# Copyright (c) 2023, Salesforce, Inc.
# SPDX-License-Identifier: Apache-2
#
# 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 math
from collections.abc import Iterable, Iterator
from enum import Enum
from functools import cached_property
from pathlib import Path

import datasets
import pyarrow.compute as pc
from gluonts.dataset import DataEntry
from gluonts.dataset.common import ProcessDataEntry
from gluonts.dataset.split import TestData, TrainingDataset, split
from gluonts.itertools import Map
from gluonts.time_feature import norm_freq_str
from gluonts.transform import Transformation
from pandas.tseries.frequencies import to_offset
from toolz import compose

TEST_SPLIT = 0.1
MAX_WINDOW = 20

M4_PRED_LENGTH_MAP = {
    "A": 6,
    "Q": 8,
    "M": 18,
    "W": 13,
    "D": 14,
    "H": 48,
    "h": 48,
    "Y": 6,
}

PRED_LENGTH_MAP = {
    "M": 12,
    "W": 8,
    "D": 30,
    "H": 48,
    "h": 48,
    "T": 48,
    "S": 60,
    "s": 60,
    "min": 48,
}

TFB_PRED_LENGTH_MAP = {
    "A": 6,
    "Y": 6,
    "H": 48,
    "h": 48,
    "Q": 8,
    "D": 14,
    "M": 18,
    "W": 13,
    "U": 8,
    "T": 8,
    "min": 8,
    "us": 8,
}


class Term(Enum):
    SHORT = "short"
    MEDIUM = "medium"
    LONG = "long"

    @property
    def multiplier(self) -> int:
        if self == Term.SHORT:
            return 1
        elif self == Term.MEDIUM:
            return 10
        elif self == Term.LONG:
            return 15


def itemize_start(data_entry: DataEntry) -> DataEntry:
    data_entry["start"] = data_entry["start"].item()
    return data_entry


class MultivariateToUnivariate(Transformation):
    def __init__(self, field):
        self.field = field

    def __call__(self, data_it: Iterable[DataEntry], is_train: bool = False) -> Iterator:
        for data_entry in data_it:
            item_id = data_entry["item_id"]
            val_ls = list(data_entry[self.field])
            for id, val in enumerate(val_ls):
                univariate_entry = data_entry.copy()
                univariate_entry[self.field] = val
                univariate_entry["item_id"] = item_id + "_dim" + str(id)
                yield univariate_entry


class Dataset:
    def __init__(
        self,
        name: str,
        term: Term | str = Term.SHORT,
        to_univariate: bool = False,
        storage_path: str = None,
        max_windows: int | None = None,
    ):
        storage_path = Path(storage_path)
        self.hf_dataset = datasets.load_from_disk(str(storage_path / name)).with_format("numpy")
        process = ProcessDataEntry(
            self.freq,
            one_dim_target=self.target_dim == 1,
        )

        self.gluonts_dataset = Map(compose(process, itemize_start), self.hf_dataset)
        if to_univariate:
            self.gluonts_dataset = MultivariateToUnivariate("target").apply(self.gluonts_dataset)

        self.term = Term(term)
        self.name = name
        self.max_windows = max_windows if max_windows is not None else MAX_WINDOW

    @cached_property
    def prediction_length(self) -> int:
        freq = norm_freq_str(to_offset(self.freq).name)
        if freq.endswith("E"):
            freq = freq[:-1]
        pred_len = M4_PRED_LENGTH_MAP[freq] if "m4" in self.name else PRED_LENGTH_MAP[freq]
        return self.term.multiplier * pred_len

    @cached_property
    def freq(self) -> str:
        return self.hf_dataset[0]["freq"]

    @cached_property
    def target_dim(self) -> int:
        return target.shape[0] if len((target := self.hf_dataset[0]["target"]).shape) > 1 else 1

    @cached_property
    def past_feat_dynamic_real_dim(self) -> int:
        if "past_feat_dynamic_real" not in self.hf_dataset[0]:
            return 0
        elif len((past_feat_dynamic_real := self.hf_dataset[0]["past_feat_dynamic_real"]).shape) > 1:
            return past_feat_dynamic_real.shape[0]
        else:
            return 1

    @cached_property
    def windows(self) -> int:
        if "m4" in self.name:
            return 1
        w = math.ceil(TEST_SPLIT * self._min_series_length / self.prediction_length)
        return min(max(1, w), self.max_windows)

    @cached_property
    def _min_series_length(self) -> int:
        if self.hf_dataset[0]["target"].ndim > 1:
            lengths = pc.list_value_length(pc.list_flatten(pc.list_slice(self.hf_dataset.data.column("target"), 0, 1)))
        else:
            lengths = pc.list_value_length(self.hf_dataset.data.column("target"))
        return min(lengths.to_numpy())

    @cached_property
    def sum_series_length(self) -> int:
        if self.hf_dataset[0]["target"].ndim > 1:
            lengths = pc.list_value_length(pc.list_flatten(self.hf_dataset.data.column("target")))
        else:
            lengths = pc.list_value_length(self.hf_dataset.data.column("target"))
        return sum(lengths.to_numpy())

    @property
    def training_dataset(self) -> TrainingDataset:
        training_dataset, _ = split(self.gluonts_dataset, offset=-self.prediction_length * (self.windows + 1))
        return training_dataset

    @property
    def validation_dataset(self) -> TrainingDataset:
        validation_dataset, _ = split(self.gluonts_dataset, offset=-self.prediction_length * self.windows)
        return validation_dataset

    @property
    def test_data(self) -> TestData:
        _, test_template = split(self.gluonts_dataset, offset=-self.prediction_length * self.windows)
        test_data = test_template.generate_instances(
            prediction_length=self.prediction_length,
            windows=self.windows,
            distance=self.prediction_length,
        )
        return test_data