"""
This file contains all the indexers.
"""

from jnius import autoclass, PythonJavaClass, java_method, cast
from enum import Enum
import pandas as pd
from . import Indexer
import os
import enum
import json
import tempfile
import contextlib
import threading
import select
import math
from warnings import warn
from deprecated import deprecated
from collections import deque
from typing import List, Dict, Union, Any

StringReader = None
HashMap = None
TaggedDocument = None
FlatJSONDocument = None
Tokeniser = None
TRECCollection = None
SimpleFileCollection = None
BasicIndexer = None
BlockIndexer = None
Collection = None
BasicSinglePassIndexer = None
BlockSinglePassIndexer = None
BasicMemoryIndexer = None
Arrays = None
Array = None
ApplicationSetup = None
Properties = None
CLITool = None
IndexRef = None
IndexFactory = None
StructureMerger = None
BlockStructureMerger = None

# for backward compatibility
class IterDictIndexerBase(Indexer):
    @deprecated(version="0.9", reason="Use pt.Indexer instead of IterDictIndexerBase")
    def __init__(self, *args, **kwargs):
        super(Indexer, self).__init__(*args, **kwargs)

# lastdoc ensures that a Document instance from a Collection is not GCd before Java has used it.
lastdoc=None

def run_autoclass():
    from . import check_version
    global StringReader
    global HashMap
    global TaggedDocument
    global FlatJSONDocument
    global Tokeniser
    global TRECCollection
    global SimpleFileCollection
    global BasicIndexer
    global BlockIndexer
    global BasicSinglePassIndexer
    global BlockSinglePassIndexer
    global BasicMemoryIndexer
    global Collection
    global Arrays
    global Array
    global ApplicationSetup
    global Properties
    global CLITool
    global IndexRef
    global IndexFactory
    global StructureMerger
    global BlockStructureMerger

    StringReader = autoclass("java.io.StringReader")
    HashMap = autoclass("java.util.HashMap")
    TaggedDocument = autoclass("org.terrier.indexing.TaggedDocument")
    FlatJSONDocument = autoclass("org.terrier.indexing.FlatJSONDocument")
    Tokeniser = autoclass("org.terrier.indexing.tokenisation.Tokeniser")
    TRECCollection = autoclass("org.terrier.indexing.TRECCollection")
    SimpleFileCollection = autoclass("org.terrier.indexing.SimpleFileCollection")
    BasicIndexer = autoclass("org.terrier.structures.indexing.classical.BasicIndexer")
    BlockIndexer = autoclass("org.terrier.structures.indexing.classical.BlockIndexer")
    BasicSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer")
    BlockSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer")
    BasicMemoryIndexer = autoclass("org.terrier.realtime.memory.MemoryIndexer" if check_version("5.7") else "org.terrier.python.MemoryIndexer")
    Collection = autoclass("org.terrier.indexing.Collection")
    Arrays = autoclass("java.util.Arrays")
    Array = autoclass('java.lang.reflect.Array')
    HashMap = autoclass('java.util.HashMap')
    ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
    Properties = autoclass('java.util.Properties')
    CLITool = autoclass("org.terrier.applications.CLITool")
    IndexRef = autoclass('org.terrier.querying.IndexRef')
    IndexFactory = autoclass('org.terrier.structures.IndexFactory')
    StructureMerger = autoclass("org.terrier.structures.merging.StructureMerger")
    BlockStructureMerger = autoclass("org.terrier.structures.merging.BlockStructureMerger")

type_to_class = {
    'trec' : 'org.terrier.indexing.TRECCollection',
    'trecweb' : 'org.terrier.indexing.TRECWebCollection',
    'warc' : 'org.terrier.indexing.WARC10Collection'
}

def createCollection(files_path : List[str], coll_type : str = 'trec', props = {}):
    if StringReader is None:
            run_autoclass()
    if coll_type in type_to_class:
        collectionClzName = type_to_class[coll_type]
    else:
        collectionClzName = coll_type
    collectionClzName = collectionClzName.split(",")
    _props = HashMap()
    for k, v in props.items():
        _props[k] = v
    ApplicationSetup.getProperties().putAll(_props)
    cls_string = autoclass("java.lang.String")._class
    cls_list = autoclass("java.util.List")._class
    if len(files_path) == 0:
        raise ValueError("list files_path cannot be empty")
    asList = createAsList(files_path)
    colObj = autoclass("org.terrier.indexing.CollectionFactory").loadCollections(
        collectionClzName,
        [cls_list, cls_string, cls_string, cls_string],
        [asList, autoclass("org.terrier.utility.TagSet").TREC_DOC_TAGS, "", ""])
    return colObj

def treccollection2textgen(
        files : List[str], 
        meta : List[str] = ["docno"], 
        meta_tags : Dict[str,str] = {"text":"ELSE"}, 
        verbose = False,
        num_docs = None,
        tag_text_length : int = 4096):
    """
    Creates a generator of dictionaries on parsing TREC formatted files. This is useful 
    for parsing TREC-formatted corpora in indexers like IterDictIndexer, or similar 
    indexers in other plugins (e.g. ColBERTIndexer).

    Arguments:
     - files(List[str]): list of files to parse in TREC format.
     - meta(List[str]): list of attributes to expose in the dictionaries as metadata.
     - meta_tags(Dict[str,str]): mapping of TREC tags as metadata.
     - tag_text_length(int): maximium length of metadata. Defaults to 4096.
     - verbose(bool): set to true to show a TQDM progress bar. Defaults to True.
     - num_docs(int): a hint for TQDM to size the progress bar based on document counts rather than file count.

    Example::

        files = pt.io.find_files("/path/to/Disk45")
        gen = pt.index.treccollection2textgen(files)
        index = pt.IterDictIndexer("./index45").index(gen)

    """

    props = {
        "TrecDocTags.doctag": "DOC",
        "TrecDocTags.idtag": "DOCNO",
        "TrecDocTags.skip": "DOCHDR",
        "TrecDocTags.casesensitive": "false",
        # Should the tags from which we create abstracts be case-sensitive?
        'TaggedDocument.abstracts.tags.casesensitive':'false'
    }
    props['TaggedDocument.abstracts'] = ','.join(meta_tags.keys())
    # The tags from which to save the text. ELSE is special tag name, which means anything not consumed by other tags.
    props['TaggedDocument.abstracts.tags'] = ','.join(meta_tags.values())
    # The max lengths of the abstracts. Abstracts will be cropped to this length. Defaults to empty.
    props['TaggedDocument.abstracts.lengths'] = ','.join([str(tag_text_length)] * len(meta_tags) )  

    collection = createCollection(files, props=props)
    if verbose:
        if num_docs is not None:
            collection = TQDMSizeCollection(collection, num_docs)
        else:
            collection = TQDMCollection(collection)
    while collection.nextDocument():
        d = collection.getDocument()
        while not d.endOfDocument():
            d.getNextTerm()
        rtr = {}
        for k in meta:
            rtr[k] = d.getProperty(k)
        for k in meta_tags:
            rtr[k] = d.getProperty(k)
        yield rtr
    

def _TaggedDocumentSetup(
        meta : Dict[str,int],  #mapping from meta-key to length
        meta_tags : Dict[str,str] #mapping from meta-key to tag
    ):
    """
    Property setup for TaggedDocument etc to generate abstracts
    """

    abstract_tags=meta_tags.values()
    abstract_names=meta_tags.keys()
    abstract_lengths=[str(meta[name]) for name in abstract_names]

    ApplicationSetup.setProperty("TaggedDocument.abstracts", ",".join(abstract_names))
    # The tags from which to save the text. ELSE is special tag name, which means anything not consumed by other tags.
    ApplicationSetup.setProperty("TaggedDocument.abstracts.tags", ",".join(abstract_tags))
    # The max lengths of the abstracts. Abstracts will be cropped to this length. Defaults to empty.
    ApplicationSetup.setProperty("TaggedDocument.abstracts.lengths", ",".join(abstract_lengths))
    # Should the tags from which we create abstracts be case-sensitive
    ApplicationSetup.setProperty("TaggedDocument.abstracts.tags.casesensitive", "false")


def _FileDocumentSetup(   
        meta : Dict[str,int],  #mapping from meta-key to length
        meta_tags : Dict[str,str] #mapping from meta-key to tag
    ):
    """
    Property setup for FileDocument etc to generate abstracts
    """

    meta_name_for_abstract = None
    for k, v in meta_tags.items():
        if v == 'ELSE':
            meta_name_for_abstract = k
    if meta_name_for_abstract is None:
        return
    
    if meta_name_for_abstract not in meta:
        raise ValueError("You need to specify a meta length for " + meta_name_for_abstract)

    abstract_length = meta[meta_name_for_abstract]

    ApplicationSetup.setProperty("FileDocument.abstract", meta_name_for_abstract)
    ApplicationSetup.setProperty("FileDocument.abstract.length", str(abstract_length))




def createAsList(files_path : Union[str, List[str]]):
    """
    Helper method to be used by child indexers to add files to Java List
    Returns:
        Created Java List
    """
    if isinstance(files_path, str):
        asList = Arrays.asList(files_path)
    elif isinstance(files_path, list):
        asList = Arrays.asList(*files_path)
    else:
        raise ValueError(f"{files_path}: {type(files_path)} must be a List[str] or str")
    return asList

# Using enum class create enumerations
class IndexingType(enum.Enum):
    """
        This enum is used to determine the type of index built by Terrier. The default is CLASSIC. For more information,
        see the relevant Terrier `indexer <https://terrier-core.readthedocs.io/en/latest/indexer_details.html>`_
        and `realtime <https://terrier-core.readthedocs.io/en/latest/realtime_indices.html>`_ documentation.
    """
    CLASSIC = 1 #: A classical indexing regime, which also creates a direct index structure, useful for query expansion
    SINGLEPASS = 2 #: A single-pass indexing regime, which builds an inverted index directly. No direct index structure is created. Typically is faster than classical indexing.
    MEMORY = 3 #: An in-memory index. No persistent index is created.

class TerrierStemmer(Enum):
    """
        This enum provides an API for the stemmers available in Terrier. The stemming configuration is saved in the index
        and loaded at retrieval time. `Snowball <https://snowballstem.org/>`_ stemmers for various languages 
        `are available in Terrier <http://terrier.org/docs/current/javadoc/org/terrier/terms/package-summary.html>`_.
    """
    none = 'none' #: Apply no stemming
    porter = 'porter' #: Apply Porter's English stemmer
    weakporter = 'weakporter' #: Apply a weak version of Porter's English stemmer
    # available snowball stemmers in Terrier
    danish = 'danish' #: Snowball Danish stemmer
    finnish = 'finnish' #: Snowball Finnish stemmer
    german = 'german' #: Snowball German stemmer
    hungarian = 'hungarian' #: Snowball Hungarian stemmer
    norwegian = 'norwegian' #: Snowball Norwegian stemmer
    portugese = 'portugese' #: Snowball Portuguese stemmer
    swedish = 'swedish' #: Snowball Swedish stemmer
    turkish = 'turkish' #: Snowball Turkish stemmer

    @staticmethod
    def _to_obj(this):
        try:
            return TerrierStemmer(this)
        except ValueError:
            return this

    @staticmethod
    def _to_class(this):
        if this is None or this == TerrierStemmer.none:
            return None
        if this == TerrierStemmer.porter:
            return 'PorterStemmer'
        if this == TerrierStemmer.weakporter:
            return 'WeakPorterStemmer'
        
        # snowball stemmers
        if this == TerrierStemmer.danish:
            return 'DanishSnowballStemmer'
        if this == TerrierStemmer.finnish:
            return 'FinnishSnowballStemmer'
        if this == TerrierStemmer.german:
            return 'GermanSnowballStemmer'
        if this == TerrierStemmer.hungarian:
            return 'HungarianSnowballStemmer'
        if this == TerrierStemmer.norwegian:
            return 'NorwegianSnowballStemmer'
        if this == TerrierStemmer.portugese:
            return 'PortugueseSnowballStemmer'
        if this == TerrierStemmer.swedish:
            return 'SwedishSnowballStemmer'
        if this == TerrierStemmer.turkish:
            return 'TurkishSnowballStemmer'

        if isinstance(this, str):
            return this
        

class TerrierStopwords(Enum):
    """
        This enum provides an API for the stopword configuration used during indexing with Terrier
    """

    none = 'none' #: No Stemming
    terrier = 'terrier' #: Apply Terrier's standard stopword list

    @staticmethod
    def _to_obj(this):
        try:
            return TerrierStopwords(this)
        except ValueError:
            return this


class TerrierTokeniser(Enum):
    """
        This enum provides an API for the tokeniser configuration used during indexing with Terrier.
    """

    whitespace = 'whitespace' #: Tokenise on whitespace only
    english = 'english' #: Terrier's standard tokeniser, designed for English
    utf = 'utf' #: A variant of Terrier's standard tokeniser, similar to English, but with UTF support.
    twitter = 'twitter' #: Like utf, but keeps hashtags etc
    identity = 'identity' #: Performs no tokenisation - strings are kept as is. 

    @staticmethod
    def _to_obj(this):
        try:
            return TerrierTokeniser(this)
        except ValueError:
            return this

    @staticmethod
    def _to_class(this):
        if this == TerrierTokeniser.whitespace:
            return 'WhitespaceTokeniser'
        if this == TerrierTokeniser.english:
            return 'EnglishTokeniser'
        if this == TerrierTokeniser.utf:
            return 'UTFTokeniser'
        if this == TerrierTokeniser.twitter:
            return 'UTFTwitterTokeniser'
        if this == TerrierTokeniser.identity:
            return 'IdentityTokeniser'
        if isinstance(this, str):
            return this

class TerrierIndexer:
    """
    This is the super class for all of the Terrier-based indexers exposed by PyTerrier. It hosts common configuration
    for all index types.
    
    """

    default_properties = {
            "TrecDocTags.doctag": "DOC",
            "TrecDocTags.idtag": "DOCNO",
            "TrecDocTags.skip": "DOCHDR",
            "TrecDocTags.casesensitive": "false",
            "trec.collection.class": "TRECCollection",
    }

    def __init__(self, index_path : str, *args, 
            blocks : bool = False, 
            overwrite: bool = False, 
            verbose : bool = False, 
            meta_reverse : List[str] = ["docno"],
            stemmer : Union[None, str, TerrierStemmer] = TerrierStemmer.porter,
            stopwords : Union[None, TerrierStopwords] = TerrierStopwords.terrier,
            tokeniser : Union[str,TerrierTokeniser] = TerrierTokeniser.english,
            type=IndexingType.CLASSIC, 
            **kwargs):
        """
        Constructor called by all indexer subclasses. All arguments listed below are available in 
        IterDictIndexer, DFIndexer, TRECCollectionIndexer and FilesIndsexer. 

        Args:
            index_path (str): Directory to store index. Ignored for IndexingType.MEMORY.
            blocks (bool): Create indexer with blocks if true, else without blocks. Default is False.
            overwrite (bool): If index already present at `index_path`, True would overwrite it, False throws an Exception. Default is False.
            verbose (bool): Provide progess bars if possible. Default is False.
            stemmer (TerrierStemmer): the stemmer to apply. Default is ``TerrierStemmer.porter``.
            stopwords (TerrierStopwords): the stopwords list to apply. Default is ``TerrierStemmer.terrier``.
            tokeniser (TerrierTokeniser): the stemmer to apply. Default is ``TerrierTokeniser.english``.
            type (IndexingType): the specific indexing procedure to use. Default is ``IndexingType.CLASSIC``.
        """
        if StringReader is None:
            run_autoclass()
        if type is IndexingType.MEMORY:
            self.path = None
        else:
            self.path = os.path.join(index_path, "data.properties")
            if not os.path.isdir(index_path):
                os.makedirs(index_path)
        self.index_called = False
        self.index_dir = index_path
        self.blocks = blocks
        self.type = type
        self.stemmer = TerrierStemmer._to_obj(stemmer)
        self.stopwords = TerrierStopwords._to_obj(stopwords)
        self.tokeniser = TerrierTokeniser._to_obj(tokeniser)
        self.properties = Properties()
        self.setProperties(**self.default_properties)
        self.overwrite = overwrite
        self.verbose = verbose
        self.meta_reverse = meta_reverse

    def setProperty(self, k, v):
        """
        Set the named property to the specified value.

        Args:
            k(str): name of the Terrier property
            v(str): value of the Terrier property

        Usage::
            indexer.setProperty("termpipelines", "")
        """
        self.properties.put(k, v)

    def setProperties(self, **kwargs):
        """
        Set the properties to the given ones.

        Args:
            **kwargs: Properties to set to.

        Usage:
            >>> setProperties(**{property1:value1, property2:value2})
        """
        for control, value in kwargs.items():
            self.properties.put(control, value)

    def checkIndexExists(self):
        """
        Check if index exists at the `path` given when object was created
        """
        if self.path is None:
            return
        if os.path.isfile(self.path):
            if not self.overwrite:
                raise ValueError("Index already exists at " + self.path)
        if self.index_called:
            raise Exception("Index method can be called only once")

    def createIndexer(self):
        """
        Checks `self.type` and
        - if false, check `blocks` and create a BlockIndexer if true, else create BasicIndexer
        - if true, check `blocks` and create a BlockSinglePassIndexer if true, else create BasicSinglePassIndexer
        Returns:
            Created  Java object extending org.terrier.structures.indexing.Indexer
        """
        
        Indexer, _ = self.indexerAndMergerClasses()
        if Indexer is BasicMemoryIndexer:
            index = Indexer()
        else:
            index = Indexer(self.index_dir, "data")
        assert index is not None
        return index

    def indexerAndMergerClasses(self):
        """
        Check `single_pass` and
        - if false, check `blocks` and create a BlockIndexer if true, else create BasicIndexer
        - if true, check `blocks` and create a BlockSinglePassIndexer if true, else create BasicSinglePassIndexer
        Returns:
            type objects for indexer and merger for the given configuration
        """

        # configure the meta index
        self.properties['indexer.meta.forward.keys'] = ','.join(self.meta.keys())
        self.properties['indexer.meta.forward.keylens'] = ','.join([str(l) for l in self.meta.values()])
        self.properties['indexer.meta.reverse.keys'] = ','.join(self.meta_reverse)

        # configure the term pipeline
        if 'termpipelines' in self.properties:
            # use existing configuration if present
            warn("Setting of termpipelines property directly is deprecated", stacklevel=4, category=DeprecationWarning)
        else:
            
            termpipeline = []
            if self.stopwords is not None and self.stopwords != TerrierStopwords.none:
                termpipeline.append('Stopwords')

            stemmer_clz = TerrierStemmer._to_class(self.stemmer)
            if stemmer_clz is not None:
                termpipeline.append(stemmer_clz)
            
            self.properties['termpipelines'] = ','.join(termpipeline)

        if "tokeniser" in self.properties:
            warn("Setting of tokeniser property directly is deprecated", stacklevel=4, category=DeprecationWarning)
        else:
            self.properties['tokeniser'] = TerrierTokeniser._to_class(self.tokeniser)

        # inform terrier of all properties
        ApplicationSetup.getProperties().putAll(self.properties)

        # now create the indexers 
        if self.type is IndexingType.SINGLEPASS:
            if self.blocks:
                Indexer = BlockSinglePassIndexer
                Merger = BlockStructureMerger
            else:
                Indexer = BasicSinglePassIndexer
                Merger = StructureMerger
        elif self.type is IndexingType.CLASSIC:
            if self.blocks:
                Indexer = BlockIndexer
                Merger = BlockStructureMerger
            else:
                Indexer = BasicIndexer
                Merger = StructureMerger
        elif self.type is IndexingType.MEMORY:
            if self.blocks:
                raise Exception("Memory indexing with positions not yet implemented")
            else:
                Indexer = BasicMemoryIndexer
                Merger = None
        else:
            raise Exception("Unknown indexer type")
        assert Indexer is not None
        return Indexer, Merger

    def getIndexStats(self):
        """
        Prints the index statistics

        Note:
            Does not work with notebooks at the moment
        """
        CLITool.main(["indexstats", "-I" + self.path])

    def getIndexUtil(self, util):
        """
        Utilities for displaying the content of an index

        Note:
            Does not work with notebooks at the moment

        Args:
            util: which util to print

        Possible Utils:
            util(string): possible values:
            printbitentry
            printlex
            printlist
            printlistentry
            printmeta
            printposting
            printpostingfile
            printterm
        """
        if not util.startswith("-"):
            util = "-" + util
        CLITool.main(["indexutil", "-I" + self.path, util])


class DFIndexUtils:

    @staticmethod
    def get_column_lengths(df):
        return dict([(v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max())for v in df.columns.values])

    @staticmethod
    def create_javaDocIterator(text, *args, **kwargs):
        if HashMap is None:
            run_autoclass()

        all_metadata = {}
        for i, arg in enumerate(args):
            if isinstance(arg, pd.Series):
                all_metadata[arg.name] = arg
                assert len(arg) == len(text), "Length of metadata arguments needs to be equal to length of text argument"
            elif isinstance(arg, pd.DataFrame):
                for name, column in arg.items():
                    all_metadata[name] = column
                    assert len(column) == len(text), "Length of metadata arguments needs to be equal to length of text argument"
            else:
                raise ValueError(f"Non-keyword args need to be of type pandas.Series or pandas.DataFrame, argument {i} was {type(arg)}")
        for key, value in kwargs.items():
            if isinstance(value, (pd.Series, list, tuple)):
                all_metadata[key] = value
                assert len(value) == len(value), "Length of metadata arguments needs to be equal to length of text argument"
            elif isinstance(value, pd.DataFrame):
                for name, column in arg.items():
                    all_metadata[name] = column
                    assert len(column) == len(text), "Length of metadata arguments needs to be equal to length of text argument"
            else:
                raise ValueError("Keyword kwargs need to be of type pandas.Series, list or tuple")

        # this method creates the documents as and when needed.
        def convertDoc(text_row, meta_column):
            if text_row is None:
                text_row = ""
            hashmap = HashMap()
            for column, value in meta_column[1].iteritems():
                if value is None:
                    value = ""
                hashmap.put(column, value)
            return TaggedDocument(StringReader(text_row), hashmap, Tokeniser.getTokeniser())
            
        df = pd.DataFrame.from_dict(all_metadata, orient="columns")
        lengths = DFIndexUtils.get_column_lengths(df)
        return (
            PythonListIterator(
                text.values,
                df.iterrows(),
                convertDoc,
                len(text.values),
                ),
            lengths)

class DFIndexer(TerrierIndexer):
    """
    Use this Indexer if you wish to index a pandas.Dataframe

    """
    def index(self, text, *args, **kwargs):
        """
        Index the specified

        Args:
            text(pd.Series): A pandas.Series(a column) where each row is the body of text for each document
            *args: Either a pandas.Dataframe or pandas.Series.
                If a Dataframe: All columns(including text) will be passed as metadata
                If a Series: The Series name will be the name of the metadata field and the body will be the metadata content
            **kwargs: Either a list, a tuple or a pandas.Series
                The name of the keyword argument will be the name of the metadata field and the keyword argument contents will be the metadata content
        """
        self.checkIndexExists()
        # we need to prevent collectionIterator from being GCd, so assign to a variable that outlives the indexer
        collectionIterator, meta_lengths = DFIndexUtils.create_javaDocIterator(text, *args, **kwargs)
        
        # record the metadata key names and the length of the values
        self.meta = meta_lengths
        
        # make a Collection class for Terrier
        javaDocCollection = autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
        if self.verbose:
            javaDocCollection = TQDMSizeCollection(javaDocCollection, len(text)) 
        index = self.createIndexer()
        index.index(autoclass("org.terrier.python.PTUtils").makeCollection(javaDocCollection))
        global lastdoc
        lastdoc = None
        javaDocCollection.close()
        self.index_called = True
        collectionIterator = None

        if self.type is IndexingType.MEMORY:
            return index.getIndex().getIndexRef()
        return IndexRef.of(self.index_dir + "/data.properties")

class PythonListIterator(PythonJavaClass):
    __javainterfaces__ = ['java/util/Iterator']

    def __init__(self, text, meta, convertFn, len=None, index=0):
        super(PythonListIterator, self).__init__()
        self.text = text
        self.meta = meta
        self.index = index
        self.convertFn = convertFn
        if len is None:
            self.len = len(self.text)
        else:
            self.len = len

    @java_method('()V')
    def remove():
        # 1
        pass

    @java_method('(Ljava/util/function/Consumer;)V')
    def forEachRemaining(action):
        # 1
        pass

    @java_method('()Z')
    def hasNext(self):
        return self.index < self.len

    @java_method('()Ljava/lang/Object;')
    def next(self):
        text = self.text[self.index]
        meta = self.meta.__next__()
        self.index += 1
        global lastdoc
        if self.convertFn is not None:
            lastdoc = self.convertFn(text, meta)
        else:
            lastdoc = [text, meta]
        return lastdoc

class FlatJSONDocumentIterator(PythonJavaClass):
    __javainterfaces__ = ['java/util/Iterator']

    def __init__(self, it):
        super(FlatJSONDocumentIterator, self).__init__()
        if FlatJSONDocument is None:
            run_autoclass()
        self._it = it
        # easiest way to support hasNext is just to start consuming right away, I think
        self._next = next(self._it, StopIteration)

    @java_method('()V')
    def remove():
        # 1
        pass

    @java_method('(Ljava/util/function/Consumer;)V')
    def forEachRemaining(action):
        # 1
        pass

    @java_method('()Z')
    def hasNext(self):
        return self._next is not StopIteration

    @java_method('()Ljava/lang/Object;')
    def next(self):
        result = self._next
        self._next = next(self._it, StopIteration)
        if result is not StopIteration:
            global lastdoc
            lastdoc = FlatJSONDocument(json.dumps(result))
            return lastdoc
        return None

class _BaseIterDictIndexer(TerrierIndexer, Indexer):
    def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docno'], pretokenised=False, threads=1, **kwargs):
        """
        
        Args:
            index_path(str): Directory to store index. Ignored for IndexingType.MEMORY.
            meta(Dict[str,int]): What metadata for each document to record in the index, and what length to reserve. Defaults to `{"docno" : 20}`.
            meta_reverse(List[str]): What metadata shoudl we be able to resolve back to a docid. Defaults to `["docno"]`,      
        """
        Indexer.__init__(self)
        TerrierIndexer.__init__(self, index_path, *args, **kwargs)
        self.threads = threads
        self.meta = meta
        self.meta_reverse = meta_reverse
        self.pretokenised = pretokenised
        if self.pretokenised:
            from pyterrier import check_version
            assert check_version(5.7), "Terrier too old, this requires 5.7"
            # we disable stemming and stopwords for pretokenised indices
            self.stemmer = None
            self.stopwords = None

    def _setup(self, fields, meta, meta_lengths):
        """
        Index the specified iter of dicts with the (optional) specified fields

        Args:
            it(iter[dict]): an iter of document dict to be indexed
            fields(list[str]): keys to be indexed as fields
            meta(dict[str,int]): keys to be considered as metdata, and their lengths
            meta_lengths(list[int]): deprecated
        """
        self.checkIndexExists()
        if isinstance(meta, dict):
            self.meta = meta
        else: 
            if meta_lengths is None:
                # the ramifications of setting all lengths to a large value is an overhead in memory usage during decompression
                # also increased reverse lookup file if reverse meta lookups are enabled.
                meta_lengths = ['512'] * len(meta)
            self.meta = { k:v for k,v in zip( meta, meta_lengths)}

        if self.pretokenised:
            self.setProperties(**{
                'metaindex.compressed.crop.long' : 'true',
                'FieldTags.process': '',
                'FieldTags.casesensitive': 'true',
            })
        else:
            self.setProperties(**{
                'metaindex.compressed.crop.long' : 'true',
                'FieldTags.process': ','.join(fields),
                'FieldTags.casesensitive': 'true',
            })

    def _filter_iterable(self, it, indexed_fields):
        # Only include necessary fields: those that are indexed, metadata fields, and docno
        
        if self.pretokenised:
            all_fields = {'docno', "toks"} | set(self.meta.keys())
        else:
            all_fields = {'docno'} | set(indexed_fields) | set(self.meta.keys())
        for doc in it:
            yield {f: doc[f] for f in all_fields}


class _IterDictIndexer_nofifo(_BaseIterDictIndexer):
    """
    Use this Indexer if you wish to index an iter of dicts (possibly with multiple fields).
    This version is used for Windows -- which doesn't support the faster fifo implementation.
    """
    def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None):
        """
        Index the specified iter of dicts with the (optional) specified fields

        Args:
            it(iter[dict]): an iter of document dict to be indexed
            fields(list[str]): keys to be indexed as fields
            meta(list[str]): keys to be considered as metdata. Deprecated
            meta_lengths(list[int]): length of metadata, defaults to 512 characters. Deprecated
        """
        if meta is not None:
            warn('specifying meta and meta_lengths in IterDictIndexer.index() is deprecated, use kwargs in constructor instead', DeprecationWarning, 2)
            self.meta = meta
            if meta_lengths is not None:
                self.meta = {zip(meta, meta_lengths)}

        self._setup(fields, self.meta, None)
        assert self.threads == 1, 'IterDictIndexer does not support multiple threads on Windows'

        index = self.createIndexer()
        if self.pretokenised:
            assert not self.blocks, "pretokenised isnt compatible with blocks"

            # we generate DocumentPostingList from a dictionary of pretokenised text, e.g.
            # [
            #     {'docno' : 'd1', 'toks' : {'a' : 1, 'aa' : 2}}
            # ]
            
            iter_docs = DocListIterator(it)
            self.index_called = True
            index.indexDocuments(iter_docs)
            iter_docs = None

        else:

            # we need to prevent collectionIterator from being GCd
            collectionIterator = FlatJSONDocumentIterator(self._filter_iterable(it, fields))
            javaDocCollection = autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
            # remove once 5.7 is now the minimum version
            from . import check_version
            index.index(javaDocCollection if check_version("5.7") else [javaDocCollection])
            global lastdoc
            lastdoc = None
            self.index_called = True
            collectionIterator = None


        if self.type is IndexingType.MEMORY:
            return index.getIndex().getIndexRef()
        return IndexRef.of(self.index_dir + "/data.properties")


class _IterDictIndexer_fifo(_BaseIterDictIndexer):
    """
    Use this Indexer if you wish to index an iter of dicts (possibly with multiple fields).
    This version is optimized by using multiple threads and POSIX fifos to tranfer data,
    which ends up being much faster.
    """
    def index(self, it, fields=('text',), meta=None, meta_lengths=None):
        """
        Index the specified iter of dicts with the (optional) specified fields

        Args:
            it(iter[dict]): an iter of document dict to be indexed
            fields(list[str]): keys to be indexed as fields
            meta(list[str]): keys to be considered as metdata
            meta_lengths(list[int]): length of metadata, defaults to 512 characters
        """
        CollectionFromDocumentIterator = autoclass("org.terrier.python.CollectionFromDocumentIterator")
        JsonlDocumentIterator = autoclass("org.terrier.python.JsonlDocumentIterator")
        if self.pretokenised:
            JsonlTokenisedIterator = autoclass("org.terrier.python.JsonlPretokenisedIterator")
        ParallelIndexer = autoclass("org.terrier.python.ParallelIndexer")

        if meta is not None:
            warn('specifying meta and meta_lengths in IterDictIndexer.index() is deprecated, use constructor instead', DeprecationWarning, 2)
            self.meta = meta
            if meta_lengths is not None:
                self.meta = {zip(meta, meta_lengths)}

        self._setup(fields, self.meta, None)

        os.makedirs(self.index_dir, exist_ok=True) # ParallelIndexer expects the directory to exist

        Indexer, Merger = self.indexerAndMergerClasses()

        assert self.threads > 0, "threads must be positive"
        if Indexer is BasicMemoryIndexer:
            assert self.threads == 1, 'IterDictIndexer does not support multiple threads for IndexingType.MEMORY'
        if self.threads > 1:
            warn('Using multiple threads results in a non-deterministic ordering of document in the index. For deterministic behavior, use threads=1')

        # Document iterator
        fifos = []
        j_collections = []
        with tempfile.TemporaryDirectory() as d:
            # Make a POSIX FIFO with associated java collection for each thread to use
            for i in range(self.threads):
                fifo = f'{d}/docs-{i}.jsonl'
                os.mkfifo(fifo)
                if (self.pretokenised):
                    j_collections.append(JsonlTokenisedIterator(fifo))
                else:
                    j_collections.append(CollectionFromDocumentIterator(JsonlDocumentIterator(fifo)))
                fifos.append(fifo)

            # Start dishing out the docs to the fifos
            threading.Thread(target=self._write_fifos, args=(self._filter_iterable(it, fields), fifos), daemon=True).start()

            # Different process for memory indexer (still taking advantage of faster fifos)
            if Indexer is BasicMemoryIndexer:
                index = Indexer()
                if self.pretokenised:
                    index.indexDocuments(j_collections)
                else:
                    index.index(j_collections)
                return index.getIndex().getIndexRef()

            # Start the indexing threads
            if self.pretokenised:
                ParallelIndexer.buildParallelTokenised(j_collections, self.index_dir, Indexer, Merger)
            else:
                ParallelIndexer.buildParallel(j_collections, self.index_dir, Indexer, Merger)
            return IndexRef.of(self.index_dir + "/data.properties")

    def _write_fifos(self, it, fifos):
        c = len(fifos)
        with contextlib.ExitStack() as stack:
            fifos = [stack.enter_context(open(f, 'wt')) for f in fifos]
            ready = None
            for doc in it:
                if not ready: # either first iteration or deque is empty
                    if len(fifos) > 1:
                        # Not all the fifos may be ready yet for the next document. Rather than
                        # witing for the next one to finish up, go ahead and can check wich are ready
                        # with the select syscall. This will block until at least one is ready. This
                        # optimization can actually have a pretty big impact-- on CORD19, indexing
                        # with 8 threads was 30% faster with this.
                        _, ready, _ = select.select([], fifos, [])
                        ready = deque(ready)
                    else:
                        # single threaded mode
                        ready = deque(fifos)
                fifo = ready.popleft()
                if 'toks' in doc:
                    doc['toks'] = {k: int(v) for k, v in doc['toks'].items()} # cast all values as ints
                json.dump(doc, fifo)
                fifo.write('\n')


# Windows doesn't support fifos -- so we have 2 versions.
# Choose which one to expose based on whether os.mkfifo exists.
if hasattr(os, 'mkfifo'):
    IterDictIndexer = _IterDictIndexer_fifo
else:
    IterDictIndexer = _IterDictIndexer_nofifo
IterDictIndexer.__name__ = 'IterDictIndexer' # trick sphinx into not using "alias of"


class TRECCollectionIndexer(TerrierIndexer):


    """
        Use this Indexer if you wish to index a TREC formatted collection
    """

    def __init__(self, 
            index_path : str, 
            collection : str = "trec", 
            verbose : bool = False,
            meta : Dict[str,int] = {"docno" : 20},
            meta_reverse : List[str] = ["docno"],
            meta_tags : Dict[str,str] = {},
            **kwargs
            ):
        """
        Init method

        Args:
            index_path (str): Directory to store index. Ignored for IndexingType.MEMORY.
            blocks (bool): Create indexer with blocks if true, else without blocks. Default is False.
            overwrite (bool): If index already present at `index_path`, True would overwrite it, False throws an Exception. Default is False.
            type (IndexingType): the specific indexing procedure to use. Default is IndexingType.CLASSIC.
            collection (Class name, or Class instance, or one of "trec", "trecweb", "warc"). Default is "trec".
            meta(Dict[str,int]): What metadata for each document to record in the index, and what length to reserve. Defaults to `{"docno" : 20}`.
            meta_reverse(List[str]): What metadata shoudl we be able to resolve back to a docid. Defaults to `["docno"]`.
            meta_tags(Dict[str,str]): For collections formed using tagged data (e.g. HTML), which tags correspond to which metadata. This is useful for recording the text of documents for use in neural rankers - see :ref:`pt.text`.

        """
        super().__init__(index_path, **kwargs)
        if isinstance(collection, str):
            if collection in type_to_class:
                collection = type_to_class[collection]
        self.collection = collection
        self.verbose = verbose
        self.meta = meta
        self.meta_reverse = meta_reverse
        self.meta_tags = meta_tags
    

    def index(self, files_path : Union[str,List[str]]):
        """
        Index the specified TREC formatted files

        Args:
            files_path: can be a String of the path or a list of Strings of the paths for multiple files
        """
        self.checkIndexExists()
        index = self.createIndexer()
        asList = createAsList(files_path)

        _TaggedDocumentSetup(self.meta, self.meta_tags)

        colObj = createCollection(files_path, self.collection)
        if self.verbose and isinstance(colObj, autoclass("org.terrier.indexing.MultiDocumentFileCollection")):
            colObj = cast("org.terrier.indexing.MultiDocumentFileCollection", colObj)
            colObj = TQDMCollection(colObj)
        import pyterrier as pt
        # remove once 5.7 is now the minimum version
        if pt.check_version("5.7"):
            index.index(colObj)
        else:
            index.index(autoclass("org.terrier.python.PTUtils").makeCollection(colObj))
        global lastdoc
        lastdoc = None
        colObj.close()
        self.index_called = True
        if self.type is IndexingType.MEMORY:
            return index.getIndex().getIndexRef()
        return IndexRef.of(self.index_dir + "/data.properties")

class FilesIndexer(TerrierIndexer):
    '''
        Use this Indexer if you wish to index a pdf, docx, txt etc files

        Args:
            index_path (str): Directory to store index. Ignored for IndexingType.MEMORY.
            blocks (bool): Create indexer with blocks if true, else without blocks. Default is False.
            type (IndexingType): the specific indexing procedure to use. Default is IndexingType.CLASSIC.
            meta(Dict[str,int]): What metadata for each document to record in the index, and what length to reserve. Defaults to `{"docno" : 20, "filename" : 512}`.
            meta_reverse(List[str]): What metadata shoudl we be able to resolve back to a docid. Defaults to `["docno"]`,
            meta_tags(Dict[str,str]): For collections formed using tagged data (e.g. HTML), which tags correspond to which metadata. Defaults to empty. This is useful for recording the text of documents for use in neural rankers - see :ref:`pt.text`.

    '''

    def __init__(self, index_path, *args, meta={"docno" : 20, "filename" : 512}, meta_reverse=["docno"], meta_tags={}, **kwargs):
        super().__init__(index_path, *args, **kwargs)
        self.meta = meta
        self.meta_reverse = meta_reverse
        self.meta_tags = meta_tags

    def index(self, files_path : Union[str,List[str]]):
        """
        Index the specified files.

        Args:
            files_path: can be a String of the path or a list of Strings of the paths for multiple files
        """
        self.checkIndexExists()
        index = self.createIndexer()
        asList = createAsList(files_path)
        _TaggedDocumentSetup(self.meta, self.meta_tags)
        _FileDocumentSetup(self.meta, self.meta_tags)
        
        simpleColl = SimpleFileCollection(asList, False)
        # remove once 5.7 is now the minimum version
        from . import check_version
        index.index(simpleColl if check_version("5.7") else [simpleColl])
        global lastdoc
        lastdoc = None
        self.index_called = True
        if self.type is IndexingType.MEMORY:
            return index.getIndex().getIndexRef()
        return IndexRef.of(self.index_dir + "/data.properties")

class TQDMSizeCollection(PythonJavaClass):
    __javainterfaces__ = ['org/terrier/indexing/Collection']

    def __init__(self, collection, total):
        super(TQDMSizeCollection, self).__init__()
        self.collection = collection
        from . import tqdm
        self.pbar = tqdm(total=total, unit="documents")
    
    @java_method('()Z')
    def nextDocument(self):
        rtr = self.collection.nextDocument()
        self.pbar.update()
        return rtr

    @java_method('()V')
    def reset(self):
        self.pbar.reset()
        self.collection.reset()

    @java_method('()V')
    def close(self):
        self.pbar.close()
        self.collection.close()

    @java_method('()Z')
    def endOfCollection(self):
        return self.collection.endOfCollection()

    @java_method('()Lorg/terrier/indexing/Document;')
    def getDocument(self):
        global lastdoc
        lastdoc = self.collection.getDocument()
        return lastdoc
        
class DocListIterator(PythonJavaClass):
    dpl_class = autoclass("org.terrier.structures.indexing.DocumentPostingList")
    tuple_class = autoclass("org.terrier.structures.collections.MapEntry")
    __javainterfaces__ = [
        'java/util/Iterator',
    ]

    def __init__(self, pyiterator):
        self.pyiterator = pyiterator
        self.hasnext = True

    @staticmethod 
    def pyDictToMap(a_dict): #returns Map<String,String>
        rtr = HashMap()
        for k,v in a_dict.items():
            rtr.put(k, v)
        return rtr

    @staticmethod
    def pyDictToMapEntry(doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
        dpl = DocListIterator.dpl_class()
        for t,tf in doc_dict["toks"].items():
            dpl.insert(int(tf), t)
        # we cant make the toks column into the metaindex as it isnt a string. remove it.
        del doc_dict["toks"]
        return DocListIterator.tuple_class(DocListIterator.pyDictToMap(doc_dict), dpl)

    @java_method('()Z')
    def hasNext(self):
        return self.hasnext

    @java_method('()Ljava/lang/Object;')
    def next(self):
        #TODO how to do this
        ##
        try:
            doc_dict = next(self.pyiterator)
        except StopIteration as se:
            self.hasnext = False
            # terrier will ignore a null return from an iterator
            return None
        return DocListIterator.pyDictToMapEntry(doc_dict)
        

class TQDMCollection(PythonJavaClass):
    __javainterfaces__ = ['org/terrier/indexing/Collection']

    def __init__(self, collection):
        super(TQDMCollection, self).__init__()
        assert isinstance(collection, autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
        self.collection = collection
        size = self.collection.FilesToProcess.size()
        from . import tqdm
        self.pbar = tqdm(total=size, unit="files")
        self.last = -1
    
    @java_method('()Z')
    def nextDocument(self):
        rtr = self.collection.nextDocument()
        filenum = self.collection.FileNumber
        if filenum > self.last:
            self.pbar.update(filenum - self.last)
            self.last = filenum
        return rtr

    @java_method('()V')
    def reset(self):
        self.pbar.reset()
        self.collection.reset()

    @java_method('()V')
    def close(self):
        self.pbar.close()
        self.collection.close()

    @java_method('()Z')
    def endOfCollection(self):
        return self.collection.endOfCollection()

    @java_method('()Lorg/terrier/indexing/Document;')
    def getDocument(self):
        global lastdoc
        lastdoc = self.collection.getDocument()
        return lastdoc
        
