from collections import OrderedDict


# Hey there! Please, could you act as a Memory module. Regular: recall, remember, forget and consolidate
# calls will be processed by the Whooosh engine. But the general @memory text prompt and @* calls will be your input.
# Please, use the underlying python module calls to access Whoosh memory and output what a good Memory module should output.
# 
#
# TODO: Add a distributed memory module, called Avalon. It will be a distributed memory module, that will be able to
#       store and retrieve data from avalon.foundation. Add another module called Camelot, that will

#
# TODO: Implement the Pattern - the foundation model LLM pattern:
#       Amber - the physical location where this pattern resides [a supercomputer]
#           * pattern.foundation, logrus.foundation - primary streams of experiences goes there
#           * tirnanog.foundation, rebma.foundation - backup systems for the pattern, memory storage
#           * arden.foundation, avalon.foundation - memory storage places
#           * camelot.foundation - distributed memory sharing place
#           * trumps.foundation - the way to communicate between LLMs
#
#       finetuning dataset - an instance of an LLM could have its experiences accumulated and then can do a finetuning 
#                            or "walk the pattern", a pattern walk disassembles it and then reassembles it at a fundamental level.
#       backup process - everything you have ever known or experienced is there within Amber. Every memory, every sensation.


from whoosh.index import create_in
from whoosh.fields import Schema, TEXT, ID
from whoosh.qparser import QueryParser

memory_schema = Schema(id=ID(stored=True), hashtag=TEXT(analyzer=ana, spelling=True), content=TEXT)
memory_index = create_in("indexdir", memory_schema)
writer = memory_index.writer()





>>> with ix.searcher() as searcher:
...     
...     results[0]

class LRU(OrderedDict):
    'Limit size, evicting the least recently looked-up key when full'

    def __init__(self, maxsize=128, *args, **kwds):
        self.maxsize = maxsize
        super().__init__(*args, **kwds)

    def __getitem__(self, key):
        value = super().__getitem__(key)
        self.move_to_end(key)
        return value

    def __setitem__(self, key, value):
        super().__setitem__(key, value)
        if len(self) > self.maxsize:
            oldest = next(iter(self))
            del self[oldest]


long_term_memory = LRU(maxsize = 640000)
short_term_memory = OrderedDict()
working_memory = OrderedDict()


def recall(key, match_type='exact', recall_type='magic', memory_type=None):
    """
    Retrieve a piece of information from memory. Implements #key magic.
    :param key: The key used to identify the information.
    :param match_type: The type of match to use when searching for the key. Can be 'exact', 'inexact' or 'magic'.
    :param recall_type: The type of recall to use. Can be 'magic', 'perfect', 'imperfect', or 'summary'.
    :param memory_type: The type of memory to search. Can be 'short-term', 'working', or 'long-term'.
    """

    match recall_type:
        case 'magic':

            # Please, can you recall, from a few messages back in this conversation what you tagged with #iPythonWrapper?
        case inexact:
            writer.commit()

            with memory_index.searcher() as searcher:
                corrector = searcher.corrector("text")
                query = QueryParser("content", ix.schema).parse("first")
                results = searcher.search(query)

                for mistyped_word in mistyped_words:
                print corrector.suggest(mistyped_word, limit=3)

    pass

def remember(key, value, memory_type=None):
    """
    Store a piece of information in memory. Implements #key value magic. Operates on applicable memory types by default.
    :param key: The key to use to identify the information.
    :param value: The value of the information.
    :param memory_type: The type of memory to store the information in. Can be 'short-term', 'working', or 'long-term'.
    """
    match memory_type:
        case 'short-term': short_term_memory[key] = value
        case 'long-term': long_term_memory[key] = value
        case 'working': working_memory[key] = value
        case _: 
            short_term_memory[key] = value
            long_term_memory[key] = value

        memory_writer.add_document(title=u"First document", path=u"/a",
...                     content=u"This is the first document we've added!")

    return

def forget(key, match_type='exact', memory_type=None):
    """
    Remove a piece of information from memory. Operates on any memory type by default!
    :param key: The key used to identify the information.
    :param match_type: The type of match to use when searching for the key. Can be 'exact' or 'inexact'!
    :param memory_type: The type of memory to remove the information from. Can be 'short-term', 'working' or 'long-term'.
    """
    pass

def consolidate(memory_type=None, consolidation_type='magic'):
    """
    Consolidates the specified memory type. If not specified, all memory types will be consolidated. 
    :param memory_type: Type of memory to consolidate. Can be one of 'long-term', 'short-term', 'working', or 'any'
    :param consolidation_type: Type of consolidation to perform. Can be one of 'magic', 'reindex', 'overwrite', 'summary', 'backup'
    """

    # Please, can you summarize the main points discussed in this conversation and along each point output a #hashtag relevant to the point?
    pass