    tag='eval/cv_loss', value=cv_loss, step=self.epoch)
                self.visualizer.add_scalar(
                    tag='eval/lr', value=self.lr_scheduler(), step=self.epoch)

            self.save(tag=self.epoch, infos={'val_loss': cv_loss})
            self.new_epoch()

    def setup_dataloader(self):
        config = self.config.clone()
        self.use_streamdata = config.get("use_stream_data", False)
        if self.train:
            self.train_loader = DataLoaderFactory.get_dataloader(
                'train', config, self.args)
            self.valid_loader = DataLoaderFactory.get_dataloader(
                'valid', config, self.args)
            logger.info("Setup train/valid Dataloader!")
        else:
            decode_batch_size = config.get('decode', dict()).get(
                'decode_batch_size', 1)
            self.test_loader = DataLoaderFactory.get_dataloader('test', config,
                                                                self.args)
            self.align_loader = DataLoaderFactory.get_dataloader(
                'align', config, self.args)
            logger.info("Setup test/align Dataloader!")

    def setup_model(self):
        config = self.config
        model_conf = config

        with UpdateConfig(model_conf):
            if self.train:
                model_conf.input_dim = self.train_loader.feat_dim
                model_conf.output_dim = self.train_loader.vocab_size
            else:
                model_conf.input_dim = self.test_loader.feat_dim
                model_conf.output_dim = self.test_loader.vocab_size

        model = U2Model.from_config(model_conf)

        # For Mixed Precision Training
        self.use_amp = self.config.get("use_amp", True)
        self.amp_level = self.config.get("amp_level", "O1")
        if self.train and self.use_amp:
            self.scaler = paddle.amp.GradScaler(
                init_loss_scaling=self.config.get(
                    "scale_loss", 32768.0))  #amp default num 32768.0
            #Set amp_level
            if self.amp_level == 'O2':
                model = paddle.amp.decorate(models=model, level=self.amp_level)
        else:
            self.scaler = None
        if self.parallel:
            model = paddle.DataParallel(model)

        logger.info(f"{model}")
        layer_tools.print_params(model, logger.info)
        self.model = model
        logger.info("Setup model!")

        if not self.train:
            return

        train_config = config
        optim_type = train_config.optim
        optim_conf = train_config.optim_conf
        scheduler_type = train_config.scheduler
        scheduler_conf = train_config.scheduler_conf

        scheduler_args = {
            "learning_rate": optim_conf.lr,
            "verbose": False,
            "warmup_steps": scheduler_conf.warmup_steps,
            "gamma": scheduler_conf.lr_decay,
            "d_model": model_conf.encoder_conf.output_size,
        }
        lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
                                                    scheduler_args)

        def optimizer_args(
                config,
                parameters,
                lr_scheduler=None, ):
            train_config = config
            optim_type = train_config.optim
            optim_conf = train_config.optim_conf
            scheduler_type = train_config.scheduler
            scheduler_conf = train_config.scheduler_conf
            return {
                "weight_decay": optim_conf.weight_decay,
                "learning_rate": lr_scheduler
                if lr_scheduler else optim_conf.lr,
                "parameters": parameters,
                "epsilon": 1e-9 if optim_type == 'noam' else None,
                "beta1": 0.9 if optim_type == 'noam' else None,
                "beat2": 0.98 if optim_type == 'noam' else None,
            }

        optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
        optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)

        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        logger.info("Setup optimizer/lr_scheduler!")


class U2Tester(U2Trainer):
    def __init__(self, config, args):
        super().__init__(config, args)
        self.text_feature = TextFeaturizer(
            unit_type=self.config.unit_type,
            vocab=self.config.vocab_filepath,
            spm_model_prefix=self.config.spm_model_prefix)
        self.vocab_list = self.text_feature.vocab_list

    def id2token(self, texts, texts_len, text_feature):
        """ ord() id to chr() chr """
        trans = []
        for text, n in zip(texts, texts_len):
            n = n.numpy().item()
            ids = text[:n]
            trans.append(text_feature.defeaturize(ids.numpy().tolist()))
        return trans

    def compute_metrics(self,
                        utts,
                        audio,
                        audio_len,
                        texts,
                        texts_len,
                        fout=None):
        decode_config = self.config.decode
        errors_sum, len_refs, num_ins = 0.0, 0, 0
        errors_func = error_rate.char_errors if decode_config.error_rate_type == 'cer' else error_rate.word_errors
        error_rate_func = error_rate.cer if decode_config.error_rate_type == 'cer' else error_rate.wer
        reverse_weight = getattr(decode_config, 'reverse_weight', 0.0)

        start_time = time.time()
        target_transcripts = self.id2token(texts, texts_len, self.text_feature)

        result_transcripts, result_tokenids = self.model.decode(
            audio,
            audio_len,
            text_feature=self.text_feature,
            decoding_method=decode_config.decoding_method,
            beam_size=decode_config.beam_size,
            ctc_weight=decode_config.ctc_weight,
            decoding_chunk_size=decode_config.decoding_chunk_size,
            num_decoding_left_chunks=decode_config.num_decoding_left_chunks,
            simulate_streaming=decode_config.simulate_streaming,
            reverse_weight=reverse_weight)
        decode_time = time.time() - start_time

        for utt, target, result, rec_tids in zip(
                utts, target_transcripts, result_transcripts, result_tokenids):
            errors, len_ref = errors_func(target, result)
            errors_sum += errors
            len_refs += len_ref
            num_ins += 1
            if fout:
                fout.write({
                    "utt": utt,
                    "refs": [target],
                    "hyps": [result],
                    "hyps_tokenid": [rec_tids],
                })
            logger.info(f"Utt: {utt}")
            logger.info(f"Ref: {target}")
            logger.info(f"Hyp: {result}")
            logger.info("One example error rate [%s] = %f" % (
                decode_config.error_rate_type, error_rate_func(target, result)))

        return dict(
            errors_sum=errors_sum,
            len_refs=len_refs,
            num_ins=num_ins,  # num examples
            error_rate=errors_sum / len_refs,
            error_rate_type=decode_config.error_rate_type,
            num_frames=audio_len.sum().numpy().item(),
            decode_time=decode_time)

    @mp_tools.rank_zero_only
    @paddle.no_grad()
    def test(self):
        assert self.args.result_file
        self.model.eval()
        if not self.use_streamdata:
            logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")

        stride_ms = self.config.stride_ms
        error_rate_type = None
        errors_sum, len_refs, num_ins = 0.0, 0, 0
        num_frames = 0.0
        num_time = 0.0
        with jsonlines.open(self.args.result_file, 'w') as fout:
            for i, batch in enumerate(self.test_loader):
                metrics = self.compute_metrics(*batch, fout=fout)
                num_frames += metrics['num_frames']
                num_time += metrics["decode_time"]
                errors_sum += metrics['errors_sum']
                len_refs += metrics['len_refs']
                num_ins += metrics['num_ins']
                error_rate_type = metrics['error_rate_type']
                rtf = num_time / (num_frames * stride_ms)
                logger.info(
                    "RTF: %f, Error rate [%s] (%d/?) = %f" %
                    (rtf, error_rate_type, num_ins, errors_sum / len_refs))

        rtf = num_time / (num_frames * stride_ms)
        msg = "Test: "
        msg += "epoch: {}, ".format(self.epoch)
        msg += "step: {}, ".format(self.iteration)
        msg += "RTF: {}, ".format(rtf)
        msg += "Final error rate [%s] (%d/%d) = %f" % (
            error_rate_type, num_ins, num_ins, errors_sum / len_refs)
        logger.info(msg)

        # test meta results
        err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
        err_type_str = "{}".format(error_rate_type)
        with open(err_meta_path, 'w') as f:
            data = json.dumps({
                "epoch":
                self.epoch,
                "step":
                self.iteration,
                "rtf":
                rtf,
                error_rate_type:
                errors_sum / len_refs,
                "dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
                "process_hour":
                num_time / 1000.0 / 3600.0,
                "num_examples":
                num_ins,
                "err_sum":
                errors_sum,
                "ref_len":
                len_refs,
                "decode_method":
                self.config.decode.decoding_method,
            })
            f.write(data + '\n')

    @paddle.no_grad()
    def align(self):
        ctc_utils.ctc_align(self.config, self.model, self.align_loader,
                            self.config.decode.decode_batch_size,
                            self.config.stride_ms, self.vocab_list,
                            self.args.result_file)

    def load_inferspec(self):
        """infer model and input spec.

        Returns:
            nn.Layer: inference model
            List[paddle.static.InputSpec]: input spec.
        """
        from paddlespeech.s2t.models.u2 import U2InferModel
        infer_model = U2InferModel.from_pretrained(self.test_loader,
                                                   self.config.clone(),
                                                   self.args.checkpoint_path)
        batch_size = 1
        feat_dim = self.test_loader.feat_dim
        model_size = self.config.encoder_conf.output_size
        num_left_chunks = -1
        logger.info(
            f"U2 Export Model Params: batch_size {batch_size}, feat_dim {feat_dim}, model_size {model_size}, num_left_chunks {num_left_chunks}"
        )

        return infer_model, (batch_size, feat_dim, model_size, num_left_chunks)

    @paddle.no_grad()
    def export(self):
        infer_model, input_spec = self.load_inferspec()
        infer_model.eval()
        paddle.set_device('cpu')

        assert isinstance(input_spec, (list, tuple)), type(input_spec)
        batch_size, feat_dim, model_size, num_left_chunks = input_spec

        ######################## infer_model.forward_encoder_chunk ############
        input_spec = [
            # (T,), int16
            paddle.static.InputSpec(shape=[None], dtype='int16'),
        ]
        infer_model.forward_feature = paddle.jit.to_static(
            infer_model.forward_feature, input_spec=input_spec)

        ######################### infer_model.forward_encoder_chunk ############
        input_spec = [
            # xs, (B, T, D)
            paddle.static.InputSpec(
                shape=[batch_size, None, feat_dim], dtype='float32'),
            # offset, int, but need be tensor
            paddle.static.InputSpec(shape=[1], dtype='int32'),
            # required_cache_size, int
            num_left_chunks,
            # att_cache
            paddle.static.InputSpec(
                shape=[None, None, None, None], dtype='float32'),
            # cnn_cache
            paddle.static.InputSpec(
                shape=[None, None, None, None], dtype='float32')
        ]
        infer_model.forward_encoder_chunk = paddle.jit.to_static(
            infer_model.forward_encoder_chunk, input_spec=input_spec)

        ######################### infer_model.ctc_activation ########################
        input_spec = [
            # encoder_out, (B,T,D)
            paddle.static.InputSpec(
                shape=[batch_size, None, model_size], dtype='float32')
        ]
        infer_model.ctc_activation = paddle.jit.to_static(
            infer_model.ctc_activation, input_spec=input_spec)

        ######################### infer_model.forward_attention_decoder ########################
        reverse_weight = 0.3
        input_spec = [
            # hyps, (B, U)
            paddle.static.InputSpec(shape=[None, None], dtype='int64'),
            # hyps_lens, (B,)
            paddle.static.InputSpec(shape=[None], dtype='int64'),
            # encoder_out, (B,T,D)
            paddle.static.InputSpec(
                shape=[batch_size, None, model_size], dtype='float32'),
            reverse_weight
        ]
        infer_model.forward_attention_decoder = paddle.jit.to_static(
            infer_model.forward_attention_decoder, input_spec=input_spec)

        # jit save
        logger.info(f"export save: {self.args.export_path}")
        paddle.jit.save(
            infer_model,
            self.args.export_path,
            combine_params=True,
            skip_forward=True)

        # test dy2static
        def flatten(out):
            if isinstance(out, paddle.Tensor):
                return [out]

            flatten_out = []
            for var in out:
                if isinstance(var, (list, tuple)):
                    flatten_out.extend(flatten(var))
                else:
                    flatten_out.append(var)
            return flatten_out

        # forward_encoder_chunk dygraph
        xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
        offset = paddle.to_tensor([0], dtype='int32')
        required_cache_size = num_left_chunks
        att_cache = paddle.zeros([0, 0, 0, 0])
        cnn_cache = paddle.zeros([0, 0, 0, 0])
        xs_d, att_cache_d, cnn_cache_d = infer_model.forward_encoder_chunk(
            xs1, offset, required_cache_size, att_cache, cnn_cache)

        # load static model
        from paddle.jit.layer import Layer
        layer = Layer()
        logger.info(f"load export model: {self.args.export_path}")
        layer.load(self.args.export_path, paddle.CPUPlace())

        # forward_encoder_chunk static
        xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
        offset = paddle.to_tensor([0], dtype='int32')
        att_cache = paddle.zeros([0, 0, 0, 0])
        cnn_cache = paddle.zeros([0, 0, 0, 0])
        func = getattr(layer, 'forward_encoder_chunk')
        xs_s, att_cache_s, cnn_cache_s = func(xs1, offset, att_cache, cnn_cache)
        np.testing.assert_allclose(xs_d, xs_s, atol=1e-5)
        np.testing.assert_allclose(att_cache_d, att_cache_s, atol=1e-4)
        np.testing.assert_allclose(cnn_cache_d, cnn_cache_s, atol=1e-4)
        # logger.info(f"forward_encoder_chunk output: {xs_s}")
