import html
import sys
import time
import xml.etree.ElementTree
from typing import Any, Dict, List, Optional

import requests
from grobid_tei_xml import GrobidBiblio, parse_citation_list_xml, parse_document_xml

from .ia import WaybackClient
from .misc import gen_file_metadata, requests_retry_session
from .workers import SandcrawlerFetchWorker, SandcrawlerWorker

MAX_GROBID_BLOB_SIZE: int = 256 * 1024 * 1024  # ~256 MByte


def clean_crossref_unstructured(raw: str) -> str:
    """
    Applies Crossref-specific cleanups to an 'unstructured' citation string.
    """

    # detect repeated strings with double space separating them
    subs = raw.split("  ")
    if len(subs) == 2 and subs[0] == subs[1]:
        raw = subs[0]
    else:
        raw = " ".join(subs)

    # remove HTML/XML numeric characters
    if "&#" in raw or "&" in raw or ">" in raw or "<" in raw:
        raw = html.unescape(raw)

    raw.replace("  ", " ")
    raw = raw.strip()
    return raw


def test_clean_ref_str() -> None:
    # NOTE: this as emdash, non-breaking string characters in it
    raw_with_nbsp = """Qingyao Ai Keping Bi Cheng Luo Jiafeng Guo and W.\u00a0Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. (2018) 385\u2013394.  Qingyao Ai Keping Bi Cheng Luo Jiafeng Guo and W.\u00a0Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. (2018) 385\u2013394."""
    cleaned = """Qingyao Ai Keping Bi Cheng Luo Jiafeng Guo and W.\u00a0Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. (2018) 385\u2013394."""
    assert clean_crossref_unstructured(raw_with_nbsp) == cleaned

    # HTML escape characters
    assert (
        clean_crossref_unstructured(
            "J-B Champion, C.Collin, INSEE Première N°1710 september 2018 - National Institute of Statistics and Economic Studies"
        )
        == "J-B Champion, C.Collin, INSEE Première N°1710 september 2018 - National Institute of Statistics and Economic Studies"
    )

    # simple doubling
    assert (
        clean_crossref_unstructured("https://graph500.org/.  https://graph500.org/.")
        == "https://graph500.org/."
    )
    assert (
        clean_crossref_unstructured(
            """Ronald L. Rivest and Butler W. Lampson. 1996. SDSI: A Simple Distributed Security Infrastructure. In Advances in Cryptology — CRYPTO ’96. Springer Berlin Heidelberg.  Ronald L. Rivest and Butler W. Lampson. 1996. SDSI: A Simple Distributed Security Infrastructure. In Advances in Cryptology — CRYPTO ’96. Springer Berlin Heidelberg."""
        )
        == """Ronald L. Rivest and Butler W. Lampson. 1996. SDSI: A Simple Distributed Security Infrastructure. In Advances in Cryptology — CRYPTO ’96. Springer Berlin Heidelberg."""
    )

    # all non-breaking whitespace
    assert (
        clean_crossref_unstructured(
            "\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0"
        )
        == ""
    )


class GrobidClient(object):
    def __init__(self, host_url: str = "https://grobid.qa.fatcat.wiki", **kwargs):
        self.host_url = host_url
        self.consolidate_mode = int(kwargs.get("consolidate_mode", 0))
        self.session = requests_retry_session()

    def process_fulltext(
        self, blob: bytes, consolidate_mode: Optional[int] = None
    ) -> Dict[str, Any]:
        """
        Returns dict with keys:
            - status_code
            - status (slug)
            - error_msg (if status == 'error')
            - tei_xml (if status is 200)
        """
        assert blob

        if len(blob) > MAX_GROBID_BLOB_SIZE:
            return {
                "status": "blob-too-large",
                "error_msg": f"Not going to process very large file ({len(blob)} bytes)",
            }

        if consolidate_mode is None:
            consolidate_mode = self.consolidate_mode
        assert consolidate_mode is not None

        try:
            grobid_response = self.session.post(
                self.host_url + "/api/processFulltextDocument",
                files={
                    "input": blob,
                },
                data={
                    "consolidateHeader": consolidate_mode,
                    "consolidateCitations": 0,  # too expensive for now
                    "includeRawCitations": 1,
                    "includeRawAffiliations": 1,
                    "teiCoordinates": ["ref", "figure", "persName", "formula", "biblStruct"],
                    "segmentSentences": 1,
                },
                timeout=180.0,
            )
        except requests.Timeout:
            return {
                "status": "error-timeout",
                "status_code": -4,  # heritrix3 "HTTP timeout" code
                "error_msg": "GROBID request (HTTP POST) timeout",
            }
        except requests.exceptions.ConnectionError as ce:
            # intentionally raising this, so workers crash when GROBID
            # unavailable. but do add a sleep to slow things down.
            print(
                "GROBID ConnectionError. sleeping as a slow-down before crashing",
                file=sys.stderr,
            )
            time.sleep(5.0)
            raise ce

        info: Dict[str, Any] = dict(status_code=grobid_response.status_code)
        if grobid_response.status_code == 200:
            info["status"] = "success"
            info["tei_xml"] = grobid_response.text
            if len(info["tei_xml"]) > 12000000:
                # XML is larger than Kafka message size, and much larger than
                # an article in general; bail out
                info["status"] = "error"
                info["error_msg"] = "response XML too large: {} bytes".format(
                    len(info["tei_xml"])
                )
                info.pop("tei_xml")
        else:
            # response.text is .content decoded as utf-8
            info["status"] = "error"
            info["error_msg"] = grobid_response.text[:10000]
        return info

    def process_citation_list(self, unstructured_list: List[str]) -> List[GrobidBiblio]:
        if not unstructured_list:
            return []
        if len(unstructured_list) > 5000:
            raise ValueError("more than 5,000 references in a batch is just too much")

        try:
            grobid_response = self.session.post(
                self.host_url + "/api/processCitationList",
                data={
                    "citations": unstructured_list,
                    "consolidateCitations": 0,
                    "includeRawCitations": 1,
                },
                timeout=30.0,
            )
        except requests.Timeout as te:
            # TODO: handle somehow?
            raise te

        grobid_response.raise_for_status()
        return parse_citation_list_xml(grobid_response.text)

    def metadata(self, result: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        if result["status"] != "success":
            return None
        try:
            tei_doc = parse_document_xml(result["tei_xml"])
        except xml.etree.ElementTree.ParseError as pe:
            result["status"] = "bad-grobid-xml"
            return dict(error_msg=str(pe)[:1000])
        tei_doc.remove_encumbered()
        tei_json = tei_doc.to_legacy_dict()
        meta = dict()
        biblio = dict()
        for k in (
            "title",
            "authors",
            "journal",
            "date",
            "doi",
        ):
            if tei_json.get(k):
                biblio[k] = tei_json[k]
        meta["biblio"] = biblio
        for k in ("grobid_version", "grobid_timestamp", "fatcat_release", "language_code"):
            if tei_json.get(k):
                meta[k] = tei_json[k]
        return meta

    def should_parse_crossref_ref(self, ref: Dict[str, Any]) -> bool:
        """
        Helper function to decide whether to run GROBID parsing on an crossref
        reference.

        For example, if there is already a DOI in the ref metadata, could skip.
        Or, if there is sufficient structured metadata, or only depending on
        the source of the DOI linkage.
        """
        if ref.get("DOI"):
            return False
        if len(ref.get("unstructured", "").strip()) <= 6:
            return False

        if (
            ref.get("year")
            and ref.get("author")
            and (ref.get("article-title") or ref.get("series-title") or ref.get("volume-title"))
        ):
            return False
        elif ref.get("year") and ref.get("author") and ref.get("journal-title"):
            return False
        elif ref.get("journal-title") and ref.get("volume") and ref.get("first-page"):
            return False

        return True

    def crossref_refs(self, record: Dict[str, Any]) -> Dict[str, Any]:
        """
        Given a complete Crossref metadata record, inspects the

        The returned dict is in the schema of the `grobid_refs` database table,
        in dict form:

            source: 'crossref'
            source_id: doi, as lower-case string
            source_ts: Crossref indexed timestamp, if available
            ('updated' is not set)
            refs_json: list of dicts
        """

        # remove API wrapper around record, if necessary
        if "message" in record and "DOI" not in record:
            record = record["message"]

        ret = dict(
            source="crossref",
            source_id=record["DOI"].lower(),
            source_ts=record["indexed"]["date-time"],
            refs_json=[],
        )
        all_refs = record.get("reference", [])
        unstructured_refs = []
        for r in all_refs:
            if not r.get("unstructured"):
                continue
            if not self.should_parse_crossref_ref(r):
                continue
            unstructured_refs.append(r)
        if not unstructured_refs:
            return ret

        # some reasonable cap on length of refs per work
        if len(unstructured_refs) > 2000:
            print(
                f"truncating very large reference list for doi:{record['DOI']} len:{len(unstructured_refs)}",
                file=sys.stderr,
            )
            unstructured_refs = unstructured_refs[:2000]

        clean_refs = [clean_crossref_unstructured(r["unstructured"]) for r in unstructured_refs]
        refs = self.process_citation_list(clean_refs)

        assert len(refs) == len(unstructured_refs)
        refs_json = []
        for i in range(len(refs)):
            refs[i].id = unstructured_refs[i].get("key")
            refs[i].index = None
            refs_json.append(refs[i].to_dict())
        ret["refs_json"] = refs_json
        return ret


class GrobidWorker(SandcrawlerFetchWorker):
    def __init__(
        self,
        grobid_client: GrobidClient,
        wayback_client: Optional[WaybackClient] = None,
        sink: Optional[SandcrawlerWorker] = None,
        **kwargs,
    ):
        super().__init__(wayback_client=wayback_client)
        self.grobid_client = grobid_client
        self.sink = sink
        self.consolidate_mode = 0

    def timeout_response(self, task: Any) -> Any:
        default_key = task["sha1hex"]
        return dict(
            status="error-timeout",
            error_msg="internal GROBID worker timeout",
            source=task,
            key=default_key,
        )

    def process(self, record: Any, key: Optional[str] = None) -> Any:
        fetch_result = self.fetch_blob(record)
        if fetch_result["status"] != "success":
            return fetch_result
        blob: bytes = fetch_result["blob"]
        assert blob and isinstance(blob, bytes)

        result = self.grobid_client.process_fulltext(
            blob, consolidate_mode=self.consolidate_mode
        )
        result["file_meta"] = gen_file_metadata(blob)
        result["source"] = record
        result["key"] = result["file_meta"]["sha1hex"]
        return result


class CrossrefRefsWorker(SandcrawlerWorker):
    def __init__(
        self, grobid_client: GrobidClient, sink: Optional[SandcrawlerWorker] = None, **kwargs
    ):
        super().__init__(**kwargs)
        self.grobid_client = grobid_client
        self.sink = sink

    def process(self, record: Any, key: Optional[str] = None) -> Any:
        # handle the rare case of bad TEI-XML response
        # eg: https://github.com/kermitt2/grobid/issues/848
        try:
            return self.grobid_client.crossref_refs(record)
        except xml.etree.ElementTree.ParseError:
            print(
                f"GROBID returned bad XML for Crossref DOI: {record.get('DOI')}",
                file=sys.stderr,
            )
            # but add a small slow-down so we don't churn through these if
            # GROBID is just misconfigured or something
            time.sleep(3)
            return None
        except requests.exceptions.HTTPError:
            print(f"GROBID HTTP error for Crossref DOI: {record.get('DOI')}", file=sys.stderr)
            time.sleep(3)
            return None
        except requests.exceptions.ReadTimeout:
            print(f"GROBID HTTP timeout for Crossref DOI: {record.get('DOI')}", file=sys.stderr)
            time.sleep(3)
            return None


class GrobidBlobWorker(SandcrawlerWorker):
    """
    This is sort of like GrobidWorker, except it receives blobs directly,
    instead of fetching blobs from some remote store.
    """

    def __init__(
        self, grobid_client: GrobidClient, sink: Optional[SandcrawlerWorker] = None, **kwargs
    ):
        super().__init__()
        self.grobid_client = grobid_client
        self.sink = sink
        self.consolidate_mode = 0

    def process(self, blob: Any, key: Optional[str] = None) -> Any:
        if not blob:
            return None
        result = self.grobid_client.process_fulltext(
            blob, consolidate_mode=self.consolidate_mode
        )
        result["file_meta"] = gen_file_metadata(blob)
        result["key"] = result["file_meta"]["sha1hex"]
        return result