import json
import logging
from typing import Iterable, Optional, Sequence, Union

import gradio as gr
import pandas as pd
from pie_datasets import Dataset, IterableDataset, load_dataset
from pie_modules.document.processing import RegexPartitioner, SpansViaRelationMerger
from pytorch_ie import Pipeline
from pytorch_ie.annotations import LabeledSpan
from pytorch_ie.auto import AutoPipeline
from pytorch_ie.documents import (
    TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
    TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
)
from typing_extensions import Protocol

from src.langchain_modules import DocumentAwareSpanRetriever
from src.langchain_modules.span_retriever import (
    DocumentAwareSpanRetrieverWithRelations,
    _parse_config,
)

logger = logging.getLogger(__name__)


def annotate_document(
    document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
    argumentation_model: Pipeline,
    handle_parts_of_same: bool = False,
) -> Union[
    TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
    TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
]:
    """Annotate a document with the provided pipeline.

    Args:
        document: The document to annotate.
        argumentation_model: The pipeline to use for annotation.
        handle_parts_of_same: Whether to merge spans that are part of the same entity into a single multi span.
    """

    # execute prediction pipeline
    argumentation_model(document)

    if handle_parts_of_same:
        merger = SpansViaRelationMerger(
            relation_layer="binary_relations",
            link_relation_label="parts_of_same",
            create_multi_spans=True,
            result_document_type=TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
            result_field_mapping={
                "labeled_spans": "labeled_multi_spans",
                "binary_relations": "binary_relations",
                "labeled_partitions": "labeled_partitions",
            },
        )
        document = merger(document)

    return document


def create_document(
    text: str, doc_id: str, split_regex: Optional[str] = None
) -> TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions:
    """Create a TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions from the provided
    text.

    Parameters:
        text: The text to process.
        doc_id: The ID of the document.
        split_regex: A regular expression pattern to use for splitting the text into partitions.

    Returns:
        The processed document.
    """

    document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions(
        id=doc_id, text=text, metadata={}
    )
    if split_regex is not None:
        partitioner = RegexPartitioner(
            pattern=split_regex, partition_layer_name="labeled_partitions"
        )
        document = partitioner(document)
    else:
        # add single partition from the whole text (the model only considers text in partitions)
        document.labeled_partitions.append(LabeledSpan(start=0, end=len(text), label="text"))
    return document


def add_annotated_pie_documents(
    retriever: DocumentAwareSpanRetriever,
    pie_documents: Sequence[TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions],
    use_predicted_annotations: bool,
    verbose: bool = False,
) -> None:
    if verbose:
        gr.Info(f"Create span embeddings for {len(pie_documents)} documents...")
    num_docs_before = len(retriever.docstore)
    retriever.add_pie_documents(pie_documents, use_predicted_annotations=use_predicted_annotations)
    # number of documents that were overwritten
    num_overwritten_docs = num_docs_before + len(pie_documents) - len(retriever.docstore)
    # warn if documents were overwritten
    if num_overwritten_docs > 0:
        gr.Warning(f"{num_overwritten_docs} documents were overwritten.")


def process_texts(
    texts: Iterable[str],
    doc_ids: Iterable[str],
    argumentation_model: Pipeline,
    retriever: DocumentAwareSpanRetriever,
    split_regex_escaped: Optional[str],
    handle_parts_of_same: bool = False,
    verbose: bool = False,
) -> None:
    # check that doc_ids are unique
    if len(set(doc_ids)) != len(list(doc_ids)):
        raise gr.Error("Document IDs must be unique.")
    pie_documents = [
        create_document(text=text, doc_id=doc_id, split_regex=split_regex_escaped)
        for text, doc_id in zip(texts, doc_ids)
    ]
    if verbose:
        gr.Info(f"Annotate {len(pie_documents)} documents...")
    pie_documents = [
        annotate_document(
            document=pie_document,
            argumentation_model=argumentation_model,
            handle_parts_of_same=handle_parts_of_same,
        )
        for pie_document in pie_documents
    ]
    add_annotated_pie_documents(
        retriever=retriever,
        pie_documents=pie_documents,
        use_predicted_annotations=True,
        verbose=verbose,
    )


def add_annotated_pie_documents_from_dataset(
    retriever: DocumentAwareSpanRetriever, verbose: bool = False, **load_dataset_kwargs
) -> None:
    try:
        gr.Info(
            "Loading PIE dataset with parameters:\n" + json.dumps(load_dataset_kwargs, indent=2)
        )
        dataset = load_dataset(**load_dataset_kwargs)
        if not isinstance(dataset, (Dataset, IterableDataset)):
            raise gr.Error("Loaded dataset is not of type PIE (Iterable)Dataset.")
        dataset_converted = dataset.to_document_type(
            TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions
        )
        add_annotated_pie_documents(
            retriever=retriever,
            pie_documents=dataset_converted,
            use_predicted_annotations=False,
            verbose=verbose,
        )
    except Exception as e:
        raise gr.Error(f"Failed to load dataset: {e}")


def load_argumentation_model(
    model_name: str,
    revision: Optional[str] = None,
    device: str = "cpu",
) -> Pipeline:
    try:
        # the Pipeline class expects an integer for the device
        if device == "cuda":
            pipeline_device = 0
        elif device.startswith("cuda:"):
            pipeline_device = int(device.split(":")[1])
        elif device == "cpu":
            pipeline_device = -1
        else:
            raise gr.Error(f"Invalid device: {device}")

        model = AutoPipeline.from_pretrained(
            model_name,
            device=pipeline_device,
            num_workers=0,
            taskmodule_kwargs=dict(revision=revision),
            model_kwargs=dict(revision=revision),
        )
        gr.Info(
            f"Loaded argumentation model: model_name={model_name}, revision={revision}, device={device}"
        )
    except Exception as e:
        raise gr.Error(f"Failed to load argumentation model: {e}")

    return model


def load_retriever(
    retriever_config: str,
    config_format: str,
    device: str = "cpu",
    previous_retriever: Optional[DocumentAwareSpanRetrieverWithRelations] = None,
) -> DocumentAwareSpanRetrieverWithRelations:
    try:
        retriever_config = _parse_config(retriever_config, format=config_format)
        # set device for the embeddings pipeline
        retriever_config["vectorstore"]["embedding"]["pipeline_kwargs"]["device"] = device
        result = DocumentAwareSpanRetrieverWithRelations.instantiate_from_config(retriever_config)
        # if a previous retriever is provided, load all documents and vectors from the previous retriever
        if previous_retriever is not None:
            # documents
            all_doc_ids = list(previous_retriever.docstore.yield_keys())
            gr.Info(f"Storing {len(all_doc_ids)} documents from previous retriever...")
            all_docs = previous_retriever.docstore.mget(all_doc_ids)
            result.docstore.mset([(doc.id, doc) for doc in all_docs])
            # spans (with vectors)
            all_span_ids = list(previous_retriever.vectorstore.yield_keys())
            all_spans = previous_retriever.vectorstore.mget(all_span_ids)
            result.vectorstore.mset([(span.id, span) for span in all_spans])

        gr.Info("Retriever loaded successfully.")
        return result
    except Exception as e:
        raise gr.Error(f"Failed to load retriever: {e}")


def retrieve_similar_spans(
    retriever: DocumentAwareSpanRetriever,
    query_span_id: str,
    **kwargs,
) -> pd.DataFrame:
    if not query_span_id.strip():
        raise gr.Error("No query span selected.")
    try:
        retrieval_result = retriever.invoke(input=query_span_id, **kwargs)
        records = []
        for similar_span_doc in retrieval_result:
            pie_doc, metadata = retriever.docstore.unwrap_with_metadata(similar_span_doc)
            span_ann = metadata["attached_span"]
            records.append(
                {
                    "doc_id": pie_doc.id,
                    "span_id": similar_span_doc.id,
                    "score": metadata["relevance_score"],
                    "label": span_ann.label,
                    "text": str(span_ann),
                }
            )
        return (
            pd.DataFrame(records, columns=["doc_id", "score", "label", "text", "span_id"])
            .sort_values(by="score", ascending=False)
            .round(3)
        )
    except Exception as e:
        raise gr.Error(f"Failed to retrieve similar ADUs: {e}")


def retrieve_relevant_spans(
    retriever: DocumentAwareSpanRetriever,
    query_span_id: str,
    relation_label_mapping: Optional[dict[str, str]] = None,
    **kwargs,
) -> pd.DataFrame:
    if not query_span_id.strip():
        raise gr.Error("No query span selected.")
    try:
        relation_label_mapping = relation_label_mapping or {}
        retrieval_result = retriever.invoke(input=query_span_id, return_related=True, **kwargs)
        records = []
        for relevant_span_doc in retrieval_result:
            pie_doc, metadata = retriever.docstore.unwrap_with_metadata(relevant_span_doc)
            span_ann = metadata["attached_span"]
            tail_span_ann = metadata["attached_tail_span"]
            mapped_relation_label = relation_label_mapping.get(
                metadata["relation_label"], metadata["relation_label"]
            )
            records.append(
                {
                    "doc_id": pie_doc.id,
                    "type": mapped_relation_label,
                    "rel_score": metadata["relation_score"],
                    "text": str(tail_span_ann),
                    "span_id": relevant_span_doc.id,
                    "label": tail_span_ann.label,
                    "ref_score": metadata["relevance_score"],
                    "ref_label": span_ann.label,
                    "ref_text": str(span_ann),
                    "ref_span_id": metadata["head_id"],
                }
            )
        return (
            pd.DataFrame(
                records,
                columns=[
                    "type",
                    # omitted for now, we get no valid relation scores for the generative model
                    # "rel_score",
                    "ref_score",
                    "label",
                    "text",
                    "ref_label",
                    "ref_text",
                    "doc_id",
                    "span_id",
                    "ref_span_id",
                ],
            )
            .sort_values(by=["ref_score"], ascending=False)
            .round(3)
        )
    except Exception as e:
        raise gr.Error(f"Failed to retrieve relevant ADUs: {e}")


class RetrieverCallable(Protocol):
    def __call__(
        self,
        retriever: DocumentAwareSpanRetriever,
        query_span_id: str,
        **kwargs,
    ) -> Optional[pd.DataFrame]:
        pass


def _retrieve_for_all_spans(
    retriever: DocumentAwareSpanRetriever,
    query_doc_id: str,
    retrieve_func: RetrieverCallable,
    query_span_id_column: str = "query_span_id",
    **kwargs,
) -> Optional[pd.DataFrame]:
    if not query_doc_id.strip():
        raise gr.Error("No query document selected.")
    try:
        span_id2idx = retriever.get_span_id2idx_from_doc(query_doc_id)
        gr.Info(f"Retrieving results for {len(span_id2idx)} ADUs in document {query_doc_id}...")
        span_results = {
            query_span_id: retrieve_func(
                retriever=retriever,
                query_span_id=query_span_id,
                **kwargs,
            )
            for query_span_id in span_id2idx.keys()
        }
        span_results_not_empty = {
            query_span_id: df
            for query_span_id, df in span_results.items()
            if df is not None and not df.empty
        }

        # add column with query_span_id
        for query_span_id, query_span_result in span_results_not_empty.items():
            query_span_result[query_span_id_column] = query_span_id

        if len(span_results_not_empty) == 0:
            gr.Info(f"No results found for any ADU in document {query_doc_id}.")
            return None
        else:
            result = pd.concat(span_results_not_empty.values(), ignore_index=True)
            gr.Info(f"Retrieved {len(result)} ADUs for document {query_doc_id}.")
            return result
    except Exception as e:
        raise gr.Error(
            f'Failed to retrieve results for all ADUs in document "{query_doc_id}": {e}'
        )


def retrieve_all_similar_spans(
    retriever: DocumentAwareSpanRetriever,
    query_doc_id: str,
    **kwargs,
) -> Optional[pd.DataFrame]:
    return _retrieve_for_all_spans(
        retriever=retriever,
        query_doc_id=query_doc_id,
        retrieve_func=retrieve_similar_spans,
        **kwargs,
    )


def retrieve_all_relevant_spans(
    retriever: DocumentAwareSpanRetriever,
    query_doc_id: str,
    **kwargs,
) -> Optional[pd.DataFrame]:
    return _retrieve_for_all_spans(
        retriever=retriever,
        query_doc_id=query_doc_id,
        retrieve_func=retrieve_relevant_spans,
        **kwargs,
    )


class RetrieverForAllSpansCallable(Protocol):
    def __call__(
        self,
        retriever: DocumentAwareSpanRetriever,
        query_doc_id: str,
        **kwargs,
    ) -> Optional[pd.DataFrame]:
        pass


def _retrieve_for_all_documents(
    retriever: DocumentAwareSpanRetriever,
    retrieve_func: RetrieverForAllSpansCallable,
    query_doc_id_column: str = "query_doc_id",
    **kwargs,
) -> Optional[pd.DataFrame]:
    try:
        all_doc_ids = list(retriever.docstore.yield_keys())
        gr.Info(f"Retrieving results for {len(all_doc_ids)} documents...")
        doc_results = {
            doc_id: retrieve_func(retriever=retriever, query_doc_id=doc_id, **kwargs)
            for doc_id in all_doc_ids
        }
        doc_results_not_empty = {
            doc_id: df for doc_id, df in doc_results.items() if df is not None and not df.empty
        }
        # add column with query_doc_id
        for doc_id, doc_result in doc_results_not_empty.items():
            doc_result[query_doc_id_column] = doc_id

        if len(doc_results_not_empty) == 0:
            gr.Info("No results found for any document.")
            return None
        else:
            result = pd.concat(doc_results_not_empty, ignore_index=True)
            gr.Info(f"Retrieved {len(result)} ADUs for all documents.")
            return result
    except Exception as e:
        raise gr.Error(f"Failed to retrieve results for all documents: {e}")


def retrieve_all_similar_spans_for_all_documents(
    retriever: DocumentAwareSpanRetriever,
    **kwargs,
) -> Optional[pd.DataFrame]:
    return _retrieve_for_all_documents(
        retriever=retriever,
        retrieve_func=retrieve_all_similar_spans,
        **kwargs,
    )


def retrieve_all_relevant_spans_for_all_documents(
    retriever: DocumentAwareSpanRetriever,
    **kwargs,
) -> Optional[pd.DataFrame]:
    return _retrieve_for_all_documents(
        retriever=retriever,
        retrieve_func=retrieve_all_relevant_spans,
        **kwargs,
    )