# Copyright 2024 Google LLC
#
# 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.
"""Evaluation script for timesfm."""

import os
import sys
import time

from absl import flags
import numpy as np
import pandas as pd
from paxml import checkpoints
import timesfm

from .utils import ExperimentHandler

dataset_names = [
    "m1_monthly",
    "m1_quarterly",
    "m1_yearly",
    "m3_monthly",
    "m3_other",
    "m3_quarterly",
    "m3_yearly",
    "m4_quarterly",
    "m4_yearly",
    "tourism_monthly",
    "tourism_quarterly",
    "tourism_yearly",
    "nn5_daily_without_missing",
    "m5",
    "nn5_weekly",
    "traffic",
    "weather",
    "australian_electricity_demand",
    "car_parts_without_missing",
    "cif_2016",
    "covid_deaths",
    "ercot",
    "ett_small_15min",
    "ett_small_1h",
    "exchange_rate",
    "fred_md",
    "hospital",
]

context_dict = {
    "cif_2016": 32,
    "tourism_yearly": 64,
    "covid_deaths": 64,
    "tourism_quarterly": 64,
    "tourism_monthly": 64,
    "m1_monthly": 64,
    "m1_quarterly": 64,
    "m1_yearly": 64,
    "m3_monthly": 64,
    "m3_other": 64,
    "m3_quarterly": 64,
    "m3_yearly": 64,
    "m4_quarterly": 64,
    "m4_yearly": 64,
}

_MODEL_PATH = flags.DEFINE_string("model_path", "/home/timesfm_q10_20240501",
                                  "Path to model")
_BATCH_SIZE = flags.DEFINE_integer("batch_size", 64, "Batch size")
_HORIZON = flags.DEFINE_integer("horizon", 128, "Horizon")
_BACKEND = flags.DEFINE_string("backend", "gpu", "Backend")
_NUM_JOBS = flags.DEFINE_integer("num_jobs", 1, "Number of jobs")
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")

QUANTILES = list(np.arange(1, 10) / 10.0)


def main():
  results_list = []
  tfm = timesfm.TimesFm(
      context_len=512,
      horizon_len=_HORIZON.value,
      input_patch_len=32,
      output_patch_len=128,
      num_layers=20,
      model_dims=1280,
      backend=_BACKEND.value,
      per_core_batch_size=_BATCH_SIZE.value,
      quantiles=QUANTILES,
  )
  tfm.load_from_checkpoint(
      _MODEL_PATH.value,
      checkpoint_type=checkpoints.CheckpointType.FLAX,
  )
  run_id = np.random.randint(100000)
  model_name = "timesfm"
  for dataset in dataset_names:
    print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
    exp = ExperimentHandler(dataset, quantiles=QUANTILES)

    if dataset in context_dict:
      context_len = context_dict[dataset]
    else:
      context_len = 512
    train_df = exp.train_df
    freq = exp.freq
    init_time = time.time()
    fcsts_df = tfm.forecast_on_df(
        inputs=train_df,
        freq=freq,
        value_name="y",
        model_name=model_name,
        forecast_context_len=context_len,
        num_jobs=_NUM_JOBS.value,
    )
    total_time = time.time() - init_time
    time_df = pd.DataFrame({"time": [total_time], "model": model_name})
    results = exp.evaluate_from_predictions(models=[model_name],
                                            fcsts_df=fcsts_df,
                                            times_df=time_df)
    print(results, flush=True)
    results_list.append(results)
    results_full = pd.concat(results_list)
    save_path = os.path.join(_SAVE_DIR.value, str(run_id))
    print(f"Saving results to {save_path}", flush=True)
    os.makedirs(save_path, exist_ok=True)
    results_full.to_csv(f"{save_path}/results.csv")


if __name__ == "__main__":
  FLAGS = flags.FLAGS
  FLAGS(sys.argv)
  main()