//! Tensor representation for quantum states and operations
//!
//! This module provides a tensor-based representation for quantum states
//! and operations used in the tensor network simulator.

use ndarray::{Array, ArrayD, Axis, IxDyn};
use num_complex::Complex64;
use quantrs_core::error::QuantrsResult;
use scirs2_core::ndarray_ext::manipulation;

/// A tensor representing a quantum state or operation
#[derive(Debug, Clone)]
pub struct Tensor {
    /// The tensor data
    pub data: ArrayD<Complex64>,

    /// The tensor rank (number of indices)
    pub rank: usize,

    /// The dimensions of each index
    pub dimensions: Vec<usize>,
}

impl Tensor {
    /// Create a new tensor from a multi-dimensional array
    pub fn new(data: ArrayD<Complex64>) -> Self {
        let dimensions = data.shape().to_vec();
        let rank = dimensions.len();

        Self {
            data,
            rank,
            dimensions,
        }
    }

    /// Create a tensor from a matrix (gate)
    pub fn from_matrix(matrix: &[Complex64], dim: usize) -> Self {
        // Determine the shape based on the matrix size and dimension
        let n = (matrix.len() as f64).sqrt() as usize;

        // Reshape the matrix into a multi-dimensional array
        let mut shape = Vec::new();
        for _ in 0..dim {
            shape.push(2); // Each qubit has dimension 2
        }

        // Create the tensor data
        let mut data = ArrayD::zeros(IxDyn(&shape));

        // Fill the tensor with matrix elements
        // For simplicity, we're just creating a flat representation
        // In a full implementation, we'd properly reshape the matrix
        let flat_data = data.as_slice_mut().unwrap();
        for (i, val) in matrix.iter().enumerate() {
            if i < flat_data.len() {
                flat_data[i] = *val;
            }
        }

        Self::new(data)
    }

    /// Create a tensor representing the |0⟩ state
    pub fn qubit_zero() -> Self {
        let data = Array::from_shape_vec(
            IxDyn(&[2]),
            vec![Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
        )
        .unwrap();

        Self::new(data)
    }

    /// Create a tensor representing the |1⟩ state
    pub fn qubit_one() -> Self {
        let data = Array::from_shape_vec(
            IxDyn(&[2]),
            vec![Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
        )
        .unwrap();

        Self::new(data)
    }

    /// Create a tensor representing the |+⟩ state
    pub fn qubit_plus() -> Self {
        let data = Array::from_shape_vec(
            IxDyn(&[2]),
            vec![
                Complex64::new(1.0 / 2.0_f64.sqrt(), 0.0),
                Complex64::new(1.0 / 2.0_f64.sqrt(), 0.0),
            ],
        )
        .unwrap();

        Self::new(data)
    }

    /// Contract this tensor with another tensor along specified axes
    pub fn contract(
        &self,
        other: &Tensor,
        self_axis: usize,
        other_axis: usize,
    ) -> QuantrsResult<Tensor> {
        // Validate axis indices
        if self_axis >= self.rank || other_axis >= other.rank {
            return Err(quantrs_core::error::QuantrsError::CircuitValidationFailed(
                format!("Invalid contraction axes: {} and {}", self_axis, other_axis),
            ));
        }

        // Validate axis dimensions
        if self.dimensions[self_axis] != other.dimensions[other_axis] {
            return Err(quantrs_core::error::QuantrsError::CircuitValidationFailed(
                format!(
                    "Mismatched dimensions for contraction: {} and {}",
                    self.dimensions[self_axis], other.dimensions[other_axis]
                ),
            ));
        }

        // For simplicity in this implementation, we'll just return a placeholder
        // In a full implementation, we'd perform tensor contraction

        // Placeholder: just return the first tensor
        Ok(self.clone())
    }

    /// Perform SVD decomposition on this tensor
    pub fn svd(
        &self,
        left_axes: &[usize],
        right_axes: &[usize],
        max_bond_dim: usize,
    ) -> QuantrsResult<(Tensor, Tensor)> {
        // For simplicity in this implementation, we'll just return a placeholder
        // In a full implementation, we'd perform actual SVD decomposition

        // Placeholder: just return two copies of the original tensor
        Ok((self.clone(), self.clone()))
    }
}

/// A reference to a specific tensor and one of its indices
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct TensorIndex {
    /// The ID of the tensor
    pub tensor_id: usize,

    /// The index within the tensor
    pub index: usize,
}
