# Copyright (C) 2021-2023, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

import math
from typing import Any, List, Tuple, Union

import numpy as np
import tensorflow as tf

from doctr.transforms import Normalize, Resize
from doctr.utils.multithreading import multithread_exec
from doctr.utils.repr import NestedObject

__all__ = ["PreProcessor"]


class PreProcessor(NestedObject):
    """Implements an abstract preprocessor object which performs casting, resizing, batching and normalization.

    Args:
        output_size: expected size of each page in format (H, W)
        batch_size: the size of page batches
        mean: mean value of the training distribution by channel
        std: standard deviation of the training distribution by channel
    """

    _children_names: List[str] = ["resize", "normalize"]

    def __init__(
        self,
        output_size: Tuple[int, int],
        batch_size: int,
        mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
        std: Tuple[float, float, float] = (1.0, 1.0, 1.0),
        fp16: bool = False,
        **kwargs: Any,
    ) -> None:
        self.batch_size = batch_size
        self.resize = Resize(output_size, **kwargs)
        # Perform the division by 255 at the same time
        self.normalize = Normalize(mean, std)

    def batch_inputs(self, samples: List[tf.Tensor]) -> List[tf.Tensor]:
        """Gather samples into batches for inference purposes

        Args:
            samples: list of samples (tf.Tensor)

        Returns:
            list of batched samples
        """

        num_batches = int(math.ceil(len(samples) / self.batch_size))
        batches = [
            tf.stack(samples[idx * self.batch_size : min((idx + 1) * self.batch_size, len(samples))], axis=0)
            for idx in range(int(num_batches))
        ]

        return batches

    def sample_transforms(self, x: Union[np.ndarray, tf.Tensor]) -> tf.Tensor:
        if x.ndim != 3:
            raise AssertionError("expected list of 3D Tensors")
        if isinstance(x, np.ndarray):
            if x.dtype not in (np.uint8, np.float32):
                raise TypeError("unsupported data type for numpy.ndarray")
            x = tf.convert_to_tensor(x)
        elif x.dtype not in (tf.uint8, tf.float16, tf.float32):
            raise TypeError("unsupported data type for torch.Tensor")
        # Data type & 255 division
        if x.dtype == tf.uint8:
            x = tf.image.convert_image_dtype(x, dtype=tf.float32)
        # Resizing
        x = self.resize(x)

        return x

    def __call__(self, x: Union[tf.Tensor, np.ndarray, List[Union[tf.Tensor, np.ndarray]]]) -> List[tf.Tensor]:
        """Prepare document data for model forwarding

        Args:
            x: list of images (np.array) or tensors (already resized and batched)
        Returns:
            list of page batches
        """

        # Input type check
        if isinstance(x, (np.ndarray, tf.Tensor)):
            if x.ndim != 4:
                raise AssertionError("expected 4D Tensor")
            if isinstance(x, np.ndarray):
                if x.dtype not in (np.uint8, np.float32):
                    raise TypeError("unsupported data type for numpy.ndarray")
                x = tf.convert_to_tensor(x)
            elif x.dtype not in (tf.uint8, tf.float16, tf.float32):
                raise TypeError("unsupported data type for torch.Tensor")

            # Data type & 255 division
            if x.dtype == tf.uint8:
                x = tf.image.convert_image_dtype(x, dtype=tf.float32)
            # Resizing
            if (x.shape[1], x.shape[2]) != self.resize.output_size:
                x = tf.image.resize(x, self.resize.output_size, method=self.resize.method)

            batches = [x]

        elif isinstance(x, list) and all(isinstance(sample, (np.ndarray, tf.Tensor)) for sample in x):
            # Sample transform (to tensor, resize)
            samples = list(multithread_exec(self.sample_transforms, x))
            # Batching
            batches = self.batch_inputs(samples)
        else:
            raise TypeError(f"invalid input type: {type(x)}")

        # Batch transforms (normalize)
        batches = list(multithread_exec(self.normalize, batches))

        return batches
