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_currency_conversion_step import CurrencyConversionConfig
from ...core.base.builder_base import StepBuilderBase
from ...core.deps.registry_manager import RegistryManager
from ...core.deps.dependency_resolver import UnifiedDependencyResolver

# Import the unified currency conversion specification
from ..specs.currency_conversion_spec import CURRENCY_CONVERSION_SPEC

logger = logging.getLogger(__name__)


class CurrencyConversionStepBuilder(StepBuilderBase):
    """
    Builder for a Currency Conversion 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: CurrencyConversionConfig,
        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 CURRENCY_CONVERSION_SPEC for all job types")

        super().__init__(
            config=config,
            spec=CURRENCY_CONVERSION_SPEC,
            sagemaker_session=sagemaker_session,
            role=role,
            registry_manager=registry_manager,
            dependency_resolver=dependency_resolver,
        )
        self.config: CurrencyConversionConfig = 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",
        ]

        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}")

        # Currency-specific validation is now handled by the config class itself

    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.

        Uses the configuration's environment_variables property which automatically
        generates all required environment variables from the config fields.

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

        # Get environment variables from config (includes all currency conversion settings)
        config_env_vars = self.config.environment_variables
        env_vars.update(config_env_vars)

        # Add environment variables from config.env if they exist
        if hasattr(self.config, "env") and self.config.env:
            env_vars.update(self.config.env)

        self.log_info("Processing environment variables: %s", env_vars)
        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}")

            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,
                        "currency_conversion",
                        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 command-line arguments for 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.

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

        # Return job_type argument - the script uses this to determine processing mode
        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., data_input from template)
        for key in ["data_input"]:
            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
