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#
# 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
#
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"""Roformer experiment configurations."""
# pylint: disable=g-doc-return-or-yield,line-too-long
import dataclasses

from official.core import config_definitions as cfg
from official.core import exp_factory
from official.modeling import optimization
from official.nlp.configs import encoders
from official.projects.fffner import fffner_dataloader
from official.projects.fffner import fffner_prediction
from official.projects.fffner.fffner import FFFNerEncoderConfig

AdamWeightDecay = optimization.AdamWeightDecayConfig
PolynomialLr = optimization.PolynomialLrConfig
PolynomialWarmupConfig = optimization.PolynomialWarmupConfig

@exp_factory.register_config_factory('fffner/ner')
def fffner_glue() -> cfg.ExperimentConfig:
  config = cfg.ExperimentConfig(
      task=fffner_prediction.FFFNerPredictionConfig(
          model=fffner_prediction.FFFNerModelConfig(
              encoder=encoders.EncoderConfig(
                  type='any', any=FFFNerEncoderConfig())),
          train_data=fffner_dataloader
          .FFFNerDataConfig(),
          validation_data=fffner_dataloader
          .FFFNerDataConfig(
              is_training=False, drop_remainder=False, include_example_id=True)),
      trainer=cfg.TrainerConfig(
          optimizer_config=optimization.OptimizationConfig({
              'optimizer': {
                  'type': 'adamw',
                  'adamw': {
                      'weight_decay_rate':
                          0.01,
                      'exclude_from_weight_decay':
                          ['LayerNorm', 'layer_norm', 'bias'],
                  }
              },
              'learning_rate': {
                  'type': 'polynomial',
                  'polynomial': {
                      'initial_learning_rate': 2e-5,
                      'end_learning_rate': 0.0,
                  }
              },
              'warmup': {
                  'type': 'polynomial'
              }
          })),
      restrictions=[
          'task.train_data.is_training != None',
          'task.validation_data.is_training != None'
      ])
  return config
