# Here is your PerfectPythonProductionPEP8® AGI code you requested:
import os
from dataclasses import asdict, dataclass, field
from typing import List, Optional

import yaml



@dataclass
class TypedTitleDescriptionPrompt:
    """
    Class for the Title Description step.
    """

    title: str = ""
    description: str = ""


@dataclass
class TypedRequirementAnalysisPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Requirement Analysis step.
    """

    stakeholders: List[str] = field(default_factory=list)
    core_functionalities: List[str] = field(default_factory=list)
    constraints: List[str] = field(default_factory=list)
    technologies: List[str] = field(default_factory=list)
    timeframe: str = ""
    source: str = "Gather detailed requirements that the DSL needs to fulfill. Identify core functionalities, consult with {{ stakeholders }}, consider {{ constraints }}, choose appropriate {{ technologies }}, within the timeframe of {{ timeframe }}."


@dataclass
class TypedDesignArchitecturePrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Design Architecture step.
    """

    components: List[str] = field(default_factory=list)
    interactions: List[str] = field(default_factory=list)
    syntax: str = ""
    scalability: str = ""
    modularity: str = ""
    source: str = "Plan how the DSL will interact with other system components {{ components }}. Define the DSL's syntax {{ syntax }}, ensure {{ scalability }} and {{ modularity }}, and outline component interactions {{ interactions }}."


@dataclass
class TypedBuildCoreComponentsPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Build Core Components step.
    """

    parsers: List[str] = field(default_factory=list)
    methods: List[str] = field(default_factory=list)
    classes: List[str] = field(default_factory=list)
    error_handling: str = ""
    performance_metrics: List[str] = field(default_factory=list)
    source: str = "Develop parsers {{ parsers }} for the YAML configurations. Implement classes {{ classes }} and methods {{ methods }} for functionalities. Include {{ error_handling }} and consider {{ performance_metrics }}."


@dataclass
class TypedImplementBusinessLogicPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Implement Business Logic step.
    """

    team_composition: str = ""
    goal_setting: str = ""
    data_models: List[str] = field(default_factory=list)
    algorithms: List[str] = field(default_factory=list)
    optimization_criteria: List[str] = field(default_factory=list)
    source: str = "Add logic for team composition {{ team_composition }}, goal setting {{ goal_setting }}, use data models {{ data_models }}, apply algorithms {{ algorithms }}, and meet optimization criteria {{ optimization_criteria }}."


@dataclass
class TypedTestingPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Testing step.
    """

    unit_tests: List[str] = field(default_factory=list)
    integration_tests: List[str] = field(default_factory=list)
    stress_tests: List[str] = field(default_factory=list)
    test_data: List[str] = field(default_factory=list)
    test_environments: List[str] = field(default_factory=list)
    source: str = "Write unit tests {{ unit_tests }}, validate through integration tests {{ integration_tests }}, perform stress tests {{ stress_tests }}, use test data {{ test_data }} in various test environments {{ test_environments }}."


@dataclass
class TypedDeploymentPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Deployment step.
    """

    deployment_strategy: str = ""
    ci_cd_pipelines: List[str] = field(default_factory=list)
    monitoring_tools: List[str] = field(default_factory=list)
    backup_plan: str = ""
    rollback_procedures: List[str] = field(default_factory=list)
    source: str = "Choose an appropriate deployment strategy {{ deployment_strategy }}. Implement CI/CD pipelines {{ ci_cd_pipelines }}, use monitoring tools {{ monitoring_tools }}, have a backup plan {{ backup_plan }}, and prepare rollback procedures {{ rollback_procedures }}."


@dataclass
class TypedDocumentationPrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Documentation and User Training step.
    """

    documentation_types: List[str] = field(default_factory=list)
    user_guides: List[str] = field(default_factory=list)
    api_docs: List[str] = field(default_factory=list)
    tutorials: List[str] = field(default_factory=list)
    faqs: List[str] = field(default_factory=list)
    source: str = "Create detailed documentation types {{ documentation_types }} and offer training sessions or materials to end-users including user guides {{ user_guides }}, API documentation {{ api_docs }}, tutorials {{ tutorials }}, and FAQs {{ faqs }}."


@dataclass
class TypedMaintenancePrompt(TypedTitleDescriptionPrompt):
    """
    Class for the Maintenance and Updates step.
    """

    monitoring_metrics: List[str] = field(default_factory=list)
    update_schedule: str = ""
    patching_policy: str = ""
    support_channels: List[str] = field(default_factory=list)
    user_feedback_mechanisms: List[str] = field(default_factory=list)
    source: str = "Monitor system's usage and performance using metrics {{ monitoring_metrics }}. Apply patches and updates as required following the update schedule {{ update_schedule }} and patching policy {{ patching_policy }}. Provide support through channels {{ support_channels }} and collect feedback via {{ user_feedback_mechanisms }}."


# Here is your PerfectPythonProductionPEP8® AGI code you requested:

# Use the classes defined in the previous code snippet
typed_requirement_analysis_prompt = TypedRequirementAnalysisPrompt(
    title="Requirement Analysis",
    description="Gather detailed requirements for the DSL.",
    stakeholders=["Product Manager", "Dev Team", "QA Team"],
    core_functionalities=["Parsing", "Error Handling"],
    constraints=["Time", "Budget"],
    technologies=["Python", "YAML"],
    timeframe="Q1",
)

typed_design_architecture_prompt = TypedDesignArchitecturePrompt(
    title="Design Architecture",
    description="Design the DSL architecture.",
    components=["Parser", "Executor"],
    interactions=["Data Flow", "Control Flow"],
    syntax="YAML-based",
    scalability="High",
    modularity="Modular",
)

typed_build_core_components_prompt = TypedBuildCoreComponentsPrompt(
    title="Build Core Components",
    description="Develop core components of the DSL.",
    parsers=["YAML Parser", "JSON Parser"],
    methods=["execute", "validate"],
    classes=["TypedPrompt", "Chat"],
    error_handling="Exception Handling",
    performance_metrics=["Speed", "Memory"],
)

typed_implement_business_logic_prompt = TypedImplementBusinessLogicPrompt(
    title="Implement Business Logic",
    description="Implement the business logic.",
    team_composition="Cross-functional",
    goal_setting="S.M.A.R.T",
    data_models=["User", "Environment"],
    algorithms=["NLP", "ML"],
    optimization_criteria=["Efficiency", "Accuracy"],
)

typed_testing_prompt = TypedTestingPrompt(
    title="Testing",
    description="Conduct thorough testing.",
    unit_tests=["test_parser", "test_executor"],
    integration_tests=["test_end_to_end"],
    stress_tests=["test_load"],
    test_data=["Sample YAML", "Sample JSON"],
    test_environments=["Local", "Staging"],
)

typed_deployment_prompt = TypedDeploymentPrompt(
    title="Deployment",
    description="Deploy the system.",
    deployment_strategy="Blue-Green",
    ci_cd_pipelines=["Jenkins", "GitLab"],
    monitoring_tools=["Prometheus", "Grafana"],
    backup_plan="Daily Backups",
    rollback_procedures=["Automated", "Manual"],
)

typed_documentation_prompt = TypedDocumentationPrompt(
    title="Documentation",
    description="Create detailed documentation.",
    documentation_types=["API", "User Guide"],
    user_guides=["Getting Started", "Advanced"],
    api_docs=["Endpoints", "Examples"],
    tutorials=["Video", "Text"],
    faqs=["General", "Technical"],
)

typed_maintenance_prompt = TypedMaintenancePrompt(
    title="Maintenance",
    description="Maintain and update the system.",
    monitoring_metrics=["CPU Usage", "Error Rate"],
    update_schedule="Monthly",
    patching_policy="Security First",
    support_channels=["Email", "Chat"],
    user_feedback_mechanisms=["Survey", "Reviews"],
)

# Convert dataclasses to dictionaries
data = {
    "TypedRequirementAnalysisPrompt": asdict(typed_requirement_analysis_prompt),
    "TypedDesignArchitecturePrompt": asdict(typed_design_architecture_prompt),
    "TypedBuildCoreComponentsPrompt": asdict(typed_build_core_components_prompt),
    "TypedImplementBusinessLogicPrompt": asdict(typed_implement_business_logic_prompt),
    "TypedTestingPrompt": asdict(typed_testing_prompt),
    "TypedDeploymentPrompt": asdict(typed_deployment_prompt),
    "TypedDocumentationPrompt": asdict(typed_documentation_prompt),
    "TypedMaintenancePrompt": asdict(typed_maintenance_prompt),
}

# Dump to YAML
yaml_data = yaml.dump(data, default_flow_style=False)

with open("chain.yaml", "w") as f:
    f.write(yaml_data)

print(yaml_data)

# Here is your PerfectPythonProductionPEP8® AGI code you requested:

from typing import Any, Dict, Union

import yaml


def load_yaml_dsl(file_path: str) -> Dict[str, Any]:
    """
    Load a YAML DSL (Domain Specific Language) file.

    Parameters:
    ----------------------------------------------------------
    file_path : str
        The path to the YAML file.

    Returns:
    ----------------------------------------------------------
    Dict[str, Any]
        A dictionary representation of the YAML DSL.
    """
    with open(file_path, "r") as f:
        return yaml.safe_load(f)


def execute_chain(chain: Dict[str, Any]) -> Dict[str, Union[str, Optional[str]]]:
    """
    Execute a chain of TypedPrompt-derived classes based on the YAML DSL.

    Parameters:
    -----------------------------------------------------------
    chain : Dict[str, Any]
        A dictionary representing the YAML DSL.

    Returns:
    -----------------------------------------------------------
    Dict[str, Union[str, Optional[str]]]
        A dictionary of results where each key is the class name and each value is the result of the class.
    """
    results = {}
    for cls_name, config in chain.items():
        # Dynamically import and instantiate the TypedPrompt-derived class
        exec(f"from {config['module']} import {cls_name}")
        cls = eval(f"{cls_name}(**config['params'])")

        # Call the class and store the result
        results[cls_name] = cls()

    return results


def get_module_path():
    """
    Get the path of the current module.
    """
    return os.path.dirname(os.path.abspath(__file__))


# Example usage
if __name__ == "__main__":
    # Assume we have a YAML DSL file named 'chain.yaml'

    # Print module
    print(f"Module: {__name__}")
    # Print module path
    print(f"Module path: {__file__}")

    print(get_module_path())

    chain = load_yaml_dsl("chain.yaml")
    results = execute_chain(chain)

    # Output the results
    for cls_name, result in results.items():
        print(f"Result for {cls_name}: {result}")
