"""A class to automatically identify a comic archive"""
#
# Copyright 2012-2014 ComicTagger Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import io
import logging
from operator import attrgetter
from typing import Any, Callable

from typing_extensions import NotRequired, TypedDict

from comicapi import utils
from comicapi.comicarchive import ComicArchive
from comicapi.genericmetadata import GenericMetadata
from comicapi.issuestring import IssueString
from comictaggerlib.ctsettings import ct_ns
from comictaggerlib.imagefetcher import ImageFetcher, ImageFetcherException
from comictaggerlib.imagehasher import ImageHasher
from comictaggerlib.resulttypes import IssueResult
from comictalker.comictalker import ComicTalker, TalkerError

logger = logging.getLogger(__name__)

try:
    from PIL import Image, ImageChops

    pil_available = True
except ImportError:
    pil_available = False


class SearchKeys(TypedDict):
    series: str | None
    issue_number: str | None
    month: int | None
    year: int | None
    issue_count: int | None


class Score(TypedDict):
    score: NotRequired[int]
    url: str
    hash: int


class IssueIdentifierNetworkError(Exception):
    ...


class IssueIdentifierCancelled(Exception):
    ...


class IssueIdentifier:
    result_no_matches = 0
    result_found_match_but_bad_cover_score = 1
    result_found_match_but_not_first_page = 2
    result_multiple_matches_with_bad_image_scores = 3
    result_one_good_match = 4
    result_multiple_good_matches = 5

    def __init__(self, comic_archive: ComicArchive, config: ct_ns, talker: ComicTalker) -> None:
        self.config = config
        self.talker = talker
        self.comic_archive: ComicArchive = comic_archive
        self.image_hasher = 1

        self.only_use_additional_meta_data = False

        # a decent hamming score, good enough to call it a match
        self.min_score_thresh: int = 16

        # for alternate covers, be more stringent, since we're a bit more
        # scattershot in comparisons
        self.min_alternate_score_thresh = 12

        # the min distance a hamming score must be to separate itself from
        # closest neighbor
        self.min_score_distance = 4

        # a very strong hamming score, almost certainly the same image
        self.strong_score_thresh = 8

        # used to eliminate series names that are too long based on our search
        # string
        self.series_match_thresh = config.Issue_Identifier__series_match_identify_thresh

        # used to eliminate unlikely publishers
        self.publisher_filter = [s.strip().casefold() for s in config.Issue_Identifier__publisher_filter]

        self.additional_metadata = GenericMetadata()
        self.output_function: Callable[[str], None] = print
        self.progress_callback: Callable[[int, int], None] | None = None
        self.cover_url_callback: Callable[[bytes], None] | None = None
        self.search_result = self.result_no_matches
        self.cover_page_index = 0
        self.cancel = False

        self.match_list: list[IssueResult] = []

    def set_score_min_threshold(self, thresh: int) -> None:
        self.min_score_thresh = thresh

    def set_score_min_distance(self, distance: int) -> None:
        self.min_score_distance = distance

    def set_additional_metadata(self, md: GenericMetadata) -> None:
        self.additional_metadata = md

    def set_name_series_match_threshold(self, delta: int) -> None:
        self.series_match_thresh = delta

    def set_publisher_filter(self, flt: list[str]) -> None:
        self.publisher_filter = flt

    def set_hasher_algorithm(self, algo: int) -> None:
        self.image_hasher = algo

    def set_output_function(self, func: Callable[[str], None]) -> None:
        self.output_function = func

    def calculate_hash(self, image_data: bytes) -> int:
        if self.image_hasher == 3:
            return ImageHasher(data=image_data).p_hash()
        if self.image_hasher == 2:
            return -1  # ImageHasher(data=image_data).average_hash2()

        return ImageHasher(data=image_data).average_hash()

    def get_aspect_ratio(self, image_data: bytes) -> float:
        try:
            im = Image.open(io.BytesIO(image_data))
            w, h = im.size
            return float(h) / float(w)
        except Exception:
            return 1.5

    def crop_cover(self, image_data: bytes) -> bytes:
        im = Image.open(io.BytesIO(image_data))
        w, h = im.size

        try:
            cropped_im = im.crop((int(w / 2), 0, w, h))
        except Exception:
            logger.exception("cropCover() error")
            return b""

        output = io.BytesIO()
        cropped_im.convert("RGB").save(output, format="PNG")
        cropped_image_data = output.getvalue()
        output.close()

        return cropped_image_data

    # Adapted from https://stackoverflow.com/a/10616717/20629671
    def crop_border(self, image_data: bytes, ratio: int) -> bytes | None:
        im = Image.open(io.BytesIO(image_data))

        # RGBA doesn't work????
        tmp = im.convert("RGB")

        bg = Image.new("RGB", tmp.size, "black")
        diff = ImageChops.difference(tmp, bg)
        diff = ImageChops.add(diff, diff, 2.0, -100)

        bbox = diff.getbbox()

        width_percent = 0
        height_percent = 0

        # If bbox is None that should mean it's solid black
        if bbox:
            width = bbox[2] - bbox[0]
            height = bbox[3] - bbox[1]

            # Convert to percent
            width_percent = int(100 - ((width / im.width) * 100))
            height_percent = int(100 - ((height / im.height) * 100))
            logger.debug(
                "Width: %s Height: %s, ratio: %s  %s ratio met: %s",
                im.width,
                im.height,
                width_percent,
                height_percent,
                width_percent > ratio or height_percent > ratio,
            )

        # If there is a difference return the image otherwise return None
        if width_percent > ratio or height_percent > ratio:
            output = io.BytesIO()
            im.crop(bbox).save(output, format="PNG")
            cropped_image_data = output.getvalue()
            output.close()
            return cropped_image_data
        return None

    def set_progress_callback(self, cb_func: Callable[[int, int], None]) -> None:
        self.progress_callback = cb_func

    def set_cover_url_callback(self, cb_func: Callable[[bytes], None]) -> None:
        self.cover_url_callback = cb_func

    def get_search_keys(self) -> SearchKeys:
        ca = self.comic_archive

        search_keys: SearchKeys
        if self.only_use_additional_meta_data:
            search_keys = SearchKeys(
                series=self.additional_metadata.series,
                issue_number=self.additional_metadata.issue,
                year=self.additional_metadata.year,
                month=self.additional_metadata.month,
                issue_count=self.additional_metadata.issue_count,
            )
            return search_keys

        # see if the archive has any useful meta data for searching with
        try:
            if ca.has_cix():
                internal_metadata = ca.read_cix()
            else:
                internal_metadata = ca.read_cbi()
        except Exception as e:
            internal_metadata = GenericMetadata()
            logger.error("Failed to load metadata for %s: %s", ca.path, e)

        # try to get some metadata from filename
        md_from_filename = ca.metadata_from_filename(
            self.config.Filename_Parsing__complicated_parser,
            self.config.Filename_Parsing__remove_c2c,
            self.config.Filename_Parsing__remove_fcbd,
            self.config.Filename_Parsing__remove_publisher,
        )

        working_md = md_from_filename.copy()

        working_md.overlay(internal_metadata)
        working_md.overlay(self.additional_metadata)

        # preference order:
        # 1. Additional metadata
        # 1. Internal metadata
        # 1. Filename metadata
        search_keys = SearchKeys(
            series=working_md.series,
            issue_number=working_md.issue,
            year=working_md.year,
            month=working_md.month,
            issue_count=working_md.issue_count,
        )

        return search_keys

    def log_msg(self, msg: Any) -> None:
        msg = str(msg)
        for handler in logging.getLogger().handlers:
            handler.flush()
        self.output(msg)

    def output(self, *args: Any, file: Any = None, **kwargs: Any) -> None:
        # We intercept and discard the file argument otherwise everything is passed to self.output_function

        # Ensure args[0] is defined and is a string for logger.info
        if not args:
            log_args: tuple[Any, ...] = ("",)
        elif isinstance(args[0], str):
            log_args = (args[0].strip("\n"), *args[1:])
        else:
            log_args = args
        log_msg = " ".join([str(x) for x in log_args])

        # Always send to logger so that we have a record for troubleshooting
        logger.info(log_msg, **kwargs)

        # If we are verbose or quiet we don't need to call the output function
        if self.config.Runtime_Options__verbose > 0 or self.config.Runtime_Options__quiet:
            return

        # default output is stdout
        self.output_function(*args, **kwargs)

    def get_issue_cover_match_score(
        self,
        primary_img_url: str,
        alt_urls: list[str],
        local_cover_hash_list: list[int],
        use_remote_alternates: bool = False,
    ) -> Score:
        # local_cover_hash_list is a list of pre-calculated hashes.
        # use_remote_alternates - indicates to use alternate covers from CV

        # If there is no URL return 100
        if not primary_img_url:
            return Score(score=100, url="", hash=0)

        try:
            url_image_data = ImageFetcher(self.config.Runtime_Options__config.user_cache_dir).fetch(
                primary_img_url, blocking=True
            )
        except ImageFetcherException as e:
            self.log_msg(f"Network issue while fetching cover image from {self.talker.name}. Aborting...")
            raise IssueIdentifierNetworkError from e

        if self.cancel:
            raise IssueIdentifierCancelled

        # alert the GUI, if needed
        if self.cover_url_callback is not None:
            self.cover_url_callback(url_image_data)

        remote_cover_list = [Score(url=primary_img_url, hash=self.calculate_hash(url_image_data))]

        if self.cancel:
            raise IssueIdentifierCancelled

        if use_remote_alternates:
            for alt_url in alt_urls:
                try:
                    alt_url_image_data = ImageFetcher(self.config.Runtime_Options__config.user_cache_dir).fetch(
                        alt_url, blocking=True
                    )
                except ImageFetcherException as e:
                    self.log_msg(f"Network issue while fetching alt. cover image from {self.talker.name}. Aborting...")
                    raise IssueIdentifierNetworkError from e

                if self.cancel:
                    raise IssueIdentifierCancelled

                # alert the GUI, if needed
                if self.cover_url_callback is not None:
                    self.cover_url_callback(alt_url_image_data)

                remote_cover_list.append(Score(url=alt_url, hash=self.calculate_hash(alt_url_image_data)))

                if self.cancel:
                    raise IssueIdentifierCancelled

            self.log_msg(f"[{len(remote_cover_list) - 1} alt. covers]")

        score_list = []
        done = False
        for local_cover_hash in local_cover_hash_list:
            for remote_cover_item in remote_cover_list:
                score = ImageHasher.hamming_distance(local_cover_hash, remote_cover_item["hash"])
                score_list.append(Score(score=score, url=remote_cover_item["url"], hash=remote_cover_item["hash"]))

                self.log_msg(f" -  {score:03}")

                if score <= self.strong_score_thresh:
                    # such a good score, we can quit now, since for sure we have a winner
                    done = True
                    break
            if done:
                break

        best_score_item = min(score_list, key=lambda x: x["score"])

        return best_score_item

    def search(self) -> list[IssueResult]:
        ca = self.comic_archive
        self.match_list = []
        self.cancel = False
        self.search_result = self.result_no_matches

        if not pil_available:
            self.log_msg("Python Imaging Library (PIL) is not available and is needed for issue identification.")
            return self.match_list

        if not ca.seems_to_be_a_comic_archive():
            self.log_msg(f"Sorry, but {ca.path} is not a comic archive!")
            return self.match_list

        cover_image_data = ca.get_page(self.cover_page_index)
        cover_hash = self.calculate_hash(cover_image_data)

        # check the aspect ratio
        # if it's wider than it is high, it's probably a two page spread
        # if so, crop it and calculate a second hash
        narrow_cover_hash = None
        aspect_ratio = self.get_aspect_ratio(cover_image_data)
        if aspect_ratio < 1.0:
            right_side_image_data = self.crop_cover(cover_image_data)
            if right_side_image_data is not None:
                narrow_cover_hash = self.calculate_hash(right_side_image_data)

        keys = self.get_search_keys()
        # normalize the issue number, None will return as ""
        keys["issue_number"] = IssueString(keys["issue_number"]).as_string()

        # we need, at minimum, a series and issue number
        if not (keys["series"] and keys["issue_number"]):
            self.log_msg("Not enough info for a search!")
            return []

        self.log_msg(f"Using {self.talker.name} to search for:")
        self.log_msg("\tSeries: " + keys["series"])
        self.log_msg("\tIssue:  " + keys["issue_number"])
        if keys["issue_count"] is not None:
            self.log_msg("\tCount:  " + str(keys["issue_count"]))
        if keys["year"] is not None:
            self.log_msg("\tYear:   " + str(keys["year"]))
        if keys["month"] is not None:
            self.log_msg("\tMonth:  " + str(keys["month"]))

        self.log_msg(f"Searching for {keys['series']} #{keys['issue_number']} ...")
        try:
            ct_search_results = self.talker.search_for_series(keys["series"])
        except TalkerError as e:
            self.log_msg(f"Error searching for series.\n{e}")
            return []

        if self.cancel:
            return []

        if ct_search_results is None:
            return []

        series_second_round_list = []

        for item in ct_search_results:
            length_approved = False
            publisher_approved = True
            date_approved = True

            # remove any series that starts after the issue year
            if keys["year"] is not None and item.start_year is not None:
                if keys["year"] < item.start_year:
                    date_approved = False

            for name in [item.name, *item.aliases]:
                if utils.titles_match(keys["series"], name, self.series_match_thresh):
                    length_approved = True
                    break
            # remove any series from publishers on the filter
            if item.publisher is not None:
                publisher = item.publisher
                if publisher is not None and publisher.casefold() in self.publisher_filter:
                    publisher_approved = False

            if length_approved and publisher_approved and date_approved:
                series_second_round_list.append(item)

        self.log_msg("Searching in " + str(len(series_second_round_list)) + " series")

        if self.progress_callback is not None:
            self.progress_callback(0, len(series_second_round_list))

        # now sort the list by name length
        series_second_round_list.sort(key=lambda x: len(x.name), reverse=False)

        series_by_id = {series.id: series for series in series_second_round_list}

        issue_list = None
        try:
            if len(series_by_id) > 0:
                issue_list = self.talker.fetch_issues_by_series_issue_num_and_year(
                    list(series_by_id.keys()), keys["issue_number"], keys["year"]
                )
        except TalkerError as e:
            self.log_msg(f"Issue with while searching for series details. Aborting...\n{e}")
            return []

        if issue_list is None:
            return []

        shortlist = []
        # now re-associate the issues and series
        # is this really needed?
        for issue in issue_list:
            if issue.series_id in series_by_id:
                shortlist.append((series_by_id[issue.series_id], issue))

        if keys["year"] is None:
            self.log_msg(f"Found {len(shortlist)} series that have an issue #{keys['issue_number']}")
        else:
            self.log_msg(
                f"Found {len(shortlist)} series that have an issue #{keys['issue_number']} from {keys['year']}"
            )

        # now we have a shortlist of series with the desired issue number
        # Do first round of cover matching
        counter = len(shortlist)
        for series, issue in shortlist:
            if self.progress_callback is not None:
                self.progress_callback(counter, len(shortlist) * 3)
                counter += 1

            self.log_msg(
                f"Examining covers for  ID: {series.id} {series.name} ({series.start_year}):",
            )

            # Now check the cover match against the primary image
            hash_list = [cover_hash]
            if narrow_cover_hash is not None:
                hash_list.append(narrow_cover_hash)

            cropped_border = self.crop_border(cover_image_data, self.config.Issue_Identifier__border_crop_percent)
            if cropped_border is not None:
                hash_list.append(self.calculate_hash(cropped_border))
                logger.info("Adding cropped cover to the hashlist")

            try:
                image_url = issue._cover_image or ""
                alt_urls = issue._alternate_images

                score_item = self.get_issue_cover_match_score(
                    image_url, alt_urls, hash_list, use_remote_alternates=False
                )
            except Exception:
                logger.exception("Scoring series failed")
                self.match_list = []
                return self.match_list

            match = IssueResult(
                series=f"{series.name} ({series.start_year})",
                distance=score_item["score"],
                issue_number=keys["issue_number"],
                cv_issue_count=series.count_of_issues,
                url_image_hash=score_item["hash"],
                issue_title=issue.title or "",
                issue_id=issue.issue_id or "",
                series_id=series.id,
                month=issue.month,
                year=issue.year,
                publisher=None,
                image_url=image_url,
                alt_image_urls=alt_urls,
                description=issue.description or "",
            )
            if series.publisher is not None:
                match.publisher = series.publisher

            self.match_list.append(match)

            self.log_msg(f"best score {match.distance:03}")

            self.log_msg("")

        if len(self.match_list) == 0:
            self.log_msg(":-( no matches!")
            self.search_result = self.result_no_matches
            return self.match_list

        # sort list by image match scores
        self.match_list.sort(key=attrgetter("distance"))

        lst = []
        for i in self.match_list:
            lst.append(i.distance)

        self.log_msg(f"Compared to covers in {len(self.match_list)} issue(s): {lst}")

        def print_match(item: IssueResult) -> None:
            self.log_msg(
                "-----> {} #{} {} ({}/{}) -- score: {}".format(
                    item.series,
                    item.issue_number,
                    item.issue_title,
                    item.month,
                    item.year,
                    item.distance,
                )
            )

        best_score: int = self.match_list[0].distance

        if best_score >= self.min_score_thresh:
            # we have 1 or more low-confidence matches (all bad cover scores)
            # look at a few more pages in the archive, and also alternate covers online
            self.log_msg("Very weak scores for the cover. Analyzing alternate pages and covers...")
            hash_list = [cover_hash]
            if narrow_cover_hash is not None:
                hash_list.append(narrow_cover_hash)
            for page_index in range(1, min(3, ca.get_number_of_pages())):
                image_data = ca.get_page(page_index)
                page_hash = self.calculate_hash(image_data)
                hash_list.append(page_hash)

            second_match_list = []
            counter = 2 * len(self.match_list)
            for m in self.match_list:
                if self.progress_callback is not None:
                    self.progress_callback(counter, len(self.match_list) * 3)
                    counter += 1
                self.log_msg(f"Examining alternate covers for ID: {m.series_id} {m.series}:")
                try:
                    score_item = self.get_issue_cover_match_score(
                        m.image_url,
                        m.alt_image_urls,
                        hash_list,
                        use_remote_alternates=True,
                    )
                except Exception:
                    logger.exception("failed examining alt covers")
                    self.match_list = []
                    return self.match_list
                self.log_msg(f"--->{score_item['score']}")
                self.log_msg("")

                if score_item["score"] < self.min_alternate_score_thresh:
                    second_match_list.append(m)
                    m.distance = score_item["score"]

            if len(second_match_list) == 0:
                if len(self.match_list) == 1:
                    self.log_msg("No matching pages in the issue.")
                    self.log_msg("--------------------------------------------------------------------------")
                    print_match(self.match_list[0])
                    self.log_msg("--------------------------------------------------------------------------")
                    self.search_result = self.result_found_match_but_bad_cover_score
                else:
                    self.log_msg("--------------------------------------------------------------------------")
                    self.log_msg("Multiple bad cover matches!  Need to use other info...")
                    self.log_msg("--------------------------------------------------------------------------")
                    self.search_result = self.result_multiple_matches_with_bad_image_scores
                return self.match_list

            # We did good, found something!
            self.log_msg("Success in secondary/alternate cover matching!")

            self.match_list = second_match_list
            # sort new list by image match scores
            self.match_list.sort(key=attrgetter("distance"))
            best_score = self.match_list[0].distance
            self.log_msg("[Second round cover matching: best score = {best_score}]")
            # now drop down into the rest of the processing

        if self.progress_callback is not None:
            self.progress_callback(99, 100)

        # now pare down list, remove any item more than specified distant from the top scores
        for match_item in reversed(self.match_list):
            if match_item.distance > best_score + self.min_score_distance:
                self.match_list.remove(match_item)

        # One more test for the case choosing limited series first issue vs a trade with the same cover:
        # if we have a given issue count > 1 and the series from CV has count==1, remove it from match list
        if len(self.match_list) >= 2 and keys["issue_count"] is not None and keys["issue_count"] != 1:
            new_list = []
            for match in self.match_list:
                if match.cv_issue_count != 1:
                    new_list.append(match)
                else:
                    self.log_msg(
                        f"Removing series {match.series} [{match.series_id}] from consideration (only 1 issue)"
                    )

            if len(new_list) > 0:
                self.match_list = new_list

        if len(self.match_list) == 1:
            self.log_msg("--------------------------------------------------------------------------")
            print_match(self.match_list[0])
            self.log_msg("--------------------------------------------------------------------------")
            self.search_result = self.result_one_good_match

        elif len(self.match_list) == 0:
            self.log_msg("--------------------------------------------------------------------------")
            self.log_msg("No matches found :(")
            self.log_msg("--------------------------------------------------------------------------")
            self.search_result = self.result_no_matches
        else:
            # we've got multiple good matches:
            self.log_msg("More than one likely candidate.")
            self.search_result = self.result_multiple_good_matches
            self.log_msg("--------------------------------------------------------------------------")
            for match_item in self.match_list:
                print_match(match_item)
            self.log_msg("--------------------------------------------------------------------------")

        return self.match_list