# -*- coding: utf-8 -*-
# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

# mypy: disable-error-code="no-redef"

import numbers
import logging
import copy
import numpy as np
from abc import ABCMeta, abstractmethod
from functools import singledispatch
from typing import Any, Union
from collections.abc import Callable, Sequence
from PIL import Image

import torch
import torchvision.transforms as transforms
from torchvision.transforms import InterpolationMode

import openvino as ov
import openvino.opset11 as ops
from openvino import Layout, Type, Output, Model
from openvino.utils.decorators import custom_preprocess_function
from openvino.preprocess import PrePostProcessor, ResizeAlgorithm, ColorFormat


TORCHTYPE_TO_OVTYPE = {
    float: ov.Type.f32,
    int: ov.Type.i32,
    bool: ov.Type.boolean,
    torch.float16: ov.Type.f16,
    torch.float32: ov.Type.f32,
    torch.float64: ov.Type.f64,
    torch.uint8: ov.Type.u8,
    torch.int8: ov.Type.i8,
    torch.int32: ov.Type.i32,
    torch.int64: ov.Type.i64,
    torch.bool: ov.Type.boolean,
    torch.DoubleTensor: ov.Type.f64,
    torch.FloatTensor: ov.Type.f32,
    torch.IntTensor: ov.Type.i32,
    torch.LongTensor: ov.Type.i64,
    torch.BoolTensor: ov.Type.boolean,
}


@singledispatch
def _setup_size(size: Any, error_msg: str) -> Sequence[int]:
    raise ValueError(error_msg)


@_setup_size.register
def _setup_size_number(size: numbers.Number, error_msg: str) -> Sequence[int]:
    return int(size), int(size)  # type: ignore


@_setup_size.register
def _setup_size_sequence(size: Sequence, error_msg: str) -> Sequence[int]:
    if len(size) == 1:
        return size[0], size[0]
    elif len(size) == 2:
        return size[0], size[1]
    raise ValueError(error_msg)


def _NHWC_to_NCHW(input_shape: list) -> list:  # noqa N802
    new_shape = copy.deepcopy(input_shape)
    new_shape[1] = input_shape[3]
    new_shape[2] = input_shape[1]
    new_shape[3] = input_shape[2]
    return new_shape


@singledispatch
def _to_list(transform: Callable) -> list:
    raise TypeError(f"Unsupported transform type: {type(transform)}")


@_to_list.register
def _to_list_torch_sequential(transform: torch.nn.Sequential) -> list:
    return list(transform)


@_to_list.register
def _to_list_transforms_compose(transform: transforms.Compose) -> list:
    return transform.transforms


def _get_shape_layout_from_data(input_example: Union[torch.Tensor, np.ndarray, Image.Image]) -> tuple[list, Layout]:
    if isinstance(input_example, (torch.Tensor, np.ndarray, Image.Image)):  # PyTorch, OpenCV, numpy, PILLOW
        shape = list(np.array(input_example, copy=False).shape)
        layout = Layout("NCHW") if isinstance(input_example, torch.Tensor) else Layout("NHWC")
    else:
        raise TypeError(f"Unsupported input type: {type(input_example)}")

    if len(shape) == 3:
        shape = [1] + shape
    elif len(shape) != 4:
        raise ValueError(f"Unsupported number of input dimensions: {len(shape)}")

    return shape, layout


class TransformConverterBase(metaclass=ABCMeta):

    def __init__(self, **kwargs: Any) -> None:  # noqa B027
        pass

    @abstractmethod
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        pass


class TransformConverterFactory:

    registry: dict[str, Callable] = {}

    @classmethod
    def register(cls: Callable, target_type: Union[Callable, None] = None) -> Callable:
        def inner_wrapper(wrapped_class: TransformConverterBase) -> Callable:
            registered_name = wrapped_class.__name__ if target_type is None else target_type.__name__
            if registered_name in cls.registry:
                logging.warning(f"Executor {registered_name} already exists. {wrapped_class.__name__} will replace it.")
            cls.registry[registered_name] = wrapped_class
            return wrapped_class  # type: ignore

        return inner_wrapper

    @classmethod
    def convert(cls: Callable, converter_type: Callable, *args: Any, **kwargs: Any) -> Callable:
        transform_name = converter_type.__name__
        if transform_name not in cls.registry:
            raise ValueError(f"{transform_name} is not supported.")

        converter = cls.registry[transform_name]()
        return converter.convert(*args, **kwargs)


@TransformConverterFactory.register(transforms.Normalize)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        if transform.inplace:
            raise ValueError("Inplace Normaliziation is not supported.")
        ppp.input(input_idx).preprocess().mean(transform.mean).scale(transform.std)


@TransformConverterFactory.register(transforms.ConvertImageDtype)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        ppp.input(input_idx).preprocess().convert_element_type(TORCHTYPE_TO_OVTYPE[transform.dtype])


@TransformConverterFactory.register(transforms.Grayscale)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        input_shape = meta["input_shape"]
        layout = meta["layout"]

        input_shape[layout.get_index_by_name("C")] = 1

        ppp.input(input_idx).preprocess().convert_color(ColorFormat.GRAY)
        if transform.num_output_channels != 1:
            input_shape[layout.get_index_by_name("C")] = transform.num_output_channels

            @custom_preprocess_function
            def broadcast_node(output: Output) -> Callable:  # type: ignore[name-defined]
                return ops.broadcast(  # type: ignore
                    data=output,
                    target_shape=input_shape,
                )
            ppp.input(input_idx).preprocess().custom(broadcast_node)

        meta["input_shape"] = input_shape


@TransformConverterFactory.register(transforms.Pad)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        image_dimensions = list(meta["image_dimensions"])
        layout = meta["layout"]
        torch_padding = transform.padding
        pad_mode = transform.padding_mode

        if pad_mode == "constant":
            if isinstance(transform.fill, tuple):
                raise ValueError("Different fill values for R, G, B channels are not supported.")

        pads_begin = [0 for _ in meta["input_shape"]]
        pads_end = [0 for _ in meta["input_shape"]]

        # padding equal on all sides
        if isinstance(torch_padding, int):
            image_dimensions[0] += 2 * torch_padding
            image_dimensions[1] += 2 * torch_padding

            pads_begin[layout.get_index_by_name("H")] = torch_padding
            pads_begin[layout.get_index_by_name("W")] = torch_padding
            pads_end[layout.get_index_by_name("H")] = torch_padding
            pads_end[layout.get_index_by_name("W")] = torch_padding

        # padding different in horizontal and vertical axis
        elif len(torch_padding) == 2:
            image_dimensions[0] += sum(torch_padding)
            image_dimensions[1] += sum(torch_padding)

            pads_begin[layout.get_index_by_name("H")] = torch_padding[1]
            pads_begin[layout.get_index_by_name("W")] = torch_padding[0]
            pads_end[layout.get_index_by_name("H")] = torch_padding[1]
            pads_end[layout.get_index_by_name("W")] = torch_padding[0]

        # padding different on top, bottom, left and right of image
        else:
            image_dimensions[0] += torch_padding[1] + torch_padding[3]
            image_dimensions[1] += torch_padding[0] + torch_padding[2]

            pads_begin[layout.get_index_by_name("H")] = torch_padding[1]
            pads_begin[layout.get_index_by_name("W")] = torch_padding[0]
            pads_end[layout.get_index_by_name("H")] = torch_padding[3]
            pads_end[layout.get_index_by_name("W")] = torch_padding[2]

        @custom_preprocess_function
        def pad_node(output: Output) -> Callable:
            return ops.pad(  # type: ignore
                output,
                pad_mode=pad_mode,
                pads_begin=pads_begin,
                pads_end=pads_end,
                arg_pad_value=np.array(transform.fill, dtype=np.uint8) if pad_mode == "constant" else None,
            )

        ppp.input(input_idx).preprocess().custom(pad_node)
        meta["image_dimensions"] = tuple(image_dimensions)


@TransformConverterFactory.register(transforms.ToTensor)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        input_shape = meta["input_shape"]
        layout = meta["layout"]

        ppp.input(input_idx).tensor().set_element_type(Type.u8).set_layout(Layout("NHWC")).set_color_format(ColorFormat.RGB)  # noqa ECE001

        if layout == Layout("NHWC"):
            input_shape = _NHWC_to_NCHW(input_shape)
            layout = Layout("NCHW")
            ppp.input(input_idx).preprocess().convert_layout(layout)
        ppp.input(input_idx).preprocess().convert_element_type(Type.f32)
        ppp.input(input_idx).preprocess().scale(255.0)

        meta["input_shape"] = input_shape
        meta["layout"] = layout


@TransformConverterFactory.register(transforms.CenterCrop)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        input_shape = meta["input_shape"]
        source_size = meta["image_dimensions"]
        target_size = _setup_size(transform.size, "Incorrect size type for CenterCrop operation")

        if target_size[0] > source_size[0] or target_size[1] > source_size[1]:
            ValueError(f"CenterCrop size={target_size} is greater than source_size={source_size}")

        bottom_left = []
        bottom_left.append(int((source_size[0] - target_size[0]) / 2))
        bottom_left.append(int((source_size[1] - target_size[1]) / 2))

        top_right = []
        top_right.append(min(bottom_left[0] + target_size[0], source_size[0] - 1))
        top_right.append(min(bottom_left[1] + target_size[1], source_size[1] - 1))

        bottom_left = [0] * len(input_shape[:-2]) + bottom_left if meta["layout"] == Layout("NCHW") else [0] + bottom_left + [0]  # noqa ECE001
        top_right = input_shape[:-2] + top_right if meta["layout"] == Layout("NCHW") else input_shape[:1] + top_right + input_shape[-1:]

        ppp.input(input_idx).preprocess().crop(bottom_left, top_right)
        meta["image_dimensions"] = (target_size[-2], target_size[-1])


@TransformConverterFactory.register(transforms.Resize)
class _(TransformConverterBase):
    def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, meta: dict) -> None:
        resize_mode_map = {
            InterpolationMode.NEAREST: ResizeAlgorithm.RESIZE_NEAREST,
            InterpolationMode.BILINEAR: ResizeAlgorithm.RESIZE_BILINEAR_PILLOW,
            InterpolationMode.BICUBIC: ResizeAlgorithm.RESIZE_BICUBIC_PILLOW,
        }
        if transform.max_size:
            raise ValueError("Resize with max_size if not supported")
        if transform.interpolation not in resize_mode_map.keys():
            raise ValueError(f"Interpolation mode {transform.interpolation} is not supported.")

        target_h, target_w = _setup_size(transform.size, "Incorrect size type for Resize operation")

        if isinstance(transform.size, int):
            # rescale the smaller image edge
            current_h, current_w = meta["image_dimensions"]
            if current_h > current_w:
                target_h = int(transform.size * (current_h / current_w))
            elif current_w > current_h:
                target_w = int(transform.size * (current_w / current_h))

        ppp.input(input_idx).tensor().set_layout(Layout("NCHW"))

        input_shape = meta["input_shape"]

        input_shape[meta["layout"].get_index_by_name("H")] = -1
        input_shape[meta["layout"].get_index_by_name("W")] = -1

        ppp.input(input_idx).tensor().set_shape(input_shape)
        ppp.input(input_idx).preprocess().resize(resize_mode_map[transform.interpolation], target_h, target_w)
        meta["input_shape"] = input_shape
        meta["image_dimensions"] = (target_h, target_w)


def _from_torchvision(model: Model, transform: Callable, input_example: Any, input_name: Union[str, None] = None) -> Model:

    if input_name is not None:
        input_idx = next((i for i, p in enumerate(model.get_parameters()) if p.get_friendly_name() == input_name), None)
    else:
        if len(model.get_parameters()) == 1:
            input_idx = 0
        else:
            raise ValueError("Model contains multiple inputs. Please specify the name of the input to which prepocessing is added.")

    if input_idx is None:
        raise ValueError(f"Input with name {input_name} is not found")

    input_shape, layout = _get_shape_layout_from_data(input_example)

    ppp = PrePostProcessor(model)
    ppp.input(input_idx).tensor().set_layout(layout)
    ppp.input(input_idx).tensor().set_shape(input_shape)

    image_dimensions = [input_shape[layout.get_index_by_name("H")], input_shape[layout.get_index_by_name("W")]]
    global_meta = {
        "input_shape": input_shape,
        "image_dimensions": image_dimensions,
        "layout": layout,
    }

    for tm in _to_list(transform):
        TransformConverterFactory.convert(type(tm), input_idx, ppp, tm, global_meta)

    updated_model = ppp.build()
    return updated_model
