# coding: utf8
from __future__ import unicode_literals

from .render import DependencyRenderer, EntityRenderer
from ..tokens import Doc, Span
from ..compat import b_to_str
from ..errors import Errors, Warnings, user_warning
from ..util import is_in_jupyter


_html = {}
IS_JUPYTER = is_in_jupyter()


def render(
    docs,
    style="dep",
    page=False,
    minify=False,
    jupyter=IS_JUPYTER,
    options={},
    manual=False,
):
    """Render displaCy visualisation.

    docs (list or Doc): Document(s) to visualise.
    style (unicode): Visualisation style, 'dep' or 'ent'.
    page (bool): Render markup as full HTML page.
    minify (bool): Minify HTML markup.
    jupyter (bool): Experimental, use Jupyter's `display()` to output markup.
    options (dict): Visualiser-specific options, e.g. colors.
    manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
    RETURNS (unicode): Rendered HTML markup.
    """
    factories = {
        "dep": (DependencyRenderer, parse_deps),
        "ent": (EntityRenderer, parse_ents),
    }
    if style not in factories:
        raise ValueError(Errors.E087.format(style=style))
    if isinstance(docs, (Doc, Span, dict)):
        docs = [docs]
    docs = [obj if not isinstance(obj, Span) else obj.as_doc() for obj in docs]
    if not all(isinstance(obj, (Doc, Span, dict)) for obj in docs):
        raise ValueError(Errors.E096)
    renderer, converter = factories[style]
    renderer = renderer(options=options)
    parsed = [converter(doc, options) for doc in docs] if not manual else docs
    _html["parsed"] = renderer.render(parsed, page=page, minify=minify).strip()
    html = _html["parsed"]
    if jupyter:  # return HTML rendered by IPython display()
        from IPython.core.display import display, HTML

        return display(HTML(html))
    return html


def serve(
    docs, style="dep", page=True, minify=False, options={}, manual=False, port=5000
):
    """Serve displaCy visualisation.

    docs (list or Doc): Document(s) to visualise.
    style (unicode): Visualisation style, 'dep' or 'ent'.
    page (bool): Render markup as full HTML page.
    minify (bool): Minify HTML markup.
    options (dict): Visualiser-specific options, e.g. colors.
    manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
    port (int): Port to serve visualisation.
    """
    from wsgiref import simple_server

    render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
    httpd = simple_server.make_server("0.0.0.0", port, app)
    print("\nUsing the '{}' visualizer".format(style))
    print("Serving on port {}...\n".format(port))
    try:
        httpd.serve_forever()
    except KeyboardInterrupt:
        print("Shutting down server on port {}.".format(port))
    finally:
        httpd.server_close()


def app(environ, start_response):
    # Headers and status need to be bytes in Python 2, see #1227
    headers = [(b_to_str(b"Content-type"), b_to_str(b"text/html; charset=utf-8"))]
    start_response(b_to_str(b"200 OK"), headers)
    res = _html["parsed"].encode(encoding="utf-8")
    return [res]


def parse_deps(orig_doc, options={}):
    """Generate dependency parse in {'words': [], 'arcs': []} format.

    doc (Doc): Document do parse.
    RETURNS (dict): Generated dependency parse keyed by words and arcs.
    """
    doc = Doc(orig_doc.vocab).from_bytes(orig_doc.to_bytes())
    if not doc.is_parsed:
        user_warning(Warnings.W005)
    if options.get("collapse_phrases", False):
        for np in list(doc.noun_chunks):
            np.merge(tag=np.root.tag_, lemma=np.root.lemma_, ent_type=np.root.ent_type_)
    if options.get("collapse_punct", True):
        spans = []
        for word in doc[:-1]:
            if word.is_punct or not word.nbor(1).is_punct:
                continue
            start = word.i
            end = word.i + 1
            while end < len(doc) and doc[end].is_punct:
                end += 1
            span = doc[start:end]
            spans.append(
                (span.start_char, span.end_char, word.tag_, word.lemma_, word.ent_type_)
            )
        for start, end, tag, lemma, ent_type in spans:
            doc.merge(start, end, tag=tag, lemma=lemma, ent_type=ent_type)
    if options.get("fine_grained"):
        words = [{"text": w.text, "tag": w.tag_} for w in doc]
    else:
        words = [{"text": w.text, "tag": w.pos_} for w in doc]
    arcs = []
    for word in doc:
        if word.i < word.head.i:
            arcs.append(
                {"start": word.i, "end": word.head.i, "label": word.dep_, "dir": "left"}
            )
        elif word.i > word.head.i:
            arcs.append(
                {
                    "start": word.head.i,
                    "end": word.i,
                    "label": word.dep_,
                    "dir": "right",
                }
            )
    return {"words": words, "arcs": arcs}


def parse_ents(doc, options={}):
    """Generate named entities in [{start: i, end: i, label: 'label'}] format.

    doc (Doc): Document do parse.
    RETURNS (dict): Generated entities keyed by text (original text) and ents.
    """
    ents = [
        {"start": ent.start_char, "end": ent.end_char, "label": ent.label_}
        for ent in doc.ents
    ]
    if not ents:
        user_warning(Warnings.W006)
    title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
    return {"text": doc.text, "ents": ents, "title": title}