from typing import Dict, Optional, Any, List
from pathlib import Path
import logging
import importlib

from sagemaker.workflow.steps import ProcessingStep, Step
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.sklearn import SKLearnProcessor

from ..configs.config_stratified_sampling_step import StratifiedSamplingConfig
from ...core.base.builder_base import StepBuilderBase

# Import the unified stratified sampling specification
from ..specs.stratified_sampling_spec import STRATIFIED_SAMPLING_SPEC

logger = logging.getLogger(__name__)


class StratifiedSamplingStepBuilder(StepBuilderBase):
    """
    Builder for a Stratified Sampling ProcessingStep.

    This implementation uses the fully specification-driven approach where inputs, outputs,
    and behavior are defined by step specifications and script contracts.
    """

    def __init__(
        self,
        config: StratifiedSamplingConfig,
        sagemaker_session=None,
        role: Optional[str] = None,
        registry_manager: Optional["RegistryManager"] = None,
        dependency_resolver: Optional["UnifiedDependencyResolver"] = None,
    ):
        """
        Initialize with unified specification for all job types.

        Args:
            config: Configuration for the step
            sagemaker_session: SageMaker session
            role: IAM role
            registry_manager: Optional registry manager for dependency injection
            dependency_resolver: Optional dependency resolver for dependency injection
        """
        if not hasattr(config, "job_type"):
            raise ValueError("config.job_type must be specified")

        # Use unified specification for all job types
        logger.info("Using unified STRATIFIED_SAMPLING_SPEC for all job types")

        super().__init__(
            config=config,
            spec=STRATIFIED_SAMPLING_SPEC,
            sagemaker_session=sagemaker_session,
            role=role,
            registry_manager=registry_manager,
            dependency_resolver=dependency_resolver,
        )
        self.config: StratifiedSamplingConfig = config

    def validate_configuration(self) -> None:
        """
        Validate required configuration.

        Raises:
            ValueError: If required attributes are missing
        """
        # Required processing configuration
        required_attrs = [
            "processing_instance_count",
            "processing_volume_size",
            "processing_instance_type_large",
            "processing_instance_type_small",
            "processing_framework_version",
            "use_large_processing_instance",
            "job_type",
            "strata_column",
            "sampling_strategy",
            "target_sample_size",
            "min_samples_per_stratum",
            "random_state",
        ]

        for attr in required_attrs:
            if not hasattr(self.config, attr) or getattr(self.config, attr) is None:
                raise ValueError(f"Missing required attribute: {attr}")

        # Validate job type
        if self.config.job_type not in [
            "training",
            "validation",
            "testing",
            "calibration",
        ]:
            raise ValueError(f"Invalid job_type: {self.config.job_type}")

        # Validate strata_column
        if not self.config.strata_column or not self.config.strata_column.strip():
            raise ValueError("strata_column must be provided and non-empty")

        # Validate sampling_strategy
        if self.config.sampling_strategy not in [
            "balanced",
            "proportional_min",
            "optimal",
        ]:
            raise ValueError(
                f"Invalid sampling_strategy: {self.config.sampling_strategy}"
            )

        # Validate target_sample_size
        if self.config.target_sample_size <= 0:
            raise ValueError("target_sample_size must be positive")

        # Validate min_samples_per_stratum
        if self.config.min_samples_per_stratum <= 0:
            raise ValueError("min_samples_per_stratum must be positive")

        # Cross-validation: optimal strategy recommendation
        if (
            self.config.sampling_strategy == "optimal"
            and not self.config.variance_column
        ):
            self.log_warning(
                "optimal sampling strategy works best with variance_column specified. "
                "Using default variance if variance_column is not provided."
            )

    def _create_processor(self) -> SKLearnProcessor:
        """
        Create the SKLearn processor for the processing job.

        Returns:
            SKLearnProcessor: Configured processor for the step
        """
        instance_type = (
            self.config.processing_instance_type_large
            if self.config.use_large_processing_instance
            else self.config.processing_instance_type_small
        )

        return SKLearnProcessor(
            framework_version=self.config.processing_framework_version,
            role=self.role,
            instance_type=instance_type,
            instance_count=self.config.processing_instance_count,
            volume_size_in_gb=self.config.processing_volume_size,
            base_job_name=self._generate_job_name(),  # Use standardized method with auto-detection
            sagemaker_session=self.session,
            env=self._get_environment_variables(),
        )

    def _get_environment_variables(self) -> Dict[str, str]:
        """
        Create environment variables for the processing job.

        Returns:
            Dict[str, str]: Environment variables for the processing job
        """
        # Get base environment variables from contract
        env_vars = super()._get_environment_variables()

        # Add required environment variables specific to stratified sampling
        env_vars["STRATA_COLUMN"] = self.config.strata_column

        # Add optional environment variables with defaults
        env_vars["SAMPLING_STRATEGY"] = self.config.sampling_strategy
        env_vars["TARGET_SAMPLE_SIZE"] = str(self.config.target_sample_size)
        env_vars["MIN_SAMPLES_PER_STRATUM"] = str(self.config.min_samples_per_stratum)
        env_vars["RANDOM_STATE"] = str(self.config.random_state)

        # Add variance_column if specified
        if self.config.variance_column:
            env_vars["VARIANCE_COLUMN"] = self.config.variance_column

        return env_vars

    def _get_inputs(self, inputs: Dict[str, Any]) -> List[ProcessingInput]:
        """
        Get inputs for the step using specification and contract.

        This method creates ProcessingInput objects for each dependency defined in the specification.

        Args:
            inputs: Input data sources keyed by logical name

        Returns:
            List of ProcessingInput objects

        Raises:
            ValueError: If no specification or contract is available
        """
        if not self.spec:
            raise ValueError("Step specification is required")

        if not self.contract:
            raise ValueError("Script contract is required for input mapping")

        processing_inputs = []

        # Process each dependency in the specification
        for _, dependency_spec in self.spec.dependencies.items():
            logical_name = dependency_spec.logical_name

            # Skip if optional and not provided
            if not dependency_spec.required and logical_name not in inputs:
                continue

            # Make sure required inputs are present
            if dependency_spec.required and logical_name not in inputs:
                raise ValueError(f"Required input '{logical_name}' not provided")

            # Get container path from contract
            container_path = None
            if logical_name in self.contract.expected_input_paths:
                container_path = self.contract.expected_input_paths[logical_name]
            else:
                raise ValueError(f"No container path found for input: {logical_name}")

            # Use the input value directly - property references are handled by PipelineAssembler
            processing_inputs.append(
                ProcessingInput(
                    input_name=logical_name,
                    source=inputs[logical_name],
                    destination=container_path,
                )
            )

        return processing_inputs

    def _get_outputs(self, outputs: Dict[str, Any]) -> List[ProcessingOutput]:
        """
        Get outputs for the step using specification and contract.

        This method creates ProcessingOutput objects for each output defined in the specification.

        Args:
            outputs: Output destinations keyed by logical name

        Returns:
            List of ProcessingOutput objects

        Raises:
            ValueError: If no specification or contract is available
        """
        if not self.spec:
            raise ValueError("Step specification is required")

        if not self.contract:
            raise ValueError("Script contract is required for output mapping")

        processing_outputs = []

        # Process each output in the specification
        for _, output_spec in self.spec.outputs.items():
            logical_name = output_spec.logical_name

            # Get container path from contract
            container_path = None
            if logical_name in self.contract.expected_output_paths:
                container_path = self.contract.expected_output_paths[logical_name]
            else:
                raise ValueError(f"No container path found for output: {logical_name}")

            # Try to find destination in outputs
            destination = None

            # Look in outputs by logical name
            if logical_name in outputs:
                destination = outputs[logical_name]
            else:
                # Generate destination using base output path and Join for parameter compatibility
                from sagemaker.workflow.functions import Join

                base_output_path = self._get_base_output_path()
                destination = Join(
                    on="/",
                    values=[
                        base_output_path,
                        "stratified_sampling",
                        self.config.job_type,
                        logical_name,
                    ],
                )
                self.log_info(
                    "Using generated destination for '%s': %s",
                    logical_name,
                    destination,
                )

            processing_outputs.append(
                ProcessingOutput(
                    output_name=logical_name,
                    source=container_path,
                    destination=destination,
                )
            )

        return processing_outputs

    def _get_job_arguments(self) -> List[str]:
        """
        Constructs the list of command-line arguments to be passed to the processing script.

        This implementation uses job_type from the configuration, which is required by the script
        and also included in the contract's expected_arguments (though we prioritize config).
        This approach allows different stratified sampling jobs to use different job_type values
        based on their configuration.

        Returns:
            A list of strings representing the command-line arguments.
        """
        # Get job_type from configuration (takes precedence over contract)
        job_type = self.config.job_type
        self.log_info("Setting job_type argument to: %s", job_type)

        # For stratified sampling, we always return the job_type from config
        # The contract has a default job_type, but config value takes precedence
        return ["--job_type", job_type]

    def create_step(self, **kwargs) -> ProcessingStep:
        """
        Create the ProcessingStep.

        Args:
            **kwargs: Step parameters including:
                - inputs: Input data sources
                - outputs: Output destinations
                - dependencies: Steps this step depends on
                - enable_caching: Whether to enable caching

        Returns:
            Configured ProcessingStep
        """
        # Extract parameters
        inputs_raw = kwargs.get("inputs", {})
        outputs = kwargs.get("outputs", {})
        dependencies = kwargs.get("dependencies", [])
        enable_caching = kwargs.get("enable_caching", True)

        # Handle inputs
        inputs = {}

        # If dependencies are provided, extract inputs from them
        if dependencies:
            try:
                extracted_inputs = self.extract_inputs_from_dependencies(dependencies)
                inputs.update(extracted_inputs)
            except Exception as e:
                self.log_warning("Failed to extract inputs from dependencies: %s", e)

        # Add explicitly provided inputs (overriding any extracted ones)
        inputs.update(inputs_raw)

        # Add direct keyword arguments (e.g., processed_data from template)
        for key in ["processed_data", "DATA", "METADATA", "SIGNATURE"]:
            if key in kwargs and key not in inputs:
                inputs[key] = kwargs[key]

        # Create processor and get inputs/outputs
        processor = self._create_processor()
        proc_inputs = self._get_inputs(inputs)
        proc_outputs = self._get_outputs(outputs)
        job_args = self._get_job_arguments()

        # Get step name using standardized method with auto-detection
        step_name = self._get_step_name()

        # Get script path using modernized method with comprehensive fallbacks
        script_path = self.config.get_script_path()
        self.log_info("Using script path: %s", script_path)

        # Create step
        step = ProcessingStep(
            name=step_name,
            processor=processor,
            inputs=proc_inputs,
            outputs=proc_outputs,
            code=script_path,
            job_arguments=job_args,
            depends_on=dependencies,
            cache_config=self._get_cache_config(enable_caching),
        )

        # Attach specification to the step for future reference
        setattr(step, "_spec", self.spec)

        return step
