"""CLI commands for Hugging Face Inference Endpoints."""

import json
from typing import Annotated, Any

import typer

from huggingface_hub._inference_endpoints import InferenceEndpoint, InferenceEndpointScalingMetric
from huggingface_hub.errors import HfHubHTTPError

from ._cli_utils import (
    FormatOpt,
    OutputFormat,
    QuietOpt,
    TokenOpt,
    get_hf_api,
    print_list_output,
    typer_factory,
)


ie_cli = typer_factory(help="Manage Hugging Face Inference Endpoints.")

catalog_app = typer_factory(help="Interact with the Inference Endpoints catalog.")


NameArg = Annotated[
    str,
    typer.Argument(help="Endpoint name."),
]
NameOpt = Annotated[
    str | None,
    typer.Option(help="Endpoint name."),
]

NamespaceOpt = Annotated[
    str | None,
    typer.Option(
        help="The namespace associated with the Inference Endpoint. Defaults to the current user's namespace.",
    ),
]


def _print_endpoint(endpoint: InferenceEndpoint) -> None:
    typer.echo(json.dumps(endpoint.raw, indent=2, sort_keys=True))


@ie_cli.command("list | ls", examples=["hf endpoints ls", "hf endpoints ls --namespace my-org"])
def ls(
    namespace: NamespaceOpt = None,
    format: FormatOpt = OutputFormat.table,
    quiet: QuietOpt = False,
    token: TokenOpt = None,
) -> None:
    """Lists all Inference Endpoints for the given namespace."""
    api = get_hf_api(token=token)
    try:
        endpoints = api.list_inference_endpoints(namespace=namespace, token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Listing failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    results = [endpoint.raw for endpoint in endpoints]

    def row_fn(item: dict[str, Any]) -> list[str]:
        status = item.get("status", {})
        model = item.get("model", {})
        compute = item.get("compute", {})
        provider = item.get("provider", {})
        return [
            str(item.get("name", "")),
            str(model.get("repository", "") if isinstance(model, dict) else ""),
            str(status.get("state", "") if isinstance(status, dict) else ""),
            str(model.get("task", "") if isinstance(model, dict) else ""),
            str(model.get("framework", "") if isinstance(model, dict) else ""),
            str(compute.get("instanceType", "") if isinstance(compute, dict) else ""),
            str(provider.get("vendor", "") if isinstance(provider, dict) else ""),
            str(provider.get("region", "") if isinstance(provider, dict) else ""),
        ]

    print_list_output(
        items=results,
        format=format,
        quiet=quiet,
        id_key="name",
        headers=["NAME", "MODEL", "STATUS", "TASK", "FRAMEWORK", "INSTANCE", "VENDOR", "REGION"],
        row_fn=row_fn,
    )


@ie_cli.command(name="deploy", examples=["hf endpoints deploy my-endpoint --repo gpt2 --framework pytorch ..."])
def deploy(
    name: NameArg,
    repo: Annotated[
        str,
        typer.Option(
            help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
        ),
    ],
    framework: Annotated[
        str,
        typer.Option(
            help="The machine learning framework used for the model (e.g. 'vllm').",
        ),
    ],
    accelerator: Annotated[
        str,
        typer.Option(
            help="The hardware accelerator to be used for inference (e.g. 'cpu').",
        ),
    ],
    instance_size: Annotated[
        str,
        typer.Option(
            help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
        ),
    ],
    instance_type: Annotated[
        str,
        typer.Option(
            help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
        ),
    ],
    region: Annotated[
        str,
        typer.Option(
            help="The cloud region in which the Inference Endpoint will be created (e.g. 'us-east-1').",
        ),
    ],
    vendor: Annotated[
        str,
        typer.Option(
            help="The cloud provider or vendor where the Inference Endpoint will be hosted (e.g. 'aws').",
        ),
    ],
    *,
    namespace: NamespaceOpt = None,
    task: Annotated[
        str | None,
        typer.Option(
            help="The task on which to deploy the model (e.g. 'text-classification').",
        ),
    ] = None,
    token: TokenOpt = None,
    min_replica: Annotated[
        int,
        typer.Option(
            help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
        ),
    ] = 1,
    max_replica: Annotated[
        int,
        typer.Option(
            help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
        ),
    ] = 1,
    scale_to_zero_timeout: Annotated[
        int | None,
        typer.Option(
            help="The duration in minutes before an inactive endpoint is scaled to zero.",
        ),
    ] = None,
    scaling_metric: Annotated[
        InferenceEndpointScalingMetric | None,
        typer.Option(
            help="The metric reference for scaling.",
        ),
    ] = None,
    scaling_threshold: Annotated[
        float | None,
        typer.Option(
            help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
        ),
    ] = None,
) -> None:
    """Deploy an Inference Endpoint from a Hub repository."""
    api = get_hf_api(token=token)
    endpoint = api.create_inference_endpoint(
        name=name,
        repository=repo,
        framework=framework,
        accelerator=accelerator,
        instance_size=instance_size,
        instance_type=instance_type,
        region=region,
        vendor=vendor,
        namespace=namespace,
        task=task,
        token=token,
        min_replica=min_replica,
        max_replica=max_replica,
        scaling_metric=scaling_metric,
        scaling_threshold=scaling_threshold,
        scale_to_zero_timeout=scale_to_zero_timeout,
    )

    _print_endpoint(endpoint)


@catalog_app.command(name="deploy", examples=["hf endpoints catalog deploy --repo meta-llama/Llama-3.2-1B-Instruct"])
def deploy_from_catalog(
    repo: Annotated[
        str,
        typer.Option(
            help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
        ),
    ],
    name: NameOpt = None,
    accelerator: Annotated[
        str | None,
        typer.Option(
            help="The hardware accelerator to be used for inference (e.g. 'cpu', 'gpu', 'neuron').",
        ),
    ] = None,
    namespace: NamespaceOpt = None,
    token: TokenOpt = None,
) -> None:
    """Deploy an Inference Endpoint from the Model Catalog."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.create_inference_endpoint_from_catalog(
            repo_id=repo,
            name=name,
            accelerator=accelerator,
            namespace=namespace,
            token=token,
        )
    except HfHubHTTPError as error:
        typer.echo(f"Deployment failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    _print_endpoint(endpoint)


def list_catalog(
    token: TokenOpt = None,
) -> None:
    """List available Catalog models."""
    api = get_hf_api(token=token)
    try:
        models = api.list_inference_catalog(token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Catalog fetch failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    typer.echo(json.dumps({"models": models}, indent=2, sort_keys=True))


catalog_app.command(name="list | ls", examples=["hf endpoints catalog ls"])(list_catalog)
ie_cli.command(name="list-catalog", hidden=True)(list_catalog)


ie_cli.add_typer(catalog_app, name="catalog")


@ie_cli.command(examples=["hf endpoints describe my-endpoint"])
def describe(
    name: NameArg,
    namespace: NamespaceOpt = None,
    token: TokenOpt = None,
) -> None:
    """Get information about an existing endpoint."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.get_inference_endpoint(name=name, namespace=namespace, token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Fetch failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    _print_endpoint(endpoint)


@ie_cli.command(examples=["hf endpoints update my-endpoint --min-replica 2"])
def update(
    name: NameArg,
    namespace: NamespaceOpt = None,
    repo: Annotated[
        str | None,
        typer.Option(
            help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
        ),
    ] = None,
    accelerator: Annotated[
        str | None,
        typer.Option(
            help="The hardware accelerator to be used for inference (e.g. 'cpu').",
        ),
    ] = None,
    instance_size: Annotated[
        str | None,
        typer.Option(
            help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
        ),
    ] = None,
    instance_type: Annotated[
        str | None,
        typer.Option(
            help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
        ),
    ] = None,
    framework: Annotated[
        str | None,
        typer.Option(
            help="The machine learning framework used for the model (e.g. 'custom').",
        ),
    ] = None,
    revision: Annotated[
        str | None,
        typer.Option(
            help="The specific model revision to deploy on the Inference Endpoint (e.g. '6c0e6080953db56375760c0471a8c5f2929baf11').",
        ),
    ] = None,
    task: Annotated[
        str | None,
        typer.Option(
            help="The task on which to deploy the model (e.g. 'text-classification').",
        ),
    ] = None,
    min_replica: Annotated[
        int | None,
        typer.Option(
            help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
        ),
    ] = None,
    max_replica: Annotated[
        int | None,
        typer.Option(
            help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
        ),
    ] = None,
    scale_to_zero_timeout: Annotated[
        int | None,
        typer.Option(
            help="The duration in minutes before an inactive endpoint is scaled to zero.",
        ),
    ] = None,
    scaling_metric: Annotated[
        InferenceEndpointScalingMetric | None,
        typer.Option(
            help="The metric reference for scaling.",
        ),
    ] = None,
    scaling_threshold: Annotated[
        float | None,
        typer.Option(
            help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
        ),
    ] = None,
    token: TokenOpt = None,
) -> None:
    """Update an existing endpoint."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.update_inference_endpoint(
            name=name,
            namespace=namespace,
            repository=repo,
            framework=framework,
            revision=revision,
            task=task,
            accelerator=accelerator,
            instance_size=instance_size,
            instance_type=instance_type,
            min_replica=min_replica,
            max_replica=max_replica,
            scale_to_zero_timeout=scale_to_zero_timeout,
            scaling_metric=scaling_metric,
            scaling_threshold=scaling_threshold,
            token=token,
        )
    except HfHubHTTPError as error:
        typer.echo(f"Update failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error
    _print_endpoint(endpoint)


@ie_cli.command(examples=["hf endpoints delete my-endpoint"])
def delete(
    name: NameArg,
    namespace: NamespaceOpt = None,
    yes: Annotated[
        bool,
        typer.Option("--yes", help="Skip confirmation prompts."),
    ] = False,
    token: TokenOpt = None,
) -> None:
    """Delete an Inference Endpoint permanently."""
    if not yes:
        confirmation = typer.prompt(f"Delete endpoint '{name}'? Type the name to confirm.")
        if confirmation != name:
            typer.echo("Aborted.")
            raise typer.Exit(code=2)

    api = get_hf_api(token=token)
    try:
        api.delete_inference_endpoint(name=name, namespace=namespace, token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Delete failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    typer.echo(f"Deleted '{name}'.")


@ie_cli.command(examples=["hf endpoints pause my-endpoint"])
def pause(
    name: NameArg,
    namespace: NamespaceOpt = None,
    token: TokenOpt = None,
) -> None:
    """Pause an Inference Endpoint."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.pause_inference_endpoint(name=name, namespace=namespace, token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Pause failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    _print_endpoint(endpoint)


@ie_cli.command(examples=["hf endpoints resume my-endpoint"])
def resume(
    name: NameArg,
    namespace: NamespaceOpt = None,
    fail_if_already_running: Annotated[
        bool,
        typer.Option(
            "--fail-if-already-running",
            help="If `True`, the method will raise an error if the Inference Endpoint is already running.",
        ),
    ] = False,
    token: TokenOpt = None,
) -> None:
    """Resume an Inference Endpoint."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.resume_inference_endpoint(
            name=name,
            namespace=namespace,
            token=token,
            running_ok=not fail_if_already_running,
        )
    except HfHubHTTPError as error:
        typer.echo(f"Resume failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error
    _print_endpoint(endpoint)


@ie_cli.command(examples=["hf endpoints scale-to-zero my-endpoint"])
def scale_to_zero(
    name: NameArg,
    namespace: NamespaceOpt = None,
    token: TokenOpt = None,
) -> None:
    """Scale an Inference Endpoint to zero."""
    api = get_hf_api(token=token)
    try:
        endpoint = api.scale_to_zero_inference_endpoint(name=name, namespace=namespace, token=token)
    except HfHubHTTPError as error:
        typer.echo(f"Scale To Zero failed: {error}")
        raise typer.Exit(code=error.response.status_code) from error

    _print_endpoint(endpoint)
