import inspect
import json
from datetime import datetime
from functools import lru_cache
from typing import Any, Dict, List, Optional, Union

from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict

from .artifact import fetch_artifact
from .card import TaskCard
from .dataset_utils import get_dataset_artifact
from .error_utils import UnitxtError
from .inference import (
    InferenceEngine,
    LogProbInferenceEngine,
    OptionSelectingByLogProbsInferenceEngine,
)
from .loaders import LoadFromDictionary
from .logging_utils import get_logger
from .metric_utils import EvaluationResults, _compute, _inference_post_process
from .operator import SourceOperator
from .schema import UNITXT_DATASET_SCHEMA, loads_instance
from .settings_utils import get_constants, get_settings
from .standard import DatasetRecipe
from .task import Task

logger = get_logger()
constants = get_constants()
settings = get_settings()


def load(source: Union[SourceOperator, str]):
    assert isinstance(
        source, (SourceOperator, str)
    ), "source must be a SourceOperator or a string"
    if isinstance(source, str):
        source, _ = fetch_artifact(source)
    return source().to_dataset()


def _get_recipe_from_query(dataset_query: str) -> DatasetRecipe:
    dataset_query = dataset_query.replace("sys_prompt", "instruction")
    try:
        dataset_stream, _ = fetch_artifact(dataset_query)
    except:
        dataset_stream = get_dataset_artifact(dataset_query)
    return dataset_stream


def _get_recipe_from_dict(dataset_params: Dict[str, Any]) -> DatasetRecipe:
    recipe_attributes = list(DatasetRecipe.__dict__["__fields__"].keys())
    for param in dataset_params.keys():
        assert param in recipe_attributes, (
            f"The parameter '{param}' is not an attribute of the 'DatasetRecipe' class. "
            f"Please check if the name is correct. The available attributes are: '{recipe_attributes}'."
        )
    return DatasetRecipe(**dataset_params)


def _verify_dataset_args(dataset_query: Optional[str] = None, dataset_args=None):
    if dataset_query and dataset_args:
        raise ValueError(
            "Cannot provide 'dataset_query' and key-worded arguments at the same time. "
            "If you want to load dataset from a card in local catalog, use query only. "
            "Otherwise, use key-worded arguments only to specify properties of dataset."
        )

    if dataset_query:
        if not isinstance(dataset_query, str):
            raise ValueError(
                f"If specified, 'dataset_query' must be a string, however, "
                f"'{dataset_query}' was provided instead, which is of type "
                f"'{type(dataset_query)}'."
            )

    if not dataset_query and not dataset_args:
        raise ValueError(
            "Either 'dataset_query' or key-worded arguments must be provided."
        )


def load_recipe(dataset_query: Optional[str] = None, **kwargs) -> DatasetRecipe:
    if isinstance(dataset_query, DatasetRecipe):
        return dataset_query

    _verify_dataset_args(dataset_query, kwargs)

    if dataset_query:
        recipe = _get_recipe_from_query(dataset_query)

    if kwargs:
        recipe = _get_recipe_from_dict(kwargs)

    return recipe


def create_dataset(
    task: Union[str, Task],
    test_set: List[Dict[Any, Any]],
    train_set: Optional[List[Dict[Any, Any]]] = None,
    validation_set: Optional[List[Dict[Any, Any]]] = None,
    split: Optional[str] = None,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Creates dataset from input data based on a specific task.

    Args:
        task:  The name of the task from the Unitxt Catalog (https://www.unitxt.ai/en/latest/catalog/catalog.tasks.__dir__.html)
        test_set : required list of instances
        train_set : optional train_set
        validation_set: optional validation set
        split: optional one split to choose
        **kwargs: Arguments used to load dataset from provided datasets (see load_dataset())

    Returns:
        DatasetDict

    Example:
        template = Template(...)
        dataset = create_dataset(task="tasks.qa.open", template=template, format="formats.chatapi")
    """
    data = {"test": test_set}
    if train_set is not None:
        data["train"] = train_set
    if validation_set is not None:
        data["validation"] = validation_set
    task, _ = fetch_artifact(task)

    if "template" not in kwargs and task.default_template is None:
        raise Exception(
            f"No 'template' was passed to the create_dataset() and the given task ('{task.__id__}') has no 'default_template' field."
        )

    card = TaskCard(loader=LoadFromDictionary(data=data), task=task)
    return load_dataset(card=card, split=split, **kwargs)


def load_dataset(
    dataset_query: Optional[str] = None,
    split: Optional[str] = None,
    streaming: bool = False,
    disable_cache: Optional[bool] = None,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Loads dataset.

    If the 'dataset_query' argument is provided, then dataset is loaded from a card
    in local catalog based on parameters specified in the query.

    Alternatively, dataset is loaded from a provided card based on explicitly
    given parameters.

    Args:
        dataset_query (str, optional):
            A string query which specifies a dataset to load from
            local catalog or name of specific recipe or benchmark in the catalog. For
            example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
        streaming (bool, False):
            When True yields the data as Unitxt streams dictionary
        split (str, optional):
            The split of the data to load
        disable_cache (str, optional):
            Disable caching process of the data
        **kwargs:
            Arguments used to load dataset from provided card, which is not present in local catalog.

    Returns:
        DatasetDict

    :Example:

        .. code-block:: python

            dataset = load_dataset(
                dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
            )  # card and template must be present in local catalog

            # or built programmatically
            card = TaskCard(...)
            template = Template(...)
            loader_limit = 10
            dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)

    """
    recipe = load_recipe(dataset_query, **kwargs)

    stream = recipe()
    if split is not None:
        stream = stream[split]

    if disable_cache is None:
        disable_cache = settings.disable_hf_datasets_cache

    if streaming:
        dataset = stream.to_iterable_dataset(
            features=UNITXT_DATASET_SCHEMA,
        ).map(loads_instance, batched=True)
    else:
        dataset = stream.to_dataset(
            features=UNITXT_DATASET_SCHEMA, disable_cache=disable_cache
        ).with_transform(loads_instance)

    frame = inspect.currentframe()
    args, _, _, values = inspect.getargvalues(frame)
    all_kwargs = {key: values[key] for key in args if key != "kwargs"}
    all_kwargs.update(kwargs)
    metadata = fill_metadata(**all_kwargs)
    if isinstance(dataset, dict):
        for ds in dataset.values():
            ds.info.description = metadata.copy()
    else:
        dataset.info.description = metadata
    return dataset


def fill_metadata(**kwargs):
    metadata = kwargs.copy()
    metadata["unitxt_version"] = get_constants().version
    metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
    return metadata


def evaluate(
    predictions, dataset: Union[Dataset, IterableDataset] = None, data=None
) -> EvaluationResults:
    if dataset is None and data is None:
        raise UnitxtError(message="Specify 'dataset' in evaluate")
    if data is not None:
        dataset = data  # for backward compatibility
    evaluation_result = _compute(predictions=predictions, references=dataset)
    if hasattr(dataset, "info") and hasattr(dataset.info, "description"):
        evaluation_result.metadata["dataset"] = dataset.info.description
    if hasattr(predictions, "metadata"):
        evaluation_result.metadata["predictions"] = predictions.metadata
    evaluation_result.metadata["creation_time"] = datetime.now().strftime(
        "%Y-%m-%d %H:%M:%S.%f"
    )[:-3]
    return evaluation_result


def post_process(predictions, data) -> List[Dict[str, Any]]:
    return _inference_post_process(predictions=predictions, references=data)


@lru_cache
def _get_produce_with_cache(dataset_query: Optional[str] = None, **kwargs):
    return load_recipe(dataset_query, **kwargs).produce


def produce(
    instance_or_instances, dataset_query: Optional[str] = None, **kwargs
) -> Union[Dataset, Dict[str, Any]]:
    is_list = isinstance(instance_or_instances, list)
    if not is_list:
        instance_or_instances = [instance_or_instances]
    result = _get_produce_with_cache(dataset_query, **kwargs)(instance_or_instances)
    if not is_list:
        return result[0]
    return Dataset.from_list(result).with_transform(loads_instance)


def infer(
    instance_or_instances,
    engine: InferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    return_log_probs: bool = False,
    return_meta_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    if return_log_probs:
        if not isinstance(engine, LogProbInferenceEngine):
            raise NotImplementedError(
                f"Error in infer: return_log_probs set to True but supplied engine "
                f"{engine.__class__.__name__} does not support logprobs."
            )
        infer_outputs = engine.infer_log_probs(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
        raw_predictions = [
            json.dumps(raw_prediction) for raw_prediction in raw_predictions
        ]
    else:
        infer_outputs = engine.infer(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
    predictions = post_process(raw_predictions, dataset)
    if return_data:
        if return_meta_data:
            infer_output_list = [
                infer_output.__dict__ for infer_output in infer_outputs
            ]
            for infer_output in infer_output_list:
                del infer_output["prediction"]
            dataset = dataset.add_column("infer_meta_data", infer_output_list)
        dataset = dataset.add_column("prediction", predictions)
        return dataset.add_column("raw_prediction", raw_predictions)
    return predictions


def select(
    instance_or_instances,
    engine: OptionSelectingByLogProbsInferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    predictions = engine.select(dataset)
    # predictions = post_process(raw_predictions, dataset)
    if return_data:
        return dataset.add_column("prediction", predictions)
    return predictions