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#  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 abc
from dataclasses import dataclass
from functools import partial
from typing import Any, Optional

import numpy as np
import pandas as pd
from einops import rearrange
from gluonts.time_feature import norm_freq_str
from jaxtyping import Num

from starembed.common.typing import UnivarTimeSeries

from ._base import Transformation
from ._mixin import MapFuncMixin


class PatchSizeConstraints(abc.ABC):
    @abc.abstractmethod
    def _get_boundaries(self, n: int, offset_name: str) -> tuple[int, int]: ...

    def __call__(self, freq: str) -> range:
        offset = pd.tseries.frequencies.to_offset(freq)
        start, stop = self._get_boundaries(offset.n, norm_freq_str(offset.name))
        return range(start, stop + 1)


@dataclass
class FixedPatchSizeConstraints(PatchSizeConstraints):
    start: int
    stop: Optional[int] = None

    def __post_init__(self):
        if self.stop is None:
            self.stop = self.start
        assert self.start <= self.stop

    def _get_boundaries(self, n: int, offset_name: str) -> tuple[int, int]:
        return self.start, self.stop


class DefaultPatchSizeConstraints(PatchSizeConstraints):
    # https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
    DEFAULT_RANGES = {
        "S": (64, 128),  # 512s = 8.53min, 4096s = 68.26min
        "T": (32, 128),  # 64min = 1.07h, 512min = 8.53h
        "H": (32, 64),  # 128h = 5.33days
        "D": (16, 32),
        "B": (16, 32),
        "W": (16, 32),
        "M": (8, 32),
        "Q": (1, 8),
        "Y": (1, 8),
        "A": (1, 8),
        # for the deprecated freqs
        "s": (64, 128),  # 512s = 8.53min, 4096s = 68.26min
        "min": (32, 128),  # 64min = 1.07h, 512min = 8.53h
        "h": (32, 64),  # 128h = 5.33days
        "D": (16, 32),
        "B": (16, 32),
        "W": (16, 32),
        "ME": (8, 32),
        "QE": (1, 8),
        "YE": (1, 8),
        "A": (1, 8),
    }

    def _get_boundaries(self, n: int, offset_name: str) -> tuple[int, int]:
        start, stop = self.DEFAULT_RANGES[offset_name]
        return start, stop


@dataclass
class GetPatchSize(Transformation):
    min_time_patches: int
    target_field: str = "target"
    patch_sizes: tuple[int, ...] | list[int] | range = (8, 16, 32, 64, 128)
    patch_size_constraints: PatchSizeConstraints = DefaultPatchSizeConstraints()
    offset: bool = True

    def __call__(self, data_entry: dict[str, Any]) -> dict[str, Any]:
        # print(f"[wj debug] GetPatchSize, file path: {__file__}")
        # print(f"[wj debug] data_entry: {data_entry.keys()}")
        # print(f"[wj debug] target_field: {self.target_field}")
        # print(f"[wj debug] patch_sizes: {self.patch_sizes}")
        # print(f"[wj debug] patch_size_constraints: {self.patch_size_constraints}")
        # print(f"[wj debug] offset: {self.offset}")
        freq = data_entry["freq"]
        # print(f"[wj debug] freq: {freq}")
        constraints = self.patch_size_constraints(freq)
        # print(f"[wj debug] constraints: {constraints}")
        # largest patch size based on min_time_patches
        target: list[UnivarTimeSeries] = data_entry[self.target_field]
        # print(f"[wj debug] target length: {len(target)}")
        length = target[0].shape[0]
        # print(f"[wj debug] target 0 shape: {target[0].shape}")
        patch_size_ceil = length // self.min_time_patches
        # print(f"[wj debug] patch_size_ceil: {patch_size_ceil}, length: {length}, min_time_patches: {self.min_time_patches}")

        if isinstance(self.patch_sizes, (tuple, list)):
            patch_size_candidates = [
                patch_size
                for patch_size in self.patch_sizes
                if (patch_size in constraints) and (patch_size <= patch_size_ceil)
            ]
        elif isinstance(self.patch_sizes, range):
            patch_size_candidates = range(
                max(self.patch_sizes.start, constraints.start),
                min(self.patch_sizes.stop, constraints.stop, patch_size_ceil),
            )
        else:
            raise NotImplementedError

        if len(patch_size_candidates) <= 0:
            ts_shape = (len(target),) + target[0].shape
            raise AssertionError(
                "no valid patch size candidates for "
                f"time series shape: {ts_shape}, "
                f"freq: {freq}, "
                f"patch_sizes: {self.patch_sizes}, "
                f"constraints: {constraints}, "
                f"min_time_patches: {self.min_time_patches}, "
                f"patch_size_ceil: {patch_size_ceil}"
            )

        data_entry["patch_size"] = np.random.choice(patch_size_candidates)
        # print(f"[wj debug] data_entry['patch_size']: {data_entry['patch_size']}")

        # # wj overfit, fix the patch_size
        # data_entry["patch_size"] = np.int64(patch_size_candidates[0])
        return data_entry


@dataclass
class Patchify(MapFuncMixin, Transformation):
    max_patch_size: int
    fields: tuple[str, ...] = ("target",)
    optional_fields: tuple[str, ...] = ("past_feat_dynamic_real",)
    pad_value: int | float = 0

    def __call__(self, data_entry: dict[str, Any]) -> dict[str, Any]:
        # print(f"[wj debug] Patchify, file path: {__file__}")
        # print(f"[wj debug] data_entry: {data_entry.keys()}")
        # print(f"[wj debug] fields: {self.fields}")
        # print(f"[wj debug] optional_fields: {self.optional_fields}")
        # print(f"[wj debug] max_patch_size: {self.max_patch_size}")
        patch_size = data_entry["patch_size"]
        self.map_func(
            partial(self._patchify, patch_size=patch_size),  # noqa
            data_entry,
            self.fields,
            optional_fields=self.optional_fields,
        )
        return data_entry

    def _patchify(self, data_entry: dict[str, Any], field: str, patch_size: int):
        arr = data_entry[field]
        # print(f"[wj debug] _patchify, file path: {__file__}")
        
        if isinstance(arr, list):
            return [self._patchify_arr(a, patch_size) for a in arr]
        if isinstance(arr, dict):
            # print(f"[wj debug] arr is dict")
            # print(f"[wj debug] arr keys: {arr.keys()}")
            for k, v in arr.items():
                if k in self.fields or k in self.optional_fields:
                    arr[k] = self._patchify_arr(v, patch_size)
            return arr
        return self._patchify_arr(arr, patch_size)

    def _patchify_arr(
        self, arr: Num[np.ndarray, "var time*patch"], patch_size: int
    ) -> Num[np.ndarray, "var time max_patch"]:
        assert arr.shape[-1] % patch_size == 0
        # print(f"[wj debug] _patchify_arr, file path: {__file__}")
        # print(f"[wj debug] arr shape: {arr.shape}")
        arr = rearrange(arr, "... (time patch) -> ... time patch", patch=patch_size)
        # print(f"[wj debug] arr shape after rearrange: {arr.shape}")
        pad_width = [(0, 0) for _ in range(arr.ndim)]
        pad_width[-1] = (0, self.max_patch_size - patch_size)
        arr = np.pad(arr, pad_width, mode="constant", constant_values=self.pad_value)
        # print(f"[wj debug] arr shape after pad: {arr.shape}")
        return arr
