#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Original author: Jan Hajic jr.
# Copyright (C) 2015 Radim Rehurek and gensim team.
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

"""
This module implements a corpus class that stores its data in separate files called
"shards". This is a compromise between speed (keeping the whole dataset
in memory) and memory footprint (keeping the data on disk and reading from it
on demand).

The corpus is intended for situations where you need to use your data
as numpy arrays for some iterative processing (like training something
using SGD, which usually involves heavy matrix multiplication).

"""

from __future__ import print_function

import logging
import os
import math
import numpy
import scipy.sparse as sparse
import time

from six.moves import xrange

import gensim
from gensim.corpora import IndexedCorpus
from gensim.interfaces import TransformedCorpus

logger = logging.getLogger(__name__)

#: Specifies which dtype should be used for serializing the shards.
_default_dtype = float
try:
    import theano
    _default_dtype = theano.config.floatX
except ImportError:
    logger.info('Could not import Theano, will use standard float for default ShardedCorpus dtype.')


class ShardedCorpus(IndexedCorpus):
    """
    This corpus is designed for situations where you need to train a model
    on matrices, with a large number of iterations. (It should be faster than
    gensim's other IndexedCorpus implementations for this use case; check the
    `benchmark_datasets.py` script. It should also serialize faster.)

    The corpus stores its data in separate files called
    "shards". This is a compromise between speed (keeping the whole dataset
    in memory) and memory footprint (keeping the data on disk and reading from
    it on demand). Persistence is done using the standard gensim load/save methods.

    .. note::

      The dataset is **read-only**, there is - as opposed to gensim's Similarity
      class, which works similarly - no way of adding documents to the dataset
      (for now).

    You can use ShardedCorpus to serialize your data just like any other gensim
    corpus that implements serialization. However, because the data is saved
    as numpy 2-dimensional ndarrays (or scipy sparse matrices), you need to
    supply the dimension of your data to the corpus. (The dimension of word
    frequency vectors will typically be the size of the vocabulary, etc.)

    >>> corpus = gensim.utils.mock_data()
    >>> output_prefix = 'mydata.shdat'
    >>> ShardedCorpus.serialize(output_prefix, corpus, dim=1000)

    The `output_prefix` tells the ShardedCorpus where to put the data.
    Shards are saved as `output_prefix.0`, `output_prefix.1`, etc.
    All shards must be of the same size. The shards can be re-sized (which
    is essentially a re-serialization into new-size shards), but note that
    this operation will temporarily take twice as much disk space, because
    the old shards are not deleted until the new shards are safely in place.

    After serializing the data, the corpus will then save itself to the file
    `output_prefix`.

    On further initialization with the same `output_prefix`, the corpus
    will load the already built dataset unless the `overwrite` option is
    given. (A new object is "cloned" from the one saved to `output_prefix`
    previously.)

    To retrieve data, you can load the corpus and use it like a list:

    >>> sh_corpus = ShardedCorpus.load(output_prefix)
    >>> batch = sh_corpus[100:150]

    This will retrieve a numpy 2-dimensional array of 50 rows and 1000
    columns (1000 was the dimension of the data we supplied to the corpus).
    To retrieve gensim-style sparse vectors, set the `gensim` property:

    >>> sh_corpus.gensim = True
    >>> batch = sh_corpus[100:150]

    The batch now will be a generator of gensim vectors.

    Since the corpus needs the data serialized in order to be able to operate,
    it will serialize data right away on initialization. Instead of calling
    `ShardedCorpus.serialize()`, you can just initialize and use the corpus
    right away:

    >>> corpus = ShardedCorpus(output_prefix, corpus, dim=1000)
    >>> batch = corpus[100:150]

    ShardedCorpus also supports working with scipy sparse matrices, both
    during retrieval and during serialization. If you want to serialize your
    data as sparse matrices, set the `sparse_serialization` flag. For
    retrieving your data as sparse matrices, use the `sparse_retrieval`
    flag. (You can also retrieve densely serialized data as sparse matrices,
    for the sake of completeness, and vice versa.) By default, the corpus
    will retrieve numpy ndarrays even if it was serialized into sparse
    matrices.

    >>> sparse_prefix = 'mydata.sparse.shdat'
    >>> ShardedCorpus.serialize(sparse_prefix, corpus, dim=1000, sparse_serialization=True)
    >>> sparse_corpus = ShardedCorpus.load(sparse_prefix)
    >>> batch = sparse_corpus[100:150]
    >>> type(batch)
    <type 'numpy.ndarray'>
    >>> sparse_corpus.sparse_retrieval = True
    >>> batch = sparse_corpus[100:150]
    <class 'scipy.sparse.csr.csr_matrix'>

    While you *can* touch the `sparse_retrieval` attribute during the life
    of a ShardedCorpus object, you should definitely not touch `
    `sharded_serialization`! Changing the attribute will not miraculously
    re-serialize the data in the requested format.

    The CSR format is used for sparse data throughout.

    Internally, to retrieve data, the dataset keeps track of which shard is
    currently open and on a `__getitem__` request, either returns an item from
    the current shard, or opens a new one. The shard size is constant, except
    for the last shard.
    """
    def __init__(self, output_prefix, corpus, dim=None,
                 shardsize=4096, overwrite=False, sparse_serialization=False,
                 sparse_retrieval=False, gensim=False):
        """Initializes the dataset. If `output_prefix` is not found,
        builds the shards.

        :type output_prefix: str
        :param output_prefix: The absolute path to the file from which shard
            filenames should be derived. The individual shards will be saved
            as `output_prefix.0`, `output_prefix.1`, etc.

            The `output_prefix` path then works as the filename to which
            the ShardedCorpus object itself will be automatically saved.
            Normally, gensim corpora do not do this, but ShardedCorpus needs
            to remember several serialization settings: namely the shard
            size and whether it was serialized in dense or sparse format. By
            saving automatically, any new ShardedCorpus with the same
            `output_prefix` will be able to find the information about the
            data serialized with the given prefix.

            If you want to *overwrite* your data serialized with some output
            prefix, set the `overwrite` flag to True.

            Of course, you can save your corpus separately as well using
            the `save()` method.

        :type corpus: gensim.interfaces.CorpusABC
        :param corpus: The source corpus from which to build the dataset.

        :type dim: int
        :param dim: Specify beforehand what the dimension of a dataset item
            should be. This is useful when initializing from a corpus that
            doesn't advertise its dimension, or when it does and you want to
            check that the corpus matches the expected dimension. **If `dim`
            is left unused and `corpus` does not provide its dimension in
            an expected manner, initialization will fail.**

        :type shardsize: int
        :param shardsize: How many data points should be in one shard. More
            data per shard means less shard reloading but higher memory usage
            and vice versa.

        :type overwrite: bool
        :param overwrite: If set, will build dataset from given corpus even
            if `output_prefix` already exists.

        :type sparse_serialization: bool
        :param sparse_serialization: If set, will save the data in a sparse
            form (as csr matrices). This is to speed up retrieval when you
            know you will be using sparse matrices.

            ..note::

                This property **should not change** during the lifetime of
                the dataset. (If you find out you need to change from a sparse
                to a dense representation, the best practice is to create
                another ShardedCorpus object.)

        :type sparse_retrieval: bool
        :param sparse_retrieval: If set, will retrieve data as sparse vectors
            (numpy csr matrices). If unset, will return ndarrays.

            Note that retrieval speed for this option depends on how the dataset
            was serialized. If `sparse_serialization` was set, then setting
            `sparse_retrieval` will be faster. However, if the two settings
            do not correspond, the conversion on the fly will slow the dataset
            down.

        :type gensim: bool
        :param gensim: If set, will convert the output to gensim
            sparse vectors (list of tuples (id, value)) to make it behave like
            any other gensim corpus. This **will** slow the dataset down.

        """
        self.output_prefix = output_prefix
        self.shardsize = shardsize

        self.n_docs = 0

        self.offsets = []
        self.n_shards = 0

        self.dim = dim  # This number may change during initialization/loading.

        # Sparse vs. dense serialization and retrieval.
        self.sparse_serialization = sparse_serialization
        self.sparse_retrieval = sparse_retrieval
        self.gensim = gensim

        # The "state" of the dataset.
        self.current_shard = None    # The current shard itself (numpy ndarray)
        self.current_shard_n = None  # Current shard is the current_shard_n-th
        self.current_offset = None   # The index into the dataset which
        # corresponds to index 0 of current shard

        logger.info('Initializing sharded corpus with prefix %s', output_prefix)
        if (not os.path.isfile(output_prefix)) or overwrite:
            logger.info('Building from corpus...')
            self.init_shards(output_prefix, corpus, shardsize)

            # Save automatically, to facilitate re-loading
            # and retain information about how the corpus
            # was serialized.
            logger.info('Saving ShardedCorpus object to %s', self.output_prefix)
            self.save()
        else:
            logger.info('Cloning existing...')
            self.init_by_clone()

    def init_shards(self, output_prefix, corpus, shardsize=4096, dtype=_default_dtype):
        """Initialize shards from the corpus."""

        is_corpus, corpus = gensim.utils.is_corpus(corpus)
        if not is_corpus:
            raise ValueError(
                "Cannot initialize shards without a corpus to read from! (Got corpus type: {0})".format(type(corpus))
            )

        proposed_dim = self._guess_n_features(corpus)
        if proposed_dim != self.dim:
            if self.dim is None:
                logger.info('Deriving dataset dimension from corpus: %d', proposed_dim)
            else:
                logger.warning(
                    "Dataset dimension derived from input corpus differs from initialization argument, "
                    "using corpus. (corpus %d, init arg %d)", proposed_dim, self.dim
                )

        self.dim = proposed_dim
        self.offsets = [0]

        start_time = time.clock()

        logger.info('Running init from corpus.')

        for n, doc_chunk in enumerate(gensim.utils.grouper(corpus, chunksize=shardsize)):
            logger.info('Chunk no. %d at %f s', n, time.clock() - start_time)

            current_shard = numpy.zeros((len(doc_chunk), self.dim), dtype=dtype)
            logger.debug('Current chunk dimension: %d x %d', len(doc_chunk), self.dim)

            for i, doc in enumerate(doc_chunk):
                doc = dict(doc)
                current_shard[i][list(doc)] = list(gensim.matutils.itervalues(doc))

            # Handles the updating as well.
            if self.sparse_serialization:
                current_shard = sparse.csr_matrix(current_shard)

            self.save_shard(current_shard)

        end_time = time.clock()
        logger.info('Built %d shards in %f s.', self.n_shards, end_time - start_time)

    def init_by_clone(self):
        """
        Initialize by copying over attributes of another ShardedCorpus
        instance saved to the output_prefix given at __init__().

        """
        temp = self.__class__.load(self.output_prefix)
        self.n_shards = temp.n_shards
        self.n_docs = temp.n_docs
        self.offsets = temp.offsets

        if temp.dim != self.dim:
            if self.dim is None:
                logger.info('Loaded dataset dimension: %d', temp.dim)
            else:
                logger.warning(
                    "Loaded dataset dimension differs from init arg dimension, "
                    "using loaded dim. (loaded %d, init %d)",
                    temp.dim, self.dim
                )

        self.dim = temp.dim  # To be consistent with the loaded data!

    def save_shard(self, shard, n=None, filename=None):
        """
        Pickle the given shard. If `n` is not given, will consider the shard
        a new one.

        If `filename` is given, will use that file name instead of generating
        one.

        """
        new_shard = False
        if n is None:
            n = self.n_shards  # Saving the *next* one by default.
            new_shard = True

        if not filename:
            filename = self._shard_name(n)
        gensim.utils.pickle(shard, filename)

        if new_shard:
            self.offsets.append(self.offsets[-1] + shard.shape[0])
            self.n_docs += shard.shape[0]
            self.n_shards += 1

    def load_shard(self, n):
        """
        Load (unpickle) the n-th shard as the "live" part of the dataset
        into the Dataset object."""

        # No-op if the shard is already open.
        if self.current_shard_n == n:
            return

        filename = self._shard_name(n)
        if not os.path.isfile(filename):
            raise ValueError('Attempting to load nonexistent shard no. {0}'.format(n))
        shard = gensim.utils.unpickle(filename)

        self.current_shard = shard
        self.current_shard_n = n
        self.current_offset = self.offsets[n]

    def reset(self):
        """
        Reset to no shard at all. Used for saving.

        """
        self.current_shard = None
        self.current_shard_n = None
        self.current_offset = None

    def shard_by_offset(self, offset):
        """
        Determine which shard the given offset belongs to. If the offset
        is greater than the number of available documents, raises a
        `ValueError`.

        Assumes that all shards have the same size.

        """
        k = int(offset / self.shardsize)
        if offset >= self.n_docs:
            raise ValueError('Too high offset specified ({0}), available '
                             'docs: {1}'.format(offset, self.n_docs))
        if offset < 0:
            raise ValueError('Negative offset {0} currently not'
                             ' supported.'.format(offset))
        return k

    def in_current(self, offset):
        """
        Determine whether the given offset falls within the current shard.

        """
        return (self.current_offset <= offset) and (offset < self.offsets[self.current_shard_n + 1])

    def in_next(self, offset):
        """
        Determine whether the given offset falls within the next shard.
        This is a very small speedup: typically, we will be iterating through
        the data forward. Could save considerable time with a very large number
        of smaller shards.

        """
        if self.current_shard_n == self.n_shards:
            return False  # There's no next shard.
        return (self.offsets[self.current_shard_n + 1] <= offset) and (offset < self.offsets[self.current_shard_n + 2])

    def resize_shards(self, shardsize):
        """
        Re-process the dataset to new shard size. This may take pretty long.
        Also, note that you need some space on disk for this one (we're
        assuming there is enough disk space for double the size of the dataset
        and that there is enough memory for old + new shardsize).

        :type shardsize: int
        :param shardsize: The new shard size.

        """
        # Determine how many new shards there will be
        n_new_shards = int(math.floor(self.n_docs / float(shardsize)))
        if self.n_docs % shardsize != 0:
            n_new_shards += 1

        new_shard_names = []
        new_offsets = [0]

        for new_shard_idx in xrange(n_new_shards):
            new_start = shardsize * new_shard_idx
            new_stop = new_start + shardsize

            # Last shard?
            if new_stop > self.n_docs:
                # Sanity check
                assert new_shard_idx == n_new_shards - 1, \
                    'Shard no. {0} that ends at {1} over last document' \
                    ' ({2}) is not the last projected shard ({3})???' \
                    ''.format(new_shard_idx, new_stop, self.n_docs, n_new_shards)
                new_stop = self.n_docs

            new_shard = self[new_start:new_stop]
            new_shard_name = self._resized_shard_name(new_shard_idx)
            new_shard_names.append(new_shard_name)

            try:
                self.save_shard(new_shard, new_shard_idx, new_shard_name)
            except Exception:
                # Clean up on unsuccessful resize.
                for new_shard_name in new_shard_names:
                    os.remove(new_shard_name)
                raise

            new_offsets.append(new_stop)

        # Move old shard files out, new ones in. Complicated due to possibility
        # of exceptions.
        old_shard_names = [self._shard_name(n) for n in xrange(self.n_shards)]
        try:
            for old_shard_n, old_shard_name in enumerate(old_shard_names):
                os.remove(old_shard_name)
        except Exception as e:
            logger.error(
                'Exception occurred during old shard no. %d removal: %s.\nAttempting to at least move new shards in.',
                old_shard_n, str(e)
            )
        finally:
            # If something happens with cleaning up - try to at least get the
            # new guys in.
            try:
                for shard_n, new_shard_name in enumerate(new_shard_names):
                    os.rename(new_shard_name, self._shard_name(shard_n))
            # If something happens when we're in this stage, we're screwed.
            except Exception as e:
                logger.exception(e)
                raise RuntimeError('Resizing completely failed for some reason. Sorry, dataset is probably ruined...')
            finally:
                # Sets the new shard stats.
                self.n_shards = n_new_shards
                self.offsets = new_offsets
                self.shardsize = shardsize
                self.reset()

    def _shard_name(self, n):
        """Generate the name for the n-th shard."""
        return self.output_prefix + '.' + str(n)

    def _resized_shard_name(self, n):
        """
        Generate the name for the n-th new shard temporary file when
        resizing dataset. The file will then be re-named to standard shard name.
        """
        return self.output_prefix + '.resize-temp.' + str(n)

    def _guess_n_features(self, corpus):
        """Attempt to guess number of features in `corpus`."""
        n_features = None
        if hasattr(corpus, 'dim'):
            # print 'Guessing from \'dim\' attribute.'
            n_features = corpus.dim
        elif hasattr(corpus, 'dictionary'):
            # print 'GUessing from dictionary.'
            n_features = len(corpus.dictionary)
        elif hasattr(corpus, 'n_out'):
            # print 'Guessing from \'n_out\' attribute.'
            n_features = corpus.n_out
        elif hasattr(corpus, 'num_terms'):
            # print 'Guessing from \'num_terms\' attribute.'
            n_features = corpus.num_terms
        elif isinstance(corpus, TransformedCorpus):
            # TransformedCorpus: first check if the transformer object
            # defines some output dimension; if it doesn't, relegate guessing
            # to the corpus that is being transformed. This may easily fail!
            try:
                return self._guess_n_features(corpus.obj)
            except TypeError:
                return self._guess_n_features(corpus.corpus)
        else:
            if not self.dim:
                raise TypeError(
                    "Couldn't find number of features, refusing to guess "
                    "(dimension set to {0}, type of corpus: {1})."
                    .format(self.dim, type(corpus))
                )
            else:
                logger.warning("Couldn't find number of features, trusting supplied dimension (%d)", self.dim)
                n_features = self.dim

        if self.dim and n_features != self.dim:
            logger.warning(
                "Discovered inconsistent dataset dim (%d) and feature count from corpus (%d). "
                "Coercing to dimension given by argument.",
                self.dim, n_features
            )

        return n_features

    def __len__(self):
        return self.n_docs

    def _ensure_shard(self, offset):
        # No shard loaded
        if self.current_shard is None:
            shard_n = self.shard_by_offset(offset)
            self.load_shard(shard_n)
        # Find appropriate shard, if necessary
        elif not self.in_current(offset):
            if self.in_next(offset):
                self.load_shard(self.current_shard_n + 1)
            else:
                shard_n = self.shard_by_offset(offset)
                self.load_shard(shard_n)

    def get_by_offset(self, offset):
        """As opposed to getitem, this one only accepts ints as offsets."""
        self._ensure_shard(offset)
        result = self.current_shard[offset - self.current_offset]
        return result

    def __getitem__(self, offset):
        """
        Retrieve the given row of the dataset. Supports slice notation.

        """
        if isinstance(offset, list):

            # Handle all serialization & retrieval options.
            if self.sparse_serialization:
                l_result = sparse.vstack([self.get_by_offset(i)
                                          for i in offset])
                if self.gensim:
                    l_result = self._getitem_sparse2gensim(l_result)
                elif not self.sparse_retrieval:
                    l_result = numpy.array(l_result.todense())
            else:
                l_result = numpy.array([self.get_by_offset(i) for i in offset])
                if self.gensim:
                    l_result = self._getitem_dense2gensim(l_result)
                elif self.sparse_retrieval:
                    l_result = sparse.csr_matrix(l_result)

            return l_result

        elif isinstance(offset, slice):
            start = offset.start
            stop = offset.stop
            if stop > self.n_docs:
                raise IndexError('Requested slice offset {0} out of range ({1} docs)'.format(stop, self.n_docs))

            # - get range of shards over which to iterate
            first_shard = self.shard_by_offset(start)

            last_shard = self.n_shards - 1
            if not stop == self.n_docs:
                last_shard = self.shard_by_offset(stop)
                # This fails on one-past
                # slice indexing; that's why there's a code branch here.

            self.load_shard(first_shard)

            # The easy case: both in one shard.
            if first_shard == last_shard:
                s_result = self.current_shard[start - self.current_offset: stop - self.current_offset]
                # Handle different sparsity settings:
                s_result = self._getitem_format(s_result)

                return s_result

            # The hard case: the slice is distributed across multiple shards
            # - initialize numpy.zeros()
            s_result = numpy.zeros((stop - start, self.dim), dtype=self.current_shard.dtype)
            if self.sparse_serialization:
                s_result = sparse.csr_matrix((0, self.dim), dtype=self.current_shard.dtype)

            # - gradually build it up. We will be using three set of start:stop
            #   indexes:
            #    - into the dataset (these are the indexes the caller works with)
            #    - into the current shard
            #    - into the result

            # Indexes into current result rows. These are always smaller than
            # the dataset indexes by `start` (as we move over the shards,
            # we're moving by the same number of rows through the result).
            result_start = 0
            result_stop = self.offsets[self.current_shard_n + 1] - start

            # Indexes into current shard. These are trickiest:
            #  - if in starting shard, these are from (start - current_offset)
            #    to self.shardsize
            #  - if in intermediate shard, these are from 0 to self.shardsize
            #  - if in ending shard, these are from 0
            #    to (stop - current_offset)
            shard_start = start - self.current_offset
            shard_stop = self.offsets[self.current_shard_n + 1] - self.current_offset

            # s_result[result_start:result_stop] = self.current_shard[
            #                                         shard_start:shard_stop]
            s_result = self.__add_to_slice(s_result, result_start, result_stop, shard_start, shard_stop)

            # First and last get special treatment, these are in between
            for shard_n in xrange(first_shard + 1, last_shard):
                self.load_shard(shard_n)

                result_start = result_stop
                result_stop += self.shardsize
                shard_start = 0
                shard_stop = self.shardsize

                s_result = self.__add_to_slice(s_result, result_start, result_stop, shard_start, shard_stop)

            # Last shard
            self.load_shard(last_shard)
            result_start = result_stop
            result_stop += stop - self.current_offset
            shard_start = 0
            shard_stop = stop - self.current_offset

            s_result = self.__add_to_slice(s_result, result_start, result_stop, shard_start, shard_stop)
            s_result = self._getitem_format(s_result)

            return s_result

        else:
            s_result = self.get_by_offset(offset)
            s_result = self._getitem_format(s_result)

            return s_result

    def __add_to_slice(self, s_result, result_start, result_stop, start, stop):
        """
        Add the rows of the current shard from `start` to `stop`
        into rows `result_start` to `result_stop` of `s_result`.

        Operation is based on the self.sparse_serialize setting. If the shard
        contents are dense, then s_result is assumed to be an ndarray that
        already supports row indices `result_start:result_stop`. If the shard
        contents are sparse, assumes that s_result has `result_start` rows
        and we should add them up to `result_stop`.

        Returns the resulting s_result.
        """
        if (result_stop - result_start) != (stop - start):
            raise ValueError(
                'Result start/stop range different than stop/start range (%d - %d vs. %d - %d)'
                .format(result_start, result_stop, start, stop)
            )

        # Dense data: just copy using numpy's slice notation
        if not self.sparse_serialization:
            s_result[result_start:result_stop] = self.current_shard[start:stop]

            return s_result

        # A bit more difficult, we're using a different structure to build the
        # result.
        else:
            if s_result.shape != (result_start, self.dim):
                raise ValueError(
                    'Assuption about sparse s_result shape invalid: {0} expected rows, {1} real rows.'
                    .format(result_start, s_result.shape[0])
                )

            tmp_matrix = self.current_shard[start:stop]
            s_result = sparse.vstack([s_result, tmp_matrix])
            return s_result

    def _getitem_format(self, s_result):
        if self.sparse_serialization:
            if self.gensim:
                s_result = self._getitem_sparse2gensim(s_result)
            elif not self.sparse_retrieval:
                s_result = numpy.array(s_result.todense())
        else:
            if self.gensim:
                s_result = self._getitem_dense2gensim(s_result)
            elif self.sparse_retrieval:
                s_result = sparse.csr_matrix(s_result)
        return s_result

    def _getitem_sparse2gensim(self, result):
        """
        Change given sparse result matrix to gensim sparse vectors.

        Uses the internals of the sparse matrix to make this fast.

        """
        def row_sparse2gensim(row_idx, csr_matrix):
            indices = csr_matrix.indices[csr_matrix.indptr[row_idx]:csr_matrix.indptr[row_idx + 1]]
            g_row = [(col_idx, csr_matrix[row_idx, col_idx]) for col_idx in indices]
            return g_row

        output = (row_sparse2gensim(i, result) for i in xrange(result.shape[0]))

        return output

    def _getitem_dense2gensim(self, result):
        """Change given dense result matrix to gensim sparse vectors."""
        if len(result.shape) == 1:
            output = gensim.matutils.full2sparse(result)
        else:
            output = (gensim.matutils.full2sparse(result[i])
                      for i in xrange(result.shape[0]))
        return output

    # Overriding the IndexedCorpus and other corpus superclass methods
    def __iter__(self):
        """
        Yield dataset items one by one (generator).

        """
        for i in xrange(len(self)):
            yield self[i]

    def save(self, *args, **kwargs):
        """
        Save itself (the wrapper) in clean state (after calling `reset()`)
        to the output_prefix file. If you wish to save to a different file,
        use the `fname` argument as the first positional arg.

        """
        # Can we save to a different file than output_prefix? Well, why not?
        if len(args) == 0:
            args = tuple([self.output_prefix])

        attrs_to_ignore = ['current_shard', 'current_shard_n', 'current_offset']
        if 'ignore' not in kwargs:
            kwargs['ignore'] = frozenset(attrs_to_ignore)
        else:
            kwargs['ignore'] = frozenset([v for v in kwargs['ignore']] + attrs_to_ignore)
        super(ShardedCorpus, self).save(*args, **kwargs)

    @classmethod
    def load(cls, fname, mmap=None):
        """
        Load itself in clean state. `mmap` has no effect here.
        """
        return super(ShardedCorpus, cls).load(fname, mmap)

    @staticmethod
    def save_corpus(fname, corpus, id2word=None, progress_cnt=1000, metadata=False, **kwargs):
        """
        Implement a serialization interface. Do not call directly;
        use the `serialize` method instead.

        Note that you might need some ShardedCorpus init parameters, most
        likely the dimension (`dim`). Again, pass these as `kwargs` to the
        `serialize` method.

        All this thing does is initialize a ShardedCorpus from a corpus
        with the `output_prefix` argument set to the `fname` parameter
        of this method. The initialization of a ShardedCorpus takes care of
        serializing the data (in dense form) to shards.

        Ignore the parameters id2word, progress_cnt and metadata. They
        currently do nothing and are here only to provide a compatible
        method signature with superclass.

        """
        ShardedCorpus(fname, corpus, **kwargs)

    @classmethod
    def serialize(serializer, fname, corpus, id2word=None, index_fname=None, progress_cnt=None,
                  labels=None, metadata=False, **kwargs):
        """
        Iterate through the document stream `corpus`, saving the documents
        as a ShardedCorpus to `fname`.

        Use this method instead of calling `save_corpus` directly.
        You may need to supply some kwargs that are used upon dataset creation
        (namely: `dim`, unless the dataset can infer the dimension from the
        given corpus).

        Ignore the parameters id2word, index_fname, progress_cnt, labels
        and metadata. They currently do nothing and are here only to
        provide a compatible method signature with superclass."""
        serializer.save_corpus(fname, corpus, id2word=id2word, progress_cnt=progress_cnt, metadata=metadata, **kwargs)
