use fractions::{Fraction, FractionError};
use std::fmt::{Debug, Display};
use std::ops::Add;
use std::ops::Mul;
use std::ops::Sub;

#[derive(Debug, Eq, PartialEq)]
pub enum MatrixError {
    IndexOutOfRange,
    RowOutOfRange,
    ColumnOutOfRange,
    NotSquared,
    InvalidDataSize,
    InvalidSizeForAdd,
    InvalidSizeForSub,
    InvalidSizeForMul,
    ZeroSize,
    FailedGauss,
    FailedGaussJordan,
    FractionError(FractionError),
}

impl From<FractionError> for MatrixError {
    fn from(err: FractionError) -> Self {
        MatrixError::FractionError(err)
    }
}

#[derive(PartialEq, Eq, Debug)]
pub struct Matrix {
    rows: usize,
    columns: usize,
    data: Vec<Fraction>,
}

impl Matrix {
    pub fn new(rows: usize, columns: usize, default: Fraction) -> Result<Self, MatrixError> {
        if columns < 1 || rows < 1 {
            return Err(MatrixError::ZeroSize);
        }

        Ok(Self {
            rows,
            columns,
            data: vec![default; rows * columns],
        })
    }

    pub fn get(&self, row: usize, column: usize) -> Result<&Fraction, MatrixError> {
        if row >= self.rows || column >= self.columns {
            return Err(MatrixError::IndexOutOfRange);
        }
        let mut index = 0;
        index += row * self.columns;
        index += column;
        return Ok(&self.data[index]);
    }

    pub fn get_row(&self, row: usize) -> Result<impl Iterator<Item = &Fraction>, MatrixError> {
        if row >= self.rows {
            return Err(MatrixError::RowOutOfRange);
        }

        let start = row * self.columns;
        let end = start + self.columns;

        return Ok(self.data[start..end].iter());
    }

    pub fn get_column(
        &self,
        column: usize,
    ) -> Result<impl Iterator<Item = &Fraction>, MatrixError> {
        if column >= self.columns {
            return Err(MatrixError::ColumnOutOfRange);
        }

        Ok((0..self.rows).map(move |i| &self.data[i * self.columns + column]))
    }

    pub fn get_diagonal(&self) -> Result<impl Iterator<Item = &Fraction>, MatrixError> {
        if self.columns != self.rows {
            return Err(MatrixError::NotSquared);
        }

        Ok((0..self.rows).map(move |i| &self.data[i * self.columns + i]))
    }

    pub fn set(&mut self, row: usize, column: usize, data: Fraction) -> Result<(), MatrixError> {
        if row >= self.rows || column >= self.columns {
            return Err(MatrixError::IndexOutOfRange);
        }
        let mut index = 0;
        index += row * self.columns;
        index += column;
        self.data[index] = data;
        Ok(())
    }

    pub fn set_row(&mut self, row: usize, data: Vec<Fraction>) -> Result<(), MatrixError> {
        if row >= self.rows {
            return Err(MatrixError::IndexOutOfRange);
        }
        if data.len() != self.columns {
            return Err(MatrixError::InvalidDataSize);
        }

        for i in 0..data.len() {
            self.set(row, i, data[i])?;
        }

        Ok(())
    }

    pub fn set_column(&mut self, column: usize, data: Vec<Fraction>) -> Result<(), MatrixError> {
        if column >= self.columns {
            return Err(MatrixError::ColumnOutOfRange);
        }
        if data.len() != self.rows {
            return Err(MatrixError::InvalidDataSize);
        }

        for i in 0..data.len() {
            self.set(i, column, data[i])?;
        }

        Ok(())
    }

    pub fn exchange_rows(&mut self, row1: usize, row2: usize) -> Result<(), MatrixError> {
        if row1 >= self.rows || row2 >= self.rows {
            return Err(MatrixError::RowOutOfRange);
        }

        let start1 = row1 * self.columns;
        let start2 = row2 * self.columns;

        for i in 0..self.columns {
            self.data.swap(start1 + i, start2 + i);
        }

        Ok(())
    }

    pub fn exchange_columns(&mut self, column1: usize, column2: usize) -> Result<(), MatrixError> {
        if column1 >= self.columns || column2 >= self.columns {
            return Err(MatrixError::ColumnOutOfRange);
        }

        for i in 0..self.rows {
            let idx1 = column1 + i * self.columns;
            let idx2 = column2 + i * self.columns;

            self.data.swap(idx1, idx2);
        }

        Ok(())
    }

    pub fn insert_column(&mut self, index: usize, data: Vec<Fraction>) -> Result<(), MatrixError> {
        if index >= self.columns {
            return Err(MatrixError::ColumnOutOfRange);
        }

        if data.len() != self.rows {
            return Err(MatrixError::InvalidDataSize);
        }

        for i in 0..self.rows {
            self.data.insert((i * self.columns) + index + i, data[i]);
        }

        self.columns += 1;

        Ok(())
    }

    pub fn insert_rows(&mut self, index: usize, data: Vec<Fraction>) -> Result<(), MatrixError> {
        if index >= self.rows {
            return Err(MatrixError::RowOutOfRange);
        }

        if data.len() != self.columns {
            return Err(MatrixError::InvalidDataSize);
        }

        for i in 0..self.columns {
            self.data.insert((i * self.columns) + i, data[i]);
        }

        self.rows += 1;

        Ok(())
    }

    fn partial_pivoting(&mut self, col: usize, sign: &mut Fraction) -> Result<bool, MatrixError> {
        if col >= self.columns {
            return Err(MatrixError::ColumnOutOfRange);
        }

        let mut max_row = col;
        let mut max_value = self.get(col, col).unwrap().abs();

        for r in (col + 1)..self.rows {
            let val = self.get(r, col).unwrap().abs();
            if val > max_value {
                max_value = val;
                max_row = r;
            }
        }

        if max_value.is_zero() {
            return Ok(false);
        }

        if max_row != col {
            self.exchange_rows(col, max_row)?;
            *sign = -*sign;
        }

        Ok(true)
    }

    pub fn gaussian_elimination(&self) -> Result<(Matrix, Fraction), MatrixError> {
        let mut trig_matrix = Matrix {
            columns: self.columns,
            rows: self.rows,
            data: self.data.clone(),
        };
        let mut sign = Fraction::new(1, 1).unwrap();

        for i in 0..self.columns {
            // We do partial pivoting for better efifiency and security
            let pivot_exists = trig_matrix.partial_pivoting(i, &mut sign)?;

            // If there ain't no other thing but 0 then we're
            // fucked, determinant is zero
            if !pivot_exists {
                return Err(MatrixError::FailedGauss);
            }

            let pivot = *trig_matrix.get(i, i).unwrap();

            // The main gaussian elimination, not even I remember how
            // i did it in such a asimple way
            for x in (i + 1)..trig_matrix.rows {
                let m = (*trig_matrix.get(x, i).unwrap() / pivot).unwrap();

                let row_x = trig_matrix.get_row(x)?;
                let row_i = trig_matrix.get_row(i)?;

                let new_row = row_x
                    .zip(row_i)
                    .map(|(a, b)| *a - m * *b)
                    .collect::<Vec<Fraction>>();

                trig_matrix.set_row(x, new_row)?;
            }
        }

        Ok((trig_matrix, sign))
    }

    pub fn gauss_jordan_elimination(&self) -> Result<Matrix, MatrixError> {
        let mut new_matrix = Matrix {
            columns: self.columns,
            rows: self.rows,
            data: self.data.clone(),
        };

        let mut dummy = Fraction::from(1);

        for i in 0..self.columns {
            let pivot_exists = new_matrix.partial_pivoting(i, &mut dummy)?;

            if !pivot_exists {
                return Err(MatrixError::FailedGaussJordan);
            }

            let pivot = *new_matrix.get(i, i).unwrap();

            let new_pivot_row = new_matrix
                .get_row(i)?
                .map(|x| *x / pivot)
                .collect::<Result<Vec<_>, _>>()?;

            new_matrix.set_row(i, new_pivot_row)?;

            for r in 0..new_matrix.rows {
                if r == i {
                    continue;
                }

                let factor = *new_matrix.get(r, i).unwrap();

                if factor.is_zero() {
                    continue;
                }

                let new_row_normalized = new_matrix
                    .get_row(r)?
                    .zip(new_matrix.get_row(i)?)
                    .map(|(a, b)| *a - factor * *b)
                    .collect::<Vec<Fraction>>();

                new_matrix.set_row(r, new_row_normalized)?;
            }
        }

        Ok(new_matrix)
    }

    pub fn get_determinant(&self) -> Result<Fraction, MatrixError> {
        if self.rows != self.columns {
            return Err(MatrixError::NotSquared);
        }

        let (trig_matrix, sign) = match self.gaussian_elimination() {
            Err(MatrixError::FailedGauss) => return Ok(Fraction::new(0, 1).unwrap()),
            Ok((matrix, sign)) => (matrix, sign),
            Err(err) => return Err(err),
        };
        // YES, now we got ourselves a triangular matrix, now we just
        // take the product of the diagonal and multiply by sign, that's
        // the determinant :)
        let determinant = sign
            * trig_matrix
                .get_diagonal()?
                .copied()
                .fold(Fraction::from(1i64), |acc, x| acc * x);

        return Ok(determinant);
    }
}

impl Add for Matrix {
    type Output = Result<Self, MatrixError>;

    fn add(self, other: Self) -> Self::Output {
        if self.data.len() != other.data.len() {
            return Err(MatrixError::InvalidSizeForAdd);
        }

        let mut new_data = Vec::new();
        for i in 0..self.data.len() {
            new_data.push(self.data[i] + other.data[i]);
        }

        Ok(Matrix {
            columns: self.columns,
            rows: self.rows,
            data: new_data,
        })
    }
}

impl Sub for Matrix {
    type Output = Result<Self, MatrixError>;

    fn sub(self, other: Self) -> Self::Output {
        if self.data.len() != other.data.len() {
            return Err(MatrixError::InvalidSizeForSub);
        }

        let mut new_data = Vec::new();
        for i in 0..self.data.len() {
            new_data.push(self.data[i] - other.data[i]);
        }

        Ok(Matrix {
            columns: self.columns,
            rows: self.rows,
            data: new_data,
        })
    }
}

impl Mul for Matrix {
    type Output = Result<Self, MatrixError>;

    fn mul(self, other: Self) -> Self::Output {
        if self.columns != other.rows {
            return Err(MatrixError::InvalidSizeForMul);
        }

        let mut new_data: Vec<Fraction> = Vec::new();
        for i in 0..self.rows {
            for k in 0..other.columns {
                let current_column = other.get_column(k)?;
                let current_row = self.get_row(i)?;

                let mut new_value = Fraction::new(0, 1).unwrap();

                for (a, b) in current_row.zip(current_column) {
                    new_value = new_value + (*a * *b);
                }

                new_data.push(new_value);
            }
        }

        Ok(Matrix {
            rows: self.rows,
            columns: other.columns,
            data: new_data,
        })
    }
}

impl Display for Matrix {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        let mut display = String::new();
        let mut index = 0;
        for _i in 0..self.columns {
            display += "{";
            for _k in 0..self.rows {
                display += &format!(" {},", self.data[index]);
                index += 1;
            }
            display += " }\n";
        }
        write!(f, "{}", display)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
}