//! Property-based testing for autograd operations
//!
//! This module provides property-based testing infrastructure to verify
//! fundamental mathematical properties of automatic differentiation operations.
//! It uses the proptest crate to generate random test cases and validate
//! that autograd operations satisfy expected properties.

#[cfg(test)]
use proptest::prelude::*;
#[cfg(test)]
use proptest::strategy::{Just, Strategy};
// Allow unused imports for property testing infrastructure
#[allow(unused_imports)]
use crate::{AutogradTensor, Result};
#[allow(unused_imports)]
use scirs2_core::numeric::{Float, FromPrimitive, One, Zero};
#[allow(unused_imports)]
use torsh_core::device::CpuDevice;
#[allow(unused_imports)]
use torsh_core::dtype::{DType, TensorElement};
#[allow(unused_imports)]
use torsh_core::error::TorshError;
#[allow(unused_imports)]
use torsh_core::shape::Shape;
#[allow(unused_imports)]
use torsh_tensor::Tensor;

/// Property-based testing configuration
#[derive(Debug, Clone)]
pub struct PropertyTestConfig {
    /// Number of test cases to generate
    pub num_test_cases: u32,
    /// Maximum tensor size for generated tensors
    pub max_tensor_size: usize,
    /// Maximum number of dimensions
    pub max_dimensions: usize,
    /// Tolerance for floating point comparisons
    pub tolerance: f64,
    /// Seed for reproducible tests
    pub seed: Option<u64>,
}

impl Default for PropertyTestConfig {
    fn default() -> Self {
        Self {
            num_test_cases: 100,
            max_tensor_size: 1000,
            max_dimensions: 4,
            tolerance: 1e-6,
            seed: Some(42),
        }
    }
}

/// Generator for test tensors with various shapes and values
#[cfg(test)]
pub struct TensorGenerator {
    config: PropertyTestConfig,
}

#[cfg(test)]
impl TensorGenerator {
    pub fn new(config: PropertyTestConfig) -> Self {
        Self { config }
    }

    /// Generate a strategy for tensor shapes
    pub fn shape_strategy(&self) -> impl Strategy<Value = Vec<usize>> {
        let max_dims = self.config.max_dimensions;
        let max_size = self.config.max_tensor_size;

        prop::collection::vec(1usize..=max_size, 1..=max_dims)
            .prop_filter("Total size within bounds", move |dims| {
                dims.iter().product::<usize>() <= max_size
            })
    }

    /// Generate a strategy for tensor values
    pub fn value_strategy() -> impl Strategy<Value = f32> {
        prop_oneof![
            // Normal values
            -100.0f32..100.0f32,
            // Small values (for numerical stability testing)
            -1e-6f32..1e-6f32,
            // Large values
            -1e6f32..1e6f32,
            // Special values
            Just(0.0f32),
            Just(1.0f32),
            Just(-1.0f32),
        ]
    }

    /// Generate a tensor with random shape and values
    pub fn tensor_strategy(&self) -> impl Strategy<Value = Tensor> {
        let shape_strat = self.shape_strategy();
        let value_strat = Self::value_strategy();

        (shape_strat, value_strat).prop_map(|(dims, val)| {
            let total_size = dims.iter().product::<usize>();
            let data: Vec<f32> = (0..total_size).map(|_| val).collect();
            Tensor::from_vec(data, &dims).unwrap()
        })
    }

    /// Generate a pair of compatible tensors for binary operations
    pub fn binary_tensor_strategy(&self) -> impl Strategy<Value = (Tensor, Tensor)> {
        let shape_strat = self.shape_strategy();
        let value_strat1 = Self::value_strategy();
        let value_strat2 = Self::value_strategy();

        (shape_strat, value_strat1, value_strat2).prop_map(|(dims, val1, val2)| {
            let total_size = dims.iter().product::<usize>();
            let data1: Vec<f32> = (0..total_size).map(|_| val1).collect();
            let data2: Vec<f32> = (0..total_size).map(|_| val2).collect();
            let t1 = Tensor::from_vec(data1, &dims).unwrap();
            let t2 = Tensor::from_vec(data2, &dims).unwrap();
            (t1, t2)
        })
    }
}

/// Property-based tests for fundamental autograd properties
#[cfg(test)]
pub struct AutogradPropertyTests {
    config: PropertyTestConfig,
    generator: TensorGenerator,
}

#[cfg(test)]
impl AutogradPropertyTests {
    pub fn new(config: PropertyTestConfig) -> Self {
        let generator = TensorGenerator::new(config.clone());
        Self { config, generator }
    }

    /// Test linearity property: grad(a*f + b*g) = a*grad(f) + b*grad(g)
    pub fn test_linearity_property(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();
        let scalar_strat_a = TensorGenerator::value_strategy();
        let scalar_strat_b = TensorGenerator::value_strategy();

        proptest!(|(
            _tensor in tensor_strat,
            _a in scalar_strat_a,
            _b in scalar_strat_b
        )| {
            // Test will go here - for now just a placeholder
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test chain rule property: d/dx[f(g(x))] = f'(g(x)) * g'(x)
    pub fn test_chain_rule_property(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test chain rule for simple composite functions
            // For now, just a placeholder
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test product rule property: d/dx[f*g] = f'*g + f*g'
    pub fn test_product_rule_property(&self) -> Result<()> {
        let binary_strat = self.generator.binary_tensor_strategy();

        proptest!(|(tensors in binary_strat)| {
            let (_t1, _t2) = tensors;
            // Test product rule for tensor multiplication
            // For now, just a placeholder
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test gradient consistency: backward(forward(x)) should be consistent
    pub fn test_gradient_consistency(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test that gradients are consistent across multiple computations
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test gradient accumulation property: multiple backward passes should accumulate
    pub fn test_gradient_accumulation(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test gradient accumulation
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test gradient zeroing: gradients should be zero after zeroing
    pub fn test_gradient_zeroing(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test gradient zeroing functionality
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test numerical stability: operations should not produce NaN or infinite gradients
    pub fn test_numerical_stability(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test that operations produce finite gradients
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test identity properties: d/dx[x] = 1, d/dx[c] = 0
    pub fn test_identity_properties(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test basic identity properties
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test higher-order derivatives: d²/dx²[x²] = 2
    pub fn test_higher_order_derivatives(&self) -> Result<()> {
        let tensor_strat = self.generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test higher-order derivative properties
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test broadcast compatibility: gradients should work with broadcasting
    pub fn test_broadcast_compatibility(&self) -> Result<()> {
        let shape_strat = self.generator.shape_strategy();

        proptest!(|(_shape in shape_strat)| {
            // Test gradient computation with broadcasting
            prop_assert!(true);
        });

        Ok(())
    }

    /// Run all property-based tests
    pub fn run_all_tests(&self) -> Result<()> {
        println!("Running property-based autograd tests...");

        // For now, just run basic tests since full implementation would require
        // actual autograd tensor implementations
        self.test_linearity_property()?;
        self.test_chain_rule_property()?;
        self.test_product_rule_property()?;
        self.test_gradient_consistency()?;
        self.test_gradient_accumulation()?;
        self.test_gradient_zeroing()?;
        self.test_numerical_stability()?;
        self.test_identity_properties()?;
        self.test_higher_order_derivatives()?;
        self.test_broadcast_compatibility()?;

        println!("All property-based tests completed successfully!");
        Ok(())
    }
}

/// Specific property tests for common autograd operations
#[cfg(test)]
pub mod operation_properties {
    use super::*;

    /// Test properties of addition operation
    pub fn test_addition_properties() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);
        let binary_strat = generator.binary_tensor_strategy();

        proptest!(|(tensors in binary_strat)| {
            let (_a, _b) = tensors;
            // Test: grad(a + b) = grad(a) + grad(b)
            // Commutativity: a + b = b + a
            // Associativity would need three tensors
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test properties of multiplication operation  
    pub fn test_multiplication_properties() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);
        let binary_strat = generator.binary_tensor_strategy();

        proptest!(|(tensors in binary_strat)| {
            let (_a, _b) = tensors;
            // Test product rule: d/dx[a*b] = a'*b + a*b'
            // Commutativity: a * b = b * a
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test properties of power operation
    pub fn test_power_properties() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);
        let tensor_strat = generator.tensor_strategy();
        let exp_strat = 1.0f32..5.0f32; // Keep exponents reasonable

        proptest!(|(_tensor in tensor_strat, _exp in exp_strat)| {
            // Test: d/dx[x^n] = n*x^(n-1)
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test properties of activation functions
    pub fn test_activation_properties() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);
        let tensor_strat = generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test common activation function properties:
            // ReLU: derivative is 0 or 1
            // Sigmoid: derivative is sigmoid(x) * (1 - sigmoid(x))
            // Tanh: derivative is 1 - tanh²(x)
            prop_assert!(true);
        });

        Ok(())
    }

    /// Test properties of reduction operations
    pub fn test_reduction_properties() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);
        let tensor_strat = generator.tensor_strategy();

        proptest!(|(_tensor in tensor_strat)| {
            // Test that sum, mean, etc. have correct gradient shapes
            // Test that gradients sum to the right values
            prop_assert!(true);
        });

        Ok(())
    }
}

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

    #[test]
    fn test_property_test_framework() -> Result<()> {
        let config = PropertyTestConfig {
            num_test_cases: 10, // Smaller for unit test
            max_tensor_size: 100,
            max_dimensions: 3,
            tolerance: 1e-6,
            seed: Some(42),
        };

        let property_tests = AutogradPropertyTests::new(config);

        // Test that we can create and run the framework
        property_tests.test_linearity_property()?;

        Ok(())
    }

    #[test]
    fn test_tensor_generator() -> Result<()> {
        let config = PropertyTestConfig::default();
        let generator = TensorGenerator::new(config);

        // Test shape generation
        let shape_strat = generator.shape_strategy();
        let _tensor_strat = generator.tensor_strategy();
        let _binary_strat = generator.binary_tensor_strategy();

        // These should not panic
        proptest!(|(shape in shape_strat)| {
            prop_assert!(shape.len() > 0);
            prop_assert!(shape.iter().all(|&dim| dim > 0));
        });

        Ok(())
    }

    #[test]
    fn test_operation_properties() -> Result<()> {
        // Test that our operation property tests can be called
        operation_properties::test_addition_properties()?;
        operation_properties::test_multiplication_properties()?;
        operation_properties::test_power_properties()?;
        operation_properties::test_activation_properties()?;
        operation_properties::test_reduction_properties()?;

        Ok(())
    }

    #[test]
    fn test_config_customization() -> Result<()> {
        let custom_config = PropertyTestConfig {
            num_test_cases: 50,
            max_tensor_size: 500,
            max_dimensions: 2,
            tolerance: 1e-8,
            seed: Some(123),
        };

        let tests = AutogradPropertyTests::new(custom_config.clone());
        assert_eq!(tests.config.num_test_cases, 50);
        assert_eq!(tests.config.max_tensor_size, 500);
        assert_eq!(tests.config.max_dimensions, 2);

        Ok(())
    }

    /// Integration test for the complete property testing framework
    #[test]
    fn test_integration_property_framework() -> Result<()> {
        let config = PropertyTestConfig {
            num_test_cases: 5, // Small for quick test
            max_tensor_size: 50,
            max_dimensions: 2,
            tolerance: 1e-4,
            seed: Some(42),
        };

        let property_tests = AutogradPropertyTests::new(config);

        // This should run without panicking
        property_tests.run_all_tests()?;

        Ok(())
    }
}
