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env-llmeval/lib/python3.10/site-packages/nltk/test/bleu.doctest

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+==========
+BLEU tests
+==========
+
+>>> from nltk.translate import bleu
+
+If the candidate has no alignment to any of the references, the BLEU score is 0.
+
+>>> bleu(
+...     ['The candidate has no alignment to any of the references'.split()],
+...     'John loves Mary'.split(),
+...     (1,),
+... )
+0
+
+This is an implementation of the smoothing techniques
+for segment-level BLEU scores that was presented in
+Boxing Chen and Collin Cherry (2014) A Systematic Comparison of
+Smoothing Techniques for Sentence-Level BLEU. In WMT14.
+http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf
+>>> from nltk.translate.bleu_score import sentence_bleu,SmoothingFunction
+
+
+>>> sentence_bleu(
+...     ['It is a place of quiet contemplation .'.split()],
+...     'It is .'.split(),
+...     smoothing_function=SmoothingFunction().method4,
+... )*100
+4.4267...

env-llmeval/lib/python3.10/site-packages/nltk/test/bnc.doctest

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+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+    >>> import os.path
+
+    >>> from nltk.corpus.reader import BNCCorpusReader
+    >>> import nltk.test
+
+    >>> root = os.path.dirname(nltk.test.__file__)
+    >>> bnc = BNCCorpusReader(root=root, fileids='FX8.xml')
+
+Checking the word access.
+-------------------------
+
+    >>> len(bnc.words())
+    151
+
+    >>> bnc.words()[:6]
+    ['Ah', 'there', 'we', 'are', ',', '.']
+    >>> bnc.words(stem=True)[:6]
+    ['ah', 'there', 'we', 'be', ',', '.']
+
+    >>> bnc.tagged_words()[:6]
+    [('Ah', 'INTERJ'), ('there', 'ADV'), ('we', 'PRON'), ('are', 'VERB'), (',', 'PUN'), ('.', 'PUN')]
+
+    >>> bnc.tagged_words(c5=True)[:6]
+    [('Ah', 'ITJ'), ('there', 'AV0'), ('we', 'PNP'), ('are', 'VBB'), (',', 'PUN'), ('.', 'PUN')]
+
+Testing access to the sentences.
+--------------------------------
+
+    >>> len(bnc.sents())
+    15
+
+    >>> bnc.sents()[0]
+    ['Ah', 'there', 'we', 'are', ',', '.']
+    >>> bnc.sents(stem=True)[0]
+    ['ah', 'there', 'we', 'be', ',', '.']
+
+    >>> bnc.tagged_sents()[0]
+    [('Ah', 'INTERJ'), ('there', 'ADV'), ('we', 'PRON'), ('are', 'VERB'), (',', 'PUN'), ('.', 'PUN')]
+    >>> bnc.tagged_sents(c5=True)[0]
+    [('Ah', 'ITJ'), ('there', 'AV0'), ('we', 'PNP'), ('are', 'VBB'), (',', 'PUN'), ('.', 'PUN')]
+
+A not lazy loader.
+------------------
+
+    >>> eager = BNCCorpusReader(root=root, fileids=r'FX8.xml', lazy=False)
+
+    >>> len(eager.words())
+    151
+    >>> eager.words(stem=True)[6:17]
+    ['right', 'abdominal', 'wound', ',', 'she', 'be', 'a', 'wee', 'bit', 'confuse', '.']
+
+    >>> eager.tagged_words()[6:11]
+    [('Right', 'ADV'), ('abdominal', 'ADJ'), ('wound', 'SUBST'), (',', 'PUN'), ('she', 'PRON')]
+    >>> eager.tagged_words(c5=True)[6:17]
+    [('Right', 'AV0'), ('abdominal', 'AJ0'), ('wound', 'NN1'), (',', 'PUN'), ('she', 'PNP'), ("'s", 'VBZ'), ('a', 'AT0'), ('wee', 'AJ0-NN1'), ('bit', 'NN1'), ('confused', 'VVN-AJ0'), ('.', 'PUN')]
+    >>> len(eager.sents())
+    15

env-llmeval/lib/python3.10/site-packages/nltk/test/ccg.doctest

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+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==============================
+Combinatory Categorial Grammar
+==============================
+
+Relative Clauses
+----------------
+
+    >>> from nltk.ccg import chart, lexicon
+
+Construct a lexicon:
+
+    >>> lex = lexicon.fromstring('''
+    ...     :- S, NP, N, VP
+    ...
+    ...     Det :: NP/N
+    ...     Pro :: NP
+    ...     Modal :: S\\NP/VP
+    ...
+    ...     TV :: VP/NP
+    ...     DTV :: TV/NP
+    ...
+    ...     the => Det
+    ...
+    ...     that => Det
+    ...     that => NP
+    ...
+    ...     I => Pro
+    ...     you => Pro
+    ...     we => Pro
+    ...
+    ...     chef => N
+    ...     cake => N
+    ...     children => N
+    ...     dough => N
+    ...
+    ...     will => Modal
+    ...     should => Modal
+    ...     might => Modal
+    ...     must => Modal
+    ...
+    ...     and => var\\.,var/.,var
+    ...
+    ...     to => VP[to]/VP
+    ...
+    ...     without => (VP\\VP)/VP[ing]
+    ...
+    ...     be => TV
+    ...     cook => TV
+    ...     eat => TV
+    ...
+    ...     cooking => VP[ing]/NP
+    ...
+    ...     give => DTV
+    ...
+    ...     is => (S\\NP)/NP
+    ...     prefer => (S\\NP)/NP
+    ...
+    ...     which => (N\\N)/(S/NP)
+    ...
+    ...     persuade => (VP/VP[to])/NP
+    ...     ''')
+
+    >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet)
+    >>> for parse in parser.parse("you prefer that cake".split()):
+    ...     chart.printCCGDerivation(parse)
+    ...     break
+    ...
+     you    prefer      that   cake
+     NP   ((S\NP)/NP)  (NP/N)   N
+                      -------------->
+                            NP
+         --------------------------->
+                   (S\NP)
+    --------------------------------<
+                   S
+
+    >>> for parse in parser.parse("that is the cake which you prefer".split()):
+    ...     chart.printCCGDerivation(parse)
+    ...     break
+    ...
+     that      is        the    cake      which       you    prefer
+      NP   ((S\NP)/NP)  (NP/N)   N    ((N\N)/(S/NP))  NP   ((S\NP)/NP)
+                                                     ----->T
+                                                  (S/(S\NP))
+                                                     ------------------>B
+                                                           (S/NP)
+                                     ---------------------------------->
+                                                   (N\N)
+                               ----------------------------------------<
+                                                  N
+                       ------------------------------------------------>
+                                              NP
+          ------------------------------------------------------------->
+                                     (S\NP)
+    -------------------------------------------------------------------<
+                                     S
+
+
+Some other sentences to try:
+"that is the cake which we will persuade the chef to cook"
+"that is the cake which we will persuade the chef to give the children"
+
+    >>> sent = "that is the dough which you will eat without cooking".split()
+    >>> nosub_parser = chart.CCGChartParser(lex, chart.ApplicationRuleSet +
+    ...                       chart.CompositionRuleSet + chart.TypeRaiseRuleSet)
+
+Without Substitution (no output)
+
+    >>> for parse in nosub_parser.parse(sent):
+    ...     chart.printCCGDerivation(parse)
+
+With Substitution:
+
+    >>> for parse in parser.parse(sent):
+    ...     chart.printCCGDerivation(parse)
+    ...     break
+    ...
+     that      is        the    dough      which       you     will        eat          without           cooking
+      NP   ((S\NP)/NP)  (NP/N)    N    ((N\N)/(S/NP))  NP   ((S\NP)/VP)  (VP/NP)  ((VP\VP)/VP['ing'])  (VP['ing']/NP)
+                                                      ----->T
+                                                   (S/(S\NP))
+                                                                                 ------------------------------------->B
+                                                                                             ((VP\VP)/NP)
+                                                                        ----------------------------------------------<Sx
+                                                                                           (VP/NP)
+                                                           ----------------------------------------------------------->B
+                                                                                   ((S\NP)/NP)
+                                                      ---------------------------------------------------------------->B
+                                                                                   (S/NP)
+                                      -------------------------------------------------------------------------------->
+                                                                           (N\N)
+                               ---------------------------------------------------------------------------------------<
+                                                                          N
+                       ----------------------------------------------------------------------------------------------->
+                                                                     NP
+          ------------------------------------------------------------------------------------------------------------>
+                                                             (S\NP)
+    ------------------------------------------------------------------------------------------------------------------<
+                                                            S
+
+
+Conjunction
+-----------
+
+    >>> from nltk.ccg.chart import CCGChartParser, ApplicationRuleSet, CompositionRuleSet
+    >>> from nltk.ccg.chart import SubstitutionRuleSet, TypeRaiseRuleSet, printCCGDerivation
+    >>> from nltk.ccg import lexicon
+
+Lexicons for the tests:
+
+    >>> test1_lex = '''
+    ...        :- S,N,NP,VP
+    ...        I => NP
+    ...        you => NP
+    ...        will => S\\NP/VP
+    ...        cook => VP/NP
+    ...        which => (N\\N)/(S/NP)
+    ...        and => var\\.,var/.,var
+    ...        might => S\\NP/VP
+    ...        eat => VP/NP
+    ...        the => NP/N
+    ...        mushrooms => N
+    ...        parsnips => N'''
+    >>> test2_lex = '''
+    ...         :- N, S, NP, VP
+    ...         articles => N
+    ...         the => NP/N
+    ...         and => var\\.,var/.,var
+    ...         which => (N\\N)/(S/NP)
+    ...         I => NP
+    ...         anyone => NP
+    ...         will => (S/VP)\\NP
+    ...         file => VP/NP
+    ...         without => (VP\\VP)/VP[ing]
+    ...         forget => VP/NP
+    ...         reading => VP[ing]/NP
+    ...         '''
+
+Tests handling of conjunctions.
+Note that while the two derivations are different, they are semantically equivalent.
+
+    >>> lex = lexicon.fromstring(test1_lex)
+    >>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet)
+    >>> for parse in parser.parse("I will cook and might eat the mushrooms and parsnips".split()):
+    ...     printCCGDerivation(parse)
+     I      will       cook               and                might       eat     the    mushrooms             and             parsnips
+     NP  ((S\NP)/VP)  (VP/NP)  ((_var0\.,_var0)/.,_var0)  ((S\NP)/VP)  (VP/NP)  (NP/N)      N      ((_var0\.,_var0)/.,_var0)     N
+        ---------------------->B
+             ((S\NP)/NP)
+                                                         ---------------------->B
+                                                              ((S\NP)/NP)
+                              ------------------------------------------------->
+                                         (((S\NP)/NP)\.,((S\NP)/NP))
+        -----------------------------------------------------------------------<
+                                      ((S\NP)/NP)
+                                                                                                  ------------------------------------->
+                                                                                                                 (N\.,N)
+                                                                                       ------------------------------------------------<
+                                                                                                              N
+                                                                               -------------------------------------------------------->
+                                                                                                          NP
+        ------------------------------------------------------------------------------------------------------------------------------->
+                                                                    (S\NP)
+    -----------------------------------------------------------------------------------------------------------------------------------<
+                                                                     S
+     I      will       cook               and                might       eat     the    mushrooms             and             parsnips
+     NP  ((S\NP)/VP)  (VP/NP)  ((_var0\.,_var0)/.,_var0)  ((S\NP)/VP)  (VP/NP)  (NP/N)      N      ((_var0\.,_var0)/.,_var0)     N
+        ---------------------->B
+             ((S\NP)/NP)
+                                                         ---------------------->B
+                                                              ((S\NP)/NP)
+                              ------------------------------------------------->
+                                         (((S\NP)/NP)\.,((S\NP)/NP))
+        -----------------------------------------------------------------------<
+                                      ((S\NP)/NP)
+        ------------------------------------------------------------------------------->B
+                                          ((S\NP)/N)
+                                                                                                  ------------------------------------->
+                                                                                                                 (N\.,N)
+                                                                                       ------------------------------------------------<
+                                                                                                              N
+        ------------------------------------------------------------------------------------------------------------------------------->
+                                                                    (S\NP)
+    -----------------------------------------------------------------------------------------------------------------------------------<
+                                                                     S
+
+
+Tests handling subject extraction.
+Interesting to point that the two parses are clearly semantically different.
+
+    >>> lex = lexicon.fromstring(test2_lex)
+    >>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet)
+    >>> for parse in parser.parse("articles which I will file and forget without reading".split()):
+    ...     printCCGDerivation(parse)
+     articles      which       I      will       file               and             forget         without           reading
+        N      ((N\N)/(S/NP))  NP  ((S/VP)\NP)  (VP/NP)  ((_var0\.,_var0)/.,_var0)  (VP/NP)  ((VP\VP)/VP['ing'])  (VP['ing']/NP)
+                              -----------------<
+                                   (S/VP)
+                                                                                            ------------------------------------->B
+                                                                                                        ((VP\VP)/NP)
+                                                                                   ----------------------------------------------<Sx
+                                                                                                      (VP/NP)
+                                                        ------------------------------------------------------------------------->
+                                                                                   ((VP/NP)\.,(VP/NP))
+                                               ----------------------------------------------------------------------------------<
+                                                                                    (VP/NP)
+                              --------------------------------------------------------------------------------------------------->B
+                                                                            (S/NP)
+              ------------------------------------------------------------------------------------------------------------------->
+                                                                     (N\N)
+    -----------------------------------------------------------------------------------------------------------------------------<
+                                                                  N
+     articles      which       I      will       file               and             forget         without           reading
+        N      ((N\N)/(S/NP))  NP  ((S/VP)\NP)  (VP/NP)  ((_var0\.,_var0)/.,_var0)  (VP/NP)  ((VP\VP)/VP['ing'])  (VP['ing']/NP)
+                              -----------------<
+                                   (S/VP)
+                                                        ------------------------------------>
+                                                                ((VP/NP)\.,(VP/NP))
+                                               ---------------------------------------------<
+                                                                  (VP/NP)
+                                                                                            ------------------------------------->B
+                                                                                                        ((VP\VP)/NP)
+                                               ----------------------------------------------------------------------------------<Sx
+                                                                                    (VP/NP)
+                              --------------------------------------------------------------------------------------------------->B
+                                                                            (S/NP)
+              ------------------------------------------------------------------------------------------------------------------->
+                                                                     (N\N)
+    -----------------------------------------------------------------------------------------------------------------------------<
+                                                                  N
+
+
+Unicode support
+---------------
+
+Unicode words are supported.
+
+    >>> from nltk.ccg import chart, lexicon
+
+Lexicons for the tests:
+
+    >>> lex = lexicon.fromstring('''
+    ...        :- S, N, NP, PP
+    ...
+    ...        AdjI :: N\\N
+    ...        AdjD :: N/N
+    ...        AdvD :: S/S
+    ...        AdvI :: S\\S
+    ...        Det :: NP/N
+    ...        PrepNPCompl :: PP/NP
+    ...        PrepNAdjN :: S\\S/N
+    ...        PrepNAdjNP :: S\\S/NP
+    ...        VPNP :: S\\NP/NP
+    ...        VPPP :: S\\NP/PP
+    ...        VPser :: S\\NP/AdjI
+    ...
+    ...        auto => N
+    ...        bebidas => N
+    ...        cine => N
+    ...        ley => N
+    ...        libro => N
+    ...        ministro => N
+    ...        panadería => N
+    ...        presidente => N
+    ...        super => N
+    ...
+    ...        el => Det
+    ...        la => Det
+    ...        las => Det
+    ...        un => Det
+    ...
+    ...        Ana => NP
+    ...        Pablo => NP
+    ...
+    ...        y => var\\.,var/.,var
+    ...
+    ...        pero => (S/NP)\\(S/NP)/(S/NP)
+    ...
+    ...        anunció => VPNP
+    ...        compró => VPNP
+    ...        cree => S\\NP/S[dep]
+    ...        desmintió => VPNP
+    ...        lee => VPNP
+    ...        fueron => VPPP
+    ...
+    ...        es => VPser
+    ...
+    ...        interesante => AdjD
+    ...        interesante => AdjI
+    ...        nueva => AdjD
+    ...        nueva => AdjI
+    ...
+    ...        a => PrepNPCompl
+    ...        en => PrepNAdjN
+    ...        en => PrepNAdjNP
+    ...
+    ...        ayer => AdvI
+    ...
+    ...        que => (NP\\NP)/(S/NP)
+    ...        que => S[dep]/S
+    ...     ''')
+
+    >>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet)
+    >>> for parse in parser.parse(u"el ministro anunció pero el presidente desmintió la nueva ley".split()):
+    ...     printCCGDerivation(parse) # doctest: +SKIP
+    ...     # it fails on python2.7 because of the unicode problem explained in https://github.com/nltk/nltk/pull/1354
+    ...     break
+       el    ministro    anunció              pero              el    presidente   desmintió     la    nueva  ley
+     (NP/N)     N      ((S\NP)/NP)  (((S/NP)\(S/NP))/(S/NP))  (NP/N)      N       ((S\NP)/NP)  (NP/N)  (N/N)   N
+    ------------------>
+            NP
+    ------------------>T
+        (S/(S\NP))
+                                                             -------------------->
+                                                                      NP
+                                                             -------------------->T
+                                                                  (S/(S\NP))
+                                                             --------------------------------->B
+                                                                          (S/NP)
+                                   ----------------------------------------------------------->
+                                                         ((S/NP)\(S/NP))
+                                                                                                      ------------>
+                                                                                                           N
+                                                                                              -------------------->
+                                                                                                       NP
+                                                                                              --------------------<T
+                                                                                                   (S\(S/NP))
+                                   -------------------------------------------------------------------------------<B
+                                                                     (S\(S/NP))
+                      --------------------------------------------------------------------------------------------<B
+                                                                 (S/NP)
+    -------------------------------------------------------------------------------------------------------------->
+                                                          S

env-llmeval/lib/python3.10/site-packages/nltk/test/chat80.doctest

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+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=======
+Chat-80
+=======
+
+Chat-80 was a natural language system which allowed the user to
+interrogate a Prolog knowledge base in the domain of world
+geography. It was developed in the early '80s by Warren and Pereira; see
+`<https://aclanthology.org/J82-3002.pdf>`_ for a description and
+`<http://www.cis.upenn.edu/~pereira/oldies.html>`_ for the source
+files.
+
+The ``chat80`` module contains functions to extract data from the Chat-80
+relation files ('the world database'), and convert then into a format
+that can be incorporated in the FOL models of
+``nltk.sem.evaluate``. The code assumes that the Prolog
+input files are available in the NLTK corpora directory.
+
+The Chat-80 World Database consists of the following files::
+
+    world0.pl
+    rivers.pl
+    cities.pl
+    countries.pl
+    contain.pl
+    borders.pl
+
+This module uses a slightly modified version of ``world0.pl``, in which
+a set of Prolog rules have been omitted. The modified file is named
+``world1.pl``. Currently, the file ``rivers.pl`` is not read in, since
+it uses a list rather than a string in the second field.
+
+Reading Chat-80 Files
+=====================
+
+Chat-80 relations are like tables in a relational database. The
+relation acts as the name of the table; the first argument acts as the
+'primary key'; and subsequent arguments are further fields in the
+table. In general, the name of the table provides a label for a unary
+predicate whose extension is all the primary keys. For example,
+relations in ``cities.pl`` are of the following form::
+
+   'city(athens,greece,1368).'
+
+Here, ``'athens'`` is the key, and will be mapped to a member of the
+unary predicate *city*.
+
+By analogy with NLTK corpora, ``chat80`` defines a number of 'items'
+which correspond to the relations.
+
+    >>> from nltk.sem import chat80
+    >>> print(chat80.items)
+    ('borders', 'circle_of_lat', 'circle_of_long', 'city', ...)
+
+The fields in the table are mapped to binary predicates. The first
+argument of the predicate is the primary key, while the second
+argument is the data in the relevant field. Thus, in the above
+example, the third field is mapped to the binary predicate
+*population_of*, whose extension is a set of pairs such as
+``'(athens, 1368)'``.
+
+An exception to this general framework is required by the relations in
+the files ``borders.pl`` and ``contains.pl``. These contain facts of the
+following form::
+
+    'borders(albania,greece).'
+
+    'contains0(africa,central_africa).'
+
+We do not want to form a unary concept out the element in
+the first field of these records, and we want the label of the binary
+relation just to be ``'border'``/``'contain'`` respectively.
+
+In order to drive the extraction process, we use 'relation metadata bundles'
+which are Python dictionaries such as the following::
+
+  city = {'label': 'city',
+          'closures': [],
+          'schema': ['city', 'country', 'population'],
+          'filename': 'cities.pl'}
+
+According to this, the file ``city['filename']`` contains a list of
+relational tuples (or more accurately, the corresponding strings in
+Prolog form) whose predicate symbol is ``city['label']`` and whose
+relational schema is ``city['schema']``. The notion of a ``closure`` is
+discussed in the next section.
+
+Concepts
+========
+In order to encapsulate the results of the extraction, a class of
+``Concept``\ s is introduced.  A ``Concept`` object has a number of
+attributes, in particular a ``prefLabel``, an arity and ``extension``.
+
+    >>> c1 = chat80.Concept('dog', arity=1, extension=set(['d1', 'd2']))
+    >>> print(c1)
+    Label = 'dog'
+    Arity = 1
+    Extension = ['d1', 'd2']
+
+
+
+The ``extension`` attribute makes it easier to inspect the output of
+the extraction.
+
+    >>> schema = ['city', 'country', 'population']
+    >>> concepts = chat80.clause2concepts('cities.pl', 'city', schema)
+    >>> concepts
+    [Concept('city'), Concept('country_of'), Concept('population_of')]
+    >>> for c in concepts:
+    ...     print("%s:\n\t%s" % (c.prefLabel, c.extension[:4]))
+    city:
+        ['athens', 'bangkok', 'barcelona', 'berlin']
+    country_of:
+        [('athens', 'greece'), ('bangkok', 'thailand'), ('barcelona', 'spain'), ('berlin', 'east_germany')]
+    population_of:
+        [('athens', '1368'), ('bangkok', '1178'), ('barcelona', '1280'), ('berlin', '3481')]
+
+In addition, the ``extension`` can be further
+processed: in the case of the ``'border'`` relation, we check that the
+relation is **symmetric**, and in the case of the ``'contain'``
+relation, we carry out the **transitive closure**. The closure
+properties associated with a concept is indicated in the relation
+metadata, as indicated earlier.
+
+    >>> borders = set([('a1', 'a2'), ('a2', 'a3')])
+    >>> c2 = chat80.Concept('borders', arity=2, extension=borders)
+    >>> print(c2)
+    Label = 'borders'
+    Arity = 2
+    Extension = [('a1', 'a2'), ('a2', 'a3')]
+    >>> c3 = chat80.Concept('borders', arity=2, closures=['symmetric'], extension=borders)
+    >>> c3.close()
+    >>> print(c3)
+    Label = 'borders'
+    Arity = 2
+    Extension = [('a1', 'a2'), ('a2', 'a1'), ('a2', 'a3'), ('a3', 'a2')]
+
+The ``extension`` of a ``Concept`` object is then incorporated into a
+``Valuation`` object.
+
+Persistence
+===========
+The functions ``val_dump`` and ``val_load`` are provided to allow a
+valuation to be stored in a persistent database and re-loaded, rather
+than having to be re-computed each time.
+
+Individuals and Lexical Items
+=============================
+As well as deriving relations from the Chat-80 data, we also create a
+set of individual constants, one for each entity in the domain. The
+individual constants are string-identical to the entities. For
+example, given a data item such as ``'zloty'``, we add to the valuation
+a pair ``('zloty', 'zloty')``. In order to parse English sentences that
+refer to these entities, we also create a lexical item such as the
+following for each individual constant::
+
+   PropN[num=sg, sem=<\P.(P zloty)>] -> 'Zloty'
+
+The set of rules is written to the file ``chat_pnames.fcfg`` in the
+current directory.
+
+SQL Query
+=========
+
+The ``city`` relation is also available in RDB form and can be queried
+using SQL statements.
+
+    >>> import nltk
+    >>> q = "SELECT City, Population FROM city_table WHERE Country = 'china' and Population > 1000"
+    >>> for answer in chat80.sql_query('corpora/city_database/city.db', q):
+    ...     print("%-10s %4s" % answer)
+    canton     1496
+    chungking  1100
+    mukden     1551
+    peking     2031
+    shanghai   5407
+    tientsin   1795
+
+The (deliberately naive) grammar ``sql.fcfg`` translates from English
+to SQL:
+
+    >>> nltk.data.show_cfg('grammars/book_grammars/sql0.fcfg')
+    % start S
+    S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp]
+    VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp]
+    VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap]
+    NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n]
+    PP[SEM=(?p + ?np)] -> P[SEM=?p] NP[SEM=?np]
+    AP[SEM=?pp] -> A[SEM=?a] PP[SEM=?pp]
+    NP[SEM='Country="greece"'] -> 'Greece'
+    NP[SEM='Country="china"'] -> 'China'
+    Det[SEM='SELECT'] -> 'Which' | 'What'
+    N[SEM='City FROM city_table'] -> 'cities'
+    IV[SEM=''] -> 'are'
+    A[SEM=''] -> 'located'
+    P[SEM=''] -> 'in'
+
+Given this grammar, we can express, and then execute, queries in English.
+
+    >>> cp = nltk.parse.load_parser('grammars/book_grammars/sql0.fcfg')
+    >>> query = 'What cities are in China'
+    >>> for tree in cp.parse(query.split()):
+    ...     answer = tree.label()['SEM']
+    ...     q = " ".join(answer)
+    ...     print(q)
+    ...
+    SELECT City FROM city_table WHERE   Country="china"
+
+    >>> rows = chat80.sql_query('corpora/city_database/city.db', q)
+    >>> for r in rows: print("%s" % r, end=' ')
+    canton chungking dairen harbin kowloon mukden peking shanghai sian tientsin
+
+
+Using Valuations
+-----------------
+
+In order to convert such an extension into a valuation, we use the
+``make_valuation()`` method; setting ``read=True`` creates and returns
+a new ``Valuation`` object which contains the results.
+
+   >>> val = chat80.make_valuation(concepts, read=True)
+   >>> 'calcutta' in val['city']
+   True
+   >>> [town for (town, country) in val['country_of'] if country == 'india']
+   ['bombay', 'calcutta', 'delhi', 'hyderabad', 'madras']
+   >>> dom = val.domain
+   >>> g = nltk.sem.Assignment(dom)
+   >>> m = nltk.sem.Model(dom, val)
+   >>> m.evaluate(r'population_of(jakarta, 533)', g)
+   True

env-llmeval/lib/python3.10/site-packages/nltk/test/classify.doctest

@@ -0,0 +1,202 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=============
+ Classifiers
+=============
+
+    >>> from nltk.test.classify_fixt import setup_module
+    >>> setup_module()
+
+Classifiers label tokens with category labels (or *class labels*).
+Typically, labels are represented with strings (such as ``"health"``
+or ``"sports"``.  In NLTK, classifiers are defined using classes that
+implement the `ClassifierI` interface, which supports the following operations:
+
+- self.classify(featureset)
+- self.classify_many(featuresets)
+- self.labels()
+- self.prob_classify(featureset)
+- self.prob_classify_many(featuresets)
+
+NLTK defines several classifier classes:
+
+- `ConditionalExponentialClassifier`
+- `DecisionTreeClassifier`
+- `MaxentClassifier`
+- `NaiveBayesClassifier`
+- `WekaClassifier`
+
+Classifiers are typically created by training them on a training
+corpus.
+
+
+Regression Tests
+~~~~~~~~~~~~~~~~
+
+We define a very simple training corpus with 3 binary features: ['a',
+'b', 'c'], and are two labels: ['x', 'y'].  We use a simple feature set so
+that the correct answers can be calculated analytically (although we
+haven't done this yet for all tests).
+
+    >>> import nltk
+    >>> train = [
+    ...     (dict(a=1,b=1,c=1), 'y'),
+    ...     (dict(a=1,b=1,c=1), 'x'),
+    ...     (dict(a=1,b=1,c=0), 'y'),
+    ...     (dict(a=0,b=1,c=1), 'x'),
+    ...     (dict(a=0,b=1,c=1), 'y'),
+    ...     (dict(a=0,b=0,c=1), 'y'),
+    ...     (dict(a=0,b=1,c=0), 'x'),
+    ...     (dict(a=0,b=0,c=0), 'x'),
+    ...     (dict(a=0,b=1,c=1), 'y'),
+    ...     (dict(a=None,b=1,c=0), 'x'),
+    ...     ]
+    >>> test = [
+    ...     (dict(a=1,b=0,c=1)), # unseen
+    ...     (dict(a=1,b=0,c=0)), # unseen
+    ...     (dict(a=0,b=1,c=1)), # seen 3 times, labels=y,y,x
+    ...     (dict(a=0,b=1,c=0)), # seen 1 time, label=x
+    ...     ]
+
+Test the Naive Bayes classifier:
+
+    >>> classifier = nltk.classify.NaiveBayesClassifier.train(train)
+    >>> sorted(classifier.labels())
+    ['x', 'y']
+    >>> classifier.classify_many(test)
+    ['y', 'x', 'y', 'x']
+    >>> for pdist in classifier.prob_classify_many(test):
+    ...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
+    0.2500 0.7500
+    0.5833 0.4167
+    0.3571 0.6429
+    0.7000 0.3000
+    >>> classifier.show_most_informative_features()
+    Most Informative Features
+                           c = 0                   x : y      =      2.3 : 1.0
+                           c = 1                   y : x      =      1.8 : 1.0
+                           a = 1                   y : x      =      1.7 : 1.0
+                           a = 0                   x : y      =      1.0 : 1.0
+                           b = 0                   x : y      =      1.0 : 1.0
+                           b = 1                   x : y      =      1.0 : 1.0
+
+Test the Decision Tree classifier (without None):
+
+    >>> classifier = nltk.classify.DecisionTreeClassifier.train(
+    ...     train[:-1], entropy_cutoff=0,
+    ...     support_cutoff=0)
+    >>> sorted(classifier.labels())
+    ['x', 'y']
+    >>> print(classifier)
+    c=0? .................................................. x
+      a=0? ................................................ x
+      a=1? ................................................ y
+    c=1? .................................................. y
+    <BLANKLINE>
+    >>> classifier.classify_many(test)
+    ['y', 'y', 'y', 'x']
+    >>> for pdist in classifier.prob_classify_many(test):
+    ...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+
+
+Test the Decision Tree classifier (with None):
+
+    >>> classifier = nltk.classify.DecisionTreeClassifier.train(
+    ...     train, entropy_cutoff=0,
+    ...     support_cutoff=0)
+    >>> sorted(classifier.labels())
+    ['x', 'y']
+    >>> print(classifier)
+    c=0? .................................................. x
+      a=0? ................................................ x
+      a=1? ................................................ y
+      a=None? ............................................. x
+    c=1? .................................................. y
+    <BLANKLINE>
+
+
+Test SklearnClassifier, which requires the scikit-learn package.
+
+    >>> from nltk.classify import SklearnClassifier
+    >>> from sklearn.naive_bayes import BernoulliNB
+    >>> from sklearn.svm import SVC
+    >>> train_data = [({"a": 4, "b": 1, "c": 0}, "ham"),
+    ...               ({"a": 5, "b": 2, "c": 1}, "ham"),
+    ...               ({"a": 0, "b": 3, "c": 4}, "spam"),
+    ...               ({"a": 5, "b": 1, "c": 1}, "ham"),
+    ...               ({"a": 1, "b": 4, "c": 3}, "spam")]
+    >>> classif = SklearnClassifier(BernoulliNB()).train(train_data)
+    >>> test_data = [{"a": 3, "b": 2, "c": 1},
+    ...              {"a": 0, "b": 3, "c": 7}]
+    >>> classif.classify_many(test_data)
+    ['ham', 'spam']
+    >>> classif = SklearnClassifier(SVC(), sparse=False).train(train_data)
+    >>> classif.classify_many(test_data)
+    ['ham', 'spam']
+
+Test the Maximum Entropy classifier training algorithms; they should all
+generate the same results.
+
+    >>> def print_maxent_test_header():
+    ...     print(' '*11+''.join(['      test[%s]  ' % i
+    ...                           for i in range(len(test))]))
+    ...     print(' '*11+'     p(x)  p(y)'*len(test))
+    ...     print('-'*(11+15*len(test)))
+
+    >>> def test_maxent(algorithm):
+    ...     print('%11s' % algorithm, end=' ')
+    ...     try:
+    ...         classifier = nltk.classify.MaxentClassifier.train(
+    ...                         train, algorithm, trace=0, max_iter=1000)
+    ...     except Exception as e:
+    ...         print('Error: %r' % e)
+    ...         return
+    ...
+    ...     for featureset in test:
+    ...         pdist = classifier.prob_classify(featureset)
+    ...         print('%8.2f%6.2f' % (pdist.prob('x'), pdist.prob('y')), end=' ')
+    ...     print()
+
+    >>> print_maxent_test_header(); test_maxent('GIS'); test_maxent('IIS')
+                     test[0]        test[1]        test[2]        test[3]
+                    p(x)  p(y)     p(x)  p(y)     p(x)  p(y)     p(x)  p(y)
+    -----------------------------------------------------------------------
+            GIS     0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
+            IIS     0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
+
+    >>> test_maxent('MEGAM'); test_maxent('TADM') # doctest: +SKIP
+            MEGAM   0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
+            TADM    0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
+
+
+
+Regression tests for TypedMaxentFeatureEncoding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+    >>> from nltk.classify import maxent
+    >>> train = [
+    ...     ({'a': 1, 'b': 1, 'c': 1}, 'y'),
+    ...     ({'a': 5, 'b': 5, 'c': 5}, 'x'),
+    ...     ({'a': 0.9, 'b': 0.9, 'c': 0.9}, 'y'),
+    ...     ({'a': 5.5, 'b': 5.4, 'c': 5.3}, 'x'),
+    ...     ({'a': 0.8, 'b': 1.2, 'c': 1}, 'y'),
+    ...     ({'a': 5.1, 'b': 4.9, 'c': 5.2}, 'x')
+    ... ]
+
+    >>> test = [
+    ...     {'a': 1, 'b': 0.8, 'c': 1.2},
+    ...     {'a': 5.2, 'b': 5.1, 'c': 5}
+    ... ]
+
+    >>> encoding = maxent.TypedMaxentFeatureEncoding.train(
+    ...     train, count_cutoff=3, alwayson_features=True)
+
+    >>> classifier = maxent.MaxentClassifier.train(
+    ...     train, bernoulli=False, encoding=encoding, trace=0)
+
+    >>> classifier.classify_many(test)
+    ['y', 'x']

env-llmeval/lib/python3.10/site-packages/nltk/test/classify_fixt.py

@@ -0,0 +1,5 @@
+# most of classify.doctest requires numpy
+def setup_module():
+    import pytest
+
+    pytest.importorskip("numpy")

env-llmeval/lib/python3.10/site-packages/nltk/test/collocations.doctest

@@ -0,0 +1,307 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==============
+ Collocations
+==============
+
+Overview
+~~~~~~~~
+
+Collocations are expressions of multiple words which commonly co-occur. For
+example, the top ten bigram collocations in Genesis are listed below, as
+measured using Pointwise Mutual Information.
+
+    >>> import nltk
+    >>> from nltk.collocations import *
+    >>> bigram_measures = nltk.collocations.BigramAssocMeasures()
+    >>> trigram_measures = nltk.collocations.TrigramAssocMeasures()
+    >>> fourgram_measures = nltk.collocations.QuadgramAssocMeasures()
+    >>> finder = BigramCollocationFinder.from_words(
+    ...     nltk.corpus.genesis.words('english-web.txt'))
+    >>> finder.nbest(bigram_measures.pmi, 10)
+    [('Allon', 'Bacuth'), ('Ashteroth', 'Karnaim'), ('Ben', 'Ammi'),
+     ('En', 'Mishpat'), ('Jegar', 'Sahadutha'), ('Salt', 'Sea'),
+     ('Whoever', 'sheds'), ('appoint', 'overseers'), ('aromatic', 'resin'),
+     ('cutting', 'instrument')]
+
+While these words are highly collocated, the expressions are also very
+infrequent.  Therefore it is useful to apply filters, such as ignoring all
+bigrams which occur less than three times in the corpus:
+
+    >>> finder.apply_freq_filter(3)
+    >>> finder.nbest(bigram_measures.pmi, 10)
+    [('Beer', 'Lahai'), ('Lahai', 'Roi'), ('gray', 'hairs'),
+     ('ewe', 'lambs'), ('Most', 'High'), ('many', 'colors'),
+     ('burnt', 'offering'), ('Paddan', 'Aram'), ('east', 'wind'),
+     ('living', 'creature')]
+
+We may similarly find collocations among tagged words:
+
+    >>> finder = BigramCollocationFinder.from_words(
+    ...     nltk.corpus.brown.tagged_words('ca01', tagset='universal'))
+    >>> finder.nbest(bigram_measures.pmi, 5)
+    [(('1,119', 'NUM'), ('votes', 'NOUN')),
+     (('1962', 'NUM'), ("governor's", 'NOUN')),
+     (('637', 'NUM'), ('E.', 'NOUN')),
+     (('Alpharetta', 'NOUN'), ('prison', 'NOUN')),
+     (('Bar', 'NOUN'), ('Association', 'NOUN'))]
+
+Or tags alone:
+
+    >>> finder = BigramCollocationFinder.from_words(t for w, t in
+    ...     nltk.corpus.brown.tagged_words('ca01', tagset='universal'))
+    >>> finder.nbest(bigram_measures.pmi, 10)
+    [('PRT', 'VERB'), ('PRON', 'VERB'), ('ADP', 'DET'), ('.', 'PRON'), ('DET', 'ADJ'),
+     ('CONJ', 'PRON'), ('ADP', 'NUM'), ('NUM', '.'), ('ADV', 'ADV'), ('VERB', 'ADV')]
+
+Or spanning intervening words:
+
+    >>> finder = BigramCollocationFinder.from_words(
+    ...     nltk.corpus.genesis.words('english-web.txt'),
+    ...     window_size = 20)
+    >>> finder.apply_freq_filter(2)
+    >>> ignored_words = nltk.corpus.stopwords.words('english')
+    >>> finder.apply_word_filter(lambda w: len(w) < 3 or w.lower() in ignored_words)
+    >>> finder.nbest(bigram_measures.likelihood_ratio, 10)
+    [('chief', 'chief'), ('became', 'father'), ('years', 'became'),
+     ('hundred', 'years'), ('lived', 'became'), ('king', 'king'),
+     ('lived', 'years'), ('became', 'became'), ('chief', 'chiefs'),
+     ('hundred', 'became')]
+
+Finders
+~~~~~~~
+
+The collocations package provides collocation finders which by default
+consider all ngrams in a text as candidate collocations:
+
+    >>> text = "I do not like green eggs and ham, I do not like them Sam I am!"
+    >>> tokens = nltk.wordpunct_tokenize(text)
+    >>> finder = BigramCollocationFinder.from_words(tokens)
+    >>> scored = finder.score_ngrams(bigram_measures.raw_freq)
+    >>> sorted(bigram for bigram, score in scored)
+    [(',', 'I'), ('I', 'am'), ('I', 'do'), ('Sam', 'I'), ('am', '!'),
+     ('and', 'ham'), ('do', 'not'), ('eggs', 'and'), ('green', 'eggs'),
+     ('ham', ','), ('like', 'green'), ('like', 'them'), ('not', 'like'),
+     ('them', 'Sam')]
+
+We could otherwise construct the collocation finder from manually-derived
+FreqDists:
+
+    >>> word_fd = nltk.FreqDist(tokens)
+    >>> bigram_fd = nltk.FreqDist(nltk.bigrams(tokens))
+    >>> finder = BigramCollocationFinder(word_fd, bigram_fd)
+    >>> scored == finder.score_ngrams(bigram_measures.raw_freq)
+    True
+
+A similar interface is provided for trigrams:
+
+    >>> finder = TrigramCollocationFinder.from_words(tokens)
+    >>> scored = finder.score_ngrams(trigram_measures.raw_freq)
+    >>> set(trigram for trigram, score in scored) == set(nltk.trigrams(tokens))
+    True
+
+We may want to select only the top n results:
+
+    >>> sorted(finder.nbest(trigram_measures.raw_freq, 2))
+    [('I', 'do', 'not'), ('do', 'not', 'like')]
+
+Alternatively, we can select those above a minimum score value:
+
+    >>> sorted(finder.above_score(trigram_measures.raw_freq,
+    ...                           1.0 / len(tuple(nltk.trigrams(tokens)))))
+    [('I', 'do', 'not'), ('do', 'not', 'like')]
+
+Now spanning intervening words:
+
+    >>> finder = TrigramCollocationFinder.from_words(tokens)
+    >>> finder = TrigramCollocationFinder.from_words(tokens, window_size=4)
+    >>> sorted(finder.nbest(trigram_measures.raw_freq, 4))
+    [('I', 'do', 'like'), ('I', 'do', 'not'), ('I', 'not', 'like'), ('do', 'not', 'like')]
+
+A closer look at the finder's ngram frequencies:
+
+    >>> sorted(finder.ngram_fd.items(), key=lambda t: (-t[1], t[0]))[:10]
+    [(('I', 'do', 'like'), 2), (('I', 'do', 'not'), 2), (('I', 'not', 'like'), 2),
+     (('do', 'not', 'like'), 2), ((',', 'I', 'do'), 1), ((',', 'I', 'not'), 1),
+     ((',', 'do', 'not'), 1), (('I', 'am', '!'), 1), (('Sam', 'I', '!'), 1),
+     (('Sam', 'I', 'am'), 1)]
+
+A similar interface is provided for fourgrams:
+
+    >>> finder_4grams = QuadgramCollocationFinder.from_words(tokens)
+    >>> scored_4grams = finder_4grams.score_ngrams(fourgram_measures.raw_freq)
+    >>> set(fourgram for fourgram, score in scored_4grams) == set(nltk.ngrams(tokens, n=4))
+    True
+
+Filtering candidates
+~~~~~~~~~~~~~~~~~~~~
+
+All the ngrams in a text are often too many to be useful when finding
+collocations.  It is generally useful to remove some words or punctuation,
+and to require a minimum frequency for candidate collocations.
+
+Given our sample text above, if we remove all trigrams containing personal
+pronouns from candidature, score_ngrams should return 6 less results, and
+'do not like' will be the only candidate which occurs more than once:
+
+    >>> finder = TrigramCollocationFinder.from_words(tokens)
+    >>> len(finder.score_ngrams(trigram_measures.raw_freq))
+    14
+    >>> finder.apply_word_filter(lambda w: w in ('I', 'me'))
+    >>> len(finder.score_ngrams(trigram_measures.raw_freq))
+    8
+    >>> sorted(finder.above_score(trigram_measures.raw_freq,
+    ...                           1.0 / len(tuple(nltk.trigrams(tokens)))))
+    [('do', 'not', 'like')]
+
+Sometimes a filter is a function on the whole ngram, rather than each word,
+such as if we may permit 'and' to appear in the middle of a trigram, but
+not on either edge:
+
+    >>> finder.apply_ngram_filter(lambda w1, w2, w3: 'and' in (w1, w3))
+    >>> len(finder.score_ngrams(trigram_measures.raw_freq))
+    6
+
+Finally, it is often important to remove low frequency candidates, as we
+lack sufficient evidence about their significance as collocations:
+
+    >>> finder.apply_freq_filter(2)
+    >>> len(finder.score_ngrams(trigram_measures.raw_freq))
+    1
+
+Association measures
+~~~~~~~~~~~~~~~~~~~~
+
+A number of measures are available to score collocations or other associations.
+The arguments to measure functions are marginals of a contingency table, in the
+bigram case (n_ii, (n_ix, n_xi), n_xx)::
+
+            w1    ~w1
+         ------ ------
+     w2 | n_ii | n_oi | = n_xi
+         ------ ------
+    ~w2 | n_io | n_oo |
+         ------ ------
+         = n_ix        TOTAL = n_xx
+
+We test their calculation using some known values presented in Manning and
+Schutze's text and other papers.
+
+Student's t: examples from Manning and Schutze 5.3.2
+
+   >>> print('%0.4f' % bigram_measures.student_t(8, (15828, 4675), 14307668))
+   0.9999
+   >>> print('%0.4f' % bigram_measures.student_t(20, (42, 20), 14307668))
+   4.4721
+
+Chi-square: examples from Manning and Schutze 5.3.3
+
+   >>> print('%0.2f' % bigram_measures.chi_sq(8, (15828, 4675), 14307668))
+   1.55
+   >>> print('%0.0f' % bigram_measures.chi_sq(59, (67, 65), 571007))
+   456400
+
+Likelihood ratios: examples from Dunning, CL, 1993
+
+   >>> print('%0.2f' % bigram_measures.likelihood_ratio(110, (2552, 221), 31777))
+   270.72
+   >>> print('%0.2f' % bigram_measures.likelihood_ratio(8, (13, 32), 31777))
+   95.29
+
+Pointwise Mutual Information: examples from Manning and Schutze 5.4
+
+   >>> print('%0.2f' % bigram_measures.pmi(20, (42, 20), 14307668))
+   18.38
+   >>> print('%0.2f' % bigram_measures.pmi(20, (15019, 15629), 14307668))
+   0.29
+
+TODO: Find authoritative results for trigrams.
+
+Using contingency table values
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+While frequency counts make marginals readily available for collocation
+finding, it is common to find published contingency table values. The
+collocations package therefore provides a wrapper, ContingencyMeasures, which
+wraps an association measures class, providing association measures which
+take contingency values as arguments, (n_ii, n_io, n_oi, n_oo) in the
+bigram case.
+
+   >>> from nltk.metrics import ContingencyMeasures
+   >>> cont_bigram_measures = ContingencyMeasures(bigram_measures)
+   >>> print('%0.2f' % cont_bigram_measures.likelihood_ratio(8, 5, 24, 31740))
+   95.29
+   >>> print('%0.2f' % cont_bigram_measures.chi_sq(8, 15820, 4667, 14287173))
+   1.55
+
+Ranking and correlation
+~~~~~~~~~~~~~~~~~~~~~~~
+
+It is useful to consider the results of finding collocations as a ranking, and
+the rankings output using different association measures can be compared using
+the Spearman correlation coefficient.
+
+Ranks can be assigned to a sorted list of results trivially by assigning
+strictly increasing ranks to each result:
+
+    >>> from nltk.metrics.spearman import *
+    >>> results_list = ['item1', 'item2', 'item3', 'item4', 'item5']
+    >>> print(list(ranks_from_sequence(results_list)))
+    [('item1', 0), ('item2', 1), ('item3', 2), ('item4', 3), ('item5', 4)]
+
+If scores are available for each result, we may allow sufficiently similar
+results (differing by no more than rank_gap) to be assigned the same rank:
+
+    >>> results_scored = [('item1', 50.0), ('item2', 40.0), ('item3', 38.0),
+    ...                   ('item4', 35.0), ('item5', 14.0)]
+    >>> print(list(ranks_from_scores(results_scored, rank_gap=5)))
+    [('item1', 0), ('item2', 1), ('item3', 1), ('item4', 1), ('item5', 4)]
+
+The Spearman correlation coefficient gives a number from -1.0 to 1.0 comparing
+two rankings.  A coefficient of 1.0 indicates identical rankings; -1.0 indicates
+exact opposite rankings.
+
+    >>> print('%0.1f' % spearman_correlation(
+    ...         ranks_from_sequence(results_list),
+    ...         ranks_from_sequence(results_list)))
+    1.0
+    >>> print('%0.1f' % spearman_correlation(
+    ...         ranks_from_sequence(reversed(results_list)),
+    ...         ranks_from_sequence(results_list)))
+    -1.0
+    >>> results_list2 = ['item2', 'item3', 'item1', 'item5', 'item4']
+    >>> print('%0.1f' % spearman_correlation(
+    ...        ranks_from_sequence(results_list),
+    ...        ranks_from_sequence(results_list2)))
+    0.6
+    >>> print('%0.1f' % spearman_correlation(
+    ...        ranks_from_sequence(reversed(results_list)),
+    ...        ranks_from_sequence(results_list2)))
+    -0.6
+
+Keywords
+~~~~~~~~
+
+Bigram association metrics can also be used to perform keyword analysis. . For example, this finds the keywords
+associated with the "romance" section of the Brown corpus as measured by likelihood ratio:
+
+    >>> romance = nltk.FreqDist(w.lower() for w in nltk.corpus.brown.words(categories='romance') if w.isalpha())
+    >>> freq = nltk.FreqDist(w.lower() for w in nltk.corpus.brown.words() if w.isalpha())
+
+    >>> key = nltk.FreqDist()
+    >>> for w in romance:
+    ...     key[w] = bigram_measures.likelihood_ratio(romance[w], (freq[w], romance.N()), freq.N())
+
+    >>> for k,v in key.most_common(10):
+    ...     print(f'{k:10s} {v:9.3f}')
+    she         1163.325
+    i            995.961
+    her          930.528
+    you          513.149
+    of           501.891
+    is           463.386
+    had          421.615
+    he           411.000
+    the          347.632
+    said         300.811

env-llmeval/lib/python3.10/site-packages/nltk/test/concordance.doctest

@@ -0,0 +1,75 @@
+.. Copyright (C) 2001-2016 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==================================
+Concordance Example
+==================================
+
+A concordance view shows us every occurrence of a given
+word, together with some context. Here we look up the word monstrous
+in Moby Dick by entering text1 followed by a period, then the term
+concordance, and then placing "monstrous" in parentheses:
+
+>>> from nltk.corpus import gutenberg
+>>> from nltk.text import Text
+>>> corpus = gutenberg.words('melville-moby_dick.txt')
+>>> text = Text(corpus)
+
+>>> text.concordance("monstrous")
+Displaying 11 of 11 matches:
+ong the former , one was of a most monstrous size . ... This came towards us ,
+ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r
+ll over with a heathenish array of monstrous clubs and spears . Some were thick
+d as you gazed , and wondered what monstrous cannibal and savage could ever hav
+that has survived the flood ; most monstrous and most mountainous ! That Himmal
+they might scout at Moby Dick as a monstrous fable , or still worse and more de
+th of Radney .'" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l
+ing Scenes . In connexion with the monstrous pictures of whales , I am strongly
+ere to enter upon those still more monstrous stories of them which are to be fo
+ght have been rummaged out of this monstrous cabinet there is no telling . But
+of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u
+
+>>> text.concordance("monstrous")
+Displaying 11 of 11 matches:
+ong the former , one was of a most monstrous size . ... This came towards us ,
+ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r
+ll over with a heathenish array of monstrous clubs and spears . Some were thick
+...
+
+We can also search for a multi-word phrase by passing a list of strings:
+
+>>> text.concordance(["monstrous", "size"])
+Displaying 2 of 2 matches:
+the former , one was of a most monstrous size . ... This came towards us , op
+Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead upo
+
+=================================
+Concordance List
+=================================
+
+Often we need to store the results of concordance for further usage.
+To do so, call the concordance function with the stdout argument set
+to false:
+
+>>> from nltk.corpus import gutenberg
+>>> from nltk.text import Text
+>>> corpus = gutenberg.words('melville-moby_dick.txt')
+>>> text = Text(corpus)
+>>> con_list = text.concordance_list("monstrous")
+>>> con_list[2].line
+'ll over with a heathenish array of monstrous clubs and spears . Some were thick'
+>>> len(con_list)
+11
+
+=================================
+Patching Issue #2088
+=================================
+
+Patching https://github.com/nltk/nltk/issues/2088
+The left slice of the left context should be clip to 0 if the `i-context` < 0.
+
+>>> from nltk import Text, word_tokenize
+>>> jane_eyre = 'Chapter 1\nTHERE was no possibility of taking a walk that day. We had been wandering, indeed, in the leafless shrubbery an hour in the morning; but since dinner (Mrs. Reed, when there was no company, dined early) the cold winter wind had brought with it clouds so sombre, and a rain so penetrating, that further outdoor exercise was now out of the question.'
+>>> text = Text(word_tokenize(jane_eyre))
+>>> text.concordance_list('taking')[0].left
+['Chapter', '1', 'THERE', 'was', 'no', 'possibility', 'of']

env-llmeval/lib/python3.10/site-packages/nltk/test/data.doctest

@@ -0,0 +1,387 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=========================================
+ Loading Resources From the Data Package
+=========================================
+
+    >>> import nltk.data
+
+Overview
+~~~~~~~~
+The `nltk.data` module contains functions that can be used to load
+NLTK resource files, such as corpora, grammars, and saved processing
+objects.
+
+Loading Data Files
+~~~~~~~~~~~~~~~~~~
+Resources are loaded using the function `nltk.data.load()`, which
+takes as its first argument a URL specifying what file should be
+loaded.  The ``nltk:`` protocol loads files from the NLTK data
+distribution:
+
+    >>> tokenizer = nltk.data.load('nltk:tokenizers/punkt/english.pickle')
+    >>> tokenizer.tokenize('Hello.  This is a test.  It works!')
+    ['Hello.', 'This is a test.', 'It works!']
+
+It is important to note that there should be no space following the
+colon (':') in the URL; 'nltk: tokenizers/punkt/english.pickle' will
+not work!
+
+The ``nltk:`` protocol is used by default if no protocol is specified:
+
+    >>> nltk.data.load('tokenizers/punkt/english.pickle')
+    <nltk.tokenize.punkt.PunktSentenceTokenizer object at ...>
+
+But it is also possible to load resources from ``http:``, ``ftp:``,
+and ``file:`` URLs:
+
+    >>> # Load a grammar from the NLTK webpage.
+    >>> cfg = nltk.data.load('https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg')
+    >>> print(cfg)  # doctest: +ELLIPSIS
+    Grammar with 14 productions (start state = S)
+        S -> NP VP
+        PP -> P NP
+        ...
+        P -> 'on'
+        P -> 'in'
+
+    >>> # Load a grammar using an absolute path.
+    >>> url = 'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg')
+    >>> url.replace('\\', '/')
+    'file:...toy.cfg'
+    >>> print(nltk.data.load(url))
+    Grammar with 14 productions (start state = S)
+        S -> NP VP
+        PP -> P NP
+        ...
+        P -> 'on'
+        P -> 'in'
+
+The second argument to the `nltk.data.load()` function specifies the
+file format, which determines how the file's contents are processed
+before they are returned by ``load()``.  The formats that are
+currently supported by the data module are described by the dictionary
+`nltk.data.FORMATS`:
+
+    >>> for format, descr in sorted(nltk.data.FORMATS.items()):
+    ...     print('{0:<7} {1:}'.format(format, descr))
+    cfg     A context free grammar.
+    fcfg    A feature CFG.
+    fol     A list of first order logic expressions, parsed with
+    nltk.sem.logic.Expression.fromstring.
+    json    A serialized python object, stored using the json module.
+    logic   A list of first order logic expressions, parsed with
+    nltk.sem.logic.LogicParser.  Requires an additional logic_parser
+    parameter
+    pcfg    A probabilistic CFG.
+    pickle  A serialized python object, stored using the pickle
+    module.
+    raw     The raw (byte string) contents of a file.
+    text    The raw (unicode string) contents of a file.
+    val     A semantic valuation, parsed by
+    nltk.sem.Valuation.fromstring.
+    yaml    A serialized python object, stored using the yaml module.
+
+`nltk.data.load()` will raise a ValueError if a bad format name is
+specified:
+
+    >>> nltk.data.load('grammars/sample_grammars/toy.cfg', 'bar')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Unknown format type!
+
+By default, the ``"auto"`` format is used, which chooses a format
+based on the filename's extension.  The mapping from file extensions
+to format names is specified by `nltk.data.AUTO_FORMATS`:
+
+    >>> for ext, format in sorted(nltk.data.AUTO_FORMATS.items()):
+    ...     print('.%-7s -> %s' % (ext, format))
+    .cfg     -> cfg
+    .fcfg    -> fcfg
+    .fol     -> fol
+    .json    -> json
+    .logic   -> logic
+    .pcfg    -> pcfg
+    .pickle  -> pickle
+    .text    -> text
+    .txt     -> text
+    .val     -> val
+    .yaml    -> yaml
+
+If `nltk.data.load()` is unable to determine the format based on the
+filename's extension, it will raise a ValueError:
+
+    >>> nltk.data.load('foo.bar')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Could not determine format for foo.bar based on its file
+    extension; use the "format" argument to specify the format explicitly.
+
+Note that by explicitly specifying the ``format`` argument, you can
+override the load method's default processing behavior.  For example,
+to get the raw contents of any file, simply use ``format="raw"``:
+
+    >>> s = nltk.data.load('grammars/sample_grammars/toy.cfg', 'text')
+    >>> print(s)
+    S -> NP VP
+    PP -> P NP
+    NP -> Det N | NP PP
+    VP -> V NP | VP PP
+    ...
+
+Making Local Copies
+~~~~~~~~~~~~~~~~~~~
+..  This will not be visible in the html output: create a tempdir to
+    play in.
+    >>> import tempfile, os
+    >>> tempdir = tempfile.mkdtemp()
+    >>> old_dir = os.path.abspath('.')
+    >>> os.chdir(tempdir)
+
+The function `nltk.data.retrieve()` copies a given resource to a local
+file.  This can be useful, for example, if you want to edit one of the
+sample grammars.
+
+    >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg')
+    Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy.cfg'
+
+    >>> # Simulate editing the grammar.
+    >>> with open('toy.cfg') as inp:
+    ...     s = inp.read().replace('NP', 'DP')
+    >>> with open('toy.cfg', 'w') as out:
+    ...     _bytes_written = out.write(s)
+
+    >>> # Load the edited grammar, & display it.
+    >>> cfg = nltk.data.load('file:///' + os.path.abspath('toy.cfg'))
+    >>> print(cfg)
+    Grammar with 14 productions (start state = S)
+        S -> DP VP
+        PP -> P DP
+        ...
+        P -> 'on'
+        P -> 'in'
+
+The second argument to `nltk.data.retrieve()` specifies the filename
+for the new copy of the file.  By default, the source file's filename
+is used.
+
+    >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg', 'mytoy.cfg')
+    Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'mytoy.cfg'
+    >>> os.path.isfile('./mytoy.cfg')
+    True
+    >>> nltk.data.retrieve('grammars/sample_grammars/np.fcfg')
+    Retrieving 'nltk:grammars/sample_grammars/np.fcfg', saving to 'np.fcfg'
+    >>> os.path.isfile('./np.fcfg')
+    True
+
+If a file with the specified (or default) filename already exists in
+the current directory, then `nltk.data.retrieve()` will raise a
+ValueError exception.  It will *not* overwrite the file:
+
+    >>> os.path.isfile('./toy.cfg')
+    True
+    >>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg')
+    Traceback (most recent call last):
+      . . .
+    ValueError: File '...toy.cfg' already exists!
+
+..  This will not be visible in the html output: clean up the tempdir.
+    >>> os.chdir(old_dir)
+    >>> for f in os.listdir(tempdir):
+    ...     os.remove(os.path.join(tempdir, f))
+    >>> os.rmdir(tempdir)
+
+Finding Files in the NLTK Data Package
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The `nltk.data.find()` function searches the NLTK data package for a
+given file, and returns a pointer to that file.  This pointer can
+either be a `FileSystemPathPointer` (whose `path` attribute gives the
+absolute path of the file); or a `ZipFilePathPointer`, specifying a
+zipfile and the name of an entry within that zipfile.  Both pointer
+types define the `open()` method, which can be used to read the string
+contents of the file.
+
+    >>> path = nltk.data.find('corpora/abc/rural.txt')
+    >>> str(path)
+    '...rural.txt'
+    >>> print(path.open().read(60).decode())
+    PM denies knowledge of AWB kickbacks
+    The Prime Minister has
+
+Alternatively, the `nltk.data.load()` function can be used with the
+keyword argument ``format="raw"``:
+
+    >>> s = nltk.data.load('corpora/abc/rural.txt', format='raw')[:60]
+    >>> print(s.decode())
+    PM denies knowledge of AWB kickbacks
+    The Prime Minister has
+
+Alternatively, you can use the keyword argument ``format="text"``:
+
+    >>> s = nltk.data.load('corpora/abc/rural.txt', format='text')[:60]
+    >>> print(s)
+    PM denies knowledge of AWB kickbacks
+    The Prime Minister has
+
+Resource Caching
+~~~~~~~~~~~~~~~~
+
+NLTK uses a weakref dictionary to maintain a cache of resources that
+have been loaded.  If you load a resource that is already stored in
+the cache, then the cached copy will be returned.  This behavior can
+be seen by the trace output generated when verbose=True:
+
+    >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True)
+    <<Loading nltk:grammars/book_grammars/feat0.fcfg>>
+    >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True)
+    <<Using cached copy of nltk:grammars/book_grammars/feat0.fcfg>>
+
+If you wish to load a resource from its source, bypassing the cache,
+use the ``cache=False`` argument to `nltk.data.load()`.  This can be
+useful, for example, if the resource is loaded from a local file, and
+you are actively editing that file:
+
+    >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg',cache=False,verbose=True)
+    <<Loading nltk:grammars/book_grammars/feat0.fcfg>>
+
+The cache *no longer* uses weak references.  A resource will not be
+automatically expunged from the cache when no more objects are using
+it.  In the following example, when we clear the variable ``feat0``,
+the reference count for the feature grammar object drops to zero.
+However, the object remains cached:
+
+    >>> del feat0
+    >>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg',
+    ...                        verbose=True)
+    <<Using cached copy of nltk:grammars/book_grammars/feat0.fcfg>>
+
+You can clear the entire contents of the cache, using
+`nltk.data.clear_cache()`:
+
+    >>> nltk.data.clear_cache()
+
+Retrieving other Data Sources
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    >>> formulas = nltk.data.load('grammars/book_grammars/background.fol')
+    >>> for f in formulas: print(str(f))
+    all x.(boxerdog(x) -> dog(x))
+    all x.(boxer(x) -> person(x))
+    all x.-(dog(x) & person(x))
+    all x.(married(x) <-> exists y.marry(x,y))
+    all x.(bark(x) -> dog(x))
+    all x y.(marry(x,y) -> (person(x) & person(y)))
+    -(Vincent = Mia)
+    -(Vincent = Fido)
+    -(Mia = Fido)
+
+Regression Tests
+~~~~~~~~~~~~~~~~
+Create a temp dir for tests that write files:
+
+    >>> import tempfile, os
+    >>> tempdir = tempfile.mkdtemp()
+    >>> old_dir = os.path.abspath('.')
+    >>> os.chdir(tempdir)
+
+The `retrieve()` function accepts all url types:
+
+    >>> urls = ['https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg',
+    ...         'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg'),
+    ...         'nltk:grammars/sample_grammars/toy.cfg',
+    ...         'grammars/sample_grammars/toy.cfg']
+    >>> for i, url in enumerate(urls):
+    ...     nltk.data.retrieve(url, 'toy-%d.cfg' % i)
+    Retrieving 'https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg', saving to 'toy-0.cfg'
+    Retrieving 'file:...toy.cfg', saving to 'toy-1.cfg'
+    Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-2.cfg'
+    Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-3.cfg'
+
+Clean up the temp dir:
+
+    >>> os.chdir(old_dir)
+    >>> for f in os.listdir(tempdir):
+    ...     os.remove(os.path.join(tempdir, f))
+    >>> os.rmdir(tempdir)
+
+Lazy Loader
+-----------
+A lazy loader is a wrapper object that defers loading a resource until
+it is accessed or used in any way.  This is mainly intended for
+internal use by NLTK's corpus readers.
+
+    >>> # Create a lazy loader for toy.cfg.
+    >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg')
+
+    >>> # Show that it's not loaded yet:
+    >>> object.__repr__(ll)
+    '<nltk.data.LazyLoader object at ...>'
+
+    >>> # printing it is enough to cause it to be loaded:
+    >>> print(ll)
+    <Grammar with 14 productions>
+
+    >>> # Show that it's now been loaded:
+    >>> object.__repr__(ll)
+    '<nltk.grammar.CFG object at ...>'
+
+
+    >>> # Test that accessing an attribute also loads it:
+    >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg')
+    >>> ll.start()
+    S
+    >>> object.__repr__(ll)
+    '<nltk.grammar.CFG object at ...>'
+
+Buffered Gzip Reading and Writing
+---------------------------------
+Write performance to gzip-compressed is extremely poor when the files become large.
+File creation can become a bottleneck in those cases.
+
+Read performance from large gzipped pickle files was improved in data.py by
+buffering the reads. A similar fix can be applied to writes by buffering
+the writes to a StringIO object first.
+
+This is mainly intended for internal use. The test simply tests that reading
+and writing work as intended and does not test how much improvement buffering
+provides.
+
+    >>> from io import StringIO
+    >>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'wb', size=2**10)
+    >>> ans = []
+    >>> for i in range(10000):
+    ...     ans.append(str(i).encode('ascii'))
+    ...     test.write(str(i).encode('ascii'))
+    >>> test.close()
+    >>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'rb')
+    >>> test.read() == b''.join(ans)
+    True
+    >>> test.close()
+    >>> import os
+    >>> os.unlink('testbuf.gz')
+
+JSON Encoding and Decoding
+--------------------------
+JSON serialization is used instead of pickle for some classes.
+
+    >>> from nltk import jsontags
+    >>> from nltk.jsontags import JSONTaggedEncoder, JSONTaggedDecoder, register_tag
+    >>> @jsontags.register_tag
+    ... class JSONSerializable:
+    ...     json_tag = 'JSONSerializable'
+    ...
+    ...     def __init__(self, n):
+    ...         self.n = n
+    ...
+    ...     def encode_json_obj(self):
+    ...         return self.n
+    ...
+    ...     @classmethod
+    ...     def decode_json_obj(cls, obj):
+    ...         n = obj
+    ...         return cls(n)
+    ...
+    >>> JSONTaggedEncoder().encode(JSONSerializable(1))
+    '{"!JSONSerializable": 1}'
+    >>> JSONTaggedDecoder().decode('{"!JSONSerializable": 1}').n
+    1

env-llmeval/lib/python3.10/site-packages/nltk/test/discourse.doctest

@@ -0,0 +1,552 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==================
+Discourse Checking
+==================
+
+    >>> from nltk import *
+    >>> from nltk.sem import logic
+    >>> logic._counter._value = 0
+
+Setup
+=====
+
+    >>> from nltk.test.childes_fixt import setup_module
+    >>> setup_module()
+
+Introduction
+============
+
+The NLTK discourse module makes it possible to test consistency and
+redundancy of simple discourses, using theorem-proving and
+model-building from `nltk.inference`.
+
+The ``DiscourseTester`` constructor takes a list of sentences as a
+parameter.
+
+    >>> dt = DiscourseTester(['a boxer walks', 'every boxer chases a girl'])
+
+The ``DiscourseTester`` parses each sentence into a list of logical
+forms.  Once we have created ``DiscourseTester`` object, we can
+inspect various properties of the discourse. First off, we might want
+to double-check what sentences are currently stored as the discourse.
+
+    >>> dt.sentences()
+    s0: a boxer walks
+    s1: every boxer chases a girl
+
+As you will see, each sentence receives an identifier `s`\ :subscript:`i`.
+We might also want to check what grammar the ``DiscourseTester`` is
+using (by default, ``book_grammars/discourse.fcfg``):
+
+    >>> dt.grammar()
+    % start S
+    # Grammar Rules
+    S[SEM = <app(?subj,?vp)>] -> NP[NUM=?n,SEM=?subj] VP[NUM=?n,SEM=?vp]
+    NP[NUM=?n,SEM=<app(?det,?nom)> ] -> Det[NUM=?n,SEM=?det]  Nom[NUM=?n,SEM=?nom]
+    NP[LOC=?l,NUM=?n,SEM=?np] -> PropN[LOC=?l,NUM=?n,SEM=?np]
+    ...
+
+A different grammar can be invoked by using the optional ``gramfile``
+parameter when a ``DiscourseTester`` object is created.
+
+Readings and Threads
+====================
+
+Depending on
+the grammar used, we may find some sentences have more than one
+logical form. To check this, use the ``readings()`` method. Given a
+sentence identifier of the form `s`\ :subscript:`i`, each reading of
+that sentence is given an identifier `s`\ :sub:`i`-`r`\ :sub:`j`.
+
+
+    >>> dt.readings()
+    <BLANKLINE>
+    s0 readings:
+    <BLANKLINE>
+    s0-r0: exists z1.(boxer(z1) & walk(z1))
+    s0-r1: exists z1.(boxerdog(z1) & walk(z1))
+    <BLANKLINE>
+    s1 readings:
+    <BLANKLINE>
+    s1-r0: all z2.(boxer(z2) -> exists z3.(girl(z3) & chase(z2,z3)))
+    s1-r1: all z1.(boxerdog(z1) -> exists z2.(girl(z2) & chase(z1,z2)))
+
+
+In this case, the only source of ambiguity lies in the word *boxer*,
+which receives two translations: ``boxer`` and ``boxerdog``. The
+intention is that one of these corresponds to the ``person`` sense and
+one to the ``dog`` sense. In principle, we would also expect to see a
+quantifier scope ambiguity in ``s1``. However, the simple grammar we
+are using, namely `sem4.fcfg <sem4.fcfg>`_, doesn't support quantifier
+scope ambiguity.
+
+We can also investigate the readings of a specific sentence:
+
+    >>> dt.readings('a boxer walks')
+    The sentence 'a boxer walks' has these readings:
+        exists x.(boxer(x) & walk(x))
+        exists x.(boxerdog(x) & walk(x))
+
+Given that each sentence is two-ways ambiguous, we potentially have
+four different discourse 'threads', taking all combinations of
+readings. To see these, specify the ``threaded=True`` parameter on
+the ``readings()`` method. Again, each thread is assigned an
+identifier of the form `d`\ :sub:`i`. Following the identifier is a
+list of the readings that constitute that thread.
+
+    >>> dt.readings(threaded=True)
+    d0: ['s0-r0', 's1-r0']
+    d1: ['s0-r0', 's1-r1']
+    d2: ['s0-r1', 's1-r0']
+    d3: ['s0-r1', 's1-r1']
+
+Of course, this simple-minded approach doesn't scale: a discourse with, say, three
+sentences, each of which has 3 readings, will generate 27 different
+threads. It is an interesting exercise to consider how to manage
+discourse ambiguity more efficiently.
+
+Checking Consistency
+====================
+
+Now, we can check whether some or all of the discourse threads are
+consistent, using the ``models()`` method. With no parameter, this
+method will try to find a model for every discourse thread in the
+current discourse. However, we can also specify just one thread, say ``d1``.
+
+    >>> dt.models('d1')
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d1
+    --------------------------------------------------------------------------------
+    % number = 1
+    % seconds = 0
+    <BLANKLINE>
+    % Interpretation of size 2
+    <BLANKLINE>
+    c1 = 0.
+    <BLANKLINE>
+    f1(0) = 0.
+    f1(1) = 0.
+    <BLANKLINE>
+      boxer(0).
+    - boxer(1).
+    <BLANKLINE>
+    - boxerdog(0).
+    - boxerdog(1).
+    <BLANKLINE>
+    - girl(0).
+    - girl(1).
+    <BLANKLINE>
+      walk(0).
+    - walk(1).
+    <BLANKLINE>
+    - chase(0,0).
+    - chase(0,1).
+    - chase(1,0).
+    - chase(1,1).
+    <BLANKLINE>
+    Consistent discourse: d1 ['s0-r0', 's1-r1']:
+        s0-r0: exists z1.(boxer(z1) & walk(z1))
+        s1-r1: all z1.(boxerdog(z1) -> exists z2.(girl(z2) & chase(z1,z2)))
+    <BLANKLINE>
+
+There are various formats for rendering **Mace4** models --- here,
+we have used the 'cooked' format (which is intended to be
+human-readable). There are a number of points to note.
+
+#. The entities in the domain are all treated as non-negative
+   integers. In this case, there are only two entities, ``0`` and
+   ``1``.
+
+#. The ``-`` symbol indicates negation. So ``0`` is the only
+   ``boxerdog`` and the only thing that ``walk``\ s. Nothing is a
+   ``boxer``, or a ``girl`` or in the ``chase`` relation. Thus the
+   universal sentence is vacuously true.
+
+#. ``c1`` is an introduced constant that denotes ``0``.
+
+#. ``f1`` is a Skolem function, but it plays no significant role in
+   this model.
+
+
+We might want to now add another sentence to the discourse, and there
+is method ``add_sentence()`` for doing just this.
+
+    >>> dt.add_sentence('John is a boxer')
+    >>> dt.sentences()
+    s0: a boxer walks
+    s1: every boxer chases a girl
+    s2: John is a boxer
+
+We can now test all the properties as before; here, we just show a
+couple of them.
+
+    >>> dt.readings()
+    <BLANKLINE>
+    s0 readings:
+    <BLANKLINE>
+    s0-r0: exists z1.(boxer(z1) & walk(z1))
+    s0-r1: exists z1.(boxerdog(z1) & walk(z1))
+    <BLANKLINE>
+    s1 readings:
+    <BLANKLINE>
+    s1-r0: all z1.(boxer(z1) -> exists z2.(girl(z2) & chase(z1,z2)))
+    s1-r1: all z1.(boxerdog(z1) -> exists z2.(girl(z2) & chase(z1,z2)))
+    <BLANKLINE>
+    s2 readings:
+    <BLANKLINE>
+    s2-r0: boxer(John)
+    s2-r1: boxerdog(John)
+    >>> dt.readings(threaded=True)
+    d0: ['s0-r0', 's1-r0', 's2-r0']
+    d1: ['s0-r0', 's1-r0', 's2-r1']
+    d2: ['s0-r0', 's1-r1', 's2-r0']
+    d3: ['s0-r0', 's1-r1', 's2-r1']
+    d4: ['s0-r1', 's1-r0', 's2-r0']
+    d5: ['s0-r1', 's1-r0', 's2-r1']
+    d6: ['s0-r1', 's1-r1', 's2-r0']
+    d7: ['s0-r1', 's1-r1', 's2-r1']
+
+If you are interested in a particular thread, the ``expand_threads()``
+method will remind you of what readings it consists of:
+
+    >>> thread = dt.expand_threads('d1')
+    >>> for rid, reading in thread:
+    ...     print(rid, str(reading.normalize()))
+    s0-r0 exists z1.(boxer(z1) & walk(z1))
+    s1-r0 all z1.(boxer(z1) -> exists z2.(girl(z2) & chase(z1,z2)))
+    s2-r1 boxerdog(John)
+
+Suppose we have already defined a discourse, as follows:
+
+    >>> dt = DiscourseTester(['A student dances', 'Every student is a person'])
+
+Now, when we add a new sentence, is it consistent with what we already
+have? The `` consistchk=True`` parameter of ``add_sentence()`` allows
+us to check:
+
+    >>> dt.add_sentence('No person dances', consistchk=True)
+    Inconsistent discourse: d0 ['s0-r0', 's1-r0', 's2-r0']:
+        s0-r0: exists z1.(student(z1) & dance(z1))
+        s1-r0: all z1.(student(z1) -> person(z1))
+        s2-r0: -exists z1.(person(z1) & dance(z1))
+    <BLANKLINE>
+    >>> dt.readings()
+    <BLANKLINE>
+    s0 readings:
+    <BLANKLINE>
+    s0-r0: exists z1.(student(z1) & dance(z1))
+    <BLANKLINE>
+    s1 readings:
+    <BLANKLINE>
+    s1-r0: all z1.(student(z1) -> person(z1))
+    <BLANKLINE>
+    s2 readings:
+    <BLANKLINE>
+    s2-r0: -exists z1.(person(z1) & dance(z1))
+
+So let's retract the inconsistent sentence:
+
+    >>> dt.retract_sentence('No person dances', verbose=True)
+    Current sentences are
+    s0: A student dances
+    s1: Every student is a person
+
+We can now verify that result is consistent.
+
+    >>> dt.models()
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d0
+    --------------------------------------------------------------------------------
+    % number = 1
+    % seconds = 0
+    <BLANKLINE>
+    % Interpretation of size 2
+    <BLANKLINE>
+    c1 = 0.
+    <BLANKLINE>
+      dance(0).
+    - dance(1).
+    <BLANKLINE>
+      person(0).
+    - person(1).
+    <BLANKLINE>
+      student(0).
+    - student(1).
+    <BLANKLINE>
+    Consistent discourse: d0 ['s0-r0', 's1-r0']:
+        s0-r0: exists z1.(student(z1) & dance(z1))
+        s1-r0: all z1.(student(z1) -> person(z1))
+    <BLANKLINE>
+
+Checking Informativity
+======================
+
+Let's assume that we are still trying to extend the discourse *A
+student dances.* *Every student is a person.* We add a new sentence,
+but this time, we check whether it is informative with respect to what
+has gone before.
+
+    >>> dt.add_sentence('A person dances', informchk=True)
+    Sentence 'A person dances' under reading 'exists x.(person(x) & dance(x))':
+    Not informative relative to thread 'd0'
+
+In fact, we are just checking whether the new sentence is entailed by
+the preceding discourse.
+
+    >>> dt.models()
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d0
+    --------------------------------------------------------------------------------
+    % number = 1
+    % seconds = 0
+    <BLANKLINE>
+    % Interpretation of size 2
+    <BLANKLINE>
+    c1 = 0.
+    <BLANKLINE>
+    c2 = 0.
+    <BLANKLINE>
+      dance(0).
+    - dance(1).
+    <BLANKLINE>
+      person(0).
+    - person(1).
+    <BLANKLINE>
+      student(0).
+    - student(1).
+    <BLANKLINE>
+    Consistent discourse: d0 ['s0-r0', 's1-r0', 's2-r0']:
+        s0-r0: exists z1.(student(z1) & dance(z1))
+        s1-r0: all z1.(student(z1) -> person(z1))
+        s2-r0: exists z1.(person(z1) & dance(z1))
+    <BLANKLINE>
+
+
+
+Adding Background Knowledge
+===========================
+
+Let's build a new discourse, and look at the readings of the component sentences:
+
+    >>> dt = DiscourseTester(['Vincent is a boxer', 'Fido is a boxer', 'Vincent is married', 'Fido barks'])
+    >>> dt.readings()
+    <BLANKLINE>
+    s0 readings:
+    <BLANKLINE>
+    s0-r0: boxer(Vincent)
+    s0-r1: boxerdog(Vincent)
+    <BLANKLINE>
+    s1 readings:
+    <BLANKLINE>
+    s1-r0: boxer(Fido)
+    s1-r1: boxerdog(Fido)
+    <BLANKLINE>
+    s2 readings:
+    <BLANKLINE>
+    s2-r0: married(Vincent)
+    <BLANKLINE>
+    s3 readings:
+    <BLANKLINE>
+    s3-r0: bark(Fido)
+
+This gives us a lot of threads:
+
+    >>> dt.readings(threaded=True)
+    d0: ['s0-r0', 's1-r0', 's2-r0', 's3-r0']
+    d1: ['s0-r0', 's1-r1', 's2-r0', 's3-r0']
+    d2: ['s0-r1', 's1-r0', 's2-r0', 's3-r0']
+    d3: ['s0-r1', 's1-r1', 's2-r0', 's3-r0']
+
+
+We can eliminate some of the readings, and hence some of the threads,
+by adding background information.
+
+    >>> import nltk.data
+    >>> bg = nltk.data.load('grammars/book_grammars/background.fol')
+    >>> dt.add_background(bg)
+    >>> dt.background()
+    all x.(boxerdog(x) -> dog(x))
+    all x.(boxer(x) -> person(x))
+    all x.-(dog(x) & person(x))
+    all x.(married(x) <-> exists y.marry(x,y))
+    all x.(bark(x) -> dog(x))
+    all x y.(marry(x,y) -> (person(x) & person(y)))
+    -(Vincent = Mia)
+    -(Vincent = Fido)
+    -(Mia = Fido)
+
+The background information allows us to reject three of the threads as
+inconsistent. To see what remains, use the ``filter=True`` parameter
+on ``readings()``.
+
+    >>> dt.readings(filter=True)
+    d1: ['s0-r0', 's1-r1', 's2-r0', 's3-r0']
+
+The ``models()`` method gives us more information about the surviving thread.
+
+    >>> dt.models()
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d0
+    --------------------------------------------------------------------------------
+    No model found!
+    <BLANKLINE>
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d1
+    --------------------------------------------------------------------------------
+    % number = 1
+    % seconds = 0
+    <BLANKLINE>
+    % Interpretation of size 3
+    <BLANKLINE>
+    Fido = 0.
+    <BLANKLINE>
+    Mia = 1.
+    <BLANKLINE>
+    Vincent = 2.
+    <BLANKLINE>
+    f1(0) = 0.
+    f1(1) = 0.
+    f1(2) = 2.
+    <BLANKLINE>
+      bark(0).
+    - bark(1).
+    - bark(2).
+    <BLANKLINE>
+    - boxer(0).
+    - boxer(1).
+      boxer(2).
+    <BLANKLINE>
+      boxerdog(0).
+    - boxerdog(1).
+    - boxerdog(2).
+    <BLANKLINE>
+      dog(0).
+    - dog(1).
+    - dog(2).
+    <BLANKLINE>
+    - married(0).
+    - married(1).
+      married(2).
+    <BLANKLINE>
+    - person(0).
+    - person(1).
+      person(2).
+    <BLANKLINE>
+    - marry(0,0).
+    - marry(0,1).
+    - marry(0,2).
+    - marry(1,0).
+    - marry(1,1).
+    - marry(1,2).
+    - marry(2,0).
+    - marry(2,1).
+      marry(2,2).
+    <BLANKLINE>
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d2
+    --------------------------------------------------------------------------------
+    No model found!
+    <BLANKLINE>
+    --------------------------------------------------------------------------------
+    Model for Discourse Thread d3
+    --------------------------------------------------------------------------------
+    No model found!
+    <BLANKLINE>
+    Inconsistent discourse: d0 ['s0-r0', 's1-r0', 's2-r0', 's3-r0']:
+        s0-r0: boxer(Vincent)
+        s1-r0: boxer(Fido)
+        s2-r0: married(Vincent)
+        s3-r0: bark(Fido)
+    <BLANKLINE>
+    Consistent discourse: d1 ['s0-r0', 's1-r1', 's2-r0', 's3-r0']:
+        s0-r0: boxer(Vincent)
+        s1-r1: boxerdog(Fido)
+        s2-r0: married(Vincent)
+        s3-r0: bark(Fido)
+    <BLANKLINE>
+    Inconsistent discourse: d2 ['s0-r1', 's1-r0', 's2-r0', 's3-r0']:
+        s0-r1: boxerdog(Vincent)
+        s1-r0: boxer(Fido)
+        s2-r0: married(Vincent)
+        s3-r0: bark(Fido)
+    <BLANKLINE>
+    Inconsistent discourse: d3 ['s0-r1', 's1-r1', 's2-r0', 's3-r0']:
+        s0-r1: boxerdog(Vincent)
+        s1-r1: boxerdog(Fido)
+        s2-r0: married(Vincent)
+        s3-r0: bark(Fido)
+    <BLANKLINE>
+
+
+..  This will not be visible in the html output: create a tempdir to
+    play in.
+    >>> import tempfile, os
+    >>> tempdir = tempfile.mkdtemp()
+    >>> old_dir = os.path.abspath('.')
+    >>> os.chdir(tempdir)
+
+In order to play around with your own version of background knowledge,
+you might want to start off with a local copy of ``background.fol``:
+
+    >>> nltk.data.retrieve('grammars/book_grammars/background.fol')
+    Retrieving 'nltk:grammars/book_grammars/background.fol', saving to 'background.fol'
+
+After you have modified the file, the ``load_fol()`` function will parse
+the strings in the file into expressions of ``nltk.sem.logic``.
+
+    >>> from nltk.inference.discourse import load_fol
+    >>> mybg = load_fol(open('background.fol').read())
+
+The result can be loaded as an argument of ``add_background()`` in the
+manner shown earlier.
+
+..  This will not be visible in the html output: clean up the tempdir.
+    >>> os.chdir(old_dir)
+    >>> for f in os.listdir(tempdir):
+    ...     os.remove(os.path.join(tempdir, f))
+    >>> os.rmdir(tempdir)
+    >>> nltk.data.clear_cache()
+
+
+Regression Testing from book
+============================
+
+    >>> logic._counter._value = 0
+
+    >>> from nltk.tag import RegexpTagger
+    >>> tagger = RegexpTagger(
+    ...     [('^(chases|runs)$', 'VB'),
+    ...      ('^(a)$', 'ex_quant'),
+    ...      ('^(every)$', 'univ_quant'),
+    ...      ('^(dog|boy)$', 'NN'),
+    ...      ('^(He)$', 'PRP')
+    ... ])
+    >>> rc = DrtGlueReadingCommand(depparser=MaltParser(tagger=tagger))
+    >>> dt = DiscourseTester(map(str.split, ['Every dog chases a boy', 'He runs']), rc)
+    >>> dt.readings()
+    <BLANKLINE>
+    s0 readings:
+    <BLANKLINE>
+    s0-r0: ([z2],[boy(z2), (([z5],[dog(z5)]) -> ([],[chases(z5,z2)]))])
+    s0-r1: ([],[(([z1],[dog(z1)]) -> ([z2],[boy(z2), chases(z1,z2)]))])
+    <BLANKLINE>
+    s1 readings:
+    <BLANKLINE>
+    s1-r0: ([z1],[PRO(z1), runs(z1)])
+    >>> dt.readings(show_thread_readings=True)
+    d0: ['s0-r0', 's1-r0'] : ([z1,z2],[boy(z1), (([z3],[dog(z3)]) -> ([],[chases(z3,z1)])), (z2 = z1), runs(z2)])
+    d1: ['s0-r1', 's1-r0'] : INVALID: AnaphoraResolutionException
+    >>> dt.readings(filter=True, show_thread_readings=True)
+    d0: ['s0-r0', 's1-r0'] : ([z1,z3],[boy(z1), (([z2],[dog(z2)]) -> ([],[chases(z2,z1)])), (z3 = z1), runs(z3)])
+
+    >>> logic._counter._value = 0
+
+    >>> from nltk.parse import FeatureEarleyChartParser
+    >>> from nltk.sem.drt import DrtParser
+    >>> grammar = nltk.data.load('grammars/book_grammars/drt.fcfg', logic_parser=DrtParser())
+    >>> parser = FeatureEarleyChartParser(grammar, trace=0)
+    >>> trees = parser.parse('Angus owns a dog'.split())
+    >>> print(list(trees)[0].label()['SEM'].simplify().normalize())
+    ([z1,z2],[Angus(z1), dog(z2), own(z1,z2)])

env-llmeval/lib/python3.10/site-packages/nltk/test/featgram.doctest

@@ -0,0 +1,610 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=========================
+ Feature Grammar Parsing
+=========================
+
+.. definitions from nltk_book/definitions.rst
+
+.. role:: feat
+    :class: feature
+.. role:: fval
+     :class: fval
+.. |rarr| unicode:: U+2192 .. right arrow
+.. |dot| unicode:: U+2022 .. bullet
+.. |pi| unicode:: U+03C0
+
+Grammars can be parsed from strings.
+
+    >>> import nltk
+    >>> from nltk import grammar, parse
+    >>> g = """
+    ... % start DP
+    ... DP[AGR=?a] -> D[AGR=?a] N[AGR=?a]
+    ... D[AGR=[NUM='sg', PERS=3]] -> 'this' | 'that'
+    ... D[AGR=[NUM='pl', PERS=3]] -> 'these' | 'those'
+    ... D[AGR=[NUM='pl', PERS=1]] -> 'we'
+    ... D[AGR=[PERS=2]] -> 'you'
+    ... N[AGR=[NUM='sg', GND='m']] -> 'boy'
+    ... N[AGR=[NUM='pl', GND='m']] -> 'boys'
+    ... N[AGR=[NUM='sg', GND='f']] -> 'girl'
+    ... N[AGR=[NUM='pl', GND='f']] -> 'girls'
+    ... N[AGR=[NUM='sg']] -> 'student'
+    ... N[AGR=[NUM='pl']] -> 'students'
+    ... """
+    >>> grammar = grammar.FeatureGrammar.fromstring(g)
+    >>> tokens = 'these girls'.split()
+    >>> parser = parse.FeatureEarleyChartParser(grammar)
+    >>> trees = parser.parse(tokens)
+    >>> for tree in trees: print(tree)
+    (DP[AGR=[GND='f', NUM='pl', PERS=3]]
+      (D[AGR=[NUM='pl', PERS=3]] these)
+      (N[AGR=[GND='f', NUM='pl']] girls))
+
+In general, when we are trying to develop even a very small grammar,
+it is convenient to put the rules in a file where they can be edited,
+tested and revised. Let's assume that we have saved feat0cfg as a file named
+``'feat0.fcfg'`` and placed it in the NLTK ``data`` directory. We can
+inspect it as follows:
+
+    >>> nltk.data.show_cfg('grammars/book_grammars/feat0.fcfg')
+    % start S
+    # ###################
+    # Grammar Productions
+    # ###################
+    # S expansion productions
+    S -> NP[NUM=?n] VP[NUM=?n]
+    # NP expansion productions
+    NP[NUM=?n] -> N[NUM=?n]
+    NP[NUM=?n] -> PropN[NUM=?n]
+    NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n]
+    NP[NUM=pl] -> N[NUM=pl]
+    # VP expansion productions
+    VP[TENSE=?t, NUM=?n] -> IV[TENSE=?t, NUM=?n]
+    VP[TENSE=?t, NUM=?n] -> TV[TENSE=?t, NUM=?n] NP
+    # ###################
+    # Lexical Productions
+    # ###################
+    Det[NUM=sg] -> 'this' | 'every'
+    Det[NUM=pl] -> 'these' | 'all'
+    Det -> 'the' | 'some' | 'several'
+    PropN[NUM=sg]-> 'Kim' | 'Jody'
+    N[NUM=sg] -> 'dog' | 'girl' | 'car' | 'child'
+    N[NUM=pl] -> 'dogs' | 'girls' | 'cars' | 'children'
+    IV[TENSE=pres,  NUM=sg] -> 'disappears' | 'walks'
+    TV[TENSE=pres, NUM=sg] -> 'sees' | 'likes'
+    IV[TENSE=pres,  NUM=pl] -> 'disappear' | 'walk'
+    TV[TENSE=pres, NUM=pl] -> 'see' | 'like'
+    IV[TENSE=past] -> 'disappeared' | 'walked'
+    TV[TENSE=past] -> 'saw' | 'liked'
+
+Assuming we have saved feat0cfg as a file named
+``'feat0.fcfg'``, the function ``parse.load_parser`` allows us to
+read the grammar into NLTK, ready for use in parsing.
+
+
+    >>> cp = parse.load_parser('grammars/book_grammars/feat0.fcfg', trace=1)
+    >>> sent = 'Kim likes children'
+    >>> tokens = sent.split()
+    >>> tokens
+    ['Kim', 'likes', 'children']
+    >>> trees = cp.parse(tokens)
+    |.Kim .like.chil.|
+    |[----]    .    .| [0:1] 'Kim'
+    |.    [----]    .| [1:2] 'likes'
+    |.    .    [----]| [2:3] 'children'
+    |[----]    .    .| [0:1] PropN[NUM='sg'] -> 'Kim' *
+    |[----]    .    .| [0:1] NP[NUM='sg'] -> PropN[NUM='sg'] *
+    |[---->    .    .| [0:1] S[] -> NP[NUM=?n] * VP[NUM=?n] {?n: 'sg'}
+    |.    [----]    .| [1:2] TV[NUM='sg', TENSE='pres'] -> 'likes' *
+    |.    [---->    .| [1:2] VP[NUM=?n, TENSE=?t] -> TV[NUM=?n, TENSE=?t] * NP[] {?n: 'sg', ?t: 'pres'}
+    |.    .    [----]| [2:3] N[NUM='pl'] -> 'children' *
+    |.    .    [----]| [2:3] NP[NUM='pl'] -> N[NUM='pl'] *
+    |.    .    [---->| [2:3] S[] -> NP[NUM=?n] * VP[NUM=?n] {?n: 'pl'}
+    |.    [---------]| [1:3] VP[NUM='sg', TENSE='pres'] -> TV[NUM='sg', TENSE='pres'] NP[] *
+    |[==============]| [0:3] S[] -> NP[NUM='sg'] VP[NUM='sg'] *
+    >>> for tree in trees: print(tree)
+    (S[]
+      (NP[NUM='sg'] (PropN[NUM='sg'] Kim))
+      (VP[NUM='sg', TENSE='pres']
+        (TV[NUM='sg', TENSE='pres'] likes)
+        (NP[NUM='pl'] (N[NUM='pl'] children))))
+
+The parser works directly with
+the underspecified productions given by the grammar. That is, the
+Predictor rule does not attempt to compile out all admissible feature
+combinations before trying to expand the non-terminals on the left hand
+side of a production. However, when the Scanner matches an input word
+against a lexical production that has been predicted, the new edge will
+typically contain fully specified features; e.g., the edge
+[PropN[`num`:feat: = `sg`:fval:] |rarr| 'Kim', (0, 1)]. Recall from
+Chapter 8 that the Fundamental (or Completer) Rule in
+standard CFGs is used to combine an incomplete edge that's expecting a
+nonterminal *B* with a following, complete edge whose left hand side
+matches *B*. In our current setting, rather than checking for a
+complete match, we test whether the expected category *B* will
+unify with the left hand side *B'* of a following complete
+edge. We will explain in more detail in Section 9.2 how
+unification works; for the moment, it is enough to know that as a
+result of unification, any variable values of features in *B* will be
+instantiated by constant values in the corresponding feature structure
+in *B'*, and these instantiated values will be used in the new edge
+added by the Completer. This instantiation can be seen, for example,
+in the edge
+[NP [`num`:feat:\ =\ `sg`:fval:] |rarr| PropN[`num`:feat:\ =\ `sg`:fval:] |dot|, (0, 1)]
+in Example 9.2, where the feature `num`:feat: has been assigned the value `sg`:fval:.
+
+Feature structures in NLTK are ... Atomic feature values can be strings or
+integers.
+
+    >>> fs1 = nltk.FeatStruct(TENSE='past', NUM='sg')
+    >>> print(fs1)
+    [ NUM   = 'sg'   ]
+    [ TENSE = 'past' ]
+
+We can think of a feature structure as being like a Python dictionary,
+and access its values by indexing in the usual way.
+
+    >>> fs1 = nltk.FeatStruct(PER=3, NUM='pl', GND='fem')
+    >>> print(fs1['GND'])
+    fem
+
+We can also define feature structures which have complex values, as
+discussed earlier.
+
+    >>> fs2 = nltk.FeatStruct(POS='N', AGR=fs1)
+    >>> print(fs2)
+    [       [ GND = 'fem' ] ]
+    [ AGR = [ NUM = 'pl'  ] ]
+    [       [ PER = 3     ] ]
+    [                       ]
+    [ POS = 'N'             ]
+    >>> print(fs2['AGR'])
+    [ GND = 'fem' ]
+    [ NUM = 'pl'  ]
+    [ PER = 3     ]
+    >>> print(fs2['AGR']['PER'])
+    3
+
+Feature structures can also be constructed using the ``parse()``
+method of the ``nltk.FeatStruct`` class. Note that in this case, atomic
+feature values do not need to be enclosed in quotes.
+
+    >>> f1 = nltk.FeatStruct("[NUMBER = sg]")
+    >>> f2 = nltk.FeatStruct("[PERSON = 3]")
+    >>> print(nltk.unify(f1, f2))
+    [ NUMBER = 'sg' ]
+    [ PERSON = 3    ]
+
+    >>> f1 = nltk.FeatStruct("[A = [B = b, D = d]]")
+    >>> f2 = nltk.FeatStruct("[A = [C = c, D = d]]")
+    >>> print(nltk.unify(f1, f2))
+    [     [ B = 'b' ] ]
+    [ A = [ C = 'c' ] ]
+    [     [ D = 'd' ] ]
+
+
+Feature Structures as Graphs
+----------------------------
+
+Feature structures are not inherently tied to linguistic objects; they are
+general purpose structures for representing knowledge. For example, we
+could encode information about a person in a feature structure:
+
+    >>> person01 = nltk.FeatStruct("[NAME=Lee, TELNO='01 27 86 42 96',AGE=33]")
+    >>> print(person01)
+    [ AGE   = 33               ]
+    [ NAME  = 'Lee'            ]
+    [ TELNO = '01 27 86 42 96' ]
+
+There are a number of notations for representing reentrancy in
+matrix-style representations of feature structures. In NLTK, we adopt
+the following convention: the first occurrence of a shared feature structure
+is prefixed with an integer in parentheses, such as ``(1)``, and any
+subsequent reference to that structure uses the notation
+``->(1)``, as shown below.
+
+
+    >>> fs = nltk.FeatStruct("""[NAME=Lee, ADDRESS=(1)[NUMBER=74, STREET='rue Pascal'],
+    ...                               SPOUSE=[NAME=Kim, ADDRESS->(1)]]""")
+    >>> print(fs)
+    [ ADDRESS = (1) [ NUMBER = 74           ] ]
+    [               [ STREET = 'rue Pascal' ] ]
+    [                                         ]
+    [ NAME    = 'Lee'                         ]
+    [                                         ]
+    [ SPOUSE  = [ ADDRESS -> (1)  ]           ]
+    [           [ NAME    = 'Kim' ]           ]
+
+There can be any number of tags within a single feature structure.
+
+    >>> fs3 = nltk.FeatStruct("[A=(1)[B=b], C=(2)[], D->(1), E->(2)]")
+    >>> print(fs3)
+    [ A = (1) [ B = 'b' ] ]
+    [                     ]
+    [ C = (2) []          ]
+    [                     ]
+    [ D -> (1)            ]
+    [ E -> (2)            ]
+    >>> fs1 = nltk.FeatStruct(NUMBER=74, STREET='rue Pascal')
+    >>> fs2 = nltk.FeatStruct(CITY='Paris')
+    >>> print(nltk.unify(fs1, fs2))
+    [ CITY   = 'Paris'      ]
+    [ NUMBER = 74           ]
+    [ STREET = 'rue Pascal' ]
+
+Unification is symmetric:
+
+    >>> nltk.unify(fs1, fs2) == nltk.unify(fs2, fs1)
+    True
+
+Unification is commutative:
+
+    >>> fs3 = nltk.FeatStruct(TELNO='01 27 86 42 96')
+    >>> nltk.unify(nltk.unify(fs1, fs2), fs3) == nltk.unify(fs1, nltk.unify(fs2, fs3))
+    True
+
+Unification between *FS*:math:`_0` and *FS*:math:`_1` will fail if the
+two feature structures share a path |pi|,
+but the value of |pi| in *FS*:math:`_0` is a distinct
+atom from the value of |pi| in *FS*:math:`_1`. In NLTK,
+this is implemented by setting the result of unification to be
+``None``.
+
+    >>> fs0 = nltk.FeatStruct(A='a')
+    >>> fs1 = nltk.FeatStruct(A='b')
+    >>> print(nltk.unify(fs0, fs1))
+    None
+
+Now, if we look at how unification interacts with structure-sharing,
+things become really interesting.
+
+
+
+    >>> fs0 = nltk.FeatStruct("""[NAME=Lee,
+    ...                                ADDRESS=[NUMBER=74,
+    ...                                         STREET='rue Pascal'],
+    ...                                SPOUSE= [NAME=Kim,
+    ...                                         ADDRESS=[NUMBER=74,
+    ...                                                  STREET='rue Pascal']]]""")
+    >>> print(fs0)
+    [ ADDRESS = [ NUMBER = 74           ]               ]
+    [           [ STREET = 'rue Pascal' ]               ]
+    [                                                   ]
+    [ NAME    = 'Lee'                                   ]
+    [                                                   ]
+    [           [ ADDRESS = [ NUMBER = 74           ] ] ]
+    [ SPOUSE  = [           [ STREET = 'rue Pascal' ] ] ]
+    [           [                                     ] ]
+    [           [ NAME    = 'Kim'                     ] ]
+
+
+    >>> fs1 = nltk.FeatStruct("[SPOUSE=[ADDRESS=[CITY=Paris]]]")
+    >>> print(nltk.unify(fs0, fs1))
+    [ ADDRESS = [ NUMBER = 74           ]               ]
+    [           [ STREET = 'rue Pascal' ]               ]
+    [                                                   ]
+    [ NAME    = 'Lee'                                   ]
+    [                                                   ]
+    [           [           [ CITY   = 'Paris'      ] ] ]
+    [           [ ADDRESS = [ NUMBER = 74           ] ] ]
+    [ SPOUSE  = [           [ STREET = 'rue Pascal' ] ] ]
+    [           [                                     ] ]
+    [           [ NAME    = 'Kim'                     ] ]
+
+    >>> fs2 = nltk.FeatStruct("""[NAME=Lee, ADDRESS=(1)[NUMBER=74, STREET='rue Pascal'],
+    ...                                SPOUSE=[NAME=Kim, ADDRESS->(1)]]""")
+
+
+    >>> print(fs2)
+    [ ADDRESS = (1) [ NUMBER = 74           ] ]
+    [               [ STREET = 'rue Pascal' ] ]
+    [                                         ]
+    [ NAME    = 'Lee'                         ]
+    [                                         ]
+    [ SPOUSE  = [ ADDRESS -> (1)  ]           ]
+    [           [ NAME    = 'Kim' ]           ]
+
+
+    >>> print(nltk.unify(fs2, fs1))
+    [               [ CITY   = 'Paris'      ] ]
+    [ ADDRESS = (1) [ NUMBER = 74           ] ]
+    [               [ STREET = 'rue Pascal' ] ]
+    [                                         ]
+    [ NAME    = 'Lee'                         ]
+    [                                         ]
+    [ SPOUSE  = [ ADDRESS -> (1)  ]           ]
+    [           [ NAME    = 'Kim' ]           ]
+
+
+    >>> fs1 = nltk.FeatStruct("[ADDRESS1=[NUMBER=74, STREET='rue Pascal']]")
+    >>> fs2 = nltk.FeatStruct("[ADDRESS1=?x, ADDRESS2=?x]")
+    >>> print(fs2)
+    [ ADDRESS1 = ?x ]
+    [ ADDRESS2 = ?x ]
+    >>> print(nltk.unify(fs1, fs2))
+    [ ADDRESS1 = (1) [ NUMBER = 74           ] ]
+    [                [ STREET = 'rue Pascal' ] ]
+    [                                          ]
+    [ ADDRESS2 -> (1)                          ]
+
+
+
+
+    >>> sent = 'who do you claim that you like'
+    >>> tokens = sent.split()
+    >>> cp = parse.load_parser('grammars/book_grammars/feat1.fcfg', trace=1)
+    >>> trees = cp.parse(tokens)
+    |.w.d.y.c.t.y.l.|
+    |[-] . . . . . .| [0:1] 'who'
+    |. [-] . . . . .| [1:2] 'do'
+    |. . [-] . . . .| [2:3] 'you'
+    |. . . [-] . . .| [3:4] 'claim'
+    |. . . . [-] . .| [4:5] 'that'
+    |. . . . . [-] .| [5:6] 'you'
+    |. . . . . . [-]| [6:7] 'like'
+    |# . . . . . . .| [0:0] NP[]/NP[] -> *
+    |. # . . . . . .| [1:1] NP[]/NP[] -> *
+    |. . # . . . . .| [2:2] NP[]/NP[] -> *
+    |. . . # . . . .| [3:3] NP[]/NP[] -> *
+    |. . . . # . . .| [4:4] NP[]/NP[] -> *
+    |. . . . . # . .| [5:5] NP[]/NP[] -> *
+    |. . . . . . # .| [6:6] NP[]/NP[] -> *
+    |. . . . . . . #| [7:7] NP[]/NP[] -> *
+    |[-] . . . . . .| [0:1] NP[+WH] -> 'who' *
+    |[-> . . . . . .| [0:1] S[-INV] -> NP[] * VP[] {}
+    |[-> . . . . . .| [0:1] S[-INV]/?x[] -> NP[] * VP[]/?x[] {}
+    |[-> . . . . . .| [0:1] S[-INV] -> NP[] * S[]/NP[] {}
+    |. [-] . . . . .| [1:2] V[+AUX] -> 'do' *
+    |. [-> . . . . .| [1:2] S[+INV] -> V[+AUX] * NP[] VP[] {}
+    |. [-> . . . . .| [1:2] S[+INV]/?x[] -> V[+AUX] * NP[] VP[]/?x[] {}
+    |. [-> . . . . .| [1:2] VP[] -> V[+AUX] * VP[] {}
+    |. [-> . . . . .| [1:2] VP[]/?x[] -> V[+AUX] * VP[]/?x[] {}
+    |. . [-] . . . .| [2:3] NP[-WH] -> 'you' *
+    |. . [-> . . . .| [2:3] S[-INV] -> NP[] * VP[] {}
+    |. . [-> . . . .| [2:3] S[-INV]/?x[] -> NP[] * VP[]/?x[] {}
+    |. . [-> . . . .| [2:3] S[-INV] -> NP[] * S[]/NP[] {}
+    |. [---> . . . .| [1:3] S[+INV] -> V[+AUX] NP[] * VP[] {}
+    |. [---> . . . .| [1:3] S[+INV]/?x[] -> V[+AUX] NP[] * VP[]/?x[] {}
+    |. . . [-] . . .| [3:4] V[-AUX, SUBCAT='clause'] -> 'claim' *
+    |. . . [-> . . .| [3:4] VP[] -> V[-AUX, SUBCAT='clause'] * SBar[] {}
+    |. . . [-> . . .| [3:4] VP[]/?x[] -> V[-AUX, SUBCAT='clause'] * SBar[]/?x[] {}
+    |. . . . [-] . .| [4:5] Comp[] -> 'that' *
+    |. . . . [-> . .| [4:5] SBar[] -> Comp[] * S[-INV] {}
+    |. . . . [-> . .| [4:5] SBar[]/?x[] -> Comp[] * S[-INV]/?x[] {}
+    |. . . . . [-] .| [5:6] NP[-WH] -> 'you' *
+    |. . . . . [-> .| [5:6] S[-INV] -> NP[] * VP[] {}
+    |. . . . . [-> .| [5:6] S[-INV]/?x[] -> NP[] * VP[]/?x[] {}
+    |. . . . . [-> .| [5:6] S[-INV] -> NP[] * S[]/NP[] {}
+    |. . . . . . [-]| [6:7] V[-AUX, SUBCAT='trans'] -> 'like' *
+    |. . . . . . [->| [6:7] VP[] -> V[-AUX, SUBCAT='trans'] * NP[] {}
+    |. . . . . . [->| [6:7] VP[]/?x[] -> V[-AUX, SUBCAT='trans'] * NP[]/?x[] {}
+    |. . . . . . [-]| [6:7] VP[]/NP[] -> V[-AUX, SUBCAT='trans'] NP[]/NP[] *
+    |. . . . . [---]| [5:7] S[-INV]/NP[] -> NP[] VP[]/NP[] *
+    |. . . . [-----]| [4:7] SBar[]/NP[] -> Comp[] S[-INV]/NP[] *
+    |. . . [-------]| [3:7] VP[]/NP[] -> V[-AUX, SUBCAT='clause'] SBar[]/NP[] *
+    |. . [---------]| [2:7] S[-INV]/NP[] -> NP[] VP[]/NP[] *
+    |. [-----------]| [1:7] S[+INV]/NP[] -> V[+AUX] NP[] VP[]/NP[] *
+    |[=============]| [0:7] S[-INV] -> NP[] S[]/NP[] *
+
+    >>> trees = list(trees)
+    >>> for tree in trees: print(tree)
+    (S[-INV]
+      (NP[+WH] who)
+      (S[+INV]/NP[]
+        (V[+AUX] do)
+        (NP[-WH] you)
+        (VP[]/NP[]
+          (V[-AUX, SUBCAT='clause'] claim)
+          (SBar[]/NP[]
+            (Comp[] that)
+            (S[-INV]/NP[]
+              (NP[-WH] you)
+              (VP[]/NP[] (V[-AUX, SUBCAT='trans'] like) (NP[]/NP[] )))))))
+
+A different parser should give the same parse trees, but perhaps in a different order:
+
+    >>> cp2 = parse.load_parser('grammars/book_grammars/feat1.fcfg', trace=1,
+    ...                         parser=parse.FeatureEarleyChartParser)
+    >>> trees2 = cp2.parse(tokens)
+    |.w.d.y.c.t.y.l.|
+    |[-] . . . . . .| [0:1] 'who'
+    |. [-] . . . . .| [1:2] 'do'
+    |. . [-] . . . .| [2:3] 'you'
+    |. . . [-] . . .| [3:4] 'claim'
+    |. . . . [-] . .| [4:5] 'that'
+    |. . . . . [-] .| [5:6] 'you'
+    |. . . . . . [-]| [6:7] 'like'
+    |> . . . . . . .| [0:0] S[-INV] -> * NP[] VP[] {}
+    |> . . . . . . .| [0:0] S[-INV]/?x[] -> * NP[] VP[]/?x[] {}
+    |> . . . . . . .| [0:0] S[-INV] -> * NP[] S[]/NP[] {}
+    |> . . . . . . .| [0:0] S[-INV] -> * Adv[+NEG] S[+INV] {}
+    |> . . . . . . .| [0:0] S[+INV] -> * V[+AUX] NP[] VP[] {}
+    |> . . . . . . .| [0:0] S[+INV]/?x[] -> * V[+AUX] NP[] VP[]/?x[] {}
+    |> . . . . . . .| [0:0] NP[+WH] -> * 'who' {}
+    |[-] . . . . . .| [0:1] NP[+WH] -> 'who' *
+    |[-> . . . . . .| [0:1] S[-INV] -> NP[] * VP[] {}
+    |[-> . . . . . .| [0:1] S[-INV]/?x[] -> NP[] * VP[]/?x[] {}
+    |[-> . . . . . .| [0:1] S[-INV] -> NP[] * S[]/NP[] {}
+    |. > . . . . . .| [1:1] S[-INV]/?x[] -> * NP[] VP[]/?x[] {}
+    |. > . . . . . .| [1:1] S[+INV]/?x[] -> * V[+AUX] NP[] VP[]/?x[] {}
+    |. > . . . . . .| [1:1] V[+AUX] -> * 'do' {}
+    |. > . . . . . .| [1:1] VP[]/?x[] -> * V[-AUX, SUBCAT='trans'] NP[]/?x[] {}
+    |. > . . . . . .| [1:1] VP[]/?x[] -> * V[-AUX, SUBCAT='clause'] SBar[]/?x[] {}
+    |. > . . . . . .| [1:1] VP[]/?x[] -> * V[+AUX] VP[]/?x[] {}
+    |. > . . . . . .| [1:1] VP[] -> * V[-AUX, SUBCAT='intrans'] {}
+    |. > . . . . . .| [1:1] VP[] -> * V[-AUX, SUBCAT='trans'] NP[] {}
+    |. > . . . . . .| [1:1] VP[] -> * V[-AUX, SUBCAT='clause'] SBar[] {}
+    |. > . . . . . .| [1:1] VP[] -> * V[+AUX] VP[] {}
+    |. [-] . . . . .| [1:2] V[+AUX] -> 'do' *
+    |. [-> . . . . .| [1:2] S[+INV]/?x[] -> V[+AUX] * NP[] VP[]/?x[] {}
+    |. [-> . . . . .| [1:2] VP[]/?x[] -> V[+AUX] * VP[]/?x[] {}
+    |. [-> . . . . .| [1:2] VP[] -> V[+AUX] * VP[] {}
+    |. . > . . . . .| [2:2] VP[] -> * V[-AUX, SUBCAT='intrans'] {}
+    |. . > . . . . .| [2:2] VP[] -> * V[-AUX, SUBCAT='trans'] NP[] {}
+    |. . > . . . . .| [2:2] VP[] -> * V[-AUX, SUBCAT='clause'] SBar[] {}
+    |. . > . . . . .| [2:2] VP[] -> * V[+AUX] VP[] {}
+    |. . > . . . . .| [2:2] VP[]/?x[] -> * V[-AUX, SUBCAT='trans'] NP[]/?x[] {}
+    |. . > . . . . .| [2:2] VP[]/?x[] -> * V[-AUX, SUBCAT='clause'] SBar[]/?x[] {}
+    |. . > . . . . .| [2:2] VP[]/?x[] -> * V[+AUX] VP[]/?x[] {}
+    |. . > . . . . .| [2:2] NP[-WH] -> * 'you' {}
+    |. . [-] . . . .| [2:3] NP[-WH] -> 'you' *
+    |. [---> . . . .| [1:3] S[+INV]/?x[] -> V[+AUX] NP[] * VP[]/?x[] {}
+    |. . . > . . . .| [3:3] VP[]/?x[] -> * V[-AUX, SUBCAT='trans'] NP[]/?x[] {}
+    |. . . > . . . .| [3:3] VP[]/?x[] -> * V[-AUX, SUBCAT='clause'] SBar[]/?x[] {}
+    |. . . > . . . .| [3:3] VP[]/?x[] -> * V[+AUX] VP[]/?x[] {}
+    |. . . > . . . .| [3:3] V[-AUX, SUBCAT='clause'] -> * 'claim' {}
+    |. . . [-] . . .| [3:4] V[-AUX, SUBCAT='clause'] -> 'claim' *
+    |. . . [-> . . .| [3:4] VP[]/?x[] -> V[-AUX, SUBCAT='clause'] * SBar[]/?x[] {}
+    |. . . . > . . .| [4:4] SBar[]/?x[] -> * Comp[] S[-INV]/?x[] {}
+    |. . . . > . . .| [4:4] Comp[] -> * 'that' {}
+    |. . . . [-] . .| [4:5] Comp[] -> 'that' *
+    |. . . . [-> . .| [4:5] SBar[]/?x[] -> Comp[] * S[-INV]/?x[] {}
+    |. . . . . > . .| [5:5] S[-INV]/?x[] -> * NP[] VP[]/?x[] {}
+    |. . . . . > . .| [5:5] NP[-WH] -> * 'you' {}
+    |. . . . . [-] .| [5:6] NP[-WH] -> 'you' *
+    |. . . . . [-> .| [5:6] S[-INV]/?x[] -> NP[] * VP[]/?x[] {}
+    |. . . . . . > .| [6:6] VP[]/?x[] -> * V[-AUX, SUBCAT='trans'] NP[]/?x[] {}
+    |. . . . . . > .| [6:6] VP[]/?x[] -> * V[-AUX, SUBCAT='clause'] SBar[]/?x[] {}
+    |. . . . . . > .| [6:6] VP[]/?x[] -> * V[+AUX] VP[]/?x[] {}
+    |. . . . . . > .| [6:6] V[-AUX, SUBCAT='trans'] -> * 'like' {}
+    |. . . . . . [-]| [6:7] V[-AUX, SUBCAT='trans'] -> 'like' *
+    |. . . . . . [->| [6:7] VP[]/?x[] -> V[-AUX, SUBCAT='trans'] * NP[]/?x[] {}
+    |. . . . . . . #| [7:7] NP[]/NP[] -> *
+    |. . . . . . [-]| [6:7] VP[]/NP[] -> V[-AUX, SUBCAT='trans'] NP[]/NP[] *
+    |. . . . . [---]| [5:7] S[-INV]/NP[] -> NP[] VP[]/NP[] *
+    |. . . . [-----]| [4:7] SBar[]/NP[] -> Comp[] S[-INV]/NP[] *
+    |. . . [-------]| [3:7] VP[]/NP[] -> V[-AUX, SUBCAT='clause'] SBar[]/NP[] *
+    |. [-----------]| [1:7] S[+INV]/NP[] -> V[+AUX] NP[] VP[]/NP[] *
+    |[=============]| [0:7] S[-INV] -> NP[] S[]/NP[] *
+
+    >>> sorted(trees) == sorted(trees2)
+    True
+
+
+Let's load a German grammar:
+
+    >>> cp = parse.load_parser('grammars/book_grammars/german.fcfg', trace=0)
+    >>> sent = 'die Katze sieht den Hund'
+    >>> tokens = sent.split()
+    >>> trees = cp.parse(tokens)
+    >>> for tree in trees: print(tree)
+    (S[]
+      (NP[AGR=[GND='fem', NUM='sg', PER=3], CASE='nom']
+        (Det[AGR=[GND='fem', NUM='sg', PER=3], CASE='nom'] die)
+        (N[AGR=[GND='fem', NUM='sg', PER=3]] Katze))
+      (VP[AGR=[NUM='sg', PER=3]]
+        (TV[AGR=[NUM='sg', PER=3], OBJCASE='acc'] sieht)
+        (NP[AGR=[GND='masc', NUM='sg', PER=3], CASE='acc']
+          (Det[AGR=[GND='masc', NUM='sg', PER=3], CASE='acc'] den)
+          (N[AGR=[GND='masc', NUM='sg', PER=3]] Hund))))
+
+Grammar with Binding Operators
+------------------------------
+The bindop.fcfg grammar is a semantic grammar that uses lambda
+calculus.  Each element has a core semantics, which is a single lambda
+calculus expression; and a set of binding operators, which bind
+variables.
+
+In order to make the binding operators work right, they need to
+instantiate their bound variable every time they are added to the
+chart.  To do this, we use a special subclass of `Chart`, called
+`InstantiateVarsChart`.
+
+    >>> from nltk.parse.featurechart import InstantiateVarsChart
+    >>> cp = parse.load_parser('grammars/sample_grammars/bindop.fcfg', trace=1,
+    ...                        chart_class=InstantiateVarsChart)
+    >>> print(cp.grammar())
+    Grammar with 15 productions (start state = S[])
+        S[SEM=[BO={?b1+?b2}, CORE=<?vp(?subj)>]] -> NP[SEM=[BO=?b1, CORE=?subj]] VP[SEM=[BO=?b2, CORE=?vp]]
+        VP[SEM=[BO={?b1+?b2}, CORE=<?v(?obj)>]] -> TV[SEM=[BO=?b1, CORE=?v]] NP[SEM=[BO=?b2, CORE=?obj]]
+        VP[SEM=?s] -> IV[SEM=?s]
+        NP[SEM=[BO={?b1+?b2+{bo(?det(?n),@x)}}, CORE=<@x>]] -> Det[SEM=[BO=?b1, CORE=?det]] N[SEM=[BO=?b2, CORE=?n]]
+        Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] -> 'a'
+        N[SEM=[BO={/}, CORE=<dog>]] -> 'dog'
+        N[SEM=[BO={/}, CORE=<dog>]] -> 'cat'
+        N[SEM=[BO={/}, CORE=<dog>]] -> 'mouse'
+        IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] -> 'barks'
+        IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] -> 'eats'
+        IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] -> 'walks'
+        TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] -> 'feeds'
+        TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] -> 'walks'
+        NP[SEM=[BO={bo(\P.P(John),@x)}, CORE=<@x>]] -> 'john'
+        NP[SEM=[BO={bo(\P.P(John),@x)}, CORE=<@x>]] -> 'alex'
+
+A simple intransitive sentence:
+
+    >>> from nltk.sem import logic
+    >>> logic._counter._value = 100
+
+    >>> trees = cp.parse('john barks'.split())
+    |. john.barks.|
+    |[-----]     .| [0:1] 'john'
+    |.     [-----]| [1:2] 'barks'
+    |[-----]     .| [0:1] NP[SEM=[BO={bo(\P.P(John),z101)}, CORE=<z101>]] -> 'john' *
+    |[----->     .| [0:1] S[SEM=[BO={?b1+?b2}, CORE=<?vp(?subj)>]] -> NP[SEM=[BO=?b1, CORE=?subj]] * VP[SEM=[BO=?b2, CORE=?vp]] {?b1: {bo(\P.P(John),z2)}, ?subj: <IndividualVariableExpression z2>}
+    |.     [-----]| [1:2] IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] -> 'barks' *
+    |.     [-----]| [1:2] VP[SEM=[BO={/}, CORE=<\x.bark(x)>]] -> IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] *
+    |[===========]| [0:2] S[SEM=[BO={bo(\P.P(John),z2)}, CORE=<bark(z2)>]] -> NP[SEM=[BO={bo(\P.P(John),z2)}, CORE=<z2>]] VP[SEM=[BO={/}, CORE=<\x.bark(x)>]] *
+    >>> for tree in trees: print(tree)
+    (S[SEM=[BO={bo(\P.P(John),z2)}, CORE=<bark(z2)>]]
+      (NP[SEM=[BO={bo(\P.P(John),z101)}, CORE=<z101>]] john)
+      (VP[SEM=[BO={/}, CORE=<\x.bark(x)>]]
+        (IV[SEM=[BO={/}, CORE=<\x.bark(x)>]] barks)))
+
+A transitive sentence:
+
+    >>> trees = cp.parse('john feeds a dog'.split())
+    |.joh.fee. a .dog.|
+    |[---]   .   .   .| [0:1] 'john'
+    |.   [---]   .   .| [1:2] 'feeds'
+    |.   .   [---]   .| [2:3] 'a'
+    |.   .   .   [---]| [3:4] 'dog'
+    |[---]   .   .   .| [0:1] NP[SEM=[BO={bo(\P.P(John),z102)}, CORE=<z102>]] -> 'john' *
+    |[--->   .   .   .| [0:1] S[SEM=[BO={?b1+?b2}, CORE=<?vp(?subj)>]] -> NP[SEM=[BO=?b1, CORE=?subj]] * VP[SEM=[BO=?b2, CORE=?vp]] {?b1: {bo(\P.P(John),z2)}, ?subj: <IndividualVariableExpression z2>}
+    |.   [---]   .   .| [1:2] TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] -> 'feeds' *
+    |.   [--->   .   .| [1:2] VP[SEM=[BO={?b1+?b2}, CORE=<?v(?obj)>]] -> TV[SEM=[BO=?b1, CORE=?v]] * NP[SEM=[BO=?b2, CORE=?obj]] {?b1: {/}, ?v: <LambdaExpression \x y.feed(y,x)>}
+    |.   .   [---]   .| [2:3] Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] -> 'a' *
+    |.   .   [--->   .| [2:3] NP[SEM=[BO={?b1+?b2+{bo(?det(?n),@x)}}, CORE=<@x>]] -> Det[SEM=[BO=?b1, CORE=?det]] * N[SEM=[BO=?b2, CORE=?n]] {?b1: {/}, ?det: <LambdaExpression \Q P.exists x.(Q(x) & P(x))>}
+    |.   .   .   [---]| [3:4] N[SEM=[BO={/}, CORE=<dog>]] -> 'dog' *
+    |.   .   [-------]| [2:4] NP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z103)}, CORE=<z103>]] -> Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] N[SEM=[BO={/}, CORE=<dog>]] *
+    |.   .   [------->| [2:4] S[SEM=[BO={?b1+?b2}, CORE=<?vp(?subj)>]] -> NP[SEM=[BO=?b1, CORE=?subj]] * VP[SEM=[BO=?b2, CORE=?vp]] {?b1: {bo(\P.exists x.(dog(x) & P(x)),z2)}, ?subj: <IndividualVariableExpression z2>}
+    |.   [-----------]| [1:4] VP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z2)}, CORE=<\y.feed(y,z2)>]] -> TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] NP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z2)}, CORE=<z2>]] *
+    |[===============]| [0:4] S[SEM=[BO={bo(\P.P(John),z2), bo(\P.exists x.(dog(x) & P(x)),z3)}, CORE=<feed(z2,z3)>]] -> NP[SEM=[BO={bo(\P.P(John),z2)}, CORE=<z2>]] VP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z3)}, CORE=<\y.feed(y,z3)>]] *
+
+    >>> for tree in trees: print(tree)
+    (S[SEM=[BO={bo(\P.P(John),z2), bo(\P.exists x.(dog(x) & P(x)),z3)}, CORE=<feed(z2,z3)>]]
+      (NP[SEM=[BO={bo(\P.P(John),z102)}, CORE=<z102>]] john)
+      (VP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z2)}, CORE=<\y.feed(y,z2)>]]
+        (TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] feeds)
+        (NP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z103)}, CORE=<z103>]]
+          (Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] a)
+          (N[SEM=[BO={/}, CORE=<dog>]] dog))))
+
+Turn down the verbosity:
+
+    >>> cp = parse.load_parser('grammars/sample_grammars/bindop.fcfg', trace=0,
+    ...                       chart_class=InstantiateVarsChart)
+
+Reuse the same lexical item twice:
+
+    >>> trees = cp.parse('john feeds john'.split())
+    >>> for tree in trees: print(tree)
+    (S[SEM=[BO={bo(\P.P(John),z2), bo(\P.P(John),z3)}, CORE=<feed(z2,z3)>]]
+      (NP[SEM=[BO={bo(\P.P(John),z104)}, CORE=<z104>]] john)
+      (VP[SEM=[BO={bo(\P.P(John),z2)}, CORE=<\y.feed(y,z2)>]]
+        (TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] feeds)
+        (NP[SEM=[BO={bo(\P.P(John),z105)}, CORE=<z105>]] john)))
+
+    >>> trees = cp.parse('a dog feeds a dog'.split())
+    >>> for tree in trees: print(tree)
+    (S[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z2), bo(\P.exists x.(dog(x) & P(x)),z3)}, CORE=<feed(z2,z3)>]]
+      (NP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z106)}, CORE=<z106>]]
+        (Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] a)
+        (N[SEM=[BO={/}, CORE=<dog>]] dog))
+      (VP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z2)}, CORE=<\y.feed(y,z2)>]]
+        (TV[SEM=[BO={/}, CORE=<\x y.feed(y,x)>]] feeds)
+        (NP[SEM=[BO={bo(\P.exists x.(dog(x) & P(x)),z107)}, CORE=<z107>]]
+          (Det[SEM=[BO={/}, CORE=<\Q P.exists x.(Q(x) & P(x))>]] a)
+          (N[SEM=[BO={/}, CORE=<dog>]] dog))))

env-llmeval/lib/python3.10/site-packages/nltk/test/framenet.doctest

@@ -0,0 +1,288 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+========
+FrameNet
+========
+
+The FrameNet corpus is a lexical database of English that is both human-
+and machine-readable, based on annotating examples of how words are used
+in actual texts. FrameNet is based on a theory of meaning called Frame
+Semantics, deriving from the work of Charles J. Fillmore and colleagues.
+The basic idea is straightforward: that the meanings of most words can
+best be understood on the basis of a semantic frame: a description of a
+type of event, relation, or entity and the participants in it. For
+example, the concept of cooking typically involves a person doing the
+cooking (Cook), the food that is to be cooked (Food), something to hold
+the food while cooking (Container) and a source of heat
+(Heating_instrument). In the FrameNet project, this is represented as a
+frame called Apply_heat, and the Cook, Food, Heating_instrument and
+Container are called frame elements (FEs). Words that evoke this frame,
+such as fry, bake, boil, and broil, are called lexical units (LUs) of
+the Apply_heat frame. The job of FrameNet is to define the frames
+and to annotate sentences to show how the FEs fit syntactically around
+the word that evokes the frame.
+
+------
+Frames
+------
+
+A Frame is a script-like conceptual structure that describes a
+particular type of situation, object, or event along with the
+participants and props that are needed for that Frame. For
+example, the "Apply_heat" frame describes a common situation
+involving a Cook, some Food, and a Heating_Instrument, and is
+evoked by words such as bake, blanch, boil, broil, brown,
+simmer, steam, etc.
+
+We call the roles of a Frame "frame elements" (FEs) and the
+frame-evoking words are called "lexical units" (LUs).
+
+FrameNet includes relations between Frames. Several types of
+relations are defined, of which the most important are:
+
+- Inheritance: An IS-A relation. The child frame is a subtype
+  of the parent frame, and each FE in the parent is bound to
+  a corresponding FE in the child. An example is the
+  "Revenge" frame which inherits from the
+  "Rewards_and_punishments" frame.
+
+- Using: The child frame presupposes the parent frame as
+  background, e.g the "Speed" frame "uses" (or presupposes)
+  the "Motion" frame; however, not all parent FEs need to be
+  bound to child FEs.
+
+- Subframe: The child frame is a subevent of a complex event
+  represented by the parent, e.g. the "Criminal_process" frame
+  has subframes of "Arrest", "Arraignment", "Trial", and
+  "Sentencing".
+
+- Perspective_on: The child frame provides a particular
+  perspective on an un-perspectivized parent frame. A pair of
+  examples consists of the "Hiring" and "Get_a_job" frames,
+  which perspectivize the "Employment_start" frame from the
+  Employer's and the Employee's point of view, respectively.
+
+To get a list of all of the Frames in FrameNet, you can use the
+`frames()` function. If you supply a regular expression pattern to the
+`frames()` function, you will get a list of all Frames whose names match
+that pattern:
+
+    >>> from pprint import pprint
+    >>> from operator import itemgetter
+    >>> from nltk.corpus import framenet as fn
+    >>> from nltk.corpus.reader.framenet import PrettyList
+    >>> x = fn.frames(r'(?i)crim')
+    >>> x.sort(key=itemgetter('ID'))
+    >>> x
+    [<frame ID=200 name=Criminal_process>, <frame ID=500 name=Criminal_investigation>, ...]
+    >>> PrettyList(sorted(x, key=itemgetter('ID')))
+    [<frame ID=200 name=Criminal_process>, <frame ID=500 name=Criminal_investigation>, ...]
+
+To get the details of a particular Frame, you can use the `frame()`
+function passing in the frame number:
+
+    >>> from pprint import pprint
+    >>> from nltk.corpus import framenet as fn
+    >>> f = fn.frame(202)
+    >>> f.ID
+    202
+    >>> f.name
+    'Arrest'
+    >>> f.definition
+    "Authorities charge a Suspect, who is under suspicion of having committed a crime..."
+    >>> len(f.lexUnit)
+    11
+    >>> pprint(sorted([x for x in f.FE]))
+    ['Authorities',
+     'Charges',
+     'Co-participant',
+     'Manner',
+     'Means',
+     'Offense',
+     'Place',
+     'Purpose',
+     'Source_of_legal_authority',
+     'Suspect',
+     'Time',
+     'Type']
+    >>> pprint(f.frameRelations)
+    [<Parent=Intentionally_affect -- Inheritance -> Child=Arrest>, <Complex=Criminal_process -- Subframe -> Component=Arrest>, ...]
+
+The `frame()` function shown above returns a dict object containing
+detailed information about the Frame. See the documentation on the
+`frame()` function for the specifics.
+
+You can also search for Frames by their Lexical Units (LUs). The
+`frames_by_lemma()` function returns a list of all frames that contain
+LUs in which the 'name' attribute of the LU matches the given regular
+expression. Note that LU names are composed of "lemma.POS", where the
+"lemma" part can be made up of either a single lexeme (e.g. 'run') or
+multiple lexemes (e.g. 'a little') (see below).
+
+    >>> PrettyList(sorted(fn.frames_by_lemma(r'(?i)a little'), key=itemgetter('ID')))
+    [<frame ID=189 name=Quanti...>, <frame ID=2001 name=Degree>]
+
+-------------
+Lexical Units
+-------------
+
+A lexical unit (LU) is a pairing of a word with a meaning. For
+example, the "Apply_heat" Frame describes a common situation
+involving a Cook, some Food, and a Heating Instrument, and is
+_evoked_ by words such as bake, blanch, boil, broil, brown,
+simmer, steam, etc. These frame-evoking words are the LUs in the
+Apply_heat frame. Each sense of a polysemous word is a different
+LU.
+
+We have used the word "word" in talking about LUs. The reality
+is actually rather complex. When we say that the word "bake" is
+polysemous, we mean that the lemma "bake.v" (which has the
+word-forms "bake", "bakes", "baked", and "baking") is linked to
+three different frames:
+
+- Apply_heat: "Michelle baked the potatoes for 45 minutes."
+
+- Cooking_creation: "Michelle baked her mother a cake for her birthday."
+
+- Absorb_heat: "The potatoes have to bake for more than 30 minutes."
+
+These constitute three different LUs, with different
+definitions.
+
+Multiword expressions such as "given name" and hyphenated words
+like "shut-eye" can also be LUs. Idiomatic phrases such as
+"middle of nowhere" and "give the slip (to)" are also defined as
+LUs in the appropriate frames ("Isolated_places" and "Evading",
+respectively), and their internal structure is not analyzed.
+
+Framenet provides multiple annotated examples of each sense of a
+word (i.e. each LU).  Moreover, the set of examples
+(approximately 20 per LU) illustrates all of the combinatorial
+possibilities of the lexical unit.
+
+Each LU is linked to a Frame, and hence to the other words which
+evoke that Frame. This makes the FrameNet database similar to a
+thesaurus, grouping together semantically similar words.
+
+In the simplest case, frame-evoking words are verbs such as
+"fried" in:
+
+   "Matilde fried the catfish in a heavy iron skillet."
+
+Sometimes event nouns may evoke a Frame. For example,
+"reduction" evokes "Cause_change_of_scalar_position" in:
+
+   "...the reduction of debt levels to $665 million from $2.6 billion."
+
+Adjectives may also evoke a Frame. For example, "asleep" may
+evoke the "Sleep" frame as in:
+
+   "They were asleep for hours."
+
+Many common nouns, such as artifacts like "hat" or "tower",
+typically serve as dependents rather than clearly evoking their
+own frames.
+
+Details for a specific lexical unit can be obtained using this class's
+`lus()` function, which takes an optional regular expression
+pattern that will be matched against the name of the lexical unit:
+
+    >>> from pprint import pprint
+    >>> PrettyList(sorted(fn.lus(r'(?i)a little'), key=itemgetter('ID')))
+    [<lu ID=14733 name=a little.n>, <lu ID=14743 name=a little.adv>, ...]
+
+You can obtain detailed information on a particular LU by calling the
+`lu()` function and passing in an LU's 'ID' number:
+
+    >>> from pprint import pprint
+    >>> from nltk.corpus import framenet as fn
+    >>> fn.lu(256).name
+    'foresee.v'
+    >>> fn.lu(256).definition
+    'COD: be aware of beforehand; predict.'
+    >>> fn.lu(256).frame.name
+    'Expectation'
+    >>> fn.lu(256).lexemes[0].name
+    'foresee'
+
+Note that LU names take the form of a dotted string (e.g. "run.v" or "a
+little.adv") in which a lemma precedes the "." and a part of speech
+(POS) follows the dot. The lemma may be composed of a single lexeme
+(e.g. "run") or of multiple lexemes (e.g. "a little"). The list of
+POSs used in the LUs is:
+
+v    - verb
+n    - noun
+a    - adjective
+adv  - adverb
+prep - preposition
+num  - numbers
+intj - interjection
+art  - article
+c    - conjunction
+scon - subordinating conjunction
+
+For more detailed information about the info that is contained in the
+dict that is returned by the `lu()` function, see the documentation on
+the `lu()` function.
+
+-------------------
+Annotated Documents
+-------------------
+
+The FrameNet corpus contains a small set of annotated documents. A list
+of these documents can be obtained by calling the `docs()` function:
+
+    >>> from pprint import pprint
+    >>> from nltk.corpus import framenet as fn
+    >>> d = fn.docs('BellRinging')[0]
+    >>> d.corpname
+    'PropBank'
+    >>> d.sentence[49]
+    full-text sentence (...) in BellRinging:
+    <BLANKLINE>
+    <BLANKLINE>
+    [POS] 17 tags
+    <BLANKLINE>
+    [POS_tagset] PENN
+    <BLANKLINE>
+    [text] + [annotationSet]
+    <BLANKLINE>
+    `` I live in hopes that the ringers themselves will be drawn into
+                 *****          *******                    *****
+                 Desir          Cause_t                    Cause
+                 [1]            [3]                        [2]
+    <BLANKLINE>
+     that fuller life .
+          ******
+          Comple
+          [4]
+     (Desir=Desiring, Cause_t=Cause_to_make_noise, Cause=Cause_motion, Comple=Completeness)
+    <BLANKLINE>
+
+    >>> d.sentence[49].annotationSet[1]
+    annotation set (...):
+    <BLANKLINE>
+    [status] MANUAL
+    <BLANKLINE>
+    [LU] (6605) hope.n in Desiring
+    <BLANKLINE>
+    [frame] (366) Desiring
+    <BLANKLINE>
+    [GF] 2 relations
+    <BLANKLINE>
+    [PT] 2 phrases
+    <BLANKLINE>
+    [text] + [Target] + [FE] + [Noun]
+    <BLANKLINE>
+    `` I live in hopes that the ringers themselves will be drawn into
+       - ^^^^ ^^ ***** ----------------------------------------------
+       E supp su       Event
+    <BLANKLINE>
+     that fuller life .
+    -----------------
+    <BLANKLINE>
+     (E=Experiencer, su=supp)
+    <BLANKLINE>
+    <BLANKLINE>

env-llmeval/lib/python3.10/site-packages/nltk/test/generate.doctest

@@ -0,0 +1,78 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+===============================================
+Generating sentences from context-free grammars
+===============================================
+
+An example grammar:
+
+    >>> from nltk.parse.generate import generate, demo_grammar
+    >>> from nltk import CFG
+    >>> grammar = CFG.fromstring(demo_grammar)
+    >>> print(grammar)
+    Grammar with 13 productions (start state = S)
+        S -> NP VP
+        NP -> Det N
+        PP -> P NP
+        VP -> 'slept'
+        VP -> 'saw' NP
+        VP -> 'walked' PP
+        Det -> 'the'
+        Det -> 'a'
+        N -> 'man'
+        N -> 'park'
+        N -> 'dog'
+        P -> 'in'
+        P -> 'with'
+
+The first 10 generated sentences:
+
+    >>> for sentence in generate(grammar, n=10):
+    ...     print(' '.join(sentence))
+    the man slept
+    the man saw the man
+    the man saw the park
+    the man saw the dog
+    the man saw a man
+    the man saw a park
+    the man saw a dog
+    the man walked in the man
+    the man walked in the park
+    the man walked in the dog
+
+All sentences of max depth 4:
+
+    >>> for sentence in generate(grammar, depth=4):
+    ...     print(' '.join(sentence))
+    the man slept
+    the park slept
+    the dog slept
+    a man slept
+    a park slept
+    a dog slept
+
+The number of sentences of different max depths:
+
+    >>> len(list(generate(grammar, depth=3)))
+    0
+    >>> len(list(generate(grammar, depth=4)))
+    6
+    >>> len(list(generate(grammar, depth=5)))
+    42
+    >>> len(list(generate(grammar, depth=6)))
+    114
+    >>> len(list(generate(grammar)))
+    114
+
+Infinite grammars will throw a RecursionError when not bounded by some ``depth``:
+
+    >>> grammar = CFG.fromstring("""
+    ... S -> A B
+    ... A -> B
+    ... B -> "b" | A
+    ... """)
+    >>> list(generate(grammar))
+    Traceback (most recent call last):
+    ...
+    RuntimeError: The grammar has rule(s) that yield infinite recursion!

env-llmeval/lib/python3.10/site-packages/nltk/test/gensim.doctest

@@ -0,0 +1,141 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=======================================
+Demonstrate word embedding using Gensim
+=======================================
+
+    >>> from nltk.test.gensim_fixt import setup_module
+    >>> setup_module()
+
+We demonstrate three functions:
+- Train the word embeddings using brown corpus;
+- Load the pre-trained model and perform simple tasks; and
+- Pruning the pre-trained binary model.
+
+    >>> import gensim
+
+---------------
+Train the model
+---------------
+
+Here we train a word embedding using the Brown Corpus:
+
+    >>> from nltk.corpus import brown
+    >>> train_set = brown.sents()[:10000]
+    >>> model = gensim.models.Word2Vec(train_set)
+
+It might take some time to train the model. So, after it is trained, it can be saved as follows:
+
+    >>> model.save('brown.embedding')
+    >>> new_model = gensim.models.Word2Vec.load('brown.embedding')
+
+The model will be the list of words with their embedding. We can easily get the vector representation of a word.
+
+    >>> len(new_model.wv['university'])
+    100
+
+There are some supporting functions already implemented in Gensim to manipulate with word embeddings.
+For example, to compute the cosine similarity between 2 words:
+
+    >>> new_model.wv.similarity('university','school') > 0.3
+    True
+
+---------------------------
+Using the pre-trained model
+---------------------------
+
+NLTK includes a pre-trained model which is part of a model that is trained on 100 billion words from the Google News Dataset.
+The full model is from https://code.google.com/p/word2vec/ (about 3 GB).
+
+    >>> from nltk.data import find
+    >>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))
+    >>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False)
+
+We pruned the model to only include the most common words (~44k words).
+
+    >>> len(model)
+    43981
+
+Each word is represented in the space of 300 dimensions:
+
+    >>> len(model['university'])
+    300
+
+Finding the top n words that are similar to a target word is simple. The result is the list of n words with the score.
+
+    >>> model.most_similar(positive=['university'], topn = 3)
+    [('universities', 0.70039...), ('faculty', 0.67809...), ('undergraduate', 0.65870...)]
+
+Finding a word that is not in a list is also supported, although, implementing this by yourself is simple.
+
+    >>> model.doesnt_match('breakfast cereal dinner lunch'.split())
+    'cereal'
+
+Mikolov et al. (2013) figured out that word embedding captures much of syntactic and semantic regularities. For example,
+the vector 'King - Man + Woman' is close to 'Queen' and 'Germany - Berlin + Paris' is close to 'France'.
+
+    >>> model.most_similar(positive=['woman','king'], negative=['man'], topn = 1)
+    [('queen', 0.71181...)]
+
+    >>> model.most_similar(positive=['Paris','Germany'], negative=['Berlin'], topn = 1)
+    [('France', 0.78840...)]
+
+We can visualize the word embeddings using t-SNE (https://lvdmaaten.github.io/tsne/). For this demonstration, we visualize the first 1000 words.
+
+|    import numpy as np
+|    labels = []
+|    count = 0
+|    max_count = 1000
+|    X = np.zeros(shape=(max_count,len(model['university'])))
+|
+|    for term in model.index_to_key:
+|        X[count] = model[term]
+|        labels.append(term)
+|        count+= 1
+|        if count >= max_count: break
+|
+|    # It is recommended to use PCA first to reduce to ~50 dimensions
+|    from sklearn.decomposition import PCA
+|    pca = PCA(n_components=50)
+|    X_50 = pca.fit_transform(X)
+|
+|    # Using TSNE to further reduce to 2 dimensions
+|    from sklearn.manifold import TSNE
+|    model_tsne = TSNE(n_components=2, random_state=0)
+|    Y = model_tsne.fit_transform(X_50)
+|
+|    # Show the scatter plot
+|    import matplotlib.pyplot as plt
+|    plt.scatter(Y[:,0], Y[:,1], 20)
+|
+|    # Add labels
+|    for label, x, y in zip(labels, Y[:, 0], Y[:, 1]):
+|        plt.annotate(label, xy = (x,y), xytext = (0, 0), textcoords = 'offset points', size = 10)
+|
+|    plt.show()
+
+------------------------------
+Prune the trained binary model
+------------------------------
+
+Here is the supporting code to extract part of the binary model (GoogleNews-vectors-negative300.bin.gz) from https://code.google.com/p/word2vec/
+We use this code to get the `word2vec_sample` model.
+
+|    import gensim
+|    # Load the binary model
+|    model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary = True)
+|
+|    # Only output word that appear in the Brown corpus
+|    from nltk.corpus import brown
+|    words = set(brown.words())
+|    print(len(words))
+|
+|    # Output presented word to a temporary file
+|    out_file = 'pruned.word2vec.txt'
+|    with open(out_file,'w') as f:
+|        word_presented = words.intersection(model.index_to_key)
+|        f.write('{} {}\n'.format(len(word_presented),len(model['word'])))
+|
+|        for word in word_presented:
+|            f.write('{} {}\n'.format(word, ' '.join(str(value) for value in model[word])))

env-llmeval/lib/python3.10/site-packages/nltk/test/gluesemantics.doctest

@@ -0,0 +1,383 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==============================================================================
+ Glue Semantics
+==============================================================================
+
+
+
+======================
+Linear logic
+======================
+
+    >>> from nltk.sem import logic
+    >>> from nltk.sem.glue import *
+    >>> from nltk.sem.linearlogic import *
+
+    >>> from nltk.sem.linearlogic import Expression
+    >>> read_expr = Expression.fromstring
+
+Parser
+
+    >>> print(read_expr(r'f'))
+    f
+    >>> print(read_expr(r'(g -o f)'))
+    (g -o f)
+    >>> print(read_expr(r'(g -o (h -o f))'))
+    (g -o (h -o f))
+    >>> print(read_expr(r'((g -o G) -o G)'))
+    ((g -o G) -o G)
+    >>> print(read_expr(r'(g -o f)(g)'))
+    (g -o f)(g)
+    >>> print(read_expr(r'((g -o G) -o G)((g -o f))'))
+    ((g -o G) -o G)((g -o f))
+
+Simplify
+
+    >>> print(read_expr(r'f').simplify())
+    f
+    >>> print(read_expr(r'(g -o f)').simplify())
+    (g -o f)
+    >>> print(read_expr(r'((g -o G) -o G)').simplify())
+    ((g -o G) -o G)
+    >>> print(read_expr(r'(g -o f)(g)').simplify())
+    f
+    >>> try: read_expr(r'(g -o f)(f)').simplify()
+    ... except LinearLogicApplicationException as e: print(e)
+    ...
+    Cannot apply (g -o f) to f. Cannot unify g with f given {}
+    >>> print(read_expr(r'(G -o f)(g)').simplify())
+    f
+    >>> print(read_expr(r'((g -o G) -o G)((g -o f))').simplify())
+    f
+
+Test BindingDict
+
+    >>> h = ConstantExpression('h')
+    >>> g = ConstantExpression('g')
+    >>> f = ConstantExpression('f')
+
+    >>> H = VariableExpression('H')
+    >>> G = VariableExpression('G')
+    >>> F = VariableExpression('F')
+
+    >>> d1 = BindingDict({H: h})
+    >>> d2 = BindingDict({F: f, G: F})
+    >>> d12 = d1 + d2
+    >>> all12 = ['%s: %s' % (v, d12[v]) for v in d12.d]
+    >>> all12.sort()
+    >>> print(all12)
+    ['F: f', 'G: f', 'H: h']
+
+    >>> BindingDict([(F,f),(G,g),(H,h)]) == BindingDict({F:f, G:g, H:h})
+    True
+
+    >>> d4 = BindingDict({F: f})
+    >>> try: d4[F] = g
+    ... except VariableBindingException as e: print(e)
+    Variable F already bound to another value
+
+Test Unify
+
+    >>> try: f.unify(g, BindingDict())
+    ... except UnificationException as e: print(e)
+    ...
+    Cannot unify f with g given {}
+
+    >>> f.unify(G, BindingDict()) == BindingDict({G: f})
+    True
+    >>> try: f.unify(G, BindingDict({G: h}))
+    ... except UnificationException as e: print(e)
+    ...
+    Cannot unify f with G given {G: h}
+    >>> f.unify(G, BindingDict({G: f})) == BindingDict({G: f})
+    True
+    >>> f.unify(G, BindingDict({H: f})) == BindingDict({G: f, H: f})
+    True
+
+    >>> G.unify(f, BindingDict()) == BindingDict({G: f})
+    True
+    >>> try: G.unify(f, BindingDict({G: h}))
+    ... except UnificationException as e: print(e)
+    ...
+    Cannot unify G with f given {G: h}
+    >>> G.unify(f, BindingDict({G: f})) == BindingDict({G: f})
+    True
+    >>> G.unify(f, BindingDict({H: f})) == BindingDict({G: f, H: f})
+    True
+
+    >>> G.unify(F, BindingDict()) == BindingDict({G: F})
+    True
+    >>> try: G.unify(F, BindingDict({G: H}))
+    ... except UnificationException as e: print(e)
+    ...
+    Cannot unify G with F given {G: H}
+    >>> G.unify(F, BindingDict({G: F})) == BindingDict({G: F})
+    True
+    >>> G.unify(F, BindingDict({H: F})) == BindingDict({G: F, H: F})
+    True
+
+Test Compile
+
+    >>> print(read_expr('g').compile_pos(Counter(), GlueFormula))
+    (<ConstantExpression g>, [])
+    >>> print(read_expr('(g -o f)').compile_pos(Counter(), GlueFormula))
+    (<ImpExpression (g -o f)>, [])
+    >>> print(read_expr('(g -o (h -o f))').compile_pos(Counter(), GlueFormula))
+    (<ImpExpression (g -o (h -o f))>, [])
+
+
+======================
+Glue
+======================
+
+Demo of "John walks"
+--------------------
+
+    >>> john = GlueFormula("John", "g")
+    >>> print(john)
+    John : g
+    >>> walks = GlueFormula(r"\x.walks(x)", "(g -o f)")
+    >>> print(walks)
+    \x.walks(x) : (g -o f)
+    >>> print(walks.applyto(john))
+    \x.walks(x)(John) : (g -o f)(g)
+    >>> print(walks.applyto(john).simplify())
+    walks(John) : f
+
+
+Demo of "A dog walks"
+---------------------
+
+    >>> a = GlueFormula("\\P Q.some x.(P(x) and Q(x))", "((gv -o gr) -o ((g -o G) -o G))")
+    >>> print(a)
+    \P Q.exists x.(P(x) & Q(x)) : ((gv -o gr) -o ((g -o G) -o G))
+    >>> man = GlueFormula(r"\x.man(x)", "(gv -o gr)")
+    >>> print(man)
+    \x.man(x) : (gv -o gr)
+    >>> walks = GlueFormula(r"\x.walks(x)", "(g -o f)")
+    >>> print(walks)
+    \x.walks(x) : (g -o f)
+    >>> a_man = a.applyto(man)
+    >>> print(a_man.simplify())
+    \Q.exists x.(man(x) & Q(x)) : ((g -o G) -o G)
+    >>> a_man_walks = a_man.applyto(walks)
+    >>> print(a_man_walks.simplify())
+    exists x.(man(x) & walks(x)) : f
+
+
+Demo of 'every girl chases a dog'
+---------------------------------
+
+Individual words:
+
+    >>> every = GlueFormula("\\P Q.all x.(P(x) -> Q(x))", "((gv -o gr) -o ((g -o G) -o G))")
+    >>> print(every)
+    \P Q.all x.(P(x) -> Q(x)) : ((gv -o gr) -o ((g -o G) -o G))
+    >>> girl = GlueFormula(r"\x.girl(x)", "(gv -o gr)")
+    >>> print(girl)
+    \x.girl(x) : (gv -o gr)
+    >>> chases = GlueFormula(r"\x y.chases(x,y)", "(g -o (h -o f))")
+    >>> print(chases)
+    \x y.chases(x,y) : (g -o (h -o f))
+    >>> a = GlueFormula("\\P Q.some x.(P(x) and Q(x))", "((hv -o hr) -o ((h -o H) -o H))")
+    >>> print(a)
+    \P Q.exists x.(P(x) & Q(x)) : ((hv -o hr) -o ((h -o H) -o H))
+    >>> dog = GlueFormula(r"\x.dog(x)", "(hv -o hr)")
+    >>> print(dog)
+    \x.dog(x) : (hv -o hr)
+
+Noun Quantification can only be done one way:
+
+    >>> every_girl = every.applyto(girl)
+    >>> print(every_girl.simplify())
+    \Q.all x.(girl(x) -> Q(x)) : ((g -o G) -o G)
+    >>> a_dog = a.applyto(dog)
+    >>> print(a_dog.simplify())
+    \Q.exists x.(dog(x) & Q(x)) : ((h -o H) -o H)
+
+The first reading is achieved by combining 'chases' with 'a dog' first.
+Since 'a girl' requires something of the form '(h -o H)' we must
+get rid of the 'g' in the glue of 'see'.  We will do this with
+the '-o elimination' rule.  So, x1 will be our subject placeholder.
+
+    >>> xPrime = GlueFormula("x1", "g")
+    >>> print(xPrime)
+    x1 : g
+    >>> xPrime_chases = chases.applyto(xPrime)
+    >>> print(xPrime_chases.simplify())
+    \y.chases(x1,y) : (h -o f)
+    >>> xPrime_chases_a_dog = a_dog.applyto(xPrime_chases)
+    >>> print(xPrime_chases_a_dog.simplify())
+    exists x.(dog(x) & chases(x1,x)) : f
+
+Now we can retract our subject placeholder using lambda-abstraction and
+combine with the true subject.
+
+    >>> chases_a_dog = xPrime_chases_a_dog.lambda_abstract(xPrime)
+    >>> print(chases_a_dog.simplify())
+    \x1.exists x.(dog(x) & chases(x1,x)) : (g -o f)
+    >>> every_girl_chases_a_dog = every_girl.applyto(chases_a_dog)
+    >>> r1 = every_girl_chases_a_dog.simplify()
+    >>> r2 = GlueFormula(r'all x.(girl(x) -> exists z1.(dog(z1) & chases(x,z1)))', 'f')
+    >>> r1 == r2
+    True
+
+The second reading is achieved by combining 'every girl' with 'chases' first.
+
+    >>> xPrime = GlueFormula("x1", "g")
+    >>> print(xPrime)
+    x1 : g
+    >>> xPrime_chases = chases.applyto(xPrime)
+    >>> print(xPrime_chases.simplify())
+    \y.chases(x1,y) : (h -o f)
+    >>> yPrime = GlueFormula("x2", "h")
+    >>> print(yPrime)
+    x2 : h
+    >>> xPrime_chases_yPrime = xPrime_chases.applyto(yPrime)
+    >>> print(xPrime_chases_yPrime.simplify())
+    chases(x1,x2) : f
+    >>> chases_yPrime = xPrime_chases_yPrime.lambda_abstract(xPrime)
+    >>> print(chases_yPrime.simplify())
+    \x1.chases(x1,x2) : (g -o f)
+    >>> every_girl_chases_yPrime = every_girl.applyto(chases_yPrime)
+    >>> print(every_girl_chases_yPrime.simplify())
+    all x.(girl(x) -> chases(x,x2)) : f
+    >>> every_girl_chases = every_girl_chases_yPrime.lambda_abstract(yPrime)
+    >>> print(every_girl_chases.simplify())
+    \x2.all x.(girl(x) -> chases(x,x2)) : (h -o f)
+    >>> every_girl_chases_a_dog = a_dog.applyto(every_girl_chases)
+    >>> r1 = every_girl_chases_a_dog.simplify()
+    >>> r2 = GlueFormula(r'exists x.(dog(x) & all z2.(girl(z2) -> chases(z2,x)))', 'f')
+    >>> r1 == r2
+    True
+
+
+Compilation
+-----------
+
+    >>> for cp in GlueFormula('m', '(b -o a)').compile(Counter()): print(cp)
+    m : (b -o a) : {1}
+    >>> for cp in GlueFormula('m', '((c -o b) -o a)').compile(Counter()): print(cp)
+    v1 : c : {1}
+    m : (b[1] -o a) : {2}
+    >>> for cp in GlueFormula('m', '((d -o (c -o b)) -o a)').compile(Counter()): print(cp)
+    v1 : c : {1}
+    v2 : d : {2}
+    m : (b[1, 2] -o a) : {3}
+    >>> for cp in GlueFormula('m', '((d -o e) -o ((c -o b) -o a))').compile(Counter()): print(cp)
+    v1 : d : {1}
+    v2 : c : {2}
+    m : (e[1] -o (b[2] -o a)) : {3}
+    >>> for cp in GlueFormula('m', '(((d -o c) -o b) -o a)').compile(Counter()): print(cp)
+    v1 : (d -o c) : {1}
+    m : (b[1] -o a) : {2}
+    >>> for cp in GlueFormula('m', '((((e -o d) -o c) -o b) -o a)').compile(Counter()): print(cp)
+    v1 : e : {1}
+    v2 : (d[1] -o c) : {2}
+    m : (b[2] -o a) : {3}
+
+
+Demo of 'a man walks' using Compilation
+---------------------------------------
+
+Premises
+
+    >>> a = GlueFormula('\\P Q.some x.(P(x) and Q(x))', '((gv -o gr) -o ((g -o G) -o G))')
+    >>> print(a)
+    \P Q.exists x.(P(x) & Q(x)) : ((gv -o gr) -o ((g -o G) -o G))
+
+    >>> man = GlueFormula('\\x.man(x)', '(gv -o gr)')
+    >>> print(man)
+    \x.man(x) : (gv -o gr)
+
+    >>> walks = GlueFormula('\\x.walks(x)', '(g -o f)')
+    >>> print(walks)
+    \x.walks(x) : (g -o f)
+
+Compiled Premises:
+
+    >>> counter = Counter()
+    >>> ahc = a.compile(counter)
+    >>> g1 = ahc[0]
+    >>> print(g1)
+    v1 : gv : {1}
+    >>> g2 = ahc[1]
+    >>> print(g2)
+    v2 : g : {2}
+    >>> g3 = ahc[2]
+    >>> print(g3)
+    \P Q.exists x.(P(x) & Q(x)) : (gr[1] -o (G[2] -o G)) : {3}
+    >>> g4 = man.compile(counter)[0]
+    >>> print(g4)
+    \x.man(x) : (gv -o gr) : {4}
+    >>> g5 = walks.compile(counter)[0]
+    >>> print(g5)
+    \x.walks(x) : (g -o f) : {5}
+
+Derivation:
+
+    >>> g14 = g4.applyto(g1)
+    >>> print(g14.simplify())
+    man(v1) : gr : {1, 4}
+    >>> g134 = g3.applyto(g14)
+    >>> print(g134.simplify())
+    \Q.exists x.(man(x) & Q(x)) : (G[2] -o G) : {1, 3, 4}
+    >>> g25 = g5.applyto(g2)
+    >>> print(g25.simplify())
+    walks(v2) : f : {2, 5}
+    >>> g12345 = g134.applyto(g25)
+    >>> print(g12345.simplify())
+    exists x.(man(x) & walks(x)) : f : {1, 2, 3, 4, 5}
+
+---------------------------------
+Dependency Graph to Glue Formulas
+---------------------------------
+    >>> from nltk.corpus.reader.dependency import DependencyGraph
+
+    >>> depgraph = DependencyGraph("""1	John	_	NNP	NNP	_	2	SUBJ	_	_
+    ... 2	sees	_	VB	VB	_	0	ROOT	_	_
+    ... 3	a	_	ex_quant	ex_quant	_	4	SPEC	_	_
+    ... 4	dog	_	NN	NN	_	2	OBJ	_	_
+    ... """)
+    >>> gfl = GlueDict('nltk:grammars/sample_grammars/glue.semtype').to_glueformula_list(depgraph)
+    >>> print(gfl) # doctest: +SKIP
+    [\x y.sees(x,y) : (f -o (i -o g)),
+     \x.dog(x) : (iv -o ir),
+     \P Q.exists x.(P(x) & Q(x)) : ((iv -o ir) -o ((i -o I3) -o I3)),
+     \P Q.exists x.(P(x) & Q(x)) : ((fv -o fr) -o ((f -o F4) -o F4)),
+     \x.John(x) : (fv -o fr)]
+    >>> glue = Glue()
+    >>> for r in sorted([r.simplify().normalize() for r in glue.get_readings(glue.gfl_to_compiled(gfl))], key=str):
+    ...     print(r)
+    exists z1.(John(z1) & exists z2.(dog(z2) & sees(z1,z2)))
+    exists z1.(dog(z1) & exists z2.(John(z2) & sees(z2,z1)))
+
+-----------------------------------
+Dependency Graph to LFG f-structure
+-----------------------------------
+    >>> from nltk.sem.lfg import FStructure
+
+    >>> fstruct = FStructure.read_depgraph(depgraph)
+
+    >>> print(fstruct) # doctest: +SKIP
+    f:[pred 'sees'
+       obj h:[pred 'dog'
+              spec 'a']
+       subj g:[pred 'John']]
+
+    >>> fstruct.to_depgraph().tree().pprint()
+    (sees (dog a) John)
+
+---------------------------------
+LFG f-structure to Glue
+---------------------------------
+    >>> fstruct.to_glueformula_list(GlueDict('nltk:grammars/sample_grammars/glue.semtype')) # doctest: +SKIP
+    [\x y.sees(x,y) : (i -o (g -o f)),
+     \x.dog(x) : (gv -o gr),
+     \P Q.exists x.(P(x) & Q(x)) : ((gv -o gr) -o ((g -o G3) -o G3)),
+     \P Q.exists x.(P(x) & Q(x)) : ((iv -o ir) -o ((i -o I4) -o I4)),
+     \x.John(x) : (iv -o ir)]
+
+.. see gluesemantics_malt.doctest for more

env-llmeval/lib/python3.10/site-packages/nltk/test/gluesemantics_malt_fixt.py

@@ -0,0 +1,9 @@
+def setup_module():
+    import pytest
+
+    from nltk.parse.malt import MaltParser
+
+    try:
+        depparser = MaltParser()
+    except (AssertionError, LookupError) as e:
+        pytest.skip("MaltParser is not available")

env-llmeval/lib/python3.10/site-packages/nltk/test/index.doctest

@@ -0,0 +1,100 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+.. _align howto: align.html
+.. _ccg howto: ccg.html
+.. _chat80 howto: chat80.html
+.. _childes howto: childes.html
+.. _chunk howto: chunk.html
+.. _classify howto: classify.html
+.. _collocations howto: collocations.html
+.. _compat howto: compat.html
+.. _corpus howto: corpus.html
+.. _data howto: data.html
+.. _dependency howto: dependency.html
+.. _discourse howto: discourse.html
+.. _drt howto: drt.html
+.. _featgram howto: featgram.html
+.. _featstruct howto: featstruct.html
+.. _framenet howto: framenet.html
+.. _generate howto: generate.html
+.. _gluesemantics howto: gluesemantics.html
+.. _gluesemantics_malt howto: gluesemantics_malt.html
+.. _grammar howto: grammar.html
+.. _grammartestsuites howto: grammartestsuites.html
+.. _index howto: index.html
+.. _inference howto: inference.html
+.. _internals howto: internals.html
+.. _japanese howto: japanese.html
+.. _logic howto: logic.html
+.. _metrics howto: metrics.html
+.. _misc howto: misc.html
+.. _nonmonotonic howto: nonmonotonic.html
+.. _parse howto: parse.html
+.. _portuguese_en howto: portuguese_en.html
+.. _probability howto: probability.html
+.. _propbank howto: propbank.html
+.. _relextract howto: relextract.html
+.. _resolution howto: resolution.html
+.. _semantics howto: semantics.html
+.. _simple howto: simple.html
+.. _stem howto: stem.html
+.. _tag howto: tag.html
+.. _tokenize howto: tokenize.html
+.. _toolbox howto: toolbox.html
+.. _tree howto: tree.html
+.. _treetransforms howto: treetransforms.html
+.. _util howto: util.html
+.. _wordnet howto: wordnet.html
+.. _wordnet_lch howto: wordnet_lch.html
+
+===========
+NLTK HOWTOs
+===========
+
+* `align HOWTO`_
+* `ccg HOWTO`_
+* `chat80 HOWTO`_
+* `childes HOWTO`_
+* `chunk HOWTO`_
+* `classify HOWTO`_
+* `collocations HOWTO`_
+* `compat HOWTO`_
+* `corpus HOWTO`_
+* `data HOWTO`_
+* `dependency HOWTO`_
+* `discourse HOWTO`_
+* `drt HOWTO`_
+* `featgram HOWTO`_
+* `featstruct HOWTO`_
+* `framenet HOWTO`_
+* `generate HOWTO`_
+* `gluesemantics HOWTO`_
+* `gluesemantics_malt HOWTO`_
+* `grammar HOWTO`_
+* `grammartestsuites HOWTO`_
+* `index HOWTO`_
+* `inference HOWTO`_
+* `internals HOWTO`_
+* `japanese HOWTO`_
+* `logic HOWTO`_
+* `metrics HOWTO`_
+* `misc HOWTO`_
+* `nonmonotonic HOWTO`_
+* `parse HOWTO`_
+* `portuguese_en HOWTO`_
+* `probability HOWTO`_
+* `propbank HOWTO`_
+* `relextract HOWTO`_
+* `resolution HOWTO`_
+* `semantics HOWTO`_
+* `simple HOWTO`_
+* `stem HOWTO`_
+* `tag HOWTO`_
+* `tokenize HOWTO`_
+* `toolbox HOWTO`_
+* `tree HOWTO`_
+* `treetransforms HOWTO`_
+* `util HOWTO`_
+* `wordnet HOWTO`_
+* `wordnet_lch HOWTO`_

env-llmeval/lib/python3.10/site-packages/nltk/test/internals.doctest

@@ -0,0 +1,161 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==========================================
+ Unit tests for the nltk.utilities module
+==========================================
+
+overridden()
+~~~~~~~~~~~~
+    >>> from nltk.internals import overridden
+
+The typical use case is in defining methods for an interface or
+abstract base class, in such a way that subclasses don't have to
+implement all of the methods:
+
+    >>> class EaterI(object):
+    ...     '''Subclass must define eat() or batch_eat().'''
+    ...     def eat(self, food):
+    ...         if overridden(self.batch_eat):
+    ...             return self.batch_eat([food])[0]
+    ...         else:
+    ...             raise NotImplementedError()
+    ...     def batch_eat(self, foods):
+    ...         return [self.eat(food) for food in foods]
+
+As long as a subclass implements one method, it will be used to
+perform the other method:
+
+    >>> class GoodEater1(EaterI):
+    ...     def eat(self, food):
+    ...         return 'yum'
+    >>> GoodEater1().eat('steak')
+    'yum'
+    >>> GoodEater1().batch_eat(['steak', 'peas'])
+    ['yum', 'yum']
+
+    >>> class GoodEater2(EaterI):
+    ...     def batch_eat(self, foods):
+    ...         return ['yum' for food in foods]
+    >>> GoodEater2().eat('steak')
+    'yum'
+    >>> GoodEater2().batch_eat(['steak', 'peas'])
+    ['yum', 'yum']
+
+But if a subclass doesn't implement either one, then they'll get an
+error when they try to call them.  (nb this is better than infinite
+recursion):
+
+    >>> class BadEater1(EaterI):
+    ...     pass
+    >>> BadEater1().eat('steak')
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+    >>> BadEater1().batch_eat(['steak', 'peas'])
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+
+Trying to use the abstract base class itself will also result in an
+error:
+
+    >>> class EaterI(EaterI):
+    ...     pass
+    >>> EaterI().eat('steak')
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+    >>> EaterI().batch_eat(['steak', 'peas'])
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+
+It's ok to use intermediate abstract classes:
+
+    >>> class AbstractEater(EaterI):
+    ...     pass
+
+    >>> class GoodEater3(AbstractEater):
+    ...     def eat(self, food):
+    ...         return 'yum'
+    ...
+    >>> GoodEater3().eat('steak')
+    'yum'
+    >>> GoodEater3().batch_eat(['steak', 'peas'])
+    ['yum', 'yum']
+
+    >>> class GoodEater4(AbstractEater):
+    ...     def batch_eat(self, foods):
+    ...         return ['yum' for food in foods]
+    >>> GoodEater4().eat('steak')
+    'yum'
+    >>> GoodEater4().batch_eat(['steak', 'peas'])
+    ['yum', 'yum']
+
+    >>> class BadEater2(AbstractEater):
+    ...     pass
+    >>> BadEater2().eat('steak')
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+    >>> BadEater2().batch_eat(['steak', 'peas'])
+    Traceback (most recent call last):
+      . . .
+    NotImplementedError
+
+Here's some extra tests:
+
+    >>> class A(object):
+    ...     def f(x): pass
+    >>> class B(A):
+    ...     def f(x): pass
+    >>> class C(A): pass
+    >>> class D(B): pass
+
+    >>> overridden(A().f)
+    False
+    >>> overridden(B().f)
+    True
+    >>> overridden(C().f)
+    False
+    >>> overridden(D().f)
+    True
+
+It works for classic classes, too:
+
+    >>> class A:
+    ...     def f(x): pass
+    >>> class B(A):
+    ...     def f(x): pass
+    >>> class C(A): pass
+    >>> class D(B): pass
+    >>> overridden(A().f)
+    False
+    >>> overridden(B().f)
+    True
+    >>> overridden(C().f)
+    False
+    >>> overridden(D().f)
+    True
+
+
+read_str()
+~~~~~~~~~~~~
+    >>> from nltk.internals import read_str
+
+Test valid scenarios
+
+    >>> read_str("'valid string'", 0)
+    ('valid string', 14)
+
+Now test invalid scenarios
+
+    >>> read_str("should error", 0)
+    Traceback (most recent call last):
+    ...
+    nltk.internals.ReadError: Expected open quote at 0
+    >>> read_str("'should error", 0)
+    Traceback (most recent call last):
+    ...
+    nltk.internals.ReadError: Expected close quote at 1

env-llmeval/lib/python3.10/site-packages/nltk/test/lm.doctest

@@ -0,0 +1,135 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+.. -*- coding: utf-8 -*-
+
+
+Regression Tests
+================
+
+
+Issue 167
+---------
+https://github.com/nltk/nltk/issues/167
+
+    >>> from nltk.corpus import brown
+    >>> from nltk.lm.preprocessing import padded_everygram_pipeline
+    >>> ngram_order = 3
+    >>> train_data, vocab_data = padded_everygram_pipeline(
+    ...     ngram_order,
+    ...     brown.sents(categories="news")
+    ... )
+
+    >>> from nltk.lm import WittenBellInterpolated
+    >>> lm = WittenBellInterpolated(ngram_order)
+    >>> lm.fit(train_data, vocab_data)
+
+
+
+
+Sentence containing an unseen word should result in infinite entropy because
+Witten-Bell is based ultimately on MLE, which cannot handle unseen ngrams.
+Crucially, it shouldn't raise any exceptions for unseen words.
+
+    >>> from nltk.util import ngrams
+    >>> sent = ngrams("This is a sentence with the word aaddvark".split(), 3)
+    >>> lm.entropy(sent)
+    inf
+
+If we remove all unseen ngrams from the sentence, we'll get a non-infinite value
+for the entropy.
+
+    >>> sent = ngrams("This is a sentence".split(), 3)
+    >>> round(lm.entropy(sent), 14)
+    10.23701322869105
+
+
+Issue 367
+---------
+https://github.com/nltk/nltk/issues/367
+
+Reproducing Dan Blanchard's example:
+https://github.com/nltk/nltk/issues/367#issuecomment-14646110
+
+    >>> from nltk.lm import Lidstone, Vocabulary
+    >>> word_seq = list('aaaababaaccbacb')
+    >>> ngram_order = 2
+    >>> from nltk.util import everygrams
+    >>> train_data = [everygrams(word_seq, max_len=ngram_order)]
+    >>> V = Vocabulary(['a', 'b', 'c', ''])
+    >>> lm = Lidstone(0.2, ngram_order, vocabulary=V)
+    >>> lm.fit(train_data)
+
+For doctest to work we have to sort the vocabulary keys.
+
+    >>> V_keys = sorted(V)
+    >>> round(sum(lm.score(w, ("b",)) for w in V_keys), 6)
+    1.0
+    >>> round(sum(lm.score(w, ("a",)) for w in V_keys), 6)
+    1.0
+
+    >>> [lm.score(w, ("b",)) for w in V_keys]
+    [0.05, 0.05, 0.8, 0.05, 0.05]
+    >>> [round(lm.score(w, ("a",)), 4) for w in V_keys]
+    [0.0222, 0.0222, 0.4667, 0.2444, 0.2444]
+
+
+Here's reproducing @afourney's comment:
+https://github.com/nltk/nltk/issues/367#issuecomment-15686289
+
+    >>> sent = ['foo', 'foo', 'foo', 'foo', 'bar', 'baz']
+    >>> ngram_order = 3
+    >>> from nltk.lm.preprocessing import padded_everygram_pipeline
+    >>> train_data, vocab_data = padded_everygram_pipeline(ngram_order, [sent])
+    >>> from nltk.lm import Lidstone
+    >>> lm = Lidstone(0.2, ngram_order)
+    >>> lm.fit(train_data, vocab_data)
+
+The vocabulary includes the "UNK" symbol as well as two padding symbols.
+
+    >>> len(lm.vocab)
+    6
+    >>> word = "foo"
+    >>> context = ("bar", "baz")
+
+The raw counts.
+
+    >>> lm.context_counts(context)[word]
+    0
+    >>> lm.context_counts(context).N()
+    1
+
+Counts with Lidstone smoothing.
+
+    >>> lm.context_counts(context)[word] + lm.gamma
+    0.2
+    >>> lm.context_counts(context).N() + len(lm.vocab) * lm.gamma
+    2.2
+
+Without any backoff, just using Lidstone smoothing, P("foo" | "bar", "baz") should be:
+0.2 / 2.2 ~= 0.090909
+
+    >>> round(lm.score(word, context), 6)
+    0.090909
+
+
+Issue 380
+---------
+https://github.com/nltk/nltk/issues/380
+
+Reproducing setup akin to this comment:
+https://github.com/nltk/nltk/issues/380#issue-12879030
+
+For speed take only the first 100 sentences of reuters. Shouldn't affect the test.
+
+    >>> from nltk.corpus import reuters
+    >>> sents = reuters.sents()[:100]
+    >>> ngram_order = 3
+    >>> from nltk.lm.preprocessing import padded_everygram_pipeline
+    >>> train_data, vocab_data = padded_everygram_pipeline(ngram_order, sents)
+
+    >>> from nltk.lm import Lidstone
+    >>> lm = Lidstone(0.2, ngram_order)
+    >>> lm.fit(train_data, vocab_data)
+    >>> lm.score("said", ("",)) < 1
+    True

env-llmeval/lib/python3.10/site-packages/nltk/test/meteor.doctest

@@ -0,0 +1,54 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+.. -*- coding: utf-8 -*-
+
+=============
+METEOR tests
+=============
+
+No Alignment test
+------------------
+
+    >>> from nltk.translate import meteor
+    >>> from nltk import word_tokenize
+
+If the candidate has no alignment to any of the references, the METEOR score is 0.
+
+    >>> round(meteor(
+    ...     [word_tokenize('The candidate has no alignment to any of the references')],
+    ...     word_tokenize('John loves Mary')
+    ... ), 4)
+    0.0
+
+Tests based on wikipedia examples
+---------------------------------
+
+Testing on `wikipedia examples <https://en.wikipedia.org/wiki/METEOR#Examples>`_
+
+    >>> same_res = round(meteor(
+    ...       [word_tokenize('The cat sat on the mat')],
+    ...       word_tokenize('The cat sat on the mat')
+    ...       ), 4)
+    >>> abs(same_res - 0.9977) < 1e-2
+    True
+
+    >>> meteor(
+    ...       [word_tokenize('The cat sat on the mat')],
+    ...       word_tokenize('on the mat sat the cat')
+    ...       )
+    0.5
+
+    >>> round(meteor(
+    ...       [word_tokenize('The cat sat on the mat')],
+    ...       word_tokenize('The cat was sat on the mat')
+    ...       ), 4)
+    0.9654
+
+Test corresponding to issue #2751, where METEOR score > 1
+
+    >>> round(meteor(
+    ...       [word_tokenize('create or update a vm set')],
+    ...       word_tokenize('creates or updates a virtual machine scale set')
+    ...       ), 4)
+    0.7806

env-llmeval/lib/python3.10/site-packages/nltk/test/metrics.doctest

@@ -0,0 +1,321 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=======
+Metrics
+=======
+
+-----
+Setup
+-----
+
+    >>> import pytest
+    >>> _ = pytest.importorskip("numpy")
+
+
+The `nltk.metrics` package provides a variety of *evaluation measures*
+which can be used for a wide variety of NLP tasks.
+
+   >>> from nltk.metrics import *
+
+------------------
+Standard IR Scores
+------------------
+
+We can use standard scores from information retrieval to test the
+performance of taggers, chunkers, etc.
+
+    >>> reference = 'DET NN VB DET JJ NN NN IN DET NN'.split()
+    >>> test    = 'DET VB VB DET NN NN NN IN DET NN'.split()
+    >>> print(accuracy(reference, test))
+    0.8
+
+
+The following measures apply to sets:
+
+    >>> reference_set = set(reference)
+    >>> test_set = set(test)
+    >>> precision(reference_set, test_set)
+    1.0
+    >>> print(recall(reference_set, test_set))
+    0.8
+    >>> print(f_measure(reference_set, test_set))
+    0.88888888888...
+
+Measuring the likelihood of the data, given probability distributions:
+
+    >>> from nltk import FreqDist, MLEProbDist
+    >>> pdist1 = MLEProbDist(FreqDist("aldjfalskfjaldsf"))
+    >>> pdist2 = MLEProbDist(FreqDist("aldjfalssjjlldss"))
+    >>> print(log_likelihood(['a', 'd'], [pdist1, pdist2]))
+    -2.7075187496...
+
+
+----------------
+Distance Metrics
+----------------
+
+String edit distance (Levenshtein):
+
+    >>> edit_distance("rain", "shine")
+    3
+    >>> edit_distance_align("shine", "shine")
+    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5)]
+    >>> edit_distance_align("rain", "brainy")
+    [(0, 0), (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (4, 6)]
+    >>> edit_distance_align("", "brainy")
+    [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6)]
+    >>> edit_distance_align("", "")
+    [(0, 0)]
+
+Other distance measures:
+
+    >>> s1 = set([1,2,3,4])
+    >>> s2 = set([3,4,5])
+    >>> binary_distance(s1, s2)
+    1.0
+    >>> print(jaccard_distance(s1, s2))
+    0.6
+    >>> print(masi_distance(s1, s2))
+    0.868
+
+----------------------
+Miscellaneous Measures
+----------------------
+
+Rank Correlation works with two dictionaries mapping keys to ranks.
+The dictionaries should have the same set of keys.
+
+    >>> spearman_correlation({'e':1, 't':2, 'a':3}, {'e':1, 'a':2, 't':3})
+    0.5
+
+Windowdiff uses a sliding window in comparing two segmentations of the same input (e.g. tokenizations, chunkings).
+Segmentations are represented using strings of zeros and ones.
+
+    >>> s1 = "000100000010"
+    >>> s2 = "000010000100"
+    >>> s3 = "100000010000"
+    >>> s4 = "000000000000"
+    >>> s5 = "111111111111"
+    >>> windowdiff(s1, s1, 3)
+    0.0
+    >>> abs(windowdiff(s1, s2, 3) - 0.3)  < 1e-6  # windowdiff(s1, s2, 3) == 0.3
+    True
+    >>> abs(windowdiff(s2, s3, 3) - 0.8)  < 1e-6  # windowdiff(s2, s3, 3) == 0.8
+    True
+    >>> windowdiff(s1, s4, 3)
+    0.5
+    >>> windowdiff(s1, s5, 3)
+    1.0
+
+----------------
+Confusion Matrix
+----------------
+
+    >>> reference = 'This is the reference data.  Testing 123.  aoaeoeoe'
+    >>> test =      'Thos iz_the rifirenci data.  Testeng 123.  aoaeoeoe'
+    >>> print(ConfusionMatrix(reference, test))
+      |   . 1 2 3 T _ a c d e f g h i n o r s t z |
+    --+-------------------------------------------+
+      |<8>. . . . . 1 . . . . . . . . . . . . . . |
+    . | .<2>. . . . . . . . . . . . . . . . . . . |
+    1 | . .<1>. . . . . . . . . . . . . . . . . . |
+    2 | . . .<1>. . . . . . . . . . . . . . . . . |
+    3 | . . . .<1>. . . . . . . . . . . . . . . . |
+    T | . . . . .<2>. . . . . . . . . . . . . . . |
+    _ | . . . . . .<.>. . . . . . . . . . . . . . |
+    a | . . . . . . .<4>. . . . . . . . . . . . . |
+    c | . . . . . . . .<1>. . . . . . . . . . . . |
+    d | . . . . . . . . .<1>. . . . . . . . . . . |
+    e | . . . . . . . . . .<6>. . . 3 . . . . . . |
+    f | . . . . . . . . . . .<1>. . . . . . . . . |
+    g | . . . . . . . . . . . .<1>. . . . . . . . |
+    h | . . . . . . . . . . . . .<2>. . . . . . . |
+    i | . . . . . . . . . . 1 . . .<1>. 1 . . . . |
+    n | . . . . . . . . . . . . . . .<2>. . . . . |
+    o | . . . . . . . . . . . . . . . .<3>. . . . |
+    r | . . . . . . . . . . . . . . . . .<2>. . . |
+    s | . . . . . . . . . . . . . . . . . .<2>. 1 |
+    t | . . . . . . . . . . . . . . . . . . .<3>. |
+    z | . . . . . . . . . . . . . . . . . . . .<.>|
+    --+-------------------------------------------+
+    (row = reference; col = test)
+    <BLANKLINE>
+
+    >>> cm = ConfusionMatrix(reference, test)
+    >>> print(cm.pretty_format(sort_by_count=True))
+      |   e a i o s t . T h n r 1 2 3 c d f g _ z |
+    --+-------------------------------------------+
+      |<8>. . . . . . . . . . . . . . . . . . 1 . |
+    e | .<6>. 3 . . . . . . . . . . . . . . . . . |
+    a | . .<4>. . . . . . . . . . . . . . . . . . |
+    i | . 1 .<1>1 . . . . . . . . . . . . . . . . |
+    o | . . . .<3>. . . . . . . . . . . . . . . . |
+    s | . . . . .<2>. . . . . . . . . . . . . . 1 |
+    t | . . . . . .<3>. . . . . . . . . . . . . . |
+    . | . . . . . . .<2>. . . . . . . . . . . . . |
+    T | . . . . . . . .<2>. . . . . . . . . . . . |
+    h | . . . . . . . . .<2>. . . . . . . . . . . |
+    n | . . . . . . . . . .<2>. . . . . . . . . . |
+    r | . . . . . . . . . . .<2>. . . . . . . . . |
+    1 | . . . . . . . . . . . .<1>. . . . . . . . |
+    2 | . . . . . . . . . . . . .<1>. . . . . . . |
+    3 | . . . . . . . . . . . . . .<1>. . . . . . |
+    c | . . . . . . . . . . . . . . .<1>. . . . . |
+    d | . . . . . . . . . . . . . . . .<1>. . . . |
+    f | . . . . . . . . . . . . . . . . .<1>. . . |
+    g | . . . . . . . . . . . . . . . . . .<1>. . |
+    _ | . . . . . . . . . . . . . . . . . . .<.>. |
+    z | . . . . . . . . . . . . . . . . . . . .<.>|
+    --+-------------------------------------------+
+    (row = reference; col = test)
+    <BLANKLINE>
+
+    >>> print(cm.pretty_format(sort_by_count=True, truncate=10))
+      |   e a i o s t . T h |
+    --+---------------------+
+      |<8>. . . . . . . . . |
+    e | .<6>. 3 . . . . . . |
+    a | . .<4>. . . . . . . |
+    i | . 1 .<1>1 . . . . . |
+    o | . . . .<3>. . . . . |
+    s | . . . . .<2>. . . . |
+    t | . . . . . .<3>. . . |
+    . | . . . . . . .<2>. . |
+    T | . . . . . . . .<2>. |
+    h | . . . . . . . . .<2>|
+    --+---------------------+
+    (row = reference; col = test)
+    <BLANKLINE>
+
+    >>> print(cm.pretty_format(sort_by_count=True, truncate=10, values_in_chart=False))
+       |                   1 |
+       | 1 2 3 4 5 6 7 8 9 0 |
+    ---+---------------------+
+     1 |<8>. . . . . . . . . |
+     2 | .<6>. 3 . . . . . . |
+     3 | . .<4>. . . . . . . |
+     4 | . 1 .<1>1 . . . . . |
+     5 | . . . .<3>. . . . . |
+     6 | . . . . .<2>. . . . |
+     7 | . . . . . .<3>. . . |
+     8 | . . . . . . .<2>. . |
+     9 | . . . . . . . .<2>. |
+    10 | . . . . . . . . .<2>|
+    ---+---------------------+
+    (row = reference; col = test)
+    Value key:
+         1:
+         2: e
+         3: a
+         4: i
+         5: o
+         6: s
+         7: t
+         8: .
+         9: T
+        10: h
+    <BLANKLINE>
+
+For "e", the number of true positives should be 6, while the number of false negatives is 3.
+So, the recall ought to be 6 / (6 + 3):
+
+    >>> cm.recall("e") # doctest: +ELLIPSIS
+    0.666666...
+
+For "e", the false positive is just 1, so the precision should be 6 / (6 + 1):
+
+    >>> cm.precision("e") # doctest: +ELLIPSIS
+    0.857142...
+
+The f-measure with default value of ``alpha = 0.5`` should then be:
+
+* *1/(alpha/p + (1-alpha)/r) =*
+* *1/(0.5/p + 0.5/r) =*
+* *2pr / (p + r) =*
+* *2 * 0.857142... * 0.666666... / (0.857142... + 0.666666...) =*
+* *0.749999...*
+
+    >>> cm.f_measure("e") # doctest: +ELLIPSIS
+    0.749999...
+
+--------------------
+Association measures
+--------------------
+
+These measures are useful to determine whether the coocurrence of two random
+events is meaningful. They are used, for instance, to distinguish collocations
+from other pairs of adjacent words.
+
+We bring some examples of bigram association calculations from Manning and
+Schutze's SNLP, 2nd Ed. chapter 5.
+
+    >>> n_new_companies, n_new, n_companies, N = 8, 15828, 4675, 14307668
+    >>> bam = BigramAssocMeasures
+    >>> bam.raw_freq(20, (42, 20), N) == 20. / N
+    True
+    >>> bam.student_t(n_new_companies, (n_new, n_companies), N)
+    0.999...
+    >>> bam.chi_sq(n_new_companies, (n_new, n_companies), N)
+    1.54...
+    >>> bam.likelihood_ratio(150, (12593, 932), N)
+    1291...
+
+For other associations, we ensure the ordering of the measures:
+
+    >>> bam.mi_like(20, (42, 20), N) > bam.mi_like(20, (41, 27), N)
+    True
+    >>> bam.pmi(20, (42, 20), N) > bam.pmi(20, (41, 27), N)
+    True
+    >>> bam.phi_sq(20, (42, 20), N) > bam.phi_sq(20, (41, 27), N)
+    True
+    >>> bam.poisson_stirling(20, (42, 20), N) > bam.poisson_stirling(20, (41, 27), N)
+    True
+    >>> bam.jaccard(20, (42, 20), N) > bam.jaccard(20, (41, 27), N)
+    True
+    >>> bam.dice(20, (42, 20), N) > bam.dice(20, (41, 27), N)
+    True
+    >>> bam.fisher(20, (42, 20), N) > bam.fisher(20, (41, 27), N) # doctest: +SKIP
+    False
+
+For trigrams, we have to provide more count information:
+
+    >>> n_w1_w2_w3 = 20
+    >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40
+    >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3)
+    >>> n_w1, n_w2, n_w3 = 100, 200, 300
+    >>> uni_counts = (n_w1, n_w2, n_w3)
+    >>> N = 14307668
+    >>> tam = TrigramAssocMeasures
+    >>> tam.raw_freq(n_w1_w2_w3, pair_counts, uni_counts, N) == 1. * n_w1_w2_w3 / N
+    True
+    >>> uni_counts2 = (n_w1, n_w2, 100)
+    >>> tam.student_t(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.student_t(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.pmi(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.pmi(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+    >>> tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts, N)
+    True
+
+
+For fourgrams, we have to provide more count information:
+
+    >>> n_w1_w2_w3_w4 = 5
+    >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40
+    >>> n_w1_w2_w3, n_w2_w3_w4 = 20, 10
+    >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3)
+    >>> triplet_counts = (n_w1_w2_w3, n_w2_w3_w4)
+    >>> n_w1, n_w2, n_w3, n_w4 = 100, 200, 300, 400
+    >>> uni_counts = (n_w1, n_w2, n_w3, n_w4)
+    >>> N = 14307668
+    >>> qam = QuadgramAssocMeasures
+    >>> qam.raw_freq(n_w1_w2_w3_w4, pair_counts, triplet_counts, uni_counts, N) == 1. * n_w1_w2_w3_w4 / N
+    True

env-llmeval/lib/python3.10/site-packages/nltk/test/misc.doctest

@@ -0,0 +1,118 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+--------------------------------------------------------------------------------
+Unit tests for the miscellaneous sort functions.
+--------------------------------------------------------------------------------
+
+    >>> from copy import deepcopy
+    >>> from nltk.misc.sort import *
+
+A (very) small list of unsorted integers.
+
+    >>> test_data = [12, 67, 7, 28, 92, 56, 53, 720, 91, 57, 20, 20]
+
+Test each sorting method - each method returns the number of operations
+required to sort the data, and sorts in-place (desctructively - hence the need
+for multiple copies).
+
+    >>> sorted_data = deepcopy(test_data)
+    >>> selection(sorted_data)
+    66
+
+    >>> sorted_data
+    [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720]
+
+    >>> sorted_data = deepcopy(test_data)
+    >>> bubble(sorted_data)
+    30
+
+    >>> sorted_data
+    [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720]
+
+    >>> sorted_data = deepcopy(test_data)
+    >>> merge(sorted_data)
+    30
+
+    >>> sorted_data
+    [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720]
+
+    >>> sorted_data = deepcopy(test_data)
+    >>> quick(sorted_data)
+    13
+
+    >>> sorted_data
+    [7, 12, 20, 20, 28, 53, 56, 57, 67, 91, 92, 720]
+
+--------------------------------------------------------------------------------
+Unit tests for Wordfinder class
+--------------------------------------------------------------------------------
+
+    >>> import random
+
+    >>> # The following is not enough for reproducibility under Python 2/3
+    >>> # (see https://bugs.python.org/issue9025) so this test is skipped.
+    >>> random.seed(12345)
+
+    >>> from nltk.misc import wordfinder
+    >>> wordfinder.word_finder() # doctest: +SKIP
+    Word Finder
+    <BLANKLINE>
+    J V L A I R O T A T I S I V O D E R E T
+    H U U B E A R O E P O C S O R E T N E P
+    A D A U Z E E S R A P P A L L M E N T R
+    C X A D Q S Z T P E O R S N G P J A D E
+    I G Y K K T I A A R G F I D T E L C N S
+    R E C N B H T R L T N N B W N T A O A I
+    A Y I L O E I A M E I A A Y U R P L L D
+    G L T V S T S F E A D I P H D O O H N I
+    R L S E C I N I L R N N M E C G R U E A
+    A A Y G I C E N L L E O I G Q R T A E L
+    M R C E T I S T A E T L L E U A E N R L
+    O U O T A S E E C S O O N H Y P A T G Y
+    E M H O M M D R E S F P U L T H C F N V
+    L A C A I M A M A N L B R U T E D O M I
+    O R I L N E E E E E U A R S C R Y L I P
+    H T R K E S N N M S I L A S R E V I N U
+    T X T A A O U T K S E T A R R E S I B J
+    A E D L E L J I F O O R P E L K N I R W
+    K H A I D E Q O P R I C K T I M B E R P
+    Z K D O O H G N I H T U R V E Y D R O P
+    <BLANKLINE>
+    1: INTERCHANGER
+    2: TEARLESSNESS
+    3: UNIVERSALISM
+    4: DESENSITIZER
+    5: INTERMENTION
+    6: TRICHOCYSTIC
+    7: EXTRAMURALLY
+    8: VEGETOALKALI
+    9: PALMELLACEAE
+    10: AESTHETICISM
+    11: PETROGRAPHER
+    12: VISITATORIAL
+    13: OLEOMARGARIC
+    14: WRINKLEPROOF
+    15: PRICKTIMBER
+    16: PRESIDIALLY
+    17: SCITAMINEAE
+    18: ENTEROSCOPE
+    19: APPALLMENT
+    20: TURVEYDROP
+    21: THINGHOOD
+    22: BISERRATE
+    23: GREENLAND
+    24: BRUTEDOM
+    25: POLONIAN
+    26: ACOLHUAN
+    27: LAPORTEA
+    28: TENDING
+    29: TEREDO
+    30: MESOLE
+    31: UNLIMP
+    32: OSTARA
+    33: PILY
+    34: DUNT
+    35: ONYX
+    36: KATH
+    37: JUNE

env-llmeval/lib/python3.10/site-packages/nltk/test/parse.doctest

@@ -0,0 +1,933 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=========
+ Parsing
+=========
+
+Unit tests for the Context Free Grammar class
+---------------------------------------------
+
+    >>> import pickle
+    >>> import subprocess
+    >>> import sys
+    >>> from nltk import Nonterminal, nonterminals, Production, CFG
+
+    >>> nt1 = Nonterminal('NP')
+    >>> nt2 = Nonterminal('VP')
+
+    >>> nt1.symbol()
+    'NP'
+
+    >>> nt1 == Nonterminal('NP')
+    True
+
+    >>> nt1 == nt2
+    False
+
+    >>> S, NP, VP, PP = nonterminals('S, NP, VP, PP')
+    >>> N, V, P, DT = nonterminals('N, V, P, DT')
+
+    >>> prod1 = Production(S, [NP, VP])
+    >>> prod2 = Production(NP, [DT, NP])
+
+    >>> prod1.lhs()
+    S
+
+    >>> prod1.rhs()
+    (NP, VP)
+
+    >>> prod1 == Production(S, [NP, VP])
+    True
+
+    >>> prod1 == prod2
+    False
+
+    >>> grammar = CFG.fromstring("""
+    ... S -> NP VP
+    ... PP -> P NP
+    ... NP -> 'the' N | N PP | 'the' N PP
+    ... VP -> V NP | V PP | V NP PP
+    ... N -> 'cat'
+    ... N -> 'dog'
+    ... N -> 'rug'
+    ... V -> 'chased'
+    ... V -> 'sat'
+    ... P -> 'in'
+    ... P -> 'on'
+    ... """)
+
+    >>> cmd = """import pickle
+    ... from nltk import Production
+    ... p = Production('S', ['NP', 'VP'])
+    ... print(pickle.dumps(p))
+    ... """
+
+    >>> # Start a subprocess to simulate pickling in another process
+    >>> proc = subprocess.run([sys.executable, '-c', cmd], stdout=subprocess.PIPE)
+    >>> p1 = pickle.loads(eval(proc.stdout))
+    >>> p2 = Production('S', ['NP', 'VP'])
+    >>> print(hash(p1) == hash(p2))
+    True
+
+Unit tests for the rd (Recursive Descent Parser) class
+------------------------------------------------------
+
+Create and run a recursive descent parser over both a syntactically ambiguous
+and unambiguous sentence.
+
+    >>> from nltk.parse import RecursiveDescentParser
+    >>> rd = RecursiveDescentParser(grammar)
+
+    >>> sentence1 = 'the cat chased the dog'.split()
+    >>> sentence2 = 'the cat chased the dog on the rug'.split()
+
+    >>> for t in rd.parse(sentence1):
+    ...     print(t)
+    (S (NP the (N cat)) (VP (V chased) (NP the (N dog))))
+
+    >>> for t in rd.parse(sentence2):
+    ...     print(t)
+    (S
+      (NP the (N cat))
+      (VP (V chased) (NP the (N dog) (PP (P on) (NP the (N rug))))))
+    (S
+      (NP the (N cat))
+      (VP (V chased) (NP the (N dog)) (PP (P on) (NP the (N rug)))))
+
+
+(dolist (expr doctest-font-lock-keywords)
+  (add-to-list 'font-lock-keywords expr))
+
+  font-lock-keywords
+(add-to-list 'font-lock-keywords
+  (car doctest-font-lock-keywords))
+
+
+Unit tests for the sr (Shift Reduce Parser) class
+-------------------------------------------------
+
+Create and run a shift reduce parser over both a syntactically ambiguous
+and unambiguous sentence. Note that unlike the recursive descent parser, one
+and only one parse is ever returned.
+
+    >>> from nltk.parse import ShiftReduceParser
+    >>> sr = ShiftReduceParser(grammar)
+
+    >>> sentence1 = 'the cat chased the dog'.split()
+    >>> sentence2 = 'the cat chased the dog on the rug'.split()
+
+    >>> for t in sr.parse(sentence1):
+    ...     print(t)
+    (S (NP the (N cat)) (VP (V chased) (NP the (N dog))))
+
+
+The shift reduce parser uses heuristics to decide what to do when there are
+multiple possible shift or reduce operations available - for the supplied
+grammar clearly the wrong operation is selected.
+
+    >>> for t in sr.parse(sentence2):
+    ...     print(t)
+
+
+Unit tests for the Chart Parser class
+-------------------------------------
+
+We use the demo() function for testing.
+We must turn off showing of times.
+
+    >>> import nltk
+
+First we test tracing with a short sentence
+
+    >>> nltk.parse.chart.demo(2, print_times=False, trace=1,
+    ...                       sent='I saw a dog', numparses=1)
+    * Sentence:
+    I saw a dog
+    ['I', 'saw', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Bottom-up
+    <BLANKLINE>
+    |.    I    .   saw   .    a    .   dog   .|
+    |[---------]         .         .         .| [0:1] 'I'
+    |.         [---------]         .         .| [1:2] 'saw'
+    |.         .         [---------]         .| [2:3] 'a'
+    |.         .         .         [---------]| [3:4] 'dog'
+    |>         .         .         .         .| [0:0] NP -> * 'I'
+    |[---------]         .         .         .| [0:1] NP -> 'I' *
+    |>         .         .         .         .| [0:0] S  -> * NP VP
+    |>         .         .         .         .| [0:0] NP -> * NP PP
+    |[--------->         .         .         .| [0:1] S  -> NP * VP
+    |[--------->         .         .         .| [0:1] NP -> NP * PP
+    |.         >         .         .         .| [1:1] Verb -> * 'saw'
+    |.         [---------]         .         .| [1:2] Verb -> 'saw' *
+    |.         >         .         .         .| [1:1] VP -> * Verb NP
+    |.         >         .         .         .| [1:1] VP -> * Verb
+    |.         [--------->         .         .| [1:2] VP -> Verb * NP
+    |.         [---------]         .         .| [1:2] VP -> Verb *
+    |.         >         .         .         .| [1:1] VP -> * VP PP
+    |[-------------------]         .         .| [0:2] S  -> NP VP *
+    |.         [--------->         .         .| [1:2] VP -> VP * PP
+    |.         .         >         .         .| [2:2] Det -> * 'a'
+    |.         .         [---------]         .| [2:3] Det -> 'a' *
+    |.         .         >         .         .| [2:2] NP -> * Det Noun
+    |.         .         [--------->         .| [2:3] NP -> Det * Noun
+    |.         .         .         >         .| [3:3] Noun -> * 'dog'
+    |.         .         .         [---------]| [3:4] Noun -> 'dog' *
+    |.         .         [-------------------]| [2:4] NP -> Det Noun *
+    |.         .         >         .         .| [2:2] S  -> * NP VP
+    |.         .         >         .         .| [2:2] NP -> * NP PP
+    |.         [-----------------------------]| [1:4] VP -> Verb NP *
+    |.         .         [------------------->| [2:4] S  -> NP * VP
+    |.         .         [------------------->| [2:4] NP -> NP * PP
+    |[=======================================]| [0:4] S  -> NP VP *
+    |.         [----------------------------->| [1:4] VP -> VP * PP
+    Nr edges in chart: 33
+    (S (NP I) (VP (Verb saw) (NP (Det a) (Noun dog))))
+    <BLANKLINE>
+
+Then we test the different parsing Strategies.
+Note that the number of edges differ between the strategies.
+
+Top-down
+
+    >>> nltk.parse.chart.demo(1, print_times=False, trace=0,
+    ...                       sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Top-down
+    <BLANKLINE>
+    Nr edges in chart: 48
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    <BLANKLINE>
+
+Bottom-up
+
+    >>> nltk.parse.chart.demo(2, print_times=False, trace=0,
+    ...                       sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Bottom-up
+    <BLANKLINE>
+    Nr edges in chart: 53
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+    <BLANKLINE>
+
+Bottom-up Left-Corner
+
+    >>> nltk.parse.chart.demo(3, print_times=False, trace=0,
+    ...                       sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Bottom-up left-corner
+    <BLANKLINE>
+    Nr edges in chart: 36
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+    <BLANKLINE>
+
+Left-Corner with Bottom-Up Filter
+
+    >>> nltk.parse.chart.demo(4, print_times=False, trace=0,
+    ...                       sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Filtered left-corner
+    <BLANKLINE>
+    Nr edges in chart: 28
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+    <BLANKLINE>
+
+The stepping chart parser
+
+    >>> nltk.parse.chart.demo(5, print_times=False, trace=1,
+    ...                       sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    * Strategy: Stepping (top-down vs bottom-up)
+    <BLANKLINE>
+    *** SWITCH TO TOP DOWN
+    |[------]      .      .      .      .      .| [0:1] 'I'
+    |.      [------]      .      .      .      .| [1:2] 'saw'
+    |.      .      [------]      .      .      .| [2:3] 'John'
+    |.      .      .      [------]      .      .| [3:4] 'with'
+    |.      .      .      .      [------]      .| [4:5] 'a'
+    |.      .      .      .      .      [------]| [5:6] 'dog'
+    |>      .      .      .      .      .      .| [0:0] S  -> * NP VP
+    |>      .      .      .      .      .      .| [0:0] NP -> * NP PP
+    |>      .      .      .      .      .      .| [0:0] NP -> * Det Noun
+    |>      .      .      .      .      .      .| [0:0] NP -> * 'I'
+    |[------]      .      .      .      .      .| [0:1] NP -> 'I' *
+    |[------>      .      .      .      .      .| [0:1] S  -> NP * VP
+    |[------>      .      .      .      .      .| [0:1] NP -> NP * PP
+    |.      >      .      .      .      .      .| [1:1] VP -> * VP PP
+    |.      >      .      .      .      .      .| [1:1] VP -> * Verb NP
+    |.      >      .      .      .      .      .| [1:1] VP -> * Verb
+    |.      >      .      .      .      .      .| [1:1] Verb -> * 'saw'
+    |.      [------]      .      .      .      .| [1:2] Verb -> 'saw' *
+    |.      [------>      .      .      .      .| [1:2] VP -> Verb * NP
+    |.      [------]      .      .      .      .| [1:2] VP -> Verb *
+    |[-------------]      .      .      .      .| [0:2] S  -> NP VP *
+    |.      [------>      .      .      .      .| [1:2] VP -> VP * PP
+    *** SWITCH TO BOTTOM UP
+    |.      .      >      .      .      .      .| [2:2] NP -> * 'John'
+    |.      .      .      >      .      .      .| [3:3] PP -> * 'with' NP
+    |.      .      .      >      .      .      .| [3:3] Prep -> * 'with'
+    |.      .      .      .      >      .      .| [4:4] Det -> * 'a'
+    |.      .      .      .      .      >      .| [5:5] Noun -> * 'dog'
+    |.      .      [------]      .      .      .| [2:3] NP -> 'John' *
+    |.      .      .      [------>      .      .| [3:4] PP -> 'with' * NP
+    |.      .      .      [------]      .      .| [3:4] Prep -> 'with' *
+    |.      .      .      .      [------]      .| [4:5] Det -> 'a' *
+    |.      .      .      .      .      [------]| [5:6] Noun -> 'dog' *
+    |.      [-------------]      .      .      .| [1:3] VP -> Verb NP *
+    |[--------------------]      .      .      .| [0:3] S  -> NP VP *
+    |.      [------------->      .      .      .| [1:3] VP -> VP * PP
+    |.      .      >      .      .      .      .| [2:2] S  -> * NP VP
+    |.      .      >      .      .      .      .| [2:2] NP -> * NP PP
+    |.      .      .      .      >      .      .| [4:4] NP -> * Det Noun
+    |.      .      [------>      .      .      .| [2:3] S  -> NP * VP
+    |.      .      [------>      .      .      .| [2:3] NP -> NP * PP
+    |.      .      .      .      [------>      .| [4:5] NP -> Det * Noun
+    |.      .      .      .      [-------------]| [4:6] NP -> Det Noun *
+    |.      .      .      [--------------------]| [3:6] PP -> 'with' NP *
+    |.      [----------------------------------]| [1:6] VP -> VP PP *
+    *** SWITCH TO TOP DOWN
+    |.      .      >      .      .      .      .| [2:2] NP -> * Det Noun
+    |.      .      .      .      >      .      .| [4:4] NP -> * NP PP
+    |.      .      .      >      .      .      .| [3:3] VP -> * VP PP
+    |.      .      .      >      .      .      .| [3:3] VP -> * Verb NP
+    |.      .      .      >      .      .      .| [3:3] VP -> * Verb
+    |[=========================================]| [0:6] S  -> NP VP *
+    |.      [---------------------------------->| [1:6] VP -> VP * PP
+    |.      .      [---------------------------]| [2:6] NP -> NP PP *
+    |.      .      .      .      [------------->| [4:6] NP -> NP * PP
+    |.      [----------------------------------]| [1:6] VP -> Verb NP *
+    |.      .      [--------------------------->| [2:6] S  -> NP * VP
+    |.      .      [--------------------------->| [2:6] NP -> NP * PP
+    |[=========================================]| [0:6] S  -> NP VP *
+    |.      [---------------------------------->| [1:6] VP -> VP * PP
+    |.      .      .      .      .      .      >| [6:6] VP -> * VP PP
+    |.      .      .      .      .      .      >| [6:6] VP -> * Verb NP
+    |.      .      .      .      .      .      >| [6:6] VP -> * Verb
+    *** SWITCH TO BOTTOM UP
+    |.      .      .      .      >      .      .| [4:4] S  -> * NP VP
+    |.      .      .      .      [------------->| [4:6] S  -> NP * VP
+    *** SWITCH TO TOP DOWN
+    *** SWITCH TO BOTTOM UP
+    *** SWITCH TO TOP DOWN
+    *** SWITCH TO BOTTOM UP
+    *** SWITCH TO TOP DOWN
+    *** SWITCH TO BOTTOM UP
+    Nr edges in chart: 61
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+    <BLANKLINE>
+
+
+Unit tests for the Incremental Chart Parser class
+-------------------------------------------------
+
+The incremental chart parsers are defined in earleychart.py.
+We use the demo() function for testing. We must turn off showing of times.
+
+    >>> import nltk
+
+Earley Chart Parser
+
+    >>> nltk.parse.earleychart.demo(print_times=False, trace=1,
+    ...                             sent='I saw John with a dog', numparses=2)
+    * Sentence:
+    I saw John with a dog
+    ['I', 'saw', 'John', 'with', 'a', 'dog']
+    <BLANKLINE>
+    |.  I   . saw  . John . with .  a   . dog  .|
+    |[------]      .      .      .      .      .| [0:1] 'I'
+    |.      [------]      .      .      .      .| [1:2] 'saw'
+    |.      .      [------]      .      .      .| [2:3] 'John'
+    |.      .      .      [------]      .      .| [3:4] 'with'
+    |.      .      .      .      [------]      .| [4:5] 'a'
+    |.      .      .      .      .      [------]| [5:6] 'dog'
+    |>      .      .      .      .      .      .| [0:0] S  -> * NP VP
+    |>      .      .      .      .      .      .| [0:0] NP -> * NP PP
+    |>      .      .      .      .      .      .| [0:0] NP -> * Det Noun
+    |>      .      .      .      .      .      .| [0:0] NP -> * 'I'
+    |[------]      .      .      .      .      .| [0:1] NP -> 'I' *
+    |[------>      .      .      .      .      .| [0:1] S  -> NP * VP
+    |[------>      .      .      .      .      .| [0:1] NP -> NP * PP
+    |.      >      .      .      .      .      .| [1:1] VP -> * VP PP
+    |.      >      .      .      .      .      .| [1:1] VP -> * Verb NP
+    |.      >      .      .      .      .      .| [1:1] VP -> * Verb
+    |.      >      .      .      .      .      .| [1:1] Verb -> * 'saw'
+    |.      [------]      .      .      .      .| [1:2] Verb -> 'saw' *
+    |.      [------>      .      .      .      .| [1:2] VP -> Verb * NP
+    |.      [------]      .      .      .      .| [1:2] VP -> Verb *
+    |[-------------]      .      .      .      .| [0:2] S  -> NP VP *
+    |.      [------>      .      .      .      .| [1:2] VP -> VP * PP
+    |.      .      >      .      .      .      .| [2:2] NP -> * NP PP
+    |.      .      >      .      .      .      .| [2:2] NP -> * Det Noun
+    |.      .      >      .      .      .      .| [2:2] NP -> * 'John'
+    |.      .      [------]      .      .      .| [2:3] NP -> 'John' *
+    |.      [-------------]      .      .      .| [1:3] VP -> Verb NP *
+    |.      .      [------>      .      .      .| [2:3] NP -> NP * PP
+    |.      .      .      >      .      .      .| [3:3] PP -> * 'with' NP
+    |[--------------------]      .      .      .| [0:3] S  -> NP VP *
+    |.      [------------->      .      .      .| [1:3] VP -> VP * PP
+    |.      .      .      [------>      .      .| [3:4] PP -> 'with' * NP
+    |.      .      .      .      >      .      .| [4:4] NP -> * NP PP
+    |.      .      .      .      >      .      .| [4:4] NP -> * Det Noun
+    |.      .      .      .      >      .      .| [4:4] Det -> * 'a'
+    |.      .      .      .      [------]      .| [4:5] Det -> 'a' *
+    |.      .      .      .      [------>      .| [4:5] NP -> Det * Noun
+    |.      .      .      .      .      >      .| [5:5] Noun -> * 'dog'
+    |.      .      .      .      .      [------]| [5:6] Noun -> 'dog' *
+    |.      .      .      .      [-------------]| [4:6] NP -> Det Noun *
+    |.      .      .      [--------------------]| [3:6] PP -> 'with' NP *
+    |.      .      .      .      [------------->| [4:6] NP -> NP * PP
+    |.      .      [---------------------------]| [2:6] NP -> NP PP *
+    |.      [----------------------------------]| [1:6] VP -> VP PP *
+    |[=========================================]| [0:6] S  -> NP VP *
+    |.      [---------------------------------->| [1:6] VP -> VP * PP
+    |.      [----------------------------------]| [1:6] VP -> Verb NP *
+    |.      .      [--------------------------->| [2:6] NP -> NP * PP
+    |[=========================================]| [0:6] S  -> NP VP *
+    |.      [---------------------------------->| [1:6] VP -> VP * PP
+    (S
+      (NP I)
+      (VP (VP (Verb saw) (NP John)) (PP with (NP (Det a) (Noun dog)))))
+    (S
+      (NP I)
+      (VP (Verb saw) (NP (NP John) (PP with (NP (Det a) (Noun dog))))))
+
+
+Unit tests for LARGE context-free grammars
+------------------------------------------
+
+Reading the ATIS grammar.
+
+    >>> grammar = nltk.data.load('grammars/large_grammars/atis.cfg')
+    >>> grammar
+    <Grammar with 5517 productions>
+
+Reading the test sentences.
+
+    >>> sentences = nltk.data.load('grammars/large_grammars/atis_sentences.txt')
+    >>> sentences = nltk.parse.util.extract_test_sentences(sentences)
+    >>> len(sentences)
+    98
+    >>> testsentence = sentences[22]
+    >>> testsentence[0]
+    ['show', 'me', 'northwest', 'flights', 'to', 'detroit', '.']
+    >>> testsentence[1]
+    17
+    >>> sentence = testsentence[0]
+
+Now we test all different parsing strategies.
+Note that the number of edges differ between the strategies.
+
+Bottom-up parsing.
+
+    >>> parser = nltk.parse.BottomUpChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    7661
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Bottom-up Left-corner parsing.
+
+    >>> parser = nltk.parse.BottomUpLeftCornerChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    4986
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Left-corner parsing with bottom-up filter.
+
+    >>> parser = nltk.parse.LeftCornerChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    1342
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Top-down parsing.
+
+    >>> parser = nltk.parse.TopDownChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    28352
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Incremental Bottom-up parsing.
+
+    >>> parser = nltk.parse.IncrementalBottomUpChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    7661
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Incremental Bottom-up Left-corner parsing.
+
+    >>> parser = nltk.parse.IncrementalBottomUpLeftCornerChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    4986
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Incremental Left-corner parsing with bottom-up filter.
+
+    >>> parser = nltk.parse.IncrementalLeftCornerChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    1342
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Incremental Top-down parsing.
+
+    >>> parser = nltk.parse.IncrementalTopDownChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    28352
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+Earley parsing. This is similar to the incremental top-down algorithm.
+
+    >>> parser = nltk.parse.EarleyChartParser(grammar)
+    >>> chart = parser.chart_parse(sentence)
+    >>> print((chart.num_edges()))
+    28352
+    >>> print((len(list(chart.parses(grammar.start())))))
+    17
+
+
+Unit tests for the Probabilistic CFG class
+------------------------------------------
+
+    >>> from nltk.corpus import treebank
+    >>> from itertools import islice
+    >>> from nltk.grammar import PCFG, induce_pcfg
+    >>> toy_pcfg1 = PCFG.fromstring("""
+    ...     S -> NP VP [1.0]
+    ...     NP -> Det N [0.5] | NP PP [0.25] | 'John' [0.1] | 'I' [0.15]
+    ...     Det -> 'the' [0.8] | 'my' [0.2]
+    ...     N -> 'man' [0.5] | 'telescope' [0.5]
+    ...     VP -> VP PP [0.1] | V NP [0.7] | V [0.2]
+    ...     V -> 'ate' [0.35] | 'saw' [0.65]
+    ...     PP -> P NP [1.0]
+    ...     P -> 'with' [0.61] | 'under' [0.39]
+    ...     """)
+
+    >>> toy_pcfg2 = PCFG.fromstring("""
+    ...     S    -> NP VP         [1.0]
+    ...     VP   -> V NP          [.59]
+    ...     VP   -> V             [.40]
+    ...     VP   -> VP PP         [.01]
+    ...     NP   -> Det N         [.41]
+    ...     NP   -> Name          [.28]
+    ...     NP   -> NP PP         [.31]
+    ...     PP   -> P NP          [1.0]
+    ...     V    -> 'saw'         [.21]
+    ...     V    -> 'ate'         [.51]
+    ...     V    -> 'ran'         [.28]
+    ...     N    -> 'boy'         [.11]
+    ...     N    -> 'cookie'      [.12]
+    ...     N    -> 'table'       [.13]
+    ...     N    -> 'telescope'   [.14]
+    ...     N    -> 'hill'        [.5]
+    ...     Name -> 'Jack'        [.52]
+    ...     Name -> 'Bob'         [.48]
+    ...     P    -> 'with'        [.61]
+    ...     P    -> 'under'       [.39]
+    ...     Det  -> 'the'         [.41]
+    ...     Det  -> 'a'           [.31]
+    ...     Det  -> 'my'          [.28]
+    ...     """)
+
+Create a set of PCFG productions.
+
+    >>> grammar = PCFG.fromstring("""
+    ... A -> B B [.3] | C B C [.7]
+    ... B -> B D [.5] | C [.5]
+    ... C -> 'a' [.1] | 'b' [0.9]
+    ... D -> 'b' [1.0]
+    ... """)
+    >>> prod = grammar.productions()[0]
+    >>> prod
+    A -> B B [0.3]
+
+    >>> prod.lhs()
+    A
+
+    >>> prod.rhs()
+    (B, B)
+
+    >>> print((prod.prob()))
+    0.3
+
+    >>> grammar.start()
+    A
+
+    >>> grammar.productions()
+    [A -> B B [0.3], A -> C B C [0.7], B -> B D [0.5], B -> C [0.5], C -> 'a' [0.1], C -> 'b' [0.9], D -> 'b' [1.0]]
+
+Induce some productions using parsed Treebank data.
+
+    >>> productions = []
+    >>> for fileid in treebank.fileids()[:2]:
+    ...     for t in treebank.parsed_sents(fileid):
+    ...         productions += t.productions()
+
+    >>> grammar = induce_pcfg(S, productions)
+    >>> grammar
+    <Grammar with 71 productions>
+
+    >>> sorted(grammar.productions(lhs=Nonterminal('PP')))[:2]
+    [PP -> IN NP [1.0]]
+    >>> sorted(grammar.productions(lhs=Nonterminal('NNP')))[:2]
+    [NNP -> 'Agnew' [0.0714286], NNP -> 'Consolidated' [0.0714286]]
+    >>> sorted(grammar.productions(lhs=Nonterminal('JJ')))[:2]
+    [JJ -> 'British' [0.142857], JJ -> 'former' [0.142857]]
+    >>> sorted(grammar.productions(lhs=Nonterminal('NP')))[:2]
+    [NP -> CD NNS [0.133333], NP -> DT JJ JJ NN [0.0666667]]
+
+Unit tests for the Probabilistic Chart Parse classes
+----------------------------------------------------
+
+    >>> tokens = "Jack saw Bob with my cookie".split()
+    >>> grammar = toy_pcfg2
+    >>> print(grammar)
+    Grammar with 23 productions (start state = S)
+        S -> NP VP [1.0]
+        VP -> V NP [0.59]
+        VP -> V [0.4]
+        VP -> VP PP [0.01]
+        NP -> Det N [0.41]
+        NP -> Name [0.28]
+        NP -> NP PP [0.31]
+        PP -> P NP [1.0]
+        V -> 'saw' [0.21]
+        V -> 'ate' [0.51]
+        V -> 'ran' [0.28]
+        N -> 'boy' [0.11]
+        N -> 'cookie' [0.12]
+        N -> 'table' [0.13]
+        N -> 'telescope' [0.14]
+        N -> 'hill' [0.5]
+        Name -> 'Jack' [0.52]
+        Name -> 'Bob' [0.48]
+        P -> 'with' [0.61]
+        P -> 'under' [0.39]
+        Det -> 'the' [0.41]
+        Det -> 'a' [0.31]
+        Det -> 'my' [0.28]
+
+Create several parsers using different queuing strategies and show the
+resulting parses.
+
+    >>> from nltk.parse import pchart
+
+    >>> parser = pchart.InsideChartParser(grammar)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+    (S
+      (NP (Name Jack))
+      (VP
+        (V saw)
+        (NP
+          (NP (Name Bob))
+          (PP (P with) (NP (Det my) (N cookie)))))) (p=6.31607e-06)
+    (S
+      (NP (Name Jack))
+      (VP
+        (VP (V saw) (NP (Name Bob)))
+        (PP (P with) (NP (Det my) (N cookie))))) (p=2.03744e-07)
+
+    >>> parser = pchart.RandomChartParser(grammar)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+    (S
+      (NP (Name Jack))
+      (VP
+        (V saw)
+        (NP
+          (NP (Name Bob))
+          (PP (P with) (NP (Det my) (N cookie)))))) (p=6.31607e-06)
+    (S
+      (NP (Name Jack))
+      (VP
+        (VP (V saw) (NP (Name Bob)))
+        (PP (P with) (NP (Det my) (N cookie))))) (p=2.03744e-07)
+
+    >>> parser = pchart.UnsortedChartParser(grammar)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+    (S
+      (NP (Name Jack))
+      (VP
+        (V saw)
+        (NP
+          (NP (Name Bob))
+          (PP (P with) (NP (Det my) (N cookie)))))) (p=6.31607e-06)
+    (S
+      (NP (Name Jack))
+      (VP
+        (VP (V saw) (NP (Name Bob)))
+        (PP (P with) (NP (Det my) (N cookie))))) (p=2.03744e-07)
+
+    >>> parser = pchart.LongestChartParser(grammar)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+    (S
+      (NP (Name Jack))
+      (VP
+        (V saw)
+        (NP
+          (NP (Name Bob))
+          (PP (P with) (NP (Det my) (N cookie)))))) (p=6.31607e-06)
+    (S
+      (NP (Name Jack))
+      (VP
+        (VP (V saw) (NP (Name Bob)))
+        (PP (P with) (NP (Det my) (N cookie))))) (p=2.03744e-07)
+
+    >>> parser = pchart.InsideChartParser(grammar, beam_size = len(tokens)+1)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+
+
+Unit tests for the Viterbi Parse classes
+----------------------------------------
+
+    >>> from nltk.parse import ViterbiParser
+    >>> tokens = "Jack saw Bob with my cookie".split()
+    >>> grammar = toy_pcfg2
+
+Parse the tokenized sentence.
+
+    >>> parser = ViterbiParser(grammar)
+    >>> for t in parser.parse(tokens):
+    ...     print(t)
+    (S
+      (NP (Name Jack))
+      (VP
+        (V saw)
+        (NP
+          (NP (Name Bob))
+          (PP (P with) (NP (Det my) (N cookie)))))) (p=6.31607e-06)
+
+
+Unit tests for the FeatStructNonterminal class
+----------------------------------------------
+
+    >>> from nltk.grammar import FeatStructNonterminal
+    >>> FeatStructNonterminal(
+    ...     pos='n', agr=FeatStructNonterminal(number='pl', gender='f'))
+    [agr=[gender='f', number='pl'], pos='n']
+
+    >>> FeatStructNonterminal('VP[+fin]/NP[+pl]')
+    VP[+fin]/NP[+pl]
+
+
+Tracing the Feature Chart Parser
+--------------------------------
+
+We use the featurechart.demo() function for tracing the Feature Chart Parser.
+
+    >>> nltk.parse.featurechart.demo(print_times=False,
+    ...                              print_grammar=True,
+    ...                              parser=nltk.parse.featurechart.FeatureChartParser,
+    ...                              sent='I saw John with a dog')
+    <BLANKLINE>
+    Grammar with 18 productions (start state = S[])
+        S[] -> NP[] VP[]
+        PP[] -> Prep[] NP[]
+        NP[] -> NP[] PP[]
+        VP[] -> VP[] PP[]
+        VP[] -> Verb[] NP[]
+        VP[] -> Verb[]
+        NP[] -> Det[pl=?x] Noun[pl=?x]
+        NP[] -> 'John'
+        NP[] -> 'I'
+        Det[] -> 'the'
+        Det[] -> 'my'
+        Det[-pl] -> 'a'
+        Noun[-pl] -> 'dog'
+        Noun[-pl] -> 'cookie'
+        Verb[] -> 'ate'
+        Verb[] -> 'saw'
+        Prep[] -> 'with'
+        Prep[] -> 'under'
+    <BLANKLINE>
+    * FeatureChartParser
+    Sentence: I saw John with a dog
+    |.I.s.J.w.a.d.|
+    |[-] . . . . .| [0:1] 'I'
+    |. [-] . . . .| [1:2] 'saw'
+    |. . [-] . . .| [2:3] 'John'
+    |. . . [-] . .| [3:4] 'with'
+    |. . . . [-] .| [4:5] 'a'
+    |. . . . . [-]| [5:6] 'dog'
+    |[-] . . . . .| [0:1] NP[] -> 'I' *
+    |[-> . . . . .| [0:1] S[] -> NP[] * VP[] {}
+    |[-> . . . . .| [0:1] NP[] -> NP[] * PP[] {}
+    |. [-] . . . .| [1:2] Verb[] -> 'saw' *
+    |. [-> . . . .| [1:2] VP[] -> Verb[] * NP[] {}
+    |. [-] . . . .| [1:2] VP[] -> Verb[] *
+    |. [-> . . . .| [1:2] VP[] -> VP[] * PP[] {}
+    |[---] . . . .| [0:2] S[] -> NP[] VP[] *
+    |. . [-] . . .| [2:3] NP[] -> 'John' *
+    |. . [-> . . .| [2:3] S[] -> NP[] * VP[] {}
+    |. . [-> . . .| [2:3] NP[] -> NP[] * PP[] {}
+    |. [---] . . .| [1:3] VP[] -> Verb[] NP[] *
+    |. [---> . . .| [1:3] VP[] -> VP[] * PP[] {}
+    |[-----] . . .| [0:3] S[] -> NP[] VP[] *
+    |. . . [-] . .| [3:4] Prep[] -> 'with' *
+    |. . . [-> . .| [3:4] PP[] -> Prep[] * NP[] {}
+    |. . . . [-] .| [4:5] Det[-pl] -> 'a' *
+    |. . . . [-> .| [4:5] NP[] -> Det[pl=?x] * Noun[pl=?x] {?x: False}
+    |. . . . . [-]| [5:6] Noun[-pl] -> 'dog' *
+    |. . . . [---]| [4:6] NP[] -> Det[-pl] Noun[-pl] *
+    |. . . . [--->| [4:6] S[] -> NP[] * VP[] {}
+    |. . . . [--->| [4:6] NP[] -> NP[] * PP[] {}
+    |. . . [-----]| [3:6] PP[] -> Prep[] NP[] *
+    |. . [-------]| [2:6] NP[] -> NP[] PP[] *
+    |. [---------]| [1:6] VP[] -> VP[] PP[] *
+    |. [--------->| [1:6] VP[] -> VP[] * PP[] {}
+    |[===========]| [0:6] S[] -> NP[] VP[] *
+    |. . [------->| [2:6] S[] -> NP[] * VP[] {}
+    |. . [------->| [2:6] NP[] -> NP[] * PP[] {}
+    |. [---------]| [1:6] VP[] -> Verb[] NP[] *
+    |. [--------->| [1:6] VP[] -> VP[] * PP[] {}
+    |[===========]| [0:6] S[] -> NP[] VP[] *
+    (S[]
+      (NP[] I)
+      (VP[]
+        (VP[] (Verb[] saw) (NP[] John))
+        (PP[] (Prep[] with) (NP[] (Det[-pl] a) (Noun[-pl] dog)))))
+    (S[]
+      (NP[] I)
+      (VP[]
+        (Verb[] saw)
+        (NP[]
+          (NP[] John)
+          (PP[] (Prep[] with) (NP[] (Det[-pl] a) (Noun[-pl] dog))))))
+
+
+Unit tests for the Feature Chart Parser classes
+-----------------------------------------------
+
+The list of parsers we want to test.
+
+    >>> parsers = [nltk.parse.featurechart.FeatureChartParser,
+    ...            nltk.parse.featurechart.FeatureTopDownChartParser,
+    ...            nltk.parse.featurechart.FeatureBottomUpChartParser,
+    ...            nltk.parse.featurechart.FeatureBottomUpLeftCornerChartParser,
+    ...            nltk.parse.earleychart.FeatureIncrementalChartParser,
+    ...            nltk.parse.earleychart.FeatureEarleyChartParser,
+    ...            nltk.parse.earleychart.FeatureIncrementalTopDownChartParser,
+    ...            nltk.parse.earleychart.FeatureIncrementalBottomUpChartParser,
+    ...            nltk.parse.earleychart.FeatureIncrementalBottomUpLeftCornerChartParser,
+    ...            ]
+
+A helper function that tests each parser on the given grammar and sentence.
+We check that the number of trees are correct, and that all parsers
+return the same trees. Otherwise an error is printed.
+
+    >>> def unittest(grammar, sentence, nr_trees):
+    ...     sentence = sentence.split()
+    ...     trees = None
+    ...     for P in parsers:
+    ...         result = P(grammar).parse(sentence)
+    ...         result = set(tree.freeze() for tree in result)
+    ...         if len(result) != nr_trees:
+    ...             print("Wrong nr of trees:", len(result))
+    ...         elif trees is None:
+    ...             trees = result
+    ...         elif result != trees:
+    ...             print("Trees differ for parser:", P.__name__)
+
+The demo grammar from before, with an ambiguous sentence.
+
+    >>> isawjohn = nltk.parse.featurechart.demo_grammar()
+    >>> unittest(isawjohn, "I saw John with a dog with my cookie", 5)
+
+This grammar tests that variables in different grammar rules are renamed
+before unification. (The problematic variable is in this case ?X).
+
+    >>> whatwasthat = nltk.grammar.FeatureGrammar.fromstring('''
+    ... S[] -> NP[num=?N] VP[num=?N, slash=?X]
+    ... NP[num=?X] -> "what"
+    ... NP[num=?X] -> "that"
+    ... VP[num=?P, slash=none] -> V[num=?P] NP[]
+    ... V[num=sg] -> "was"
+    ... ''')
+    >>> unittest(whatwasthat, "what was that", 1)
+
+This grammar tests that the same rule can be used in different places
+in another rule, and that the variables are properly renamed.
+
+    >>> thislovesthat = nltk.grammar.FeatureGrammar.fromstring('''
+    ... S[] -> NP[case=nom] V[] NP[case=acc]
+    ... NP[case=?X] -> Pron[case=?X]
+    ... Pron[] -> "this"
+    ... Pron[] -> "that"
+    ... V[] -> "loves"
+    ... ''')
+    >>> unittest(thislovesthat, "this loves that", 1)
+
+
+Tests for loading feature grammar files
+---------------------------------------
+
+Alternative 1: first load the grammar, then create the parser.
+
+    >>> fcfg = nltk.data.load('grammars/book_grammars/feat0.fcfg')
+    >>> fcp1 = nltk.parse.FeatureChartParser(fcfg)
+    >>> print((type(fcp1)))
+    <class 'nltk.parse.featurechart.FeatureChartParser'>
+
+Alternative 2: directly load the parser.
+
+    >>> fcp2 = nltk.parse.load_parser('grammars/book_grammars/feat0.fcfg')
+    >>> print((type(fcp2)))
+    <class 'nltk.parse.featurechart.FeatureChartParser'>

env-llmeval/lib/python3.10/site-packages/nltk/test/portuguese_en.doctest

@@ -0,0 +1,568 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+==================================
+Examples for Portuguese Processing
+==================================
+
+This HOWTO contains a variety of examples relating to the Portuguese language.
+It is intended to be read in conjunction with the NLTK book
+(``https://www.nltk.org/book/``).  For instructions on running the Python
+interpreter, please see the section *Getting Started with Python*, in Chapter 1.
+
+--------------------------------------------
+Python Programming, with Portuguese Examples
+--------------------------------------------
+
+Chapter 1 of the NLTK book contains many elementary programming examples, all
+with English texts.  In this section, we'll see some corresponding examples
+using Portuguese.  Please refer to the chapter for full discussion.  *Vamos!*
+
+    >>> from nltk.test.portuguese_en_fixt import setup_module
+    >>> setup_module()
+
+    >>> from nltk.examples.pt import *
+    *** Introductory Examples for the NLTK Book ***
+    Loading ptext1, ... and psent1, ...
+    Type the name of the text or sentence to view it.
+    Type: 'texts()' or 'sents()' to list the materials.
+    ptext1: Memórias Póstumas de Brás Cubas (1881)
+    ptext2: Dom Casmurro (1899)
+    ptext3: Gênesis
+    ptext4: Folha de Sao Paulo (1994)
+
+
+Any time we want to find out about these texts, we just have
+to enter their names at the Python prompt:
+
+    >>> ptext2
+    <Text: Dom Casmurro (1899)>
+
+Searching Text
+--------------
+
+A concordance permits us to see words in context.
+
+    >>> ptext1.concordance('olhos')
+    Building index...
+    Displaying 25 of 138 matches:
+    De pé , à cabeceira da cama , com os olhos estúpidos , a boca entreaberta , a t
+    orelhas . Pela minha parte fechei os olhos e deixei - me ir à ventura . Já agor
+    xões de cérebro enfermo . Como ia de olhos fechados , não via o caminho ; lembr
+    gelos eternos . Com efeito , abri os olhos e vi que o meu animal galopava numa
+    me apareceu então , fitando - me uns olhos rutilantes como o sol . Tudo nessa f
+     mim mesmo . Então , encarei - a com olhos súplices , e pedi mais alguns anos .
+    ...
+
+For a given word, we can find words with a similar text distribution:
+
+    >>> ptext1.similar('chegar')
+    Building word-context index...
+    acabada acudir aludir avistar bramanismo casamento cheguei com contar
+    contrário corpo dali deixei desferirem dizer fazer filhos já leitor lhe
+    >>> ptext3.similar('chegar')
+    Building word-context index...
+    achar alumiar arrombar destruir governar guardar ir lavrar passar que
+    toda tomar ver vir
+
+We can search for the statistically significant collocations in a text:
+
+    >>> ptext1.collocations()
+    Building collocations list
+    Quincas Borba; Lobo Neves; alguma coisa; Brás Cubas; meu pai; dia
+    seguinte; não sei; Meu pai; alguns instantes; outra vez; outra coisa;
+    por exemplo; mim mesmo; coisa nenhuma; mesma coisa; não era; dias
+    depois; Passeio Público; olhar para; das coisas
+
+We can search for words in context, with the help of *regular expressions*, e.g.:
+
+    >>> ptext1.findall("<olhos> (<.*>)")
+    estúpidos; e; fechados; rutilantes; súplices; a; do; babavam;
+    na; moles; se; da; umas; espraiavam; chamejantes; espetados;
+    ...
+
+We can automatically generate random text based on a given text, e.g.:
+
+    >>> ptext3.generate() # doctest: +SKIP
+    No princípio , criou Deus os abençoou , dizendo : Onde { estão } e até
+    à ave dos céus , { que } será . Disse mais Abrão : Dá - me a mulher
+    que tomaste ; porque daquele poço Eseque , { tinha .} E disse : Não
+    poderemos descer ; mas , do campo ainda não estava na casa do teu
+    pescoço . E viveu Serugue , depois Simeão e Levi { são } estes ? E o
+    varão , porque habitava na terra de Node , da mão de Esaú : Jeús ,
+    Jalão e Corá
+
+Texts as List of Words
+----------------------
+
+A few sentences have been defined for you.
+
+    >>> psent1
+    ['o', 'amor', 'da', 'gl\xf3ria', 'era', 'a', 'coisa', 'mais',
+    'verdadeiramente', 'humana', 'que', 'h\xe1', 'no', 'homem', ',',
+    'e', ',', 'conseq\xfcentemente', ',', 'a', 'sua', 'mais',
+    'genu\xedna', 'fei\xe7\xe3o', '.']
+    >>>
+
+Notice that the sentence has been *tokenized*.  Each token is
+represented as a string, represented using quotes, e.g. ``'coisa'``.
+Some strings contain special characters, e.g. ``\xf3``,
+the internal representation for ó.
+The tokens are combined in the form of a *list*.  How long is this list?
+
+    >>> len(psent1)
+    25
+    >>>
+
+What is the vocabulary of this sentence?
+
+    >>> sorted(set(psent1))
+    [',', '.', 'a', 'amor', 'coisa', 'conseqüentemente', 'da', 'e', 'era',
+     'feição', 'genuína', 'glória', 'homem', 'humana', 'há', 'mais', 'no',
+     'o', 'que', 'sua', 'verdadeiramente']
+    >>>
+
+Let's iterate over each item in ``psent2``, and print information for each:
+
+    >>> for w in psent2:
+    ...     print(w, len(w), w[-1])
+    ...
+    Não 3 o
+    consultes 9 s
+    dicionários 11 s
+    . 1 .
+
+Observe how we make a human-readable version of a string, using ``decode()``.
+Also notice that we accessed the last character of a string ``w`` using ``w[-1]``.
+
+We just saw a ``for`` loop above.  Another useful control structure is a
+*list comprehension*.
+
+    >>> [w.upper() for w in psent2]
+    ['N\xc3O', 'CONSULTES', 'DICION\xc1RIOS', '.']
+    >>> [w for w in psent1 if w.endswith('a')]
+    ['da', 'gl\xf3ria', 'era', 'a', 'coisa', 'humana', 'a', 'sua', 'genu\xedna']
+    >>> [w for w in ptext4 if len(w) > 15]
+    ['norte-irlandeses', 'pan-nacionalismo', 'predominatemente', 'primeiro-ministro',
+    'primeiro-ministro', 'irlandesa-americana', 'responsabilidades', 'significativamente']
+
+We can examine the relative frequency of words in a text, using ``FreqDist``:
+
+    >>> fd1 = FreqDist(ptext1)
+    >>> fd1
+    <FreqDist with 10848 samples and 77098 outcomes>
+    >>> fd1['olhos']
+    137
+    >>> fd1.max()
+    ','
+    >>> fd1.samples()[:100]
+    [',', '.', 'a', 'que', 'de', 'e', '-', 'o', ';', 'me', 'um', 'n\xe3o',
+    '\x97', 'se', 'do', 'da', 'uma', 'com', 'os', '\xe9', 'era', 'as', 'eu',
+    'lhe', 'ao', 'em', 'para', 'mas', '...', '!', '\xe0', 'na', 'mais', '?',
+    'no', 'como', 'por', 'N\xe3o', 'dos', 'o', 'ele', ':', 'Virg\xedlia',
+    'me', 'disse', 'minha', 'das', 'O', '/', 'A', 'CAP\xcdTULO', 'muito',
+    'depois', 'coisa', 'foi', 'sem', 'olhos', 'ela', 'nos', 'tinha', 'nem',
+    'E', 'outro', 'vida', 'nada', 'tempo', 'menos', 'outra', 'casa', 'homem',
+    'porque', 'quando', 'mim', 'mesmo', 'ser', 'pouco', 'estava', 'dia',
+    't\xe3o', 'tudo', 'Mas', 'at\xe9', 'D', 'ainda', 's\xf3', 'alguma',
+    'la', 'vez', 'anos', 'h\xe1', 'Era', 'pai', 'esse', 'lo', 'dizer', 'assim',
+    'ent\xe3o', 'dizia', 'aos', 'Borba']
+
+---------------
+Reading Corpora
+---------------
+
+Accessing the Machado Text Corpus
+---------------------------------
+
+NLTK includes the complete works of Machado de Assis.
+
+    >>> from nltk.corpus import machado
+    >>> machado.fileids()
+    ['contos/macn001.txt', 'contos/macn002.txt', 'contos/macn003.txt', ...]
+
+Each file corresponds to one of the works of Machado de Assis.  To see a complete
+list of works, you can look at the corpus README file: ``print machado.readme()``.
+Let's access the text of the *Posthumous Memories of Brás Cubas*.
+
+We can access the text as a list of characters, and access 200 characters starting
+from position 10,000.
+
+    >>> raw_text = machado.raw('romance/marm05.txt')
+    >>> raw_text[10000:10200]
+    u', primou no\nEstado, e foi um dos amigos particulares do vice-rei Conde
+    da Cunha.\n\nComo este apelido de Cubas lhe\ncheirasse excessivamente a
+    tanoaria, alegava meu pai, bisneto de Dami\xe3o, que o\ndito ape'
+
+However, this is not a very useful way to work with a text.  We generally think
+of a text as a sequence of words and punctuation, not characters:
+
+    >>> text1 = machado.words('romance/marm05.txt')
+    >>> text1
+    ['Romance', ',', 'Mem\xf3rias', 'P\xf3stumas', 'de', ...]
+    >>> len(text1)
+    77098
+    >>> len(set(text1))
+    10848
+
+Here's a program that finds the most common ngrams that contain a
+particular target word.
+
+    >>> from nltk import ngrams, FreqDist
+    >>> target_word = 'olhos'
+    >>> fd = FreqDist(ng
+    ...               for ng in ngrams(text1, 5)
+    ...               if target_word in ng)
+    >>> for hit in fd.samples():
+    ...     print(' '.join(hit))
+    ...
+    , com os olhos no
+    com os olhos no ar
+    com os olhos no chão
+    e todos com os olhos
+    me estar com os olhos
+    os olhos estúpidos , a
+    os olhos na costura ,
+    os olhos no ar ,
+    , com os olhos espetados
+    , com os olhos estúpidos
+    , com os olhos fitos
+    , com os olhos naquele
+    , com os olhos para
+
+
+Accessing the MacMorpho Tagged Corpus
+-------------------------------------
+
+NLTK includes the MAC-MORPHO Brazilian Portuguese POS-tagged news text,
+with over a million words of
+journalistic texts extracted from ten sections of
+the daily newspaper *Folha de Sao Paulo*, 1994.
+
+We can access this corpus as a sequence of words or tagged words as follows:
+
+    >>> import nltk.corpus
+    >>> nltk.corpus.mac_morpho.words()
+    ['Jersei', 'atinge', 'm\xe9dia', 'de', 'Cr$', '1,4', ...]
+    >>> nltk.corpus.mac_morpho.sents()
+    [['Jersei', 'atinge', 'm\xe9dia', 'de', 'Cr$', '1,4', 'milh\xe3o',
+    'em', 'a', 'venda', 'de', 'a', 'Pinhal', 'em', 'S\xe3o', 'Paulo'],
+    ['Programe', 'sua', 'viagem', 'a', 'a', 'Exposi\xe7\xe3o', 'Nacional',
+    'do', 'Zeb', ',', 'que', 'come\xe7a', 'dia', '25'], ...]
+    >>> nltk.corpus.mac_morpho.tagged_words()
+    [('Jersei', 'N'), ('atinge', 'V'), ('m\xe9dia', 'N'), ...]
+
+We can also access it in sentence chunks.
+
+    >>> nltk.corpus.mac_morpho.tagged_sents()
+    [[('Jersei', 'N'), ('atinge', 'V'), ('m\xe9dia', 'N'), ('de', 'PREP'),
+      ('Cr$', 'CUR'), ('1,4', 'NUM'), ('milh\xe3o', 'N'), ('em', 'PREP|+'),
+      ('a', 'ART'), ('venda', 'N'), ('de', 'PREP|+'), ('a', 'ART'),
+      ('Pinhal', 'NPROP'), ('em', 'PREP'), ('S\xe3o', 'NPROP'),
+      ('Paulo', 'NPROP')],
+     [('Programe', 'V'), ('sua', 'PROADJ'), ('viagem', 'N'), ('a', 'PREP|+'),
+      ('a', 'ART'), ('Exposi\xe7\xe3o', 'NPROP'), ('Nacional', 'NPROP'),
+      ('do', 'NPROP'), ('Zeb', 'NPROP'), (',', ','), ('que', 'PRO-KS-REL'),
+      ('come\xe7a', 'V'), ('dia', 'N'), ('25', 'N|AP')], ...]
+
+This data can be used to train taggers (examples below for the Floresta treebank).
+
+Accessing the Floresta Portuguese Treebank
+------------------------------------------
+
+The NLTK data distribution includes the
+"Floresta Sinta(c)tica Corpus" version 7.4, available from
+``https://www.linguateca.pt/Floresta/``.
+
+We can access this corpus as a sequence of words or tagged words as follows:
+
+    >>> from nltk.corpus import floresta
+    >>> floresta.words()
+    ['Um', 'revivalismo', 'refrescante', 'O', '7_e_Meio', ...]
+    >>> floresta.tagged_words()
+    [('Um', '>N+art'), ('revivalismo', 'H+n'), ...]
+
+The tags consist of some syntactic information, followed by a plus sign,
+followed by a conventional part-of-speech tag.  Let's strip off the material before
+the plus sign:
+
+    >>> def simplify_tag(t):
+    ...     if "+" in t:
+    ...         return t[t.index("+")+1:]
+    ...     else:
+    ...         return t
+    >>> twords = floresta.tagged_words()
+    >>> twords = [(w.lower(), simplify_tag(t)) for (w,t) in twords]
+    >>> twords[:10]
+    [('um', 'art'), ('revivalismo', 'n'), ('refrescante', 'adj'), ('o', 'art'), ('7_e_meio', 'prop'),
+    ('\xe9', 'v-fin'), ('um', 'art'), ('ex-libris', 'n'), ('de', 'prp'), ('a', 'art')]
+
+Pretty printing the tagged words:
+
+    >>> print(' '.join(word + '/' + tag for (word, tag) in twords[:10]))
+    um/art revivalismo/n refrescante/adj o/art 7_e_meio/prop é/v-fin um/art ex-libris/n de/prp a/art
+
+Count the word tokens and types, and determine the most common word:
+
+    >>> words = floresta.words()
+    >>> len(words)
+    211852
+    >>> fd = nltk.FreqDist(words)
+    >>> len(fd)
+    29421
+    >>> fd.max()
+    'de'
+
+List the 20 most frequent tags, in order of decreasing frequency:
+
+    >>> tags = [simplify_tag(tag) for (word,tag) in floresta.tagged_words()]
+    >>> fd = nltk.FreqDist(tags)
+    >>> fd.keys()[:20]
+    ['n', 'prp', 'art', 'v-fin', ',', 'prop', 'adj', 'adv', '.',
+     'conj-c', 'v-inf', 'pron-det', 'v-pcp', 'num', 'pron-indp',
+     'pron-pers', '\xab', '\xbb', 'conj-s', '}']
+
+We can also access the corpus grouped by sentence:
+
+    >>> floresta.sents()
+    [['Um', 'revivalismo', 'refrescante'],
+     ['O', '7_e_Meio', '\xe9', 'um', 'ex-libris', 'de', 'a', 'noite',
+      'algarvia', '.'], ...]
+    >>> floresta.tagged_sents()
+    [[('Um', '>N+art'), ('revivalismo', 'H+n'), ('refrescante', 'N<+adj')],
+     [('O', '>N+art'), ('7_e_Meio', 'H+prop'), ('\xe9', 'P+v-fin'),
+      ('um', '>N+art'), ('ex-libris', 'H+n'), ('de', 'H+prp'),
+      ('a', '>N+art'), ('noite', 'H+n'), ('algarvia', 'N<+adj'), ('.', '.')],
+     ...]
+    >>> floresta.parsed_sents()
+    [Tree('UTT+np', [Tree('>N+art', ['Um']), Tree('H+n', ['revivalismo']),
+                     Tree('N<+adj', ['refrescante'])]),
+     Tree('STA+fcl',
+         [Tree('SUBJ+np', [Tree('>N+art', ['O']),
+                           Tree('H+prop', ['7_e_Meio'])]),
+          Tree('P+v-fin', ['\xe9']),
+          Tree('SC+np',
+             [Tree('>N+art', ['um']),
+              Tree('H+n', ['ex-libris']),
+              Tree('N<+pp', [Tree('H+prp', ['de']),
+                             Tree('P<+np', [Tree('>N+art', ['a']),
+                                            Tree('H+n', ['noite']),
+                                            Tree('N<+adj', ['algarvia'])])])]),
+          Tree('.', ['.'])]), ...]
+
+To view a parse tree, use the ``draw()`` method, e.g.:
+
+    >>> psents = floresta.parsed_sents()
+    >>> psents[5].draw() # doctest: +SKIP
+
+Character Encodings
+-------------------
+
+Python understands the common character encoding used for Portuguese, ISO 8859-1 (ISO Latin 1).
+
+    >>> import os, nltk.test
+    >>> testdir = os.path.split(nltk.test.__file__)[0]
+    >>> text = open(os.path.join(testdir, 'floresta.txt'), 'rb').read().decode('ISO 8859-1')
+    >>> text[:60]
+    'O 7 e Meio \xe9 um ex-libris da noite algarvia.\n\xc9 uma das mais '
+    >>> print(text[:60])
+    O 7 e Meio é um ex-libris da noite algarvia.
+    É uma das mais
+
+For more information about character encodings and Python, please see section 3.3 of the book.
+
+----------------
+Processing Tasks
+----------------
+
+
+Simple Concordancing
+--------------------
+
+Here's a function that takes a word and a specified amount of context (measured
+in characters), and generates a concordance for that word.
+
+    >>> def concordance(word, context=30):
+    ...     for sent in floresta.sents():
+    ...         if word in sent:
+    ...             pos = sent.index(word)
+    ...             left = ' '.join(sent[:pos])
+    ...             right = ' '.join(sent[pos+1:])
+    ...             print('%*s %s %-*s' %
+    ...                 (context, left[-context:], word, context, right[:context]))
+
+    >>> concordance("dar") # doctest: +SKIP
+    anduru , foi o suficiente para dar a volta a o resultado .
+                 1. O P?BLICO veio dar a a imprensa di?ria portuguesa
+      A fartura de pensamento pode dar maus resultados e n?s n?o quer
+                          Come?a a dar resultados a pol?tica de a Uni
+    ial come?ar a incorporar- lo e dar forma a um ' site ' que tem se
+    r com Constantino para ele lhe dar tamb?m os pap?is assinados .
+    va a brincar , pois n?o lhe ia dar procura??o nenhuma enquanto n?
+    ?rica como o ant?doto capaz de dar sentido a o seu enorme poder .
+    . . .
+    >>> concordance("vender") # doctest: +SKIP
+    er recebido uma encomenda para vender 4000 blindados a o Iraque .
+    m?rico_Amorim caso conseguisse vender o lote de ac??es de o empres?r
+    mpre ter jovens simp?ticos a ? vender ? chega ! }
+           Disse que o governo vai vender ? desde autom?vel at? particip
+    ndiciou ontem duas pessoas por vender carro com ?gio .
+            A inten??o de Fleury ? vender as a??es para equilibrar as fi
+
+Part-of-Speech Tagging
+----------------------
+
+Let's begin by getting the tagged sentence data, and simplifying the tags
+as described earlier.
+
+    >>> from nltk.corpus import floresta
+    >>> tsents = floresta.tagged_sents()
+    >>> tsents = [[(w.lower(),simplify_tag(t)) for (w,t) in sent] for sent in tsents if sent]
+    >>> train = tsents[100:]
+    >>> test = tsents[:100]
+
+We already know that ``n`` is the most common tag, so we can set up a
+default tagger that tags every word as a noun, and see how well it does:
+
+    >>> tagger0 = nltk.DefaultTagger('n')
+    >>> nltk.tag.accuracy(tagger0, test)
+    0.17697228144989338
+
+Evidently, about one in every six words is a noun.  Let's improve on this by
+training a unigram tagger:
+
+    >>> tagger1 = nltk.UnigramTagger(train, backoff=tagger0)
+    >>> nltk.tag.accuracy(tagger1, test)
+    0.87029140014214645
+
+Next a bigram tagger:
+
+    >>> tagger2 = nltk.BigramTagger(train, backoff=tagger1)
+    >>> nltk.tag.accuracy(tagger2, test)
+    0.89019189765458417
+
+
+Sentence Segmentation
+---------------------
+
+Punkt is a language-neutral sentence segmentation tool.  We
+
+    >>> sent_tokenizer=nltk.data.load('tokenizers/punkt/portuguese.pickle')
+    >>> raw_text = machado.raw('romance/marm05.txt')
+    >>> sentences = sent_tokenizer.tokenize(raw_text)
+    >>> for sent in sentences[1000:1005]:
+    ...     print("<<", sent, ">>")
+    ...
+    << Em verdade, parecia ainda mais mulher do que era;
+    seria criança nos seus folgares de moça; mas assim quieta, impassível, tinha a
+    compostura da mulher casada. >>
+    << Talvez essa circunstância lhe diminuía um pouco da
+    graça virginal. >>
+    << Depressa nos familiarizamos; a mãe fazia-lhe grandes elogios, eu
+    escutava-os de boa sombra, e ela sorria com os olhos fúlgidos, como se lá dentro
+    do cérebro lhe estivesse a voar uma borboletinha de asas de ouro e olhos de
+    diamante... >>
+    << Digo lá dentro, porque cá fora o
+    que esvoaçou foi uma borboleta preta, que subitamente penetrou na varanda, e
+    começou a bater as asas em derredor de D. Eusébia. >>
+    << D. Eusébia deu um grito,
+    levantou-se, praguejou umas palavras soltas: - T'esconjuro!... >>
+
+The sentence tokenizer can be trained and evaluated on other text.
+The source text (from the Floresta Portuguese Treebank) contains one sentence per line.
+We read the text, split it into its lines, and then join these lines together using
+spaces.  Now the information about sentence breaks has been discarded.  We split this
+material into training and testing data:
+
+    >>> import os, nltk.test
+    >>> testdir = os.path.split(nltk.test.__file__)[0]
+    >>> text = open(os.path.join(testdir, 'floresta.txt'), 'rb').read().decode('ISO-8859-1')
+    >>> lines = text.split('\n')
+    >>> train = ' '.join(lines[10:])
+    >>> test = ' '.join(lines[:10])
+
+Now we train the sentence segmenter (or sentence tokenizer) and use it on our test sentences:
+
+    >>> stok = nltk.PunktSentenceTokenizer(train)
+    >>> print(stok.tokenize(test))
+    ['O 7 e Meio \xe9 um ex-libris da noite algarvia.',
+    '\xc9 uma das mais antigas discotecas do Algarve, situada em Albufeira,
+    que continua a manter os tra\xe7os decorativos e as clientelas de sempre.',
+    '\xc9 um pouco a vers\xe3o de uma esp\xe9cie de \xaboutro lado\xbb da noite,
+    a meio caminho entre os devaneios de uma fauna perif\xe9rica, seja de Lisboa,
+    Londres, Dublin ou Faro e Portim\xe3o, e a postura circunspecta dos fi\xe9is da casa,
+    que dela esperam a m\xfasica \xabgeracionista\xbb dos 60 ou dos 70.',
+    'N\xe3o deixa de ser, nos tempos que correm, um certo \xabvery typical\xbb algarvio,
+    cabe\xe7a de cartaz para os que querem fugir a algumas movimenta\xe7\xf5es nocturnas
+    j\xe1 a caminho da ritualiza\xe7\xe3o de massas, do g\xe9nero \xabvamos todos ao
+    Calypso e encontramo-nos na Locomia\xbb.',
+    'E assim, aos 2,5 milh\xf5es que o Minist\xe9rio do Planeamento e Administra\xe7\xe3o
+    do Territ\xf3rio j\xe1 gasta no pagamento do pessoal afecto a estes organismos,
+    v\xeam juntar-se os montantes das obras propriamente ditas, que os munic\xedpios,
+    j\xe1 com projectos na m\xe3o, v\xeam reivindicar junto do Executivo, como salienta
+    aquele membro do Governo.',
+    'E o dinheiro \xabn\xe3o falta s\xf3 \xe0s c\xe2maras\xbb, lembra o secret\xe1rio de Estado,
+    que considera que a solu\xe7\xe3o para as autarquias \xe9 \xabespecializarem-se em
+    fundos comunit\xe1rios\xbb.',
+    'Mas como, se muitas n\xe3o disp\xf5em, nos seus quadros, dos t\xe9cnicos necess\xe1rios?',
+    '\xabEncomendem-nos a projectistas de fora\xbb porque, se as obras vierem a ser financiadas,
+    eles at\xe9 saem de gra\xe7a, j\xe1 que, nesse caso, \xabos fundos comunit\xe1rios pagam
+    os projectos, o mesmo n\xe3o acontecendo quando eles s\xe3o feitos pelos GAT\xbb,
+    dado serem organismos do Estado.',
+    'Essa poder\xe1 vir a ser uma hip\xf3tese, at\xe9 porque, no terreno, a capacidade dos GAT
+    est\xe1 cada vez mais enfraquecida.',
+    'Alguns at\xe9 j\xe1 desapareceram, como o de Castro Verde, e outros t\xeam vindo a perder quadros.']
+
+NLTK's data collection includes a trained model for Portuguese sentence
+segmentation, which can be loaded as follows.  It is faster to load a trained model than
+to retrain it.
+
+    >>> stok = nltk.data.load('tokenizers/punkt/portuguese.pickle')
+
+Stemming
+--------
+
+NLTK includes the RSLP Portuguese stemmer.  Here we use it to stem some Portuguese text:
+
+    >>> stemmer = nltk.stem.RSLPStemmer()
+    >>> stemmer.stem("copiar")
+    'copi'
+    >>> stemmer.stem("paisagem")
+    'pais'
+
+
+Stopwords
+---------
+
+NLTK includes Portuguese stopwords:
+
+    >>> stopwords = nltk.corpus.stopwords.words('portuguese')
+    >>> stopwords[:10]
+    ['a', 'ao', 'aos', 'aquela', 'aquelas', 'aquele', 'aqueles', 'aquilo', 'as', 'at\xe9']
+
+Now we can use these to filter text.  Let's find the most frequent words (other than stopwords)
+and print them in descending order of frequency:
+
+    >>> fd = nltk.FreqDist(w.lower() for w in floresta.words() if w not in stopwords)
+    >>> for word in list(fd.keys())[:20]:
+    ...     print(word, fd[word])
+    , 13444
+    . 7725
+    « 2369
+    » 2310
+    é 1305
+    o 1086
+    } 1047
+    { 1044
+    a 897
+    ; 633
+    em 516
+    ser 466
+    sobre 349
+    os 313
+    anos 301
+    ontem 292
+    ainda 279
+    segundo 256
+    ter 249
+    dois 231

env-llmeval/lib/python3.10/site-packages/nltk/test/semantics.doctest

@@ -0,0 +1,667 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=========
+Semantics
+=========
+
+    >>> # Setup tests by setting the counter to 0
+    >>> from nltk.sem import logic
+    >>> logic._counter._value = 0
+
+    >>> import nltk
+    >>> from nltk.sem import Valuation, Model
+    >>> v = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'),
+    ... ('girl', set(['g1', 'g2'])), ('boy', set(['b1', 'b2'])),
+    ... ('dog', set(['d1'])),
+    ... ('love', set([('b1', 'g1'), ('b2', 'g2'), ('g1', 'b1'), ('g2', 'b1')]))]
+    >>> val = Valuation(v)
+    >>> dom = val.domain
+    >>> m = Model(dom, val)
+
+Evaluation
+----------
+
+The top-level method of a ``Model`` instance is ``evaluate()``, which
+assigns a semantic value to expressions of the ``logic`` module, under
+an assignment ``g``:
+
+    >>> dom = val.domain
+    >>> g = nltk.sem.Assignment(dom)
+    >>> m.evaluate('all x.(boy(x) -> - girl(x))', g)
+    True
+
+
+``evaluate()`` calls a recursive function ``satisfy()``, which in turn
+calls a function ``i()`` to interpret non-logical constants and
+individual variables. ``i()`` delegates the interpretation of these to
+the the model's ``Valuation`` and the variable assignment ``g``
+respectively. Any atomic expression which cannot be assigned a value
+by ``i`` raises an ``Undefined`` exception; this is caught by
+``evaluate``, which returns the string ``'Undefined'``.
+
+    >>> m.evaluate('walk(adam)', g, trace=2)
+    <BLANKLINE>
+    'walk(adam)' is undefined under M, g
+    'Undefined'
+
+Batch Processing
+----------------
+
+The utility functions ``interpret_sents()`` and ``evaluate_sents()`` are intended to
+help with processing multiple sentences. Here's an example of the first of these:
+
+    >>> sents = ['Mary walks']
+    >>> results = nltk.sem.util.interpret_sents(sents, 'grammars/sample_grammars/sem2.fcfg')
+    >>> for result in results:
+    ...     for (synrep, semrep) in result:
+    ...         print(synrep)
+    (S[SEM=<walk(mary)>]
+      (NP[-LOC, NUM='sg', SEM=<\P.P(mary)>]
+        (PropN[-LOC, NUM='sg', SEM=<\P.P(mary)>] Mary))
+      (VP[NUM='sg', SEM=<\x.walk(x)>]
+        (IV[NUM='sg', SEM=<\x.walk(x)>, TNS='pres'] walks)))
+
+In order to provide backwards compatibility with 'legacy' grammars where the semantics value
+is specified with a lowercase
+``sem`` feature, the relevant feature name can be passed to the function using the
+``semkey`` parameter, as shown here:
+
+    >>> sents = ['raining']
+    >>> g = nltk.grammar.FeatureGrammar.fromstring("""
+    ... % start S
+    ... S[sem=<raining>] -> 'raining'
+    ... """)
+    >>> results = nltk.sem.util.interpret_sents(sents, g, semkey='sem')
+    >>> for result in results:
+    ...     for (synrep, semrep) in result:
+    ...         print(semrep)
+    raining
+
+The function ``evaluate_sents()`` works in a similar manner, but also needs to be
+passed a ``Model`` against which the semantic representations are evaluated.
+
+Unit Tests
+==========
+
+
+Unit tests for relations and valuations
+---------------------------------------
+
+    >>> from nltk.sem import *
+
+Relations are sets of tuples, all of the same length.
+
+    >>> s1 = set([('d1', 'd2'), ('d1', 'd1'), ('d2', 'd1')])
+    >>> is_rel(s1)
+    True
+    >>> s2 = set([('d1', 'd2'), ('d1', 'd2'), ('d1',)])
+    >>> is_rel(s2)
+    Traceback (most recent call last):
+      . . .
+    ValueError: Set set([('d1', 'd2'), ('d1',)]) contains sequences of different lengths
+    >>> s3 = set(['d1', 'd2'])
+    >>> is_rel(s3)
+    Traceback (most recent call last):
+      . . .
+    ValueError: Set set(['d2', 'd1']) contains sequences of different lengths
+    >>> s4 = set2rel(s3)
+    >>> is_rel(s4)
+    True
+    >>> is_rel(set())
+    True
+    >>> null_binary_rel = set([(None, None)])
+    >>> is_rel(null_binary_rel)
+    True
+
+Sets of entities are converted into sets of singleton tuples
+(containing strings).
+
+    >>> sorted(set2rel(s3))
+    [('d1',), ('d2',)]
+    >>> sorted(set2rel(set([1,3,5,])))
+    ['1', '3', '5']
+    >>> set2rel(set()) == set()
+    True
+    >>> set2rel(set2rel(s3)) == set2rel(s3)
+    True
+
+Predication is evaluated by set membership.
+
+    >>> ('d1', 'd2') in s1
+    True
+    >>> ('d2', 'd2') in s1
+    False
+    >>> ('d1',) in s1
+    False
+    >>> 'd2' in s1
+    False
+    >>> ('d1',) in s4
+    True
+    >>> ('d1',) in set()
+    False
+    >>> 'd1' in  null_binary_rel
+    False
+
+
+    >>> val = Valuation([('Fido', 'd1'), ('dog', set(['d1', 'd2'])), ('walk', set())])
+    >>> sorted(val['dog'])
+    [('d1',), ('d2',)]
+    >>> val.domain == set(['d1', 'd2'])
+    True
+    >>> print(val.symbols)
+    ['Fido', 'dog', 'walk']
+
+
+Parse a valuation from a string.
+
+    >>> v = """
+    ... john => b1
+    ... mary => g1
+    ... suzie => g2
+    ... fido => d1
+    ... tess => d2
+    ... noosa => n
+    ... girl => {g1, g2}
+    ... boy => {b1, b2}
+    ... dog => {d1, d2}
+    ... bark => {d1, d2}
+    ... walk => {b1, g2, d1}
+    ... chase => {(b1, g1), (b2, g1), (g1, d1), (g2, d2)}
+    ... see => {(b1, g1), (b2, d2), (g1, b1),(d2, b1), (g2, n)}
+    ... in => {(b1, n), (b2, n), (d2, n)}
+    ... with => {(b1, g1), (g1, b1), (d1, b1), (b1, d1)}
+    ... """
+    >>> val = Valuation.fromstring(v)
+
+    >>> print(val) # doctest: +SKIP
+    {'bark': set([('d1',), ('d2',)]),
+     'boy': set([('b1',), ('b2',)]),
+     'chase': set([('b1', 'g1'), ('g2', 'd2'), ('g1', 'd1'), ('b2', 'g1')]),
+     'dog': set([('d1',), ('d2',)]),
+     'fido': 'd1',
+     'girl': set([('g2',), ('g1',)]),
+     'in': set([('d2', 'n'), ('b1', 'n'), ('b2', 'n')]),
+     'john': 'b1',
+     'mary': 'g1',
+     'noosa': 'n',
+     'see': set([('b1', 'g1'), ('b2', 'd2'), ('d2', 'b1'), ('g2', 'n'), ('g1', 'b1')]),
+     'suzie': 'g2',
+     'tess': 'd2',
+     'walk': set([('d1',), ('b1',), ('g2',)]),
+     'with': set([('b1', 'g1'), ('d1', 'b1'), ('b1', 'd1'), ('g1', 'b1')])}
+
+
+Unit tests for function argument application in a Model
+-------------------------------------------------------
+
+    >>> v = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'),\
+    ...      ('girl', set(['g1', 'g2'])), ('boy', set(['b1', 'b2'])), ('dog', set(['d1'])),
+    ...      ('love', set([('b1', 'g1'), ('b2', 'g2'), ('g1', 'b1'), ('g2', 'b1')])),
+    ...      ('kiss', null_binary_rel)]
+    >>> val = Valuation(v)
+    >>> dom = val.domain
+    >>> m = Model(dom, val)
+    >>> g = Assignment(dom)
+    >>> sorted(val['boy'])
+    [('b1',), ('b2',)]
+    >>> ('b1',) in val['boy']
+    True
+    >>> ('g1',) in val['boy']
+    False
+    >>> ('foo',) in val['boy']
+    False
+    >>> ('b1', 'g1') in val['love']
+    True
+    >>> ('b1', 'b1') in val['kiss']
+    False
+    >>> sorted(val.domain)
+    ['b1', 'b2', 'd1', 'g1', 'g2']
+
+
+Model Tests
+===========
+
+Extension of Lambda expressions
+
+    >>> v0 = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'),\
+    ... ('girl', set(['g1', 'g2'])), ('boy', set(['b1', 'b2'])),
+    ... ('dog', set(['d1'])),
+    ... ('love', set([('b1', 'g1'), ('b2', 'g2'), ('g1', 'b1'), ('g2', 'b1')]))]
+
+    >>> val0 = Valuation(v0)
+    >>> dom0 = val0.domain
+    >>> m0 = Model(dom0, val0)
+    >>> g0 = Assignment(dom0)
+
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)', g0) == {'g2': {'g2': False, 'b2': False, 'b1': True, 'g1': False, 'd1': False}, 'b2': {'g2': True, 'b2': False, 'b1': False, 'g1': False, 'd1': False}, 'b1': {'g2': False, 'b2': False, 'b1': False, 'g1': True, 'd1': False}, 'g1': {'g2': False, 'b2': False, 'b1': True, 'g1': False, 'd1': False}, 'd1': {'g2': False, 'b2': False, 'b1': False, 'g1': False, 'd1': False}})
+    True
+    >>> print(m0.evaluate(r'\x. dog(x) (adam)', g0))
+    False
+    >>> print(m0.evaluate(r'\x. (dog(x) | boy(x)) (adam)', g0))
+    True
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)(fido)', g0) == {'g2': False, 'b2': False, 'b1': False, 'g1': False, 'd1': False})
+    True
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)(adam)', g0) == {'g2': False, 'b2': False, 'b1': False, 'g1': True, 'd1': False})
+    True
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)(betty)', g0) == {'g2': False, 'b2': False, 'b1': True, 'g1': False, 'd1': False})
+    True
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)(betty)(adam)', g0))
+    True
+    >>> print(m0.evaluate(r'\x. \y. love(x, y)(betty, adam)', g0))
+    True
+    >>> print(m0.evaluate(r'\y. \x. love(x, y)(fido)(adam)', g0))
+    False
+    >>> print(m0.evaluate(r'\y. \x. love(x, y)(betty, adam)', g0))
+    True
+    >>> print(m0.evaluate(r'\x. exists y. love(x, y)', g0) == {'g2': True, 'b2': True, 'b1': True, 'g1': True, 'd1': False})
+    True
+    >>> print(m0.evaluate(r'\z. adam', g0) == {'g2': 'b1', 'b2': 'b1', 'b1': 'b1', 'g1': 'b1', 'd1': 'b1'})
+    True
+    >>> print(m0.evaluate(r'\z. love(x, y)', g0) == {'g2': False, 'b2': False, 'b1': False, 'g1': False, 'd1': False})
+    True
+
+
+Propositional Model Test
+------------------------
+
+    >>> tests = [
+    ...     ('P & Q', True),
+    ...     ('P & R', False),
+    ...     ('- P', False),
+    ...     ('- R', True),
+    ...     ('- - P', True),
+    ...     ('- (P & R)', True),
+    ...     ('P | R', True),
+    ...     ('R | P', True),
+    ...     ('R | R', False),
+    ...     ('- P | R', False),
+    ...     ('P | - P', True),
+    ...     ('P -> Q', True),
+    ...     ('P -> R', False),
+    ...     ('R -> P', True),
+    ...     ('P <-> P', True),
+    ...     ('R <-> R', True),
+    ...     ('P <-> R', False),
+    ...     ]
+    >>> val1 = Valuation([('P', True), ('Q', True), ('R', False)])
+    >>> dom = set([])
+    >>> m = Model(dom, val1)
+    >>> g = Assignment(dom)
+    >>> for (sent, testvalue) in tests:
+    ...     semvalue = m.evaluate(sent, g)
+    ...     if semvalue == testvalue:
+    ...         print('*', end=' ')
+    * * * * * * * * * * * * * * * * *
+
+
+Test of i Function
+------------------
+
+    >>> from nltk.sem import Expression
+    >>> v = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'),
+    ...      ('girl', set(['g1', 'g2'])), ('boy', set(['b1', 'b2'])), ('dog', set(['d1'])),
+    ...      ('love', set([('b1', 'g1'), ('b2', 'g2'), ('g1', 'b1'), ('g2', 'b1')]))]
+    >>> val = Valuation(v)
+    >>> dom = val.domain
+    >>> m = Model(dom, val)
+    >>> g = Assignment(dom, [('x', 'b1'), ('y', 'g2')])
+    >>> exprs = ['adam', 'girl', 'love', 'walks', 'x', 'y', 'z']
+    >>> parsed_exprs = [Expression.fromstring(e) for e in exprs]
+    >>> sorted_set = lambda x: sorted(x) if isinstance(x, set) else x
+    >>> for parsed in parsed_exprs:
+    ...     try:
+    ...         print("'%s' gets value %s" % (parsed, sorted_set(m.i(parsed, g))))
+    ...     except Undefined:
+    ...         print("'%s' is Undefined" % parsed)
+    'adam' gets value b1
+    'girl' gets value [('g1',), ('g2',)]
+    'love' gets value [('b1', 'g1'), ('b2', 'g2'), ('g1', 'b1'), ('g2', 'b1')]
+    'walks' is Undefined
+    'x' gets value b1
+    'y' gets value g2
+    'z' is Undefined
+
+Test for formulas in Model
+--------------------------
+
+    >>> tests = [
+    ...     ('love(adam, betty)', True),
+    ...     ('love(adam, sue)', 'Undefined'),
+    ...     ('dog(fido)', True),
+    ...     ('- dog(fido)', False),
+    ...     ('- - dog(fido)', True),
+    ...     ('- dog(sue)', 'Undefined'),
+    ...     ('dog(fido) & boy(adam)', True),
+    ...     ('- (dog(fido) & boy(adam))', False),
+    ...     ('- dog(fido) & boy(adam)', False),
+    ...     ('dog(fido) | boy(adam)', True),
+    ...     ('- (dog(fido) | boy(adam))', False),
+    ...     ('- dog(fido) | boy(adam)', True),
+    ...     ('- dog(fido) | - boy(adam)', False),
+    ...     ('dog(fido) -> boy(adam)', True),
+    ...     ('- (dog(fido) -> boy(adam))', False),
+    ...     ('- dog(fido) -> boy(adam)', True),
+    ...     ('exists x . love(adam, x)', True),
+    ...     ('all x . love(adam, x)', False),
+    ...     ('fido = fido', True),
+    ...     ('exists x . all y. love(x, y)', False),
+    ...     ('exists x . (x = fido)', True),
+    ...     ('all x . (dog(x) | - dog(x))', True),
+    ...     ('adam = mia', 'Undefined'),
+    ...     ('\\x. (boy(x) | girl(x))', {'g2': True, 'b2': True, 'b1': True, 'g1': True, 'd1': False}),
+    ...     ('\\x. exists y. (boy(x) & love(x, y))', {'g2': False, 'b2': True, 'b1': True, 'g1': False, 'd1': False}),
+    ...     ('exists z1. boy(z1)', True),
+    ...     ('exists x. (boy(x) & - (x = adam))', True),
+    ...     ('exists x. (boy(x) & all y. love(y, x))', False),
+    ...     ('all x. (boy(x) | girl(x))', False),
+    ...     ('all x. (girl(x) -> exists y. boy(y) & love(x, y))', False),
+    ...     ('exists x. (boy(x) & all y. (girl(y) -> love(y, x)))', True),
+    ...     ('exists x. (boy(x) & all y. (girl(y) -> love(x, y)))', False),
+    ...     ('all x. (dog(x) -> - girl(x))', True),
+    ...     ('exists x. exists y. (love(x, y) & love(x, y))', True),
+    ...     ]
+    >>> for (sent, testvalue) in tests:
+    ...     semvalue = m.evaluate(sent, g)
+    ...     if semvalue == testvalue:
+    ...         print('*', end=' ')
+    ...     else:
+    ...         print(sent, semvalue)
+    * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+
+
+
+Satisfier Tests
+---------------
+
+    >>> formulas = [
+    ...     'boy(x)',
+    ...     '(x = x)',
+    ...     '(boy(x) | girl(x))',
+    ...     '(boy(x) & girl(x))',
+    ...     'love(adam, x)',
+    ...     'love(x, adam)',
+    ...     '- (x = adam)',
+    ...     'exists z22. love(x, z22)',
+    ...     'exists y. love(y, x)',
+    ...     'all y. (girl(y) -> love(x, y))',
+    ...     'all y. (girl(y) -> love(y, x))',
+    ...     'all y. (girl(y) -> (boy(x) & love(y, x)))',
+    ...     'boy(x) & all y. (girl(y) -> love(x, y))',
+    ...     'boy(x) & all y. (girl(y) -> love(y, x))',
+    ...     'boy(x) & exists y. (girl(y) & love(y, x))',
+    ...     'girl(x) -> dog(x)',
+    ...     'all y. (dog(y) -> (x = y))',
+    ...     '- exists y. love(y, x)',
+    ...     'exists y. (love(adam, y) & love(y, x))'
+    ...     ]
+    >>> g.purge()
+    >>> g.add('x', 'b1')
+    {'x': 'b1'}
+    >>> for f in formulas:
+    ...     try:
+    ...         print("'%s' gets value: %s" % (f, m.evaluate(f, g)))
+    ...     except Undefined:
+    ...         print("'%s' is Undefined" % f)
+    'boy(x)' gets value: True
+    '(x = x)' gets value: True
+    '(boy(x) | girl(x))' gets value: True
+    '(boy(x) & girl(x))' gets value: False
+    'love(adam, x)' gets value: False
+    'love(x, adam)' gets value: False
+    '- (x = adam)' gets value: False
+    'exists z22. love(x, z22)' gets value: True
+    'exists y. love(y, x)' gets value: True
+    'all y. (girl(y) -> love(x, y))' gets value: False
+    'all y. (girl(y) -> love(y, x))' gets value: True
+    'all y. (girl(y) -> (boy(x) & love(y, x)))' gets value: True
+    'boy(x) & all y. (girl(y) -> love(x, y))' gets value: False
+    'boy(x) & all y. (girl(y) -> love(y, x))' gets value: True
+    'boy(x) & exists y. (girl(y) & love(y, x))' gets value: True
+    'girl(x) -> dog(x)' gets value: True
+    'all y. (dog(y) -> (x = y))' gets value: False
+    '- exists y. love(y, x)' gets value: False
+    'exists y. (love(adam, y) & love(y, x))' gets value: True
+
+    >>> from nltk.sem import Expression
+    >>> for fmla in formulas:
+    ...     p = Expression.fromstring(fmla)
+    ...     g.purge()
+    ...     print("Satisfiers of '%s':\n\t%s" % (p, sorted(m.satisfiers(p, 'x', g))))
+    Satisfiers of 'boy(x)':
+    ['b1', 'b2']
+    Satisfiers of '(x = x)':
+    ['b1', 'b2', 'd1', 'g1', 'g2']
+    Satisfiers of '(boy(x) | girl(x))':
+    ['b1', 'b2', 'g1', 'g2']
+    Satisfiers of '(boy(x) & girl(x))':
+    []
+    Satisfiers of 'love(adam,x)':
+    ['g1']
+    Satisfiers of 'love(x,adam)':
+    ['g1', 'g2']
+    Satisfiers of '-(x = adam)':
+    ['b2', 'd1', 'g1', 'g2']
+    Satisfiers of 'exists z22.love(x,z22)':
+    ['b1', 'b2', 'g1', 'g2']
+    Satisfiers of 'exists y.love(y,x)':
+    ['b1', 'g1', 'g2']
+    Satisfiers of 'all y.(girl(y) -> love(x,y))':
+    []
+    Satisfiers of 'all y.(girl(y) -> love(y,x))':
+    ['b1']
+    Satisfiers of 'all y.(girl(y) -> (boy(x) & love(y,x)))':
+    ['b1']
+    Satisfiers of '(boy(x) & all y.(girl(y) -> love(x,y)))':
+    []
+    Satisfiers of '(boy(x) & all y.(girl(y) -> love(y,x)))':
+    ['b1']
+    Satisfiers of '(boy(x) & exists y.(girl(y) & love(y,x)))':
+    ['b1']
+    Satisfiers of '(girl(x) -> dog(x))':
+    ['b1', 'b2', 'd1']
+    Satisfiers of 'all y.(dog(y) -> (x = y))':
+    ['d1']
+    Satisfiers of '-exists y.love(y,x)':
+    ['b2', 'd1']
+    Satisfiers of 'exists y.(love(adam,y) & love(y,x))':
+    ['b1']
+
+
+Tests based on the Blackburn & Bos testsuite
+--------------------------------------------
+
+    >>> v1 = [('jules', 'd1'), ('vincent', 'd2'), ('pumpkin', 'd3'),
+    ...       ('honey_bunny', 'd4'), ('yolanda', 'd5'),
+    ...       ('customer', set(['d1', 'd2'])),
+    ...       ('robber', set(['d3', 'd4'])),
+    ...       ('love', set([('d3', 'd4')]))]
+    >>> val1 = Valuation(v1)
+    >>> dom1 = val1.domain
+    >>> m1 = Model(dom1, val1)
+    >>> g1 = Assignment(dom1)
+
+    >>> v2 = [('jules', 'd1'), ('vincent', 'd2'), ('pumpkin', 'd3'),
+    ...       ('honey_bunny', 'd4'), ('yolanda', 'd4'),
+    ...       ('customer', set(['d1', 'd2', 'd5', 'd6'])),
+    ...       ('robber', set(['d3', 'd4'])),
+    ...       ('love', set([(None, None)]))]
+    >>> val2 = Valuation(v2)
+    >>> dom2 = set(['d1', 'd2', 'd3', 'd4', 'd5', 'd6'])
+    >>> m2 = Model(dom2, val2)
+    >>> g2 = Assignment(dom2)
+    >>> g21 = Assignment(dom2)
+    >>> g21.add('y', 'd3')
+    {'y': 'd3'}
+
+    >>> v3 = [('mia', 'd1'), ('jody', 'd2'), ('jules', 'd3'),
+    ...       ('vincent', 'd4'),
+    ...       ('woman', set(['d1', 'd2'])), ('man', set(['d3', 'd4'])),
+    ...       ('joke', set(['d5', 'd6'])), ('episode', set(['d7', 'd8'])),
+    ...       ('in', set([('d5', 'd7'), ('d5', 'd8')])),
+    ...       ('tell', set([('d1', 'd5'), ('d2', 'd6')]))]
+    >>> val3 = Valuation(v3)
+    >>> dom3 = set(['d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8'])
+    >>> m3 = Model(dom3, val3)
+    >>> g3 = Assignment(dom3)
+
+    >>> tests = [
+    ...     ('exists x. robber(x)', m1, g1, True),
+    ...     ('exists x. exists y. love(y, x)', m1, g1, True),
+    ...     ('exists x0. exists x1. love(x1, x0)', m2, g2, False),
+    ...     ('all x. all y. love(y, x)', m2, g2, False),
+    ...     ('- (all x. all y. love(y, x))', m2, g2, True),
+    ...     ('all x. all y. - love(y, x)', m2, g2, True),
+    ...     ('yolanda = honey_bunny', m2, g2, True),
+    ...     ('mia = honey_bunny', m2, g2, 'Undefined'),
+    ...     ('- (yolanda = honey_bunny)', m2, g2, False),
+    ...     ('- (mia = honey_bunny)', m2, g2, 'Undefined'),
+    ...     ('all x. (robber(x) | customer(x))', m2, g2, True),
+    ...     ('- (all x. (robber(x) | customer(x)))', m2, g2, False),
+    ...     ('(robber(x) | customer(x))', m2, g2, 'Undefined'),
+    ...     ('(robber(y) | customer(y))', m2, g21, True),
+    ...     ('exists x. (man(x) & exists x. woman(x))', m3, g3, True),
+    ...     ('exists x. (man(x) & exists x. woman(x))', m3, g3, True),
+    ...     ('- exists x. woman(x)', m3, g3, False),
+    ...     ('exists x. (tasty(x) & burger(x))', m3, g3, 'Undefined'),
+    ...     ('- exists x. (tasty(x) & burger(x))', m3, g3, 'Undefined'),
+    ...     ('exists x. (man(x) & - exists y. woman(y))', m3, g3, False),
+    ...     ('exists x. (man(x) & - exists x. woman(x))', m3, g3, False),
+    ...     ('exists x. (woman(x) & - exists x. customer(x))', m2, g2, 'Undefined'),
+    ... ]
+
+    >>> for item in tests:
+    ...     sentence, model, g, testvalue = item
+    ...     semvalue = model.evaluate(sentence, g)
+    ...     if semvalue == testvalue:
+    ...         print('*', end=' ')
+    ...     g.purge()
+    * * * * * * * * * * * * * * * * * * * * * *
+
+
+Tests for mapping from syntax to semantics
+------------------------------------------
+
+Load a valuation from a file.
+
+    >>> import nltk.data
+    >>> from nltk.sem.util import parse_sents
+    >>> val = nltk.data.load('grammars/sample_grammars/valuation1.val')
+    >>> dom = val.domain
+    >>> m = Model(dom, val)
+    >>> g = Assignment(dom)
+    >>> gramfile = 'grammars/sample_grammars/sem2.fcfg'
+    >>> inputs = ['John sees a girl', 'every dog barks']
+    >>> parses = parse_sents(inputs, gramfile)
+    >>> for sent, trees in zip(inputs, parses):
+    ...     print()
+    ...     print("Sentence: %s" % sent)
+    ...     for tree in trees:
+    ...         print("Parse:\n %s" %tree)
+    ...         print("Semantics: %s" %  root_semrep(tree))
+    <BLANKLINE>
+    Sentence: John sees a girl
+    Parse:
+     (S[SEM=<exists x.(girl(x) & see(john,x))>]
+      (NP[-LOC, NUM='sg', SEM=<\P.P(john)>]
+        (PropN[-LOC, NUM='sg', SEM=<\P.P(john)>] John))
+      (VP[NUM='sg', SEM=<\y.exists x.(girl(x) & see(y,x))>]
+        (TV[NUM='sg', SEM=<\X y.X(\x.see(y,x))>, TNS='pres'] sees)
+        (NP[NUM='sg', SEM=<\Q.exists x.(girl(x) & Q(x))>]
+          (Det[NUM='sg', SEM=<\P Q.exists x.(P(x) & Q(x))>] a)
+          (Nom[NUM='sg', SEM=<\x.girl(x)>]
+            (N[NUM='sg', SEM=<\x.girl(x)>] girl)))))
+    Semantics: exists x.(girl(x) & see(john,x))
+    <BLANKLINE>
+    Sentence: every dog barks
+    Parse:
+     (S[SEM=<all x.(dog(x) -> bark(x))>]
+      (NP[NUM='sg', SEM=<\Q.all x.(dog(x) -> Q(x))>]
+        (Det[NUM='sg', SEM=<\P Q.all x.(P(x) -> Q(x))>] every)
+        (Nom[NUM='sg', SEM=<\x.dog(x)>]
+          (N[NUM='sg', SEM=<\x.dog(x)>] dog)))
+      (VP[NUM='sg', SEM=<\x.bark(x)>]
+        (IV[NUM='sg', SEM=<\x.bark(x)>, TNS='pres'] barks)))
+    Semantics: all x.(dog(x) -> bark(x))
+
+    >>> sent = "every dog barks"
+    >>> result = nltk.sem.util.interpret_sents([sent], gramfile)[0]
+    >>> for (syntree, semrep) in result:
+    ...     print(syntree)
+    ...     print()
+    ...     print(semrep)
+    (S[SEM=<all x.(dog(x) -> bark(x))>]
+      (NP[NUM='sg', SEM=<\Q.all x.(dog(x) -> Q(x))>]
+        (Det[NUM='sg', SEM=<\P Q.all x.(P(x) -> Q(x))>] every)
+        (Nom[NUM='sg', SEM=<\x.dog(x)>]
+          (N[NUM='sg', SEM=<\x.dog(x)>] dog)))
+      (VP[NUM='sg', SEM=<\x.bark(x)>]
+        (IV[NUM='sg', SEM=<\x.bark(x)>, TNS='pres'] barks)))
+    <BLANKLINE>
+    all x.(dog(x) -> bark(x))
+
+    >>> result = nltk.sem.util.evaluate_sents([sent], gramfile, m, g)[0]
+    >>> for (syntree, semrel, value) in result:
+    ...     print(syntree)
+    ...     print()
+    ...     print(semrep)
+    ...     print()
+    ...     print(value)
+    (S[SEM=<all x.(dog(x) -> bark(x))>]
+      (NP[NUM='sg', SEM=<\Q.all x.(dog(x) -> Q(x))>]
+        (Det[NUM='sg', SEM=<\P Q.all x.(P(x) -> Q(x))>] every)
+        (Nom[NUM='sg', SEM=<\x.dog(x)>]
+          (N[NUM='sg', SEM=<\x.dog(x)>] dog)))
+      (VP[NUM='sg', SEM=<\x.bark(x)>]
+        (IV[NUM='sg', SEM=<\x.bark(x)>, TNS='pres'] barks)))
+    <BLANKLINE>
+    all x.(dog(x) -> bark(x))
+    <BLANKLINE>
+    True
+
+    >>> sents = ['Mary walks', 'John sees a dog']
+    >>> results = nltk.sem.util.interpret_sents(sents, 'grammars/sample_grammars/sem2.fcfg')
+    >>> for result in results:
+    ...     for (synrep, semrep) in result:
+    ...         print(synrep)
+    (S[SEM=<walk(mary)>]
+      (NP[-LOC, NUM='sg', SEM=<\P.P(mary)>]
+        (PropN[-LOC, NUM='sg', SEM=<\P.P(mary)>] Mary))
+      (VP[NUM='sg', SEM=<\x.walk(x)>]
+        (IV[NUM='sg', SEM=<\x.walk(x)>, TNS='pres'] walks)))
+    (S[SEM=<exists x.(dog(x) & see(john,x))>]
+      (NP[-LOC, NUM='sg', SEM=<\P.P(john)>]
+        (PropN[-LOC, NUM='sg', SEM=<\P.P(john)>] John))
+      (VP[NUM='sg', SEM=<\y.exists x.(dog(x) & see(y,x))>]
+        (TV[NUM='sg', SEM=<\X y.X(\x.see(y,x))>, TNS='pres'] sees)
+        (NP[NUM='sg', SEM=<\Q.exists x.(dog(x) & Q(x))>]
+          (Det[NUM='sg', SEM=<\P Q.exists x.(P(x) & Q(x))>] a)
+          (Nom[NUM='sg', SEM=<\x.dog(x)>]
+            (N[NUM='sg', SEM=<\x.dog(x)>] dog)))))
+
+Cooper Storage
+--------------
+
+    >>> from nltk.sem import cooper_storage as cs
+    >>> sentence = 'every girl chases a dog'
+    >>> trees = cs.parse_with_bindops(sentence, grammar='grammars/book_grammars/storage.fcfg')
+    >>> semrep = trees[0].label()['SEM']
+    >>> cs_semrep = cs.CooperStore(semrep)
+    >>> print(cs_semrep.core)
+    chase(z2,z4)
+    >>> for bo in cs_semrep.store:
+    ...     print(bo)
+    bo(\P.all x.(girl(x) -> P(x)),z2)
+    bo(\P.exists x.(dog(x) & P(x)),z4)
+    >>> cs_semrep.s_retrieve(trace=True)
+    Permutation 1
+       (\P.all x.(girl(x) -> P(x)))(\z2.chase(z2,z4))
+       (\P.exists x.(dog(x) & P(x)))(\z4.all x.(girl(x) -> chase(x,z4)))
+    Permutation 2
+       (\P.exists x.(dog(x) & P(x)))(\z4.chase(z2,z4))
+       (\P.all x.(girl(x) -> P(x)))(\z2.exists x.(dog(x) & chase(z2,x)))
+
+    >>> for reading in cs_semrep.readings:
+    ...     print(reading)
+    exists x.(dog(x) & all z3.(girl(z3) -> chase(z3,x)))
+    all x.(girl(x) -> exists z4.(dog(z4) & chase(x,z4)))

env-llmeval/lib/python3.10/site-packages/nltk/test/setup_fixt.py

@@ -0,0 +1,26 @@
+from nltk.internals import find_binary, find_jar
+
+
+def check_binary(binary: str, **args):
+    """Skip a test via `pytest.skip` if the `binary` executable is not found.
+    Keyword arguments are passed to `nltk.internals.find_binary`."""
+    import pytest
+
+    try:
+        find_binary(binary, **args)
+    except LookupError:
+        pytest.skip(f"Skipping test because the {binary} binary was not found.")
+
+
+def check_jar(name_pattern: str, **args):
+    """Skip a test via `pytest.skip` if the `name_pattern` jar is not found.
+    Keyword arguments are passed to `nltk.internals.find_jar`.
+
+    TODO: Investigate why the CoreNLP tests that rely on this check_jar failed
+    on the CI. https://github.com/nltk/nltk/pull/3060#issuecomment-1268355108
+    """
+    import pytest
+
+    pytest.skip(
+        "Skipping test because the doctests requiring jars are inconsistent on the CI."
+    )

env-llmeval/lib/python3.10/site-packages/nltk/test/tokenize.doctest

@@ -0,0 +1,397 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+    >>> from nltk.tokenize import *
+
+Regression Tests: NLTKWordTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Tokenizing some test strings.
+
+    >>> s1 = "On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88."
+    >>> word_tokenize(s1)
+    ['On', 'a', '$', '50,000', 'mortgage', 'of', '30', 'years', 'at', '8', 'percent', ',', 'the', 'monthly', 'payment', 'would', 'be', '$', '366.88', '.']
+    >>> s2 = "\"We beat some pretty good teams to get here,\" Slocum said."
+    >>> word_tokenize(s2)
+    ['``', 'We', 'beat', 'some', 'pretty', 'good', 'teams', 'to', 'get', 'here', ',', "''", 'Slocum', 'said', '.']
+    >>> s3 = "Well, we couldn't have this predictable, cliche-ridden, \"Touched by an Angel\" (a show creator John Masius worked on) wanna-be if she didn't."
+    >>> word_tokenize(s3)
+    ['Well', ',', 'we', 'could', "n't", 'have', 'this', 'predictable', ',', 'cliche-ridden', ',', '``', 'Touched', 'by', 'an', 'Angel', "''", '(', 'a', 'show', 'creator', 'John', 'Masius', 'worked', 'on', ')', 'wanna-be', 'if', 'she', 'did', "n't", '.']
+    >>> s4 = "I cannot cannot work under these conditions!"
+    >>> word_tokenize(s4)
+    ['I', 'can', 'not', 'can', 'not', 'work', 'under', 'these', 'conditions', '!']
+    >>> s5 = "The company spent $30,000,000 last year."
+    >>> word_tokenize(s5)
+    ['The', 'company', 'spent', '$', '30,000,000', 'last', 'year', '.']
+    >>> s6 = "The company spent 40.75% of its income last year."
+    >>> word_tokenize(s6)
+    ['The', 'company', 'spent', '40.75', '%', 'of', 'its', 'income', 'last', 'year', '.']
+    >>> s7 = "He arrived at 3:00 pm."
+    >>> word_tokenize(s7)
+    ['He', 'arrived', 'at', '3:00', 'pm', '.']
+    >>> s8 = "I bought these items: books, pencils, and pens."
+    >>> word_tokenize(s8)
+    ['I', 'bought', 'these', 'items', ':', 'books', ',', 'pencils', ',', 'and', 'pens', '.']
+    >>> s9 = "Though there were 150, 100 of them were old."
+    >>> word_tokenize(s9)
+    ['Though', 'there', 'were', '150', ',', '100', 'of', 'them', 'were', 'old', '.']
+    >>> s10 = "There were 300,000, but that wasn't enough."
+    >>> word_tokenize(s10)
+    ['There', 'were', '300,000', ',', 'but', 'that', 'was', "n't", 'enough', '.']
+    >>> s11 = "It's more'n enough."
+    >>> word_tokenize(s11)
+    ['It', "'s", 'more', "'n", 'enough', '.']
+
+Gathering the spans of the tokenized strings.
+
+    >>> s = '''Good muffins cost $3.88\nin New (York).  Please (buy) me\ntwo of them.\n(Thanks).'''
+    >>> expected = [(0, 4), (5, 12), (13, 17), (18, 19), (19, 23),
+    ... (24, 26), (27, 30), (31, 32), (32, 36), (36, 37), (37, 38),
+    ... (40, 46), (47, 48), (48, 51), (51, 52), (53, 55), (56, 59),
+    ... (60, 62), (63, 68), (69, 70), (70, 76), (76, 77), (77, 78)]
+    >>> list(NLTKWordTokenizer().span_tokenize(s)) == expected
+    True
+    >>> expected = ['Good', 'muffins', 'cost', '$', '3.88', 'in',
+    ... 'New', '(', 'York', ')', '.', 'Please', '(', 'buy', ')',
+    ... 'me', 'two', 'of', 'them.', '(', 'Thanks', ')', '.']
+    >>> [s[start:end] for start, end in NLTKWordTokenizer().span_tokenize(s)] == expected
+    True
+
+    >>> s = '''I said, "I'd like to buy some ''good muffins" which cost $3.88\n each in New (York)."'''
+    >>> expected = [(0, 1), (2, 6), (6, 7), (8, 9), (9, 10), (10, 12),
+    ... (13, 17), (18, 20), (21, 24), (25, 29), (30, 32), (32, 36),
+    ... (37, 44), (44, 45), (46, 51), (52, 56), (57, 58), (58, 62),
+    ... (64, 68), (69, 71), (72, 75), (76, 77), (77, 81), (81, 82),
+    ... (82, 83), (83, 84)]
+    >>> list(NLTKWordTokenizer().span_tokenize(s)) == expected
+    True
+    >>> expected = ['I', 'said', ',', '"', 'I', "'d", 'like', 'to',
+    ... 'buy', 'some', "''", "good", 'muffins', '"', 'which', 'cost',
+    ... '$', '3.88', 'each', 'in', 'New', '(', 'York', ')', '.', '"']
+    >>> [s[start:end] for start, end in NLTKWordTokenizer().span_tokenize(s)] == expected
+    True
+
+Testing improvement made to the TreebankWordTokenizer
+
+    >>> sx1 = '\xabNow that I can do.\xbb'
+    >>> expected = ['\xab', 'Now', 'that', 'I', 'can', 'do', '.', '\xbb']
+    >>> word_tokenize(sx1) == expected
+    True
+    >>> sx2 = 'The unicode 201C and 201D \u201cLEFT(RIGHT) DOUBLE QUOTATION MARK\u201d is also OPEN_PUNCT and CLOSE_PUNCT.'
+    >>> expected = ['The', 'unicode', '201C', 'and', '201D', '\u201c', 'LEFT', '(', 'RIGHT', ')', 'DOUBLE', 'QUOTATION', 'MARK', '\u201d', 'is', 'also', 'OPEN_PUNCT', 'and', 'CLOSE_PUNCT', '.']
+    >>> word_tokenize(sx2) == expected
+    True
+
+
+Testing treebank's detokenizer
+
+    >>> from nltk.tokenize.treebank import TreebankWordDetokenizer
+    >>> detokenizer = TreebankWordDetokenizer()
+    >>> s = "On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'On a $50,000 mortgage of 30 years at 8 percent, the monthly payment would be $366.88.'
+    >>> s = "\"We beat some pretty good teams to get here,\" Slocum said."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    '"We beat some pretty good teams to get here," Slocum said.'
+    >>> s = "Well, we couldn't have this predictable, cliche-ridden, \"Touched by an Angel\" (a show creator John Masius worked on) wanna-be if she didn't."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'Well, we couldn\'t have this predictable, cliche-ridden, "Touched by an Angel" (a show creator John Masius worked on) wanna-be if she didn\'t.'
+    >>> s = "I cannot cannot work under these conditions!"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'I cannot cannot work under these conditions!'
+    >>> s = "The company spent $30,000,000 last year."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'The company spent $30,000,000 last year.'
+    >>> s = "The company spent 40.75% of its income last year."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'The company spent 40.75% of its income last year.'
+    >>> s = "He arrived at 3:00 pm."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'He arrived at 3:00 pm.'
+    >>> s = "I bought these items: books, pencils, and pens."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'I bought these items: books, pencils, and pens.'
+    >>> s = "Though there were 150, 100 of them were old."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'Though there were 150, 100 of them were old.'
+    >>> s = "There were 300,000, but that wasn't enough."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    "There were 300,000, but that wasn't enough."
+    >>> s = 'How "are" you?'
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'How "are" you?'
+    >>> s = "Hello (world)"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'Hello (world)'
+    >>> s = '<A sentence> with (many) [kinds] of {parentheses}. "Sometimes it\'s inside (quotes)". ("Sometimes the otherway around").'
+    >>> detokenizer.detokenize(word_tokenize(s))
+    '<A sentence> with (many) [kinds] of {parentheses}. "Sometimes it\'s inside (quotes)". ("Sometimes the otherway around").'
+    >>> s = "Sentence ending with (parentheses)"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    'Sentence ending with (parentheses)'
+    >>> s = "(Sentence) starting with parentheses."
+    >>> detokenizer.detokenize(word_tokenize(s))
+    '(Sentence) starting with parentheses.'
+    >>> s = "I've"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    "I've"
+    >>> s = "Don't"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    "Don't"
+    >>> s = "I'd"
+    >>> detokenizer.detokenize(word_tokenize(s))
+    "I'd"
+
+
+Sentence tokenization in word_tokenize:
+
+    >>> s11 = "I called Dr. Jones. I called Dr. Jones."
+    >>> word_tokenize(s11)
+    ['I', 'called', 'Dr.', 'Jones', '.', 'I', 'called', 'Dr.', 'Jones', '.']
+    >>> s12 = ("Ich muss unbedingt daran denken, Mehl, usw. fur einen "
+    ...        "Kuchen einzukaufen. Ich muss.")
+    >>> word_tokenize(s12)
+    ['Ich', 'muss', 'unbedingt', 'daran', 'denken', ',', 'Mehl', ',', 'usw',
+     '.', 'fur', 'einen', 'Kuchen', 'einzukaufen', '.', 'Ich', 'muss', '.']
+    >>> word_tokenize(s12, 'german')
+    ['Ich', 'muss', 'unbedingt', 'daran', 'denken', ',', 'Mehl', ',', 'usw.',
+     'fur', 'einen', 'Kuchen', 'einzukaufen', '.', 'Ich', 'muss', '.']
+
+
+Regression Tests: Regexp Tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Some additional test strings.
+
+    >>> s = ("Good muffins cost $3.88\nin New York.  Please buy me\n"
+    ...      "two of them.\n\nThanks.")
+    >>> s2 = ("Alas, it has not rained today. When, do you think, "
+    ...       "will it rain again?")
+    >>> s3 = ("<p>Although this is <b>not</b> the case here, we must "
+    ...       "not relax our vigilance!</p>")
+
+    >>> regexp_tokenize(s2, r'[,\.\?!"]\s*', gaps=False)
+    [', ', '. ', ', ', ', ', '?']
+    >>> regexp_tokenize(s2, r'[,\.\?!"]\s*', gaps=True)
+    ['Alas', 'it has not rained today', 'When', 'do you think',
+     'will it rain again']
+
+Take care to avoid using capturing groups:
+
+    >>> regexp_tokenize(s3, r'</?[bp]>', gaps=False)
+    ['<p>', '<b>', '</b>', '</p>']
+    >>> regexp_tokenize(s3, r'</?(?:b|p)>', gaps=False)
+    ['<p>', '<b>', '</b>', '</p>']
+    >>> regexp_tokenize(s3, r'</?(?:b|p)>', gaps=True)
+    ['Although this is ', 'not',
+     ' the case here, we must not relax our vigilance!']
+
+Named groups are capturing groups, and confuse the tokenizer:
+
+    >>> regexp_tokenize(s3, r'</?(?P<named>b|p)>', gaps=False)
+    ['p', 'b', 'b', 'p']
+    >>> regexp_tokenize(s3, r'</?(?P<named>b|p)>', gaps=True)
+    ['p', 'Although this is ', 'b', 'not', 'b',
+     ' the case here, we must not relax our vigilance!', 'p']
+
+Make sure that nested groups don't confuse the tokenizer:
+
+    >>> regexp_tokenize(s2, r'(?:h|r|l)a(?:s|(?:i|n0))', gaps=False)
+    ['las', 'has', 'rai', 'rai']
+    >>> regexp_tokenize(s2, r'(?:h|r|l)a(?:s|(?:i|n0))', gaps=True)
+    ['A', ', it ', ' not ', 'ned today. When, do you think, will it ',
+     'n again?']
+
+Back-references require capturing groups, and these are not supported:
+
+    >>> regexp_tokenize("aabbbcccc", r'(.)\1')
+    ['a', 'b', 'c', 'c']
+
+A simple sentence tokenizer '\.(\s+|$)'
+
+    >>> regexp_tokenize(s, pattern=r'\.(?:\s+|$)', gaps=True)
+    ['Good muffins cost $3.88\nin New York',
+     'Please buy me\ntwo of them', 'Thanks']
+
+
+Regression Tests: TweetTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TweetTokenizer is a tokenizer specifically designed for micro-blogging tokenization tasks.
+
+    >>> from nltk.tokenize import TweetTokenizer
+    >>> tknzr = TweetTokenizer()
+    >>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
+    >>> tknzr.tokenize(s0)
+    ['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
+    >>> s1 = "@Joyster2012 @CathStaincliffe Good for you, girl!! Best wishes :-)"
+    >>> tknzr.tokenize(s1)
+    ['@Joyster2012', '@CathStaincliffe', 'Good', 'for', 'you', ',', 'girl', '!', '!', 'Best', 'wishes', ':-)']
+    >>> s2 = "3Points for #DreamTeam Gooo BAILEY! :) #PBB737Gold @PBBabscbn"
+    >>> tknzr.tokenize(s2)
+    ['3Points', 'for', '#DreamTeam', 'Gooo', 'BAILEY', '!', ':)', '#PBB737Gold', '@PBBabscbn']
+    >>> s3 = "@Insanomania They do... Their mentality doesn't :("
+    >>> tknzr.tokenize(s3)
+    ['@Insanomania', 'They', 'do', '...', 'Their', 'mentality', "doesn't", ':(']
+    >>> s4 = "RT @facugambande: Ya por arrancar a grabar !!! #TirenTirenTiren vamoo !!"
+    >>> tknzr.tokenize(s4)
+    ['RT', '@facugambande', ':', 'Ya', 'por', 'arrancar', 'a', 'grabar', '!', '!', '!', '#TirenTirenTiren', 'vamoo', '!', '!']
+    >>> tknzr = TweetTokenizer(reduce_len=True)
+    >>> s5 = "@crushinghes the summer holidays are great but I'm so bored already :("
+    >>> tknzr.tokenize(s5)
+    ['@crushinghes', 'the', 'summer', 'holidays', 'are', 'great', 'but', "I'm", 'so', 'bored', 'already', ':(']
+
+It is possible to specify `strip_handles` and `reduce_len` parameters for a TweetTokenizer instance. Setting `strip_handles` to True, the tokenizer will remove Twitter handles (e.g. usernames). Setting `reduce_len` to True, repeated character sequences of length 3 or greater will be replaced with sequences of length 3.
+
+    >>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
+    >>> s6 = '@remy: This is waaaaayyyy too much for you!!!!!!'
+    >>> tknzr.tokenize(s6)
+    [':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
+    >>> s7 = '@_willy65: No place for @chuck tonight. Sorry.'
+    >>> tknzr.tokenize(s7)
+    [':', 'No', 'place', 'for', 'tonight', '.', 'Sorry', '.']
+    >>> s8 = '@mar_tin is a great developer. Contact him at [email protected].'
+    >>> tknzr.tokenize(s8)
+    ['is', 'a', 'great', 'developer', '.', 'Contact', 'him', 'at', '[email protected]', '.']
+
+The `preserve_case` parameter (default: True) allows to convert uppercase tokens to lowercase tokens. Emoticons are not affected:
+
+    >>> tknzr = TweetTokenizer(preserve_case=False)
+    >>> s9 = "@jrmy: I'm REALLY HAPPYYY about that! NICEEEE :D :P"
+    >>> tknzr.tokenize(s9)
+    ['@jrmy', ':', "i'm", 'really', 'happyyy', 'about', 'that', '!', 'niceeee', ':D', ':P']
+
+It should not hang on long sequences of the same punctuation character.
+
+    >>> tknzr = TweetTokenizer()
+    >>> s10 = "Photo: Aujourd'hui sur http://t.co/0gebOFDUzn Projet... http://t.co/bKfIUbydz2.............................. http://fb.me/3b6uXpz0L"
+    >>> tknzr.tokenize(s10)
+    ['Photo', ':', "Aujourd'hui", 'sur', 'http://t.co/0gebOFDUzn', 'Projet', '...', 'http://t.co/bKfIUbydz2', '...', 'http://fb.me/3b6uXpz0L']
+
+Tokenizing multiple sentences at once:
+
+    >>> tknzr = TweetTokenizer()
+    >>> sentences = [
+    ...     "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--",
+    ...     "@jrmy: I'm REALLY HAPPYYY about that! NICEEEE :D :P",
+    ...     "@_willy65: No place for @chuck tonight. Sorry."
+    ... ]
+    >>> tknzr.tokenize_sents(sentences) # doctest: +NORMALIZE_WHITESPACE
+    [['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--'],
+    ['@jrmy', ':', "I'm", 'REALLY', 'HAPPYYY', 'about', 'that', '!', 'NICEEEE', ':D', ':P'],
+    ['@_willy65', ':', 'No', 'place', 'for', '@chuck', 'tonight', '.', 'Sorry', '.']]
+
+
+Regression Tests: PunktSentenceTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The sentence splitter should remove whitespace following the sentence boundary.
+
+    >>> pst = PunktSentenceTokenizer()
+    >>> pst.tokenize('See Section 3).  Or Section 2).  ')
+    ['See Section 3).', 'Or Section 2).']
+    >>> pst.tokenize('See Section 3.)  Or Section 2.)  ')
+    ['See Section 3.)', 'Or Section 2.)']
+    >>> pst.tokenize('See Section 3.)  Or Section 2.)  ', realign_boundaries=False)
+    ['See Section 3.', ')  Or Section 2.', ')']
+
+
+Two instances of PunktSentenceTokenizer should not share PunktParameters.
+
+    >>> pst = PunktSentenceTokenizer()
+    >>> pst2 = PunktSentenceTokenizer()
+    >>> pst._params is pst2._params
+    False
+
+Testing mutable default arguments for https://github.com/nltk/nltk/pull/2067
+
+    >>> from nltk.tokenize.punkt import PunktBaseClass, PunktTrainer, PunktSentenceTokenizer
+    >>> from nltk.tokenize.punkt import PunktLanguageVars, PunktParameters
+    >>> pbc = PunktBaseClass(lang_vars=None, params=None)
+    >>> type(pbc._params)
+    <class 'nltk.tokenize.punkt.PunktParameters'>
+    >>> type(pbc._lang_vars)
+    <class 'nltk.tokenize.punkt.PunktLanguageVars'>
+    >>> pt = PunktTrainer(lang_vars=None)
+    >>> type(pt._lang_vars)
+    <class 'nltk.tokenize.punkt.PunktLanguageVars'>
+    >>> pst = PunktSentenceTokenizer(lang_vars=None)
+    >>> type(pst._lang_vars)
+    <class 'nltk.tokenize.punkt.PunktLanguageVars'>
+
+Testing that inputs can start with dots.
+
+    >>> pst = PunktSentenceTokenizer(lang_vars=None)
+    >>> pst.tokenize(". This input starts with a dot. This used to cause issues.")
+    ['.', 'This input starts with a dot.', 'This used to cause issues.']
+
+Regression Tests: align_tokens
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Post-hoc alignment of tokens with a source string
+
+    >>> from nltk.tokenize.util import align_tokens
+    >>> list(align_tokens([''], ""))
+    [(0, 0)]
+    >>> list(align_tokens([''], " "))
+    [(0, 0)]
+    >>> list(align_tokens([], ""))
+    []
+    >>> list(align_tokens([], " "))
+    []
+    >>> list(align_tokens(['a'], "a"))
+    [(0, 1)]
+    >>> list(align_tokens(['abc', 'def'], "abcdef"))
+    [(0, 3), (3, 6)]
+    >>> list(align_tokens(['abc', 'def'], "abc def"))
+    [(0, 3), (4, 7)]
+    >>> list(align_tokens(['ab', 'cd'], "ab cd ef"))
+    [(0, 2), (3, 5)]
+    >>> list(align_tokens(['ab', 'cd', 'ef'], "ab cd ef"))
+    [(0, 2), (3, 5), (6, 8)]
+    >>> list(align_tokens(['ab', 'cd', 'efg'], "ab cd ef"))
+    Traceback (most recent call last):
+    ....
+    ValueError: substring "efg" not found in "ab cd ef"
+    >>> list(align_tokens(['ab', 'cd', 'ef', 'gh'], "ab cd ef"))
+    Traceback (most recent call last):
+    ....
+    ValueError: substring "gh" not found in "ab cd ef"
+    >>> list(align_tokens(['The', 'plane', ',', 'bound', 'for', 'St', 'Petersburg', ',', 'crashed', 'in', 'Egypt', "'s", 'Sinai', 'desert', 'just', '23', 'minutes', 'after', 'take-off', 'from', 'Sharm', 'el-Sheikh', 'on', 'Saturday', '.'], "The plane, bound for St Petersburg, crashed in Egypt's Sinai desert just 23 minutes after take-off from Sharm el-Sheikh on Saturday."))
+    [(0, 3), (4, 9), (9, 10), (11, 16), (17, 20), (21, 23), (24, 34), (34, 35), (36, 43), (44, 46), (47, 52), (52, 54), (55, 60), (61, 67), (68, 72), (73, 75), (76, 83), (84, 89), (90, 98), (99, 103), (104, 109), (110, 119), (120, 122), (123, 131), (131, 132)]
+
+
+Regression Tests: MWETokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Pickle an MWETokenizer
+
+    >>> from nltk.tokenize import MWETokenizer
+    >>> import pickle
+
+    >>> tokenizer = MWETokenizer([('hors', "d'oeuvre")], separator='+')
+    >>> p = pickle.dumps(tokenizer)
+    >>> unpickeled = pickle.loads(p)
+    >>> unpickeled.tokenize("An hors d'oeuvre tonight, sir?".split())
+    ['An', "hors+d'oeuvre", 'tonight,', 'sir?']
+
+
+Regression Tests: TextTilingTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TextTilingTokenizer tokenizes text into coherent subtopic chunks based upon Hearst's TextTiling algorithm.
+
+    >>> from nltk.tokenize import TextTilingTokenizer
+    >>> from nltk.corpus import brown
+    >>> tt = TextTilingTokenizer()
+    >>> tt.tokenize(brown.raw()[0:1000])
+    ["\n\n\tThe/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at investigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd ``/`` no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.\n\n\n\tThe/at jury/nn further/rbr said/vbd in/in term-end/nn presentments/nns that/cs the/at City/nn-tl Executive/jj-tl Committee/nn-tl ,/, which/wdt had/hvd over-all/jj charge/nn of/in the/at election/nn ,/, ``/`` deserves/vbz the/at praise/nn and/cc thanks/nns of/in the/at City/nn-tl of/in-tl Atlanta/np-tl ''/'' for/in the/at manner/nn in/in which/wdt the/at election/nn was/bedz conducted/vbn ./.\n\n\n\tThe/at September-October/np term/nn jury/nn had/hvd been/ben charged/vbn by/in Fulton/np-tl Superior/jj-tl Court/nn-tl Judge/nn-tl Durwood/np Pye/np to/to investigate/vb reports/nns of/in possible/jj ``/`` irregularities/nns ''/'' in/in the/at hard-fought/jj primary/nn which/wdt was/bedz won/vbn by/in Mayor-nominate/nn-tl Ivan/np Allen/np Jr./"]
+
+Test that `ValueError` exceptions are raised when illegal arguments are used.
+
+    >>> TextTilingTokenizer(similarity_method='foo').tokenize(brown.raw()[0:1000])
+    Traceback (most recent call last):
+      ...
+    ValueError: Similarity method foo not recognized
+    >>> TextTilingTokenizer(smoothing_method='bar').tokenize(brown.raw()[0:1000])
+    Traceback (most recent call last):
+      ...
+    ValueError: Smoothing method bar not recognized

env-llmeval/lib/python3.10/site-packages/nltk/test/toolbox.doctest

@@ -0,0 +1,306 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+===============================
+Unit test cases for ``toolbox``
+===============================
+
+    >>> from nltk import toolbox
+
+--------------------------
+``toolbox.StandardFormat``
+--------------------------
+
+    >>> f = toolbox.StandardFormat()
+
+``toolbox.StandardFormat.open()``
+---------------------------------
+    >>> import os, tempfile
+    >>> (fd, fname) = tempfile.mkstemp()
+    >>> tf = os.fdopen(fd, "w")
+    >>> _ = tf.write('\\lx a value\n\\lx another value\n')
+    >>> tf.close()
+    >>> f = toolbox.StandardFormat()
+    >>> f.open(fname)
+    >>> list(f.fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+    >>> f.close()
+    >>> os.unlink(fname)
+
+``toolbox.StandardFormat.open_string()``
+----------------------------------------
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\\lx another value\n')
+    >>> list(f.fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+    >>> f.close()
+
+``toolbox.StandardFormat.close()``
+----------------------------------
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\\lx another value\n')
+    >>> list(f.fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+    >>> f.close()
+
+``toolbox.StandardFormat.line_num``
+---------------------------------------
+
+``StandardFormat.line_num`` contains the line number of the last line returned:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\\lx another value\n\\lx a third value\n')
+    >>> line_nums = []
+    >>> for l in f.raw_fields():
+    ...     line_nums.append(f.line_num)
+    >>> line_nums
+    [1, 2, 3]
+
+``StandardFormat.line_num`` contains the line number of the last line returned:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx two\nlines\n\\lx three\nlines\n\n\\lx two\nlines\n')
+    >>> line_nums = []
+    >>> for l in f.raw_fields():
+    ...     line_nums.append(f.line_num)
+    >>> line_nums
+    [2, 5, 7]
+
+``StandardFormat.line_num`` doesn't exist before opening or after closing
+a file or string:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.line_num
+    Traceback (most recent call last):
+        ...
+    AttributeError: 'StandardFormat' object has no attribute 'line_num'
+    >>> f.open_string('\\lx two\nlines\n\\lx three\nlines\n\n\\lx two\nlines\n')
+    >>> line_nums = []
+    >>> for l in f.raw_fields():
+    ...     line_nums.append(f.line_num)
+    >>> line_nums
+    [2, 5, 7]
+    >>> f.close()
+    >>> f.line_num
+    Traceback (most recent call last):
+        ...
+    AttributeError: 'StandardFormat' object has no attribute 'line_num'
+
+``toolbox.StandardFormat.raw_fields()``
+---------------------------------------
+``raw_fields()`` returns an iterator over tuples of two strings representing the
+marker and its value. The marker is given without the backslash and the value
+without its trailing newline:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\\lx another value\n')
+    >>> list(f.raw_fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+
+an empty file returns nothing:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('')
+    >>> list(f.raw_fields())
+    []
+
+file with only a newline returns WHAT SHOULD IT RETURN???:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\n')
+    >>> list(f.raw_fields())
+    [(None, '')]
+
+file with only one field should be parsed ok:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx one value\n')
+    >>> list(f.raw_fields())
+    [('lx', 'one value')]
+
+file without a trailing newline should be parsed ok:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\\lx another value')
+    >>> list(f.raw_fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+
+trailing white space is preserved except for the final newline:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx trailing space \n\\lx trailing tab\t\n\\lx extra newline\n\n')
+    >>> list(f.raw_fields())
+    [('lx', 'trailing space '), ('lx', 'trailing tab\t'), ('lx', 'extra newline\n')]
+
+line wrapping is preserved:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n')
+    >>> list(f.raw_fields())
+    [('lx', 'a value\nmore of the value\nand still more'), ('lc', 'another val')]
+
+file beginning with a multiline record should be parsed ok:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n')
+    >>> list(f.raw_fields())
+    [('lx', 'a value\nmore of the value\nand still more'), ('lc', 'another val')]
+
+file ending with a multiline record should be parsed ok:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lc a value\n\\lx another value\nmore of the value\nand still more\n')
+    >>> list(f.raw_fields())
+    [('lc', 'a value'), ('lx', 'another value\nmore of the value\nand still more')]
+
+file beginning with a BOM should be parsed ok:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\xef\xbb\xbf\\lx a value\n\\lx another value\n')
+    >>> list(f.raw_fields())
+    [('lx', 'a value'), ('lx', 'another value')]
+
+file beginning with two BOMs should ignore only the first one:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\xef\xbb\xbf\xef\xbb\xbf\\lx a value\n\\lx another value\n')
+    >>> list(f.raw_fields())
+    [(None, '\xef\xbb\xbf\\lx a value'), ('lx', 'another value')]
+
+should not ignore a BOM not at the beginning of the file:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\n\xef\xbb\xbf\\lx another value\n')
+    >>> list(f.raw_fields())
+    [('lx', 'a value\n\xef\xbb\xbf\\lx another value')]
+
+``toolbox.StandardFormat.fields()``
+-----------------------------------
+trailing white space is not preserved:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx trailing space \n\\lx trailing tab\t\n\\lx extra newline\n\n')
+    >>> list(f.fields())
+    [('lx', 'trailing space'), ('lx', 'trailing tab'), ('lx', 'extra newline')]
+
+multiline fields are unwrapped:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\lx a value\nmore of the value\nand still more\n\\lc another val\n')
+    >>> list(f.fields())
+    [('lx', 'a value more of the value and still more'), ('lc', 'another val')]
+
+markers
+-------
+A backslash in the first position on a new line indicates the start of a
+marker. The backslash is not part of the marker:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\mk a value\n')
+    >>> list(f.fields())
+    [('mk', 'a value')]
+
+If the backslash occurs later in the line it does not indicate the start
+of a marker:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\mk a value\n \\mk another one\n')
+    >>> list(f.raw_fields())
+    [('mk', 'a value\n \\mk another one')]
+
+There is no specific limit to the length of a marker:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\this_is_an_extremely_long_marker value\n')
+    >>> list(f.fields())
+    [('this_is_an_extremely_long_marker', 'value')]
+
+A marker can contain any non white space character:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\`~!@#$%^&*()_-=+[{]}\\|,<.>/?;:"0123456789 value\n')
+    >>> list(f.fields())
+    [('`~!@#$%^&*()_-=+[{]}\\|,<.>/?;:"0123456789', 'value')]
+
+A marker is terminated by any white space character:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\mk a value\n\\mk\tanother one\n\\mk\rthird one\n\\mk\ffourth one')
+    >>> list(f.fields())
+    [('mk', 'a value'), ('mk', 'another one'), ('mk', 'third one'), ('mk', 'fourth one')]
+
+Consecutive whitespace characters (except newline) are treated the same as one:
+
+    >>> f = toolbox.StandardFormat()
+    >>> f.open_string('\\mk \t\r\fa value\n')
+    >>> list(f.fields())
+    [('mk', 'a value')]
+
+-----------------------
+``toolbox.ToolboxData``
+-----------------------
+
+    >>> db = toolbox.ToolboxData()
+
+``toolbox.ToolboxData.parse()``
+-------------------------------
+check that normal parsing works:
+
+    >>> from xml.etree import ElementTree
+    >>> td = toolbox.ToolboxData()
+    >>> s = """\\_sh v3.0  400  Rotokas Dictionary
+    ... \\_DateStampHasFourDigitYear
+    ...
+    ... \\lx kaa
+    ... \\ps V.A
+    ... \\ge gag
+    ... \\gp nek i pas
+    ...
+    ... \\lx kaa
+    ... \\ps V.B
+    ... \\ge strangle
+    ... \\gp pasim nek
+    ... """
+    >>> td.open_string(s)
+    >>> tree = td.parse(key='lx')
+    >>> tree.tag
+    'toolbox_data'
+    >>> ElementTree.tostring(list(tree)[0]).decode('utf8')
+    '<header><_sh>v3.0  400  Rotokas Dictionary</_sh><_DateStampHasFourDigitYear /></header>'
+    >>> ElementTree.tostring(list(tree)[1]).decode('utf8')
+    '<record><lx>kaa</lx><ps>V.A</ps><ge>gag</ge><gp>nek i pas</gp></record>'
+    >>> ElementTree.tostring(list(tree)[2]).decode('utf8')
+    '<record><lx>kaa</lx><ps>V.B</ps><ge>strangle</ge><gp>pasim nek</gp></record>'
+
+check that guessing the key marker works:
+
+    >>> from xml.etree import ElementTree
+    >>> td = toolbox.ToolboxData()
+    >>> s = """\\_sh v3.0  400  Rotokas Dictionary
+    ... \\_DateStampHasFourDigitYear
+    ...
+    ... \\lx kaa
+    ... \\ps V.A
+    ... \\ge gag
+    ... \\gp nek i pas
+    ...
+    ... \\lx kaa
+    ... \\ps V.B
+    ... \\ge strangle
+    ... \\gp pasim nek
+    ... """
+    >>> td.open_string(s)
+    >>> tree = td.parse()
+    >>> ElementTree.tostring(list(tree)[0]).decode('utf8')
+    '<header><_sh>v3.0  400  Rotokas Dictionary</_sh><_DateStampHasFourDigitYear /></header>'
+    >>> ElementTree.tostring(list(tree)[1]).decode('utf8')
+    '<record><lx>kaa</lx><ps>V.A</ps><ge>gag</ge><gp>nek i pas</gp></record>'
+    >>> ElementTree.tostring(list(tree)[2]).decode('utf8')
+    '<record><lx>kaa</lx><ps>V.B</ps><ge>strangle</ge><gp>pasim nek</gp></record>'
+
+-----------------------
+``toolbox`` functions
+-----------------------
+
+``toolbox.to_sfm_string()``
+-------------------------------

env-llmeval/lib/python3.10/site-packages/nltk/test/tree.doctest

@@ -0,0 +1,1223 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+===============================
+ Unit tests for nltk.tree.Tree
+===============================
+
+    >>> from nltk.tree import *
+
+Some trees to run tests on:
+
+    >>> dp1 = Tree('dp', [Tree('d', ['the']), Tree('np', ['dog'])])
+    >>> dp2 = Tree('dp', [Tree('d', ['the']), Tree('np', ['cat'])])
+    >>> vp = Tree('vp', [Tree('v', ['chased']), dp2])
+    >>> tree = Tree('s', [dp1, vp])
+    >>> print(tree)
+    (s (dp (d the) (np dog)) (vp (v chased) (dp (d the) (np cat))))
+
+The node label is accessed using the `label()` method:
+
+    >>> dp1.label(), dp2.label(), vp.label(), tree.label()
+    ('dp', 'dp', 'vp', 's')
+
+    >>> print(tree[1,1,1,0])
+    cat
+
+The `treepositions` method returns a list of the tree positions of
+subtrees and leaves in a tree.  By default, it gives the position of
+every tree, subtree, and leaf, in prefix order:
+
+    >>> print(tree.treepositions())
+    [(), (0,), (0, 0), (0, 0, 0), (0, 1), (0, 1, 0), (1,), (1, 0), (1, 0, 0), (1, 1), (1, 1, 0), (1, 1, 0, 0), (1, 1, 1), (1, 1, 1, 0)]
+
+In addition to `str` and `repr`, several methods exist to convert a
+tree object to one of several standard tree encodings:
+
+    >>> print(tree.pformat_latex_qtree())
+    \Tree [.s
+            [.dp [.d the ] [.np dog ] ]
+            [.vp [.v chased ] [.dp [.d the ] [.np cat ] ] ] ]
+
+There is also a fancy ASCII art representation:
+
+    >>> tree.pretty_print()
+                  s
+          ________|_____
+         |              vp
+         |         _____|___
+         dp       |         dp
+      ___|___     |      ___|___
+     d       np   v     d       np
+     |       |    |     |       |
+    the     dog chased the     cat
+
+    >>> tree.pretty_print(unicodelines=True, nodedist=4)
+                           s
+            ┌──────────────┴────────┐
+            │                       vp
+            │              ┌────────┴──────┐
+            dp             │               dp
+     ┌──────┴──────┐       │        ┌──────┴──────┐
+     d             np      v        d             np
+     │             │       │        │             │
+    the           dog    chased    the           cat
+
+Trees can be initialized from treebank strings:
+
+    >>> tree2 = Tree.fromstring('(S (NP I) (VP (V enjoyed) (NP my cookie)))')
+    >>> print(tree2)
+    (S (NP I) (VP (V enjoyed) (NP my cookie)))
+
+Trees can be compared for equality:
+
+    >>> tree == Tree.fromstring(str(tree))
+    True
+    >>> tree2 == Tree.fromstring(str(tree2))
+    True
+    >>> tree == tree2
+    False
+    >>> tree == Tree.fromstring(str(tree2))
+    False
+    >>> tree2 == Tree.fromstring(str(tree))
+    False
+
+    >>> tree != Tree.fromstring(str(tree))
+    False
+    >>> tree2 != Tree.fromstring(str(tree2))
+    False
+    >>> tree != tree2
+    True
+    >>> tree != Tree.fromstring(str(tree2))
+    True
+    >>> tree2 != Tree.fromstring(str(tree))
+    True
+
+    >>> tree < tree2 or tree > tree2
+    True
+
+Tree Parsing
+============
+
+The class method `Tree.fromstring()` can be used to parse trees, and it
+provides some additional options.
+
+    >>> tree = Tree.fromstring('(S (NP I) (VP (V enjoyed) (NP my cookie)))')
+    >>> print(tree)
+    (S (NP I) (VP (V enjoyed) (NP my cookie)))
+
+When called on a subclass of `Tree`, it will create trees of that
+type:
+
+    >>> tree = ImmutableTree.fromstring('(VP (V enjoyed) (NP my cookie))')
+    >>> print(tree)
+    (VP (V enjoyed) (NP my cookie))
+    >>> print(type(tree))
+    <class 'nltk.tree.immutable.ImmutableTree'>
+    >>> tree[1] = 'x'
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableTree may not be modified
+    >>> del tree[0]
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableTree may not be modified
+
+The ``brackets`` parameter can be used to specify two characters that
+should be used as brackets:
+
+    >>> print(Tree.fromstring('[S [NP I] [VP [V enjoyed] [NP my cookie]]]',
+    ...                  brackets='[]'))
+    (S (NP I) (VP (V enjoyed) (NP my cookie)))
+    >>> print(Tree.fromstring('<S <NP I> <VP <V enjoyed> <NP my cookie>>>',
+    ...                  brackets='<>'))
+    (S (NP I) (VP (V enjoyed) (NP my cookie)))
+
+If ``brackets`` is not a string, or is not exactly two characters,
+then `Tree.fromstring` raises an exception:
+
+    >>> Tree.fromstring('<VP <V enjoyed> <NP my cookie>>', brackets='')
+    Traceback (most recent call last):
+      . . .
+    TypeError: brackets must be a length-2 string
+    >>> Tree.fromstring('<VP <V enjoyed> <NP my cookie>>', brackets='<<>>')
+    Traceback (most recent call last):
+      . . .
+    TypeError: brackets must be a length-2 string
+    >>> Tree.fromstring('<VP <V enjoyed> <NP my cookie>>', brackets=12)
+    Traceback (most recent call last):
+      . . .
+    TypeError: brackets must be a length-2 string
+    >>> Tree.fromstring('<<NP my cookie>>', brackets=('<<','>>'))
+    Traceback (most recent call last):
+      . . .
+    TypeError: brackets must be a length-2 string
+
+(We may add support for multi-character brackets in the future, in
+which case the ``brackets=('<<','>>')`` example would start working.)
+
+Whitespace brackets are not permitted:
+
+    >>> Tree.fromstring('(NP my cookie\n', brackets='(\n')
+    Traceback (most recent call last):
+      . . .
+    TypeError: whitespace brackets not allowed
+
+If an invalid tree is given to Tree.fromstring, then it raises a
+ValueError, with a description of the problem:
+
+    >>> Tree.fromstring('(NP my cookie) (NP my milk)')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected 'end-of-string' but got '(NP'
+                at index 15.
+                    "...y cookie) (NP my mil..."
+                                  ^
+    >>> Tree.fromstring(')NP my cookie(')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected '(' but got ')'
+                at index 0.
+                    ")NP my coo..."
+                     ^
+    >>> Tree.fromstring('(NP my cookie))')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected 'end-of-string' but got ')'
+                at index 14.
+                    "...my cookie))"
+                                  ^
+    >>> Tree.fromstring('my cookie)')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected '(' but got 'my'
+                at index 0.
+                    "my cookie)"
+                     ^
+    >>> Tree.fromstring('(NP my cookie')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected ')' but got 'end-of-string'
+                at index 13.
+                    "... my cookie"
+                                  ^
+    >>> Tree.fromstring('')
+    Traceback (most recent call last):
+      . . .
+    ValueError: Tree.fromstring(): expected '(' but got 'end-of-string'
+                at index 0.
+                    ""
+                     ^
+
+Trees with no children are supported:
+
+    >>> print(Tree.fromstring('(S)'))
+    (S )
+    >>> print(Tree.fromstring('(X (Y) (Z))'))
+    (X (Y ) (Z ))
+
+Trees with an empty node label and no children are supported:
+
+    >>> print(Tree.fromstring('()'))
+    ( )
+    >>> print(Tree.fromstring('(X () ())'))
+    (X ( ) ( ))
+
+Trees with an empty node label and children are supported, but only if the
+first child is not a leaf (otherwise, it will be treated as the node label).
+
+    >>> print(Tree.fromstring('((A) (B) (C))'))
+    ( (A ) (B ) (C ))
+    >>> print(Tree.fromstring('((A) leaf)'))
+    ( (A ) leaf)
+    >>> print(Tree.fromstring('(((())))'))
+    ( ( ( ( ))))
+
+The optional arguments `read_node` and `read_leaf` may be used to
+transform the string values of nodes or leaves.
+
+    >>> print(Tree.fromstring('(A b (C d e) (F (G h i)))',
+    ...                  read_node=lambda s: '<%s>' % s,
+    ...                  read_leaf=lambda s: '"%s"' % s))
+    (<A> "b" (<C> "d" "e") (<F> (<G> "h" "i")))
+
+These transformation functions are typically used when the node or
+leaf labels should be parsed to a non-string value (such as a feature
+structure).  If node and leaf labels need to be able to include
+whitespace, then you must also use the optional `node_pattern` and
+`leaf_pattern` arguments.
+
+    >>> from nltk.featstruct import FeatStruct
+    >>> tree = Tree.fromstring('([cat=NP] [lex=the] [lex=dog])',
+    ...                   read_node=FeatStruct, read_leaf=FeatStruct)
+    >>> tree.set_label(tree.label().unify(FeatStruct('[num=singular]')))
+    >>> print(tree)
+    ([cat='NP', num='singular'] [lex='the'] [lex='dog'])
+
+The optional argument ``remove_empty_top_bracketing`` can be used to
+remove any top-level empty bracketing that occurs.
+
+    >>> print(Tree.fromstring('((S (NP I) (VP (V enjoyed) (NP my cookie))))',
+    ...                  remove_empty_top_bracketing=True))
+    (S (NP I) (VP (V enjoyed) (NP my cookie)))
+
+It will not remove a top-level empty bracketing with multiple children:
+
+    >>> print(Tree.fromstring('((A a) (B b))'))
+    ( (A a) (B b))
+
+
+Tree.fromlist()
+---------------
+The class method `Tree.fromlist()` can be used to parse trees
+that are expressed as nested lists, such as those produced by
+the tree() function from the wordnet module.
+
+    >>> from nltk.corpus import wordnet as wn
+    >>> t=Tree.fromlist(wn.synset('dog.n.01').tree(lambda s:s.hypernyms()))
+    >>> print(t.height())
+    14
+    >>> print(t.leaves())
+    ["Synset('entity.n.01')", "Synset('entity.n.01')"]
+    >>> t.pretty_print()
+                      Synset('dog.n.01')
+             _________________|__________________
+    Synset('canine.n.                            |
+           02')                                  |
+            |                                    |
+     Synset('carnivor                            |
+         e.n.01')                                |
+            |                                    |
+     Synset('placenta                            |
+         l.n.01')                                |
+            |                                    |
+    Synset('mammal.n.                            |
+           01')                                  |
+            |                                    |
+     Synset('vertebra                            |
+        te.n.01')                                |
+            |                                    |
+    Synset('chordate.                     Synset('domestic
+          n.01')                           _animal.n.01')
+            |                                    |
+    Synset('animal.n.                    Synset('animal.n.
+           01')                                 01')
+            |                                    |
+    Synset('organism.                    Synset('organism.
+          n.01')                               n.01')
+            |                                    |
+     Synset('living_t                     Synset('living_t
+       hing.n.01')                          hing.n.01')
+            |                                    |
+     Synset('whole.n.                     Synset('whole.n.
+           02')                                 02')
+            |                                    |
+    Synset('object.n.                    Synset('object.n.
+           01')                                 01')
+            |                                    |
+     Synset('physical                     Synset('physical
+      _entity.n.01')                       _entity.n.01')
+            |                                    |
+    Synset('entity.n.                    Synset('entity.n.
+           01')                                 01')
+
+
+
+Parented Trees
+==============
+`ParentedTree` is a subclass of `Tree` that automatically maintains
+parent pointers for single-parented trees.  Parented trees can be
+created directly from a node label and a list of children:
+
+    >>> ptree = (
+    ...     ParentedTree('VP', [
+    ...         ParentedTree('VERB', ['saw']),
+    ...         ParentedTree('NP', [
+    ...             ParentedTree('DET', ['the']),
+    ...             ParentedTree('NOUN', ['dog'])])]))
+    >>> print(ptree)
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+
+Parented trees can be created from strings using the classmethod
+`ParentedTree.fromstring`:
+
+    >>> ptree = ParentedTree.fromstring('(VP (VERB saw) (NP (DET the) (NOUN dog)))')
+    >>> print(ptree)
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    >>> print(type(ptree))
+    <class 'nltk.tree.parented.ParentedTree'>
+
+Parented trees can also be created by using the classmethod
+`ParentedTree.convert` to convert another type of tree to a parented
+tree:
+
+    >>> tree = Tree.fromstring('(VP (VERB saw) (NP (DET the) (NOUN dog)))')
+    >>> ptree = ParentedTree.convert(tree)
+    >>> print(ptree)
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    >>> print(type(ptree))
+    <class 'nltk.tree.parented.ParentedTree'>
+
+.. clean-up:
+
+    >>> del tree
+
+`ParentedTree`\ s should never be used in the same tree as `Tree`\ s
+or `MultiParentedTree`\ s.  Mixing tree implementations may result in
+incorrect parent pointers and in `TypeError` exceptions:
+
+    >>> # Inserting a Tree in a ParentedTree gives an exception:
+    >>> ParentedTree('NP', [
+    ...     Tree('DET', ['the']), Tree('NOUN', ['dog'])])
+    Traceback (most recent call last):
+      . . .
+    TypeError: Can not insert a non-ParentedTree into a ParentedTree
+
+    >>> # inserting a ParentedTree in a Tree gives incorrect parent pointers:
+    >>> broken_tree = Tree('NP', [
+    ...     ParentedTree('DET', ['the']), ParentedTree('NOUN', ['dog'])])
+    >>> print(broken_tree[0].parent())
+    None
+
+Parented Tree Methods
+------------------------
+In addition to all the methods defined by the `Tree` class, the
+`ParentedTree` class adds six new methods whose values are
+automatically updated whenever a parented tree is modified: `parent()`,
+`parent_index()`, `left_sibling()`, `right_sibling()`, `root()`, and
+`treeposition()`.
+
+The `parent()` method contains a `ParentedTree`\ 's parent, if it has
+one; and ``None`` otherwise.  `ParentedTree`\ s that do not have
+parents are known as "root trees."
+
+    >>> for subtree in ptree.subtrees():
+    ...     print(subtree)
+    ...     print('  Parent = %s' % subtree.parent())
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+      Parent = None
+    (VERB saw)
+      Parent = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (NP (DET the) (NOUN dog))
+      Parent = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (DET the)
+      Parent = (NP (DET the) (NOUN dog))
+    (NOUN dog)
+      Parent = (NP (DET the) (NOUN dog))
+
+The `parent_index()` method stores the index of a tree in its parent's
+child list.  If a tree does not have a parent, then its `parent_index`
+is ``None``.
+
+    >>> for subtree in ptree.subtrees():
+    ...     print(subtree)
+    ...     print('  Parent Index = %s' % subtree.parent_index())
+    ...     assert (subtree.parent() is None or
+    ...             subtree.parent()[subtree.parent_index()] is subtree)
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+      Parent Index = None
+    (VERB saw)
+      Parent Index = 0
+    (NP (DET the) (NOUN dog))
+      Parent Index = 1
+    (DET the)
+      Parent Index = 0
+    (NOUN dog)
+      Parent Index = 1
+
+Note that ``ptree.parent().index(ptree)`` is *not* equivalent to
+``ptree.parent_index()``.  In particular, ``ptree.parent().index(ptree)``
+will return the index of the first child of ``ptree.parent()`` that is
+equal to ``ptree`` (using ``==``); and that child may not be
+``ptree``:
+
+    >>> on_and_on = ParentedTree('CONJP', [
+    ...     ParentedTree('PREP', ['on']),
+    ...     ParentedTree('COJN', ['and']),
+    ...     ParentedTree('PREP', ['on'])])
+    >>> second_on = on_and_on[2]
+    >>> print(second_on.parent_index())
+    2
+    >>> print(second_on.parent().index(second_on))
+    0
+
+The methods `left_sibling()` and `right_sibling()` can be used to get a
+parented tree's siblings.  If a tree does not have a left or right
+sibling, then the corresponding method's value is ``None``:
+
+    >>> for subtree in ptree.subtrees():
+    ...     print(subtree)
+    ...     print('  Left Sibling  = %s' % subtree.left_sibling())
+    ...     print('  Right Sibling = %s' % subtree.right_sibling())
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+      Left Sibling  = None
+      Right Sibling = None
+    (VERB saw)
+      Left Sibling  = None
+      Right Sibling = (NP (DET the) (NOUN dog))
+    (NP (DET the) (NOUN dog))
+      Left Sibling  = (VERB saw)
+      Right Sibling = None
+    (DET the)
+      Left Sibling  = None
+      Right Sibling = (NOUN dog)
+    (NOUN dog)
+      Left Sibling  = (DET the)
+      Right Sibling = None
+
+A parented tree's root tree can be accessed using the `root()`
+method.  This method follows the tree's parent pointers until it
+finds a tree without a parent.  If a tree does not have a parent, then
+it is its own root:
+
+    >>> for subtree in ptree.subtrees():
+    ...     print(subtree)
+    ...     print('  Root = %s' % subtree.root())
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+      Root = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (VERB saw)
+      Root = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (NP (DET the) (NOUN dog))
+      Root = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (DET the)
+      Root = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+    (NOUN dog)
+      Root = (VP (VERB saw) (NP (DET the) (NOUN dog)))
+
+The `treeposition()` method can be used to find a tree's treeposition
+relative to its root:
+
+    >>> for subtree in ptree.subtrees():
+    ...     print(subtree)
+    ...     print('  Tree Position = %s' % (subtree.treeposition(),))
+    ...     assert subtree.root()[subtree.treeposition()] is subtree
+    (VP (VERB saw) (NP (DET the) (NOUN dog)))
+      Tree Position = ()
+    (VERB saw)
+      Tree Position = (0,)
+    (NP (DET the) (NOUN dog))
+      Tree Position = (1,)
+    (DET the)
+      Tree Position = (1, 0)
+    (NOUN dog)
+      Tree Position = (1, 1)
+
+Whenever a parented tree is modified, all of the methods described
+above (`parent()`, `parent_index()`, `left_sibling()`, `right_sibling()`,
+`root()`, and `treeposition()`) are automatically updated.  For example,
+if we replace ``ptree``\ 's subtree for the word "dog" with a new
+subtree for "cat," the method values for both the "dog" subtree and the
+"cat" subtree get automatically updated:
+
+    >>> # Replace the dog with a cat
+    >>> dog = ptree[1,1]
+    >>> cat = ParentedTree('NOUN', ['cat'])
+    >>> ptree[1,1] = cat
+
+    >>> # the noun phrase is no longer the dog's parent:
+    >>> print(dog.parent(), dog.parent_index(), dog.left_sibling())
+    None None None
+    >>> # dog is now its own root.
+    >>> print(dog.root())
+    (NOUN dog)
+    >>> print(dog.treeposition())
+    ()
+
+    >>> # the cat's parent is now the noun phrase:
+    >>> print(cat.parent())
+    (NP (DET the) (NOUN cat))
+    >>> print(cat.parent_index())
+    1
+    >>> print(cat.left_sibling())
+    (DET the)
+    >>> print(cat.root())
+    (VP (VERB saw) (NP (DET the) (NOUN cat)))
+    >>> print(cat.treeposition())
+    (1, 1)
+
+ParentedTree Regression Tests
+-----------------------------
+Keep track of all trees that we create (including subtrees) using this
+variable:
+
+    >>> all_ptrees = []
+
+Define a helper function to create new parented trees:
+
+    >>> def make_ptree(s):
+    ...     ptree = ParentedTree.convert(Tree.fromstring(s))
+    ...     all_ptrees.extend(t for t in ptree.subtrees()
+    ...                       if isinstance(t, Tree))
+    ...     return ptree
+
+Define a test function that examines every subtree in all_ptrees; and
+checks that all six of its methods are defined correctly.  If any
+ptrees are passed as arguments, then they are printed.
+
+    >>> def pcheck(*print_ptrees):
+    ...     for ptree in all_ptrees:
+    ...         # Check ptree's methods.
+    ...         if ptree.parent() is not None:
+    ...             i = ptree.parent_index()
+    ...             assert ptree.parent()[i] is ptree
+    ...             if i > 0:
+    ...                 assert ptree.left_sibling() is ptree.parent()[i-1]
+    ...             if i < (len(ptree.parent())-1):
+    ...                 assert ptree.right_sibling() is ptree.parent()[i+1]
+    ...             assert len(ptree.treeposition()) > 0
+    ...             assert (ptree.treeposition() ==
+    ...                     ptree.parent().treeposition() + (ptree.parent_index(),))
+    ...             assert ptree.root() is not ptree
+    ...             assert ptree.root() is not None
+    ...             assert ptree.root() is ptree.parent().root()
+    ...             assert ptree.root()[ptree.treeposition()] is ptree
+    ...         else:
+    ...             assert ptree.parent_index() is None
+    ...             assert ptree.left_sibling() is None
+    ...             assert ptree.right_sibling() is None
+    ...             assert ptree.root() is ptree
+    ...             assert ptree.treeposition() == ()
+    ...         # Check ptree's children's methods:
+    ...         for i, child in enumerate(ptree):
+    ...             if isinstance(child, Tree):
+    ...                 # pcheck parent() & parent_index() methods
+    ...                 assert child.parent() is ptree
+    ...                 assert child.parent_index() == i
+    ...                 # pcheck sibling methods
+    ...                 if i == 0:
+    ...                     assert child.left_sibling() is None
+    ...                 else:
+    ...                     assert child.left_sibling() is ptree[i-1]
+    ...                 if i == len(ptree)-1:
+    ...                     assert child.right_sibling() is None
+    ...                 else:
+    ...                     assert child.right_sibling() is ptree[i+1]
+    ...     if print_ptrees:
+    ...         print('ok!', end=' ')
+    ...         for ptree in print_ptrees: print(ptree)
+    ...     else:
+    ...         print('ok!')
+
+Run our test function on a variety of newly-created trees:
+
+    >>> pcheck(make_ptree('(A)'))
+    ok! (A )
+    >>> pcheck(make_ptree('(A (B (C (D) (E f)) g) h)'))
+    ok! (A (B (C (D ) (E f)) g) h)
+    >>> pcheck(make_ptree('(A (B) (C c) (D d d) (E e e e))'))
+    ok! (A (B ) (C c) (D d d) (E e e e))
+    >>> pcheck(make_ptree('(A (B) (C (c)) (D (d) (d)) (E (e) (e) (e)))'))
+    ok! (A (B ) (C (c )) (D (d ) (d )) (E (e ) (e ) (e )))
+
+Run our test function after performing various tree-modification
+operations:
+
+**__delitem__()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> e = ptree[0,0,1]
+    >>> del ptree[0,0,1]; pcheck(ptree); pcheck(e)
+    ok! (A (B (C (D ) (Q p)) g) h)
+    ok! (E f)
+    >>> del ptree[0,0,0]; pcheck(ptree)
+    ok! (A (B (C (Q p)) g) h)
+    >>> del ptree[0,1]; pcheck(ptree)
+    ok! (A (B (C (Q p))) h)
+    >>> del ptree[-1]; pcheck(ptree)
+    ok! (A (B (C (Q p))))
+    >>> del ptree[-100]
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+    >>> del ptree[()]
+    Traceback (most recent call last):
+      . . .
+    IndexError: The tree position () may not be deleted.
+
+    >>> # With slices:
+    >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> b = ptree[0]
+    >>> del ptree[0:0]; pcheck(ptree)
+    ok! (A (B c) (D e) f g (H i) j (K l))
+    >>> del ptree[:1]; pcheck(ptree); pcheck(b)
+    ok! (A (D e) f g (H i) j (K l))
+    ok! (B c)
+    >>> del ptree[-2:]; pcheck(ptree)
+    ok! (A (D e) f g (H i))
+    >>> del ptree[1:3]; pcheck(ptree)
+    ok! (A (D e) (H i))
+    >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> del ptree[5:1000]; pcheck(ptree)
+    ok! (A (B c) (D e) f g (H i))
+    >>> del ptree[-2:1000]; pcheck(ptree)
+    ok! (A (B c) (D e) f)
+    >>> del ptree[-100:1]; pcheck(ptree)
+    ok! (A (D e) f)
+    >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> del ptree[1:-2:2]; pcheck(ptree)
+    ok! (A (B c) f (H i) j (K l))
+
+**__setitem__()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> d, e, q = ptree[0,0]
+    >>> ptree[0,0,0] = 'x'; pcheck(ptree); pcheck(d)
+    ok! (A (B (C x (E f) (Q p)) g) h)
+    ok! (D )
+    >>> ptree[0,0,1] = make_ptree('(X (Y z))'); pcheck(ptree); pcheck(e)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) h)
+    ok! (E f)
+    >>> ptree[1] = d; pcheck(ptree)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) (D ))
+    >>> ptree[-1] = 'x'; pcheck(ptree)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) x)
+    >>> ptree[-100] = 'y'
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+    >>> ptree[()] = make_ptree('(X y)')
+    Traceback (most recent call last):
+      . . .
+    IndexError: The tree position () may not be assigned to.
+
+    >>> # With slices:
+    >>> ptree = make_ptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> b = ptree[0]
+    >>> ptree[0:0] = ('x', make_ptree('(Y)')); pcheck(ptree)
+    ok! (A x (Y ) (B c) (D e) f g (H i) j (K l))
+    >>> ptree[2:6] = (); pcheck(ptree); pcheck(b)
+    ok! (A x (Y ) (H i) j (K l))
+    ok! (B c)
+    >>> ptree[-2:] = ('z', 'p'); pcheck(ptree)
+    ok! (A x (Y ) (H i) z p)
+    >>> ptree[1:3] = [make_ptree('(X)') for x in range(10)]; pcheck(ptree)
+    ok! (A x (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) z p)
+    >>> ptree[5:1000] = []; pcheck(ptree)
+    ok! (A x (X ) (X ) (X ) (X ))
+    >>> ptree[-2:1000] = ['n']; pcheck(ptree)
+    ok! (A x (X ) (X ) n)
+    >>> ptree[-100:1] = [make_ptree('(U v)')]; pcheck(ptree)
+    ok! (A (U v) (X ) (X ) n)
+    >>> ptree[-1:] = (make_ptree('(X)') for x in range(3)); pcheck(ptree)
+    ok! (A (U v) (X ) (X ) (X ) (X ) (X ))
+    >>> ptree[1:-2:2] = ['x', 'y']; pcheck(ptree)
+    ok! (A (U v) x (X ) y (X ) (X ))
+
+**append()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> ptree.append('x'); pcheck(ptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x)
+    >>> ptree.append(make_ptree('(X (Y z))')); pcheck(ptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x (X (Y z)))
+
+**extend()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> ptree.extend(['x', 'y', make_ptree('(X (Y z))')]); pcheck(ptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)))
+    >>> ptree.extend([]); pcheck(ptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)))
+    >>> ptree.extend(make_ptree('(X)') for x in range(3)); pcheck(ptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)) (X ) (X ) (X ))
+
+**insert()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> ptree.insert(0, make_ptree('(X (Y z))')); pcheck(ptree)
+    ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) h)
+    >>> ptree.insert(-1, make_ptree('(X (Y z))')); pcheck(ptree)
+    ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h)
+    >>> ptree.insert(-4, make_ptree('(X (Y z))')); pcheck(ptree)
+    ok! (A (X (Y z)) (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h)
+    >>> # Note: as with ``list``, inserting at a negative index that
+    >>> # gives a position before the start of the list does *not*
+    >>> # raise an IndexError exception; it just inserts at 0.
+    >>> ptree.insert(-400, make_ptree('(X (Y z))')); pcheck(ptree)
+    ok! (A
+      (X (Y z))
+      (X (Y z))
+      (X (Y z))
+      (B (C (D ) (E f) (Q p)) g)
+      (X (Y z))
+      h)
+
+**pop()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> ptree[0,0].pop(1); pcheck(ptree)
+    ParentedTree('E', ['f'])
+    ok! (A (B (C (D ) (Q p)) g) h)
+    >>> ptree[0].pop(-1); pcheck(ptree)
+    'g'
+    ok! (A (B (C (D ) (Q p))) h)
+    >>> ptree.pop(); pcheck(ptree)
+    'h'
+    ok! (A (B (C (D ) (Q p))))
+    >>> ptree.pop(-100)
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+
+**remove()**
+
+    >>> ptree = make_ptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> e = ptree[0,0,1]
+    >>> ptree[0,0].remove(ptree[0,0,1]); pcheck(ptree); pcheck(e)
+    ok! (A (B (C (D ) (Q p)) g) h)
+    ok! (E f)
+    >>> ptree[0,0].remove(make_ptree('(Q p)')); pcheck(ptree)
+    ok! (A (B (C (D )) g) h)
+    >>> ptree[0,0].remove(make_ptree('(Q p)'))
+    Traceback (most recent call last):
+      . . .
+    ValueError: ParentedTree('Q', ['p']) is not in list
+    >>> ptree.remove('h'); pcheck(ptree)
+    ok! (A (B (C (D )) g))
+    >>> ptree.remove('h');
+    Traceback (most recent call last):
+      . . .
+    ValueError: 'h' is not in list
+    >>> # remove() removes the first subtree that is equal (==) to the
+    >>> # given tree, which may not be the identical tree we give it:
+    >>> ptree = make_ptree('(A (X x) (Y y) (X x))')
+    >>> x1, y, x2 = ptree
+    >>> ptree.remove(ptree[-1]); pcheck(ptree)
+    ok! (A (Y y) (X x))
+    >>> print(x1.parent()); pcheck(x1)
+    None
+    ok! (X x)
+    >>> print(x2.parent())
+    (A (Y y) (X x))
+
+Test that a tree can not be given multiple parents:
+
+    >>> ptree = make_ptree('(A (X x) (Y y) (Z z))')
+    >>> ptree[0] = ptree[1]
+    Traceback (most recent call last):
+      . . .
+    ValueError: Can not insert a subtree that already has a parent.
+    >>> pcheck()
+    ok!
+
+[more to be written]
+
+Shallow copying can be tricky for Tree and several of its subclasses.
+For shallow copies of Tree, only the root node is reconstructed, while
+all the children are shared between the two trees. Modify the children
+of one tree - and the shallowly copied tree will also update.
+
+    >>> from nltk.tree import Tree, ParentedTree, MultiParentedTree
+    >>> tree = Tree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))")
+    >>> copy_tree = tree.copy(deep=False)
+    >>> tree == copy_tree # Ensure identical labels and nodes
+    True
+    >>> id(copy_tree[0]) == id(tree[0]) # Ensure shallow copy - the children are the same objects in memory
+    True
+
+For ParentedTree objects, this behaviour is not possible. With a shallow
+copy, the children of the root node would be reused for both the original
+and the shallow copy. For this to be possible, some children would need
+to have multiple parents. As this is forbidden for ParentedTree objects,
+attempting to make a shallow copy will cause a warning, and a deep copy
+is made instead.
+
+    >>> ptree = ParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))")
+    >>> copy_ptree = ptree.copy(deep=False)
+    >>> copy_ptree == ptree # Ensure identical labels and nodes
+    True
+    >>> id(copy_ptree[0]) != id(ptree[0]) # Shallow copying isn't supported - it defaults to deep copy.
+    True
+
+For MultiParentedTree objects, the issue of only allowing one parent that
+can be seen for ParentedTree objects is no more. Shallow copying a
+MultiParentedTree gives the children of the root node two parents:
+the original and the newly copied root.
+
+    >>> mptree = MultiParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))")
+    >>> len(mptree[0].parents())
+    1
+    >>> copy_mptree = mptree.copy(deep=False)
+    >>> copy_mptree == mptree # Ensure identical labels and nodes
+    True
+    >>> len(mptree[0].parents())
+    2
+    >>> len(copy_mptree[0].parents())
+    2
+
+Shallow copying a MultiParentedTree is similar to creating a second root
+which is identically labeled as the root on which the copy method was called.
+
+
+ImmutableParentedTree Regression Tests
+--------------------------------------
+
+    >>> iptree = ImmutableParentedTree.convert(ptree)
+    >>> type(iptree)
+    <class 'nltk.tree.immutable.ImmutableParentedTree'>
+    >>> del iptree[0]
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableParentedTree may not be modified
+    >>> iptree.set_label('newnode')
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableParentedTree may not be modified
+
+
+MultiParentedTree Regression Tests
+----------------------------------
+Keep track of all trees that we create (including subtrees) using this
+variable:
+
+    >>> all_mptrees = []
+
+Define a helper function to create new parented trees:
+
+    >>> def make_mptree(s):
+    ...     mptree = MultiParentedTree.convert(Tree.fromstring(s))
+    ...     all_mptrees.extend(t for t in mptree.subtrees()
+    ...                       if isinstance(t, Tree))
+    ...     return mptree
+
+Define a test function that examines every subtree in all_mptrees; and
+checks that all six of its methods are defined correctly.  If any
+mptrees are passed as arguments, then they are printed.
+
+    >>> def mpcheck(*print_mptrees):
+    ...     def has(seq, val): # uses identity comparison
+    ...         for item in seq:
+    ...             if item is val: return True
+    ...         return False
+    ...     for mptree in all_mptrees:
+    ...         # Check mptree's methods.
+    ...         if len(mptree.parents()) == 0:
+    ...             assert len(mptree.left_siblings()) == 0
+    ...             assert len(mptree.right_siblings()) == 0
+    ...             assert len(mptree.roots()) == 1
+    ...             assert mptree.roots()[0] is mptree
+    ...             assert mptree.treepositions(mptree) == [()]
+    ...             left_siblings = right_siblings = ()
+    ...             roots = {id(mptree): 1}
+    ...         else:
+    ...             roots = dict((id(r), 0) for r in mptree.roots())
+    ...             left_siblings = mptree.left_siblings()
+    ...             right_siblings = mptree.right_siblings()
+    ...         for parent in mptree.parents():
+    ...             for i in mptree.parent_indices(parent):
+    ...                 assert parent[i] is mptree
+    ...                 # check left siblings
+    ...                 if i > 0:
+    ...                     for j in range(len(left_siblings)):
+    ...                         if left_siblings[j] is parent[i-1]:
+    ...                             del left_siblings[j]
+    ...                             break
+    ...                     else:
+    ...                         assert 0, 'sibling not found!'
+    ...                 # check ight siblings
+    ...                 if i < (len(parent)-1):
+    ...                     for j in range(len(right_siblings)):
+    ...                         if right_siblings[j] is parent[i+1]:
+    ...                             del right_siblings[j]
+    ...                             break
+    ...                     else:
+    ...                         assert 0, 'sibling not found!'
+    ...             # check roots
+    ...             for root in parent.roots():
+    ...                 assert id(root) in roots, 'missing root'
+    ...                 roots[id(root)] += 1
+    ...         # check that we don't have any unexplained values
+    ...         assert len(left_siblings)==0, 'unexpected sibling'
+    ...         assert len(right_siblings)==0, 'unexpected sibling'
+    ...         for v in roots.values(): assert v>0, roots #'unexpected root'
+    ...         # check treepositions
+    ...         for root in mptree.roots():
+    ...             for treepos in mptree.treepositions(root):
+    ...                 assert root[treepos] is mptree
+    ...         # Check mptree's children's methods:
+    ...         for i, child in enumerate(mptree):
+    ...             if isinstance(child, Tree):
+    ...                 # mpcheck parent() & parent_index() methods
+    ...                 assert has(child.parents(), mptree)
+    ...                 assert i in child.parent_indices(mptree)
+    ...                 # mpcheck sibling methods
+    ...                 if i > 0:
+    ...                     assert has(child.left_siblings(), mptree[i-1])
+    ...                 if i < len(mptree)-1:
+    ...                     assert has(child.right_siblings(), mptree[i+1])
+    ...     if print_mptrees:
+    ...         print('ok!', end=' ')
+    ...         for mptree in print_mptrees: print(mptree)
+    ...     else:
+    ...         print('ok!')
+
+Run our test function on a variety of newly-created trees:
+
+    >>> mpcheck(make_mptree('(A)'))
+    ok! (A )
+    >>> mpcheck(make_mptree('(A (B (C (D) (E f)) g) h)'))
+    ok! (A (B (C (D ) (E f)) g) h)
+    >>> mpcheck(make_mptree('(A (B) (C c) (D d d) (E e e e))'))
+    ok! (A (B ) (C c) (D d d) (E e e e))
+    >>> mpcheck(make_mptree('(A (B) (C (c)) (D (d) (d)) (E (e) (e) (e)))'))
+    ok! (A (B ) (C (c )) (D (d ) (d )) (E (e ) (e ) (e )))
+    >>> subtree = make_mptree('(A (B (C (D) (E f)) g) h)')
+
+Including some trees that contain multiple parents:
+
+    >>> mpcheck(MultiParentedTree('Z', [subtree, subtree]))
+    ok! (Z (A (B (C (D ) (E f)) g) h) (A (B (C (D ) (E f)) g) h))
+
+Run our test function after performing various tree-modification
+operations (n.b., these are the same tests that we ran for
+`ParentedTree`, above; thus, none of these trees actually *uses*
+multiple parents.)
+
+**__delitem__()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> e = mptree[0,0,1]
+    >>> del mptree[0,0,1]; mpcheck(mptree); mpcheck(e)
+    ok! (A (B (C (D ) (Q p)) g) h)
+    ok! (E f)
+    >>> del mptree[0,0,0]; mpcheck(mptree)
+    ok! (A (B (C (Q p)) g) h)
+    >>> del mptree[0,1]; mpcheck(mptree)
+    ok! (A (B (C (Q p))) h)
+    >>> del mptree[-1]; mpcheck(mptree)
+    ok! (A (B (C (Q p))))
+    >>> del mptree[-100]
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+    >>> del mptree[()]
+    Traceback (most recent call last):
+      . . .
+    IndexError: The tree position () may not be deleted.
+
+    >>> # With slices:
+    >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> b = mptree[0]
+    >>> del mptree[0:0]; mpcheck(mptree)
+    ok! (A (B c) (D e) f g (H i) j (K l))
+    >>> del mptree[:1]; mpcheck(mptree); mpcheck(b)
+    ok! (A (D e) f g (H i) j (K l))
+    ok! (B c)
+    >>> del mptree[-2:]; mpcheck(mptree)
+    ok! (A (D e) f g (H i))
+    >>> del mptree[1:3]; mpcheck(mptree)
+    ok! (A (D e) (H i))
+    >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> del mptree[5:1000]; mpcheck(mptree)
+    ok! (A (B c) (D e) f g (H i))
+    >>> del mptree[-2:1000]; mpcheck(mptree)
+    ok! (A (B c) (D e) f)
+    >>> del mptree[-100:1]; mpcheck(mptree)
+    ok! (A (D e) f)
+    >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> del mptree[1:-2:2]; mpcheck(mptree)
+    ok! (A (B c) f (H i) j (K l))
+
+**__setitem__()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> d, e, q = mptree[0,0]
+    >>> mptree[0,0,0] = 'x'; mpcheck(mptree); mpcheck(d)
+    ok! (A (B (C x (E f) (Q p)) g) h)
+    ok! (D )
+    >>> mptree[0,0,1] = make_mptree('(X (Y z))'); mpcheck(mptree); mpcheck(e)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) h)
+    ok! (E f)
+    >>> mptree[1] = d; mpcheck(mptree)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) (D ))
+    >>> mptree[-1] = 'x'; mpcheck(mptree)
+    ok! (A (B (C x (X (Y z)) (Q p)) g) x)
+    >>> mptree[-100] = 'y'
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+    >>> mptree[()] = make_mptree('(X y)')
+    Traceback (most recent call last):
+      . . .
+    IndexError: The tree position () may not be assigned to.
+
+    >>> # With slices:
+    >>> mptree = make_mptree('(A (B c) (D e) f g (H i) j (K l))')
+    >>> b = mptree[0]
+    >>> mptree[0:0] = ('x', make_mptree('(Y)')); mpcheck(mptree)
+    ok! (A x (Y ) (B c) (D e) f g (H i) j (K l))
+    >>> mptree[2:6] = (); mpcheck(mptree); mpcheck(b)
+    ok! (A x (Y ) (H i) j (K l))
+    ok! (B c)
+    >>> mptree[-2:] = ('z', 'p'); mpcheck(mptree)
+    ok! (A x (Y ) (H i) z p)
+    >>> mptree[1:3] = [make_mptree('(X)') for x in range(10)]; mpcheck(mptree)
+    ok! (A x (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) z p)
+    >>> mptree[5:1000] = []; mpcheck(mptree)
+    ok! (A x (X ) (X ) (X ) (X ))
+    >>> mptree[-2:1000] = ['n']; mpcheck(mptree)
+    ok! (A x (X ) (X ) n)
+    >>> mptree[-100:1] = [make_mptree('(U v)')]; mpcheck(mptree)
+    ok! (A (U v) (X ) (X ) n)
+    >>> mptree[-1:] = (make_mptree('(X)') for x in range(3)); mpcheck(mptree)
+    ok! (A (U v) (X ) (X ) (X ) (X ) (X ))
+    >>> mptree[1:-2:2] = ['x', 'y']; mpcheck(mptree)
+    ok! (A (U v) x (X ) y (X ) (X ))
+
+**append()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> mptree.append('x'); mpcheck(mptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x)
+    >>> mptree.append(make_mptree('(X (Y z))')); mpcheck(mptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x (X (Y z)))
+
+**extend()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> mptree.extend(['x', 'y', make_mptree('(X (Y z))')]); mpcheck(mptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)))
+    >>> mptree.extend([]); mpcheck(mptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)))
+    >>> mptree.extend(make_mptree('(X)') for x in range(3)); mpcheck(mptree)
+    ok! (A (B (C (D ) (E f) (Q p)) g) h x y (X (Y z)) (X ) (X ) (X ))
+
+**insert()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> mptree.insert(0, make_mptree('(X (Y z))')); mpcheck(mptree)
+    ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) h)
+    >>> mptree.insert(-1, make_mptree('(X (Y z))')); mpcheck(mptree)
+    ok! (A (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h)
+    >>> mptree.insert(-4, make_mptree('(X (Y z))')); mpcheck(mptree)
+    ok! (A (X (Y z)) (X (Y z)) (B (C (D ) (E f) (Q p)) g) (X (Y z)) h)
+    >>> # Note: as with ``list``, inserting at a negative index that
+    >>> # gives a position before the start of the list does *not*
+    >>> # raise an IndexError exception; it just inserts at 0.
+    >>> mptree.insert(-400, make_mptree('(X (Y z))')); mpcheck(mptree)
+    ok! (A
+      (X (Y z))
+      (X (Y z))
+      (X (Y z))
+      (B (C (D ) (E f) (Q p)) g)
+      (X (Y z))
+      h)
+
+**pop()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> mptree[0,0].pop(1); mpcheck(mptree)
+    MultiParentedTree('E', ['f'])
+    ok! (A (B (C (D ) (Q p)) g) h)
+    >>> mptree[0].pop(-1); mpcheck(mptree)
+    'g'
+    ok! (A (B (C (D ) (Q p))) h)
+    >>> mptree.pop(); mpcheck(mptree)
+    'h'
+    ok! (A (B (C (D ) (Q p))))
+    >>> mptree.pop(-100)
+    Traceback (most recent call last):
+      . . .
+    IndexError: index out of range
+
+**remove()**
+
+    >>> mptree = make_mptree('(A (B (C (D) (E f) (Q p)) g) h)')
+    >>> e = mptree[0,0,1]
+    >>> mptree[0,0].remove(mptree[0,0,1]); mpcheck(mptree); mpcheck(e)
+    ok! (A (B (C (D ) (Q p)) g) h)
+    ok! (E f)
+    >>> mptree[0,0].remove(make_mptree('(Q p)')); mpcheck(mptree)
+    ok! (A (B (C (D )) g) h)
+    >>> mptree[0,0].remove(make_mptree('(Q p)'))
+    Traceback (most recent call last):
+      . . .
+    ValueError: MultiParentedTree('Q', ['p']) is not in list
+    >>> mptree.remove('h'); mpcheck(mptree)
+    ok! (A (B (C (D )) g))
+    >>> mptree.remove('h');
+    Traceback (most recent call last):
+      . . .
+    ValueError: 'h' is not in list
+    >>> # remove() removes the first subtree that is equal (==) to the
+    >>> # given tree, which may not be the identical tree we give it:
+    >>> mptree = make_mptree('(A (X x) (Y y) (X x))')
+    >>> x1, y, x2 = mptree
+    >>> mptree.remove(mptree[-1]); mpcheck(mptree)
+    ok! (A (Y y) (X x))
+    >>> print([str(p) for p in x1.parents()])
+    []
+    >>> print([str(p) for p in x2.parents()])
+    ['(A (Y y) (X x))']
+
+
+ImmutableMultiParentedTree Regression Tests
+-------------------------------------------
+
+    >>> imptree = ImmutableMultiParentedTree.convert(mptree)
+    >>> type(imptree)
+    <class 'nltk.tree.immutable.ImmutableMultiParentedTree'>
+    >>> del imptree[0]
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableMultiParentedTree may not be modified
+    >>> imptree.set_label('newnode')
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableMultiParentedTree may not be modified
+
+
+ProbabilisticTree Regression Tests
+----------------------------------
+
+    >>> prtree = ProbabilisticTree("S", [ProbabilisticTree("NP", ["N"], prob=0.3)], prob=0.6)
+    >>> print(prtree)
+    (S (NP N)) (p=0.6)
+    >>> import copy
+    >>> prtree == copy.deepcopy(prtree) == prtree.copy(deep=True) == prtree.copy()
+    True
+    >>> prtree[0] is prtree.copy()[0]
+    True
+    >>> prtree[0] is prtree.copy(deep=True)[0]
+    False
+
+    >>> imprtree = ImmutableProbabilisticTree.convert(prtree)
+    >>> type(imprtree)
+    <class 'nltk.tree.immutable.ImmutableProbabilisticTree'>
+    >>> del imprtree[0]
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableProbabilisticTree may not be modified
+    >>> imprtree.set_label('newnode')
+    Traceback (most recent call last):
+      . . .
+    ValueError: ImmutableProbabilisticTree may not be modified
+
+
+Squashed Bugs
+=============
+
+This used to discard the ``(B b)`` subtree (fixed in svn 6270):
+
+    >>> print(Tree.fromstring('((A a) (B b))'))
+    ( (A a) (B b))
+
+Pickling ParentedTree instances didn't work for Python 3.7 onwards (See #2478)
+
+    >>> import pickle
+    >>> tree = ParentedTree.fromstring('(S (NN x) (NP x) (NN x))')
+    >>> print(tree)
+    (S (NN x) (NP x) (NN x))
+
+    >>> pickled = pickle.dumps(tree)
+    >>> tree_loaded = pickle.loads(pickled)
+    >>> print(tree_loaded)
+    (S (NN x) (NP x) (NN x))
+
+ParentedTree used to be impossible to (deep)copy. (See #1324)
+
+    >>> from nltk.tree import ParentedTree
+    >>> import copy
+    >>> tree = ParentedTree.fromstring("(TOP (S (NP (NNP Bell,)) (NP (NP (DT a) (NN company)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (VP (VBN based) (PP (IN in) (NP (NNP LA,)))))))) (VP (VBZ makes) (CC and) (VBZ distributes) (NP (NN computer))) (. products.)))")
+    >>> tree == copy.deepcopy(tree) == copy.copy(tree) == tree.copy(deep=True) == tree.copy()
+    True

env-llmeval/lib/python3.10/site-packages/nltk/test/treeprettyprinter.doctest

@@ -0,0 +1,177 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+=========================================================
+ Unit tests for nltk.tree.prettyprinter.TreePrettyPrinter
+=========================================================
+
+    >>> from nltk.tree import Tree, TreePrettyPrinter
+
+Tree nr 2170 from nltk.corpus.treebank:
+
+    >>> tree = Tree.fromstring(
+    ...     '(S (NP-SBJ (PRP I)) (VP (VBP feel) (ADJP-PRD (RB pretty) '
+    ...     '(JJ good)) (PP-CLR (IN about) (NP (PRP it)))) (. .))')
+    >>> tpp = TreePrettyPrinter(tree)
+    >>> print(tpp.text())
+                                 S
+       __________________________|_____________________
+      |                          VP                    |
+      |      ____________________|___________          |
+      |     |             |                PP-CLR      |
+      |     |             |             _____|_____    |
+    NP-SBJ  |          ADJP-PRD        |           NP  |
+      |     |      _______|______      |           |   |
+     PRP   VBP    RB             JJ    IN         PRP  .
+      |     |     |              |     |           |   |
+      I    feel pretty          good about         it  .
+
+    >>> print(tpp.text(unicodelines=True))
+                                 S
+      ┌──────────────────────────┼─────────────────────┐
+      │                          VP                    │
+      │     ┌─────────────┬──────┴───────────┐         │
+      │     │             │                PP-CLR      │
+      │     │             │            ┌─────┴─────┐   │
+    NP-SBJ  │          ADJP-PRD        │           NP  │
+      │     │     ┌───────┴──────┐     │           │   │
+     PRP   VBP    RB             JJ    IN         PRP  .
+      │     │     │              │     │           │   │
+      I    feel pretty          good about         it  .
+
+A tree with long labels:
+
+    >>> tree = Tree.fromstring(
+    ...     '(sentence (plural-noun-phrase (plural-noun Superconductors)) '
+    ...     '(verb-phrase (plural-verb conduct) '
+    ...     '(noun-phrase (singular-noun electricity))))')
+    >>> tpp = TreePrettyPrinter(tree)
+    >>> print(tpp.text(abbreviate=8, nodedist=2))
+                sentence
+         __________|__________
+        |                 verb-phr.
+        |           __________|__________
+    plural-n.      |                 noun-phr.
+        |          |                     |
+    plural-n.  plural-v.             singular.
+        |          |                     |
+    Supercon.   conduct              electric.
+
+    >>> print(tpp.text(maxwidth=8, nodedist=2))
+              sentence
+        _________|________
+       |                verb-
+       |                phrase
+       |          ________|_________
+    plural-      |                noun-
+     noun-       |                phrase
+     phrase      |                  |
+       |         |                  |
+    plural-   plural-           singular-
+      noun      verb               noun
+       |         |                  |
+    Supercon  conduct            electric
+    ductors                        ity
+
+A discontinuous tree:
+
+    >>> tree = Tree.fromstring(
+    ...     '(top (punct 8) (smain (noun 0) (verb 1) (inf (verb 5) (inf (verb 6) '
+    ...     '(conj (inf (pp (prep 2) (np (det 3) (noun 4))) (verb 7)) (inf (verb 9)) '
+    ...     '(vg 10) (inf (verb 11)))))) (punct 12))', read_leaf=int)
+    >>> sentence = ('Ze had met haar moeder kunnen gaan winkelen ,'
+    ...             ' zwemmen of terrassen .'.split())
+    >>> tpp = TreePrettyPrinter(tree, sentence)
+    >>> print(tpp.text())
+                                          top
+                                      _____|______________________________________________
+                                   smain                      |                           |
+      _______________________________|_____                   |                           |
+     |    |                               inf                 |                           |
+     |    |                           _____|____              |                           |
+     |    |                          |         inf            |                           |
+     |    |                          |      ____|_____        |                           |
+     |    |                          |     |         conj     |                           |
+     |    |                    _____ | ___ | _________|______ | __________________        |
+     |    |                  inf     |     |                  |      |     |      |       |
+     |    |          _________|_____ | ___ | _________        |      |     |      |       |
+     |    |         pp               |     |          |       |      |     |      |       |
+     |    |     ____|____            |     |          |       |      |     |      |       |
+     |    |    |         np          |     |          |       |     inf    |     inf      |
+     |    |    |     ____|____       |     |          |       |      |     |      |       |
+    noun verb prep det       noun   verb  verb       verb   punct   verb   vg    verb   punct
+     |    |    |    |         |      |     |          |       |      |     |      |       |
+     Ze  had  met  haar     moeder kunnen gaan     winkelen   ,   zwemmen  of terrassen   .
+
+    >>> print(tpp.text(unicodelines=True))
+                                          top
+                                     ┌─────┴──────────────────┬───────────────────────────┐
+                                   smain                      │                           │
+     ┌────┬──────────────────────────┴─────┐                  │                           │
+     │    │                               inf                 │                           │
+     │    │                          ┌─────┴────┐             │                           │
+     │    │                          │         inf            │                           │
+     │    │                          │     ┌────┴─────┐       │                           │
+     │    │                          │     │         conj     │                           │
+     │    │                   ┌───── │ ─── │ ─────────┴────── │ ─────┬─────┬──────┐       │
+     │    │                  inf     │     │                  │      │     │      │       │
+     │    │         ┌─────────┴───── │ ─── │ ─────────┐       │      │     │      │       │
+     │    │         pp               │     │          │       │      │     │      │       │
+     │    │    ┌────┴────┐           │     │          │       │      │     │      │       │
+     │    │    │         np          │     │          │       │     inf    │     inf      │
+     │    │    │    ┌────┴────┐      │     │          │       │      │     │      │       │
+    noun verb prep det       noun   verb  verb       verb   punct   verb   vg    verb   punct
+     │    │    │    │         │      │     │          │       │      │     │      │       │
+     Ze  had  met  haar     moeder kunnen gaan     winkelen   ,   zwemmen  of terrassen   .
+
+Importing TreePrettyPrinter
+---------------------------
+
+First of all, a simple tree will be constructed::
+
+  >>> from nltk.tree import Tree
+  >>> tree = Tree.fromstring('(S (NP Mary) (VP walks))')
+
+We'll use this sample tree to show that the method of importing `TreePrettyPrinter` work correctly:
+
+- Recommended::
+
+    >>> from nltk.tree import TreePrettyPrinter
+    >>> print(TreePrettyPrinter(tree).text())
+          S
+      ____|____
+     NP        VP
+     |         |
+    Mary     walks
+
+- Alternative but valid options::
+
+    >>> from nltk import TreePrettyPrinter
+    >>> print(TreePrettyPrinter(tree).text())
+          S
+      ____|____
+     NP        VP
+     |         |
+    Mary     walks
+
+    >>> from nltk.tree.prettyprinter import TreePrettyPrinter
+    >>> print(TreePrettyPrinter(tree).text())
+          S
+      ____|____
+     NP        VP
+     |         |
+    Mary     walks
+
+- Deprecated, do not use::
+
+    >>> from nltk.treeprettyprinter import TreePrettyPrinter
+    >>> print(TreePrettyPrinter(tree).text())
+          S
+      ____|____
+     NP        VP
+     |         |
+    Mary     walks
+
+  This method will throw a DeprecationWarning::
+
+    Import `TreePrettyPrinter` using `from nltk.tree import TreePrettyPrinter` instead.

env-llmeval/lib/python3.10/site-packages/nltk/test/treetransforms.doctest

@@ -0,0 +1,154 @@
+.. Copyright (C) 2001-2023 NLTK Project
+.. For license information, see LICENSE.TXT
+
+-------------------------------------------
+Unit tests for the TreeTransformation class
+-------------------------------------------
+
+    >>> from copy import deepcopy
+    >>> from nltk.tree import Tree, collapse_unary, chomsky_normal_form, un_chomsky_normal_form
+
+    >>> tree_string = "(TOP (S (S (VP (VBN Turned) (ADVP (RB loose)) (PP (IN in) (NP (NP (NNP Shane) (NNP Longman) (POS 's)) (NN trading) (NN room))))) (, ,) (NP (DT the) (NN yuppie) (NNS dealers)) (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right)))) (. .)))"
+
+    >>> tree = Tree.fromstring(tree_string)
+    >>> print(tree)
+    (TOP
+      (S
+        (S
+          (VP
+            (VBN Turned)
+            (ADVP (RB loose))
+            (PP
+              (IN in)
+              (NP
+                (NP (NNP Shane) (NNP Longman) (POS 's))
+                (NN trading)
+                (NN room)))))
+        (, ,)
+        (NP (DT the) (NN yuppie) (NNS dealers))
+        (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
+        (. .)))
+
+Make a copy of the original tree and collapse the subtrees with only one child
+
+    >>> collapsedTree = deepcopy(tree)
+    >>> collapse_unary(collapsedTree)
+    >>> print(collapsedTree)
+    (TOP
+      (S
+        (S+VP
+          (VBN Turned)
+          (ADVP (RB loose))
+          (PP
+            (IN in)
+            (NP
+              (NP (NNP Shane) (NNP Longman) (POS 's))
+              (NN trading)
+              (NN room))))
+        (, ,)
+        (NP (DT the) (NN yuppie) (NNS dealers))
+        (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
+        (. .)))
+
+    >>> collapsedTree2 = deepcopy(tree)
+    >>> collapse_unary(collapsedTree2, collapsePOS=True, collapseRoot=True)
+    >>> print(collapsedTree2)
+    (TOP+S
+      (S+VP
+        (VBN Turned)
+        (ADVP+RB loose)
+        (PP
+          (IN in)
+          (NP
+            (NP (NNP Shane) (NNP Longman) (POS 's))
+            (NN trading)
+            (NN room))))
+      (, ,)
+      (NP (DT the) (NN yuppie) (NNS dealers))
+      (VP (AUX do) (NP (NP+RB little) (ADJP+RB right)))
+      (. .))
+
+Convert the tree to Chomsky Normal Form i.e. each subtree has either two
+subtree children or a single leaf value. This conversion can be performed
+using either left- or right-factoring.
+
+    >>> cnfTree = deepcopy(collapsedTree)
+    >>> chomsky_normal_form(cnfTree, factor='left')
+    >>> print(cnfTree)
+    (TOP
+      (S
+        (S|<S+VP-,-NP-VP>
+          (S|<S+VP-,-NP>
+            (S|<S+VP-,>
+              (S+VP
+                (S+VP|<VBN-ADVP> (VBN Turned) (ADVP (RB loose)))
+                (PP
+                  (IN in)
+                  (NP
+                    (NP|<NP-NN>
+                      (NP
+                        (NP|<NNP-NNP> (NNP Shane) (NNP Longman))
+                        (POS 's))
+                      (NN trading))
+                    (NN room))))
+              (, ,))
+            (NP (NP|<DT-NN> (DT the) (NN yuppie)) (NNS dealers)))
+          (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right)))))
+        (. .)))
+
+    >>> cnfTree = deepcopy(collapsedTree)
+    >>> chomsky_normal_form(cnfTree, factor='right')
+    >>> print(cnfTree)
+    (TOP
+      (S
+        (S+VP
+          (VBN Turned)
+          (S+VP|<ADVP-PP>
+            (ADVP (RB loose))
+            (PP
+              (IN in)
+              (NP
+                (NP (NNP Shane) (NP|<NNP-POS> (NNP Longman) (POS 's)))
+                (NP|<NN-NN> (NN trading) (NN room))))))
+        (S|<,-NP-VP-.>
+          (, ,)
+          (S|<NP-VP-.>
+            (NP (DT the) (NP|<NN-NNS> (NN yuppie) (NNS dealers)))
+            (S|<VP-.>
+              (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
+              (. .))))))
+
+Employ some Markov smoothing to make the artificial node labels a bit more
+readable. See the treetransforms.py documentation for more details.
+
+    >>> markovTree = deepcopy(collapsedTree)
+    >>> chomsky_normal_form(markovTree, horzMarkov=2, vertMarkov=1)
+    >>> print(markovTree)
+    (TOP
+      (S^<TOP>
+        (S+VP^<S>
+          (VBN Turned)
+          (S+VP|<ADVP-PP>^<S>
+            (ADVP^<S+VP> (RB loose))
+            (PP^<S+VP>
+              (IN in)
+              (NP^<PP>
+                (NP^<NP>
+                  (NNP Shane)
+                  (NP|<NNP-POS>^<NP> (NNP Longman) (POS 's)))
+                (NP|<NN-NN>^<PP> (NN trading) (NN room))))))
+        (S|<,-NP>^<TOP>
+          (, ,)
+          (S|<NP-VP>^<TOP>
+            (NP^<S> (DT the) (NP|<NN-NNS>^<S> (NN yuppie) (NNS dealers)))
+            (S|<VP-.>^<TOP>
+              (VP^<S>
+                (AUX do)
+                (NP^<VP> (NP^<NP> (RB little)) (ADJP^<NP> (RB right))))
+              (. .))))))
+
+Convert the transformed tree back to its original form
+
+    >>> un_chomsky_normal_form(markovTree)
+    >>> tree == markovTree
+    True

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_aline.py

@@ -0,0 +1,48 @@
+"""
+Test Aline algorithm for aligning phonetic sequences
+"""
+from nltk.metrics import aline
+
+
+def test_aline():
+    result = aline.align("θin", "tenwis")
+    expected = [[("θ", "t"), ("i", "e"), ("n", "n")]]
+
+    assert result == expected
+
+    result = aline.align("jo", "ʒə")
+    expected = [[("j", "ʒ"), ("o", "ə")]]
+
+    assert result == expected
+
+    result = aline.align("pematesiweni", "pematesewen")
+    expected = [
+        [
+            ("p", "p"),
+            ("e", "e"),
+            ("m", "m"),
+            ("a", "a"),
+            ("t", "t"),
+            ("e", "e"),
+            ("s", "s"),
+            ("i", "e"),
+            ("w", "w"),
+            ("e", "e"),
+            ("n", "n"),
+        ]
+    ]
+
+    assert result == expected
+
+    result = aline.align("tuwθ", "dentis")
+    expected = [[("t", "t"), ("u", "i"), ("w", "-"), ("θ", "s")]]
+
+    assert result == expected
+
+
+def test_aline_delta():
+    """
+    Test aline for computing the difference between two segments
+    """
+    assert aline.delta("p", "q") == 20.0
+    assert aline.delta("a", "A") == 0.0

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_bllip.py

@@ -0,0 +1,42 @@
+import pytest
+
+from nltk.data import find
+from nltk.parse.bllip import BllipParser
+from nltk.tree import Tree
+
+
+@pytest.fixture(scope="module")
+def parser():
+    model_dir = find("models/bllip_wsj_no_aux").path
+    return BllipParser.from_unified_model_dir(model_dir)
+
+
+def setup_module():
+    pytest.importorskip("bllipparser")
+
+
+class TestBllipParser:
+    def test_parser_loads_a_valid_tree(self, parser):
+        parsed = parser.parse("I saw the man with the telescope")
+        tree = next(parsed)
+
+        assert isinstance(tree, Tree)
+        assert (
+            tree.pformat()
+            == """
+(S1
+  (S
+    (NP (PRP I))
+    (VP
+      (VBD saw)
+      (NP (DT the) (NN man))
+      (PP (IN with) (NP (DT the) (NN telescope))))))
+""".strip()
+        )
+
+    def test_tagged_parse_finds_matching_element(self, parser):
+        parsed = parser.parse("I saw the man with the telescope")
+        tagged_tree = next(parser.tagged_parse([("telescope", "NN")]))
+
+        assert isinstance(tagged_tree, Tree)
+        assert tagged_tree.pformat() == "(S1 (NP (NN telescope)))"

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_brill.py

@@ -0,0 +1,34 @@
+"""
+Tests for Brill tagger.
+"""
+
+import unittest
+
+from nltk.corpus import treebank
+from nltk.tag import UnigramTagger, brill, brill_trainer
+from nltk.tbl import demo
+
+
+class TestBrill(unittest.TestCase):
+    def test_pos_template(self):
+        train_sents = treebank.tagged_sents()[:1000]
+        tagger = UnigramTagger(train_sents)
+        trainer = brill_trainer.BrillTaggerTrainer(
+            tagger, [brill.Template(brill.Pos([-1]))]
+        )
+        brill_tagger = trainer.train(train_sents)
+        # Example from https://github.com/nltk/nltk/issues/769
+        result = brill_tagger.tag("This is a foo bar sentence".split())
+        expected = [
+            ("This", "DT"),
+            ("is", "VBZ"),
+            ("a", "DT"),
+            ("foo", None),
+            ("bar", "NN"),
+            ("sentence", None),
+        ]
+        self.assertEqual(result, expected)
+
+    @unittest.skip("Should be tested in __main__ of nltk.tbl.demo")
+    def test_brill_demo(self):
+        demo()

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_cfd_mutation.py

@@ -0,0 +1,39 @@
+import unittest
+
+import pytest
+
+from nltk import ConditionalFreqDist, tokenize
+
+
+class TestEmptyCondFreq(unittest.TestCase):
+    def test_tabulate(self):
+        empty = ConditionalFreqDist()
+        self.assertEqual(empty.conditions(), [])
+        with pytest.raises(ValueError):
+            empty.tabulate(conditions="BUG")  # nonexistent keys shouldn't be added
+        self.assertEqual(empty.conditions(), [])
+
+    def test_plot(self):
+        empty = ConditionalFreqDist()
+        self.assertEqual(empty.conditions(), [])
+        empty.plot(conditions=["BUG"])  # nonexistent keys shouldn't be added
+        self.assertEqual(empty.conditions(), [])
+
+    def test_increment(self):
+        # make sure that we can still mutate cfd normally
+        text = "cow cat mouse cat tiger"
+        cfd = ConditionalFreqDist()
+
+        # create cfd with word length as condition
+        for word in tokenize.word_tokenize(text):
+            condition = len(word)
+            cfd[condition][word] += 1
+
+        self.assertEqual(cfd.conditions(), [3, 5])
+
+        # incrementing previously unseen key is still possible
+        cfd[2]["hi"] += 1
+        self.assertCountEqual(cfd.conditions(), [3, 5, 2])  # new condition added
+        self.assertEqual(
+            cfd[2]["hi"], 1
+        )  # key's frequency incremented from 0 (unseen) to 1

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_cfg2chomsky.py

@@ -0,0 +1,49 @@
+import unittest
+
+import nltk
+from nltk.grammar import CFG
+
+
+class ChomskyNormalFormForCFGTest(unittest.TestCase):
+    def test_simple(self):
+        grammar = CFG.fromstring(
+            """
+          S -> NP VP
+          PP -> P NP
+          NP -> Det N | NP PP P
+          VP -> V NP | VP PP
+          VP -> Det
+          Det -> 'a' | 'the'
+          N -> 'dog' | 'cat'
+          V -> 'chased' | 'sat'
+          P -> 'on' | 'in'
+        """
+        )
+        self.assertFalse(grammar.is_flexible_chomsky_normal_form())
+        self.assertFalse(grammar.is_chomsky_normal_form())
+        grammar = grammar.chomsky_normal_form(flexible=True)
+        self.assertTrue(grammar.is_flexible_chomsky_normal_form())
+        self.assertFalse(grammar.is_chomsky_normal_form())
+
+        grammar2 = CFG.fromstring(
+            """
+          S -> NP VP
+          NP -> VP N P
+          VP -> P
+          N -> 'dog' | 'cat'
+          P -> 'on' | 'in'
+        """
+        )
+        self.assertFalse(grammar2.is_flexible_chomsky_normal_form())
+        self.assertFalse(grammar2.is_chomsky_normal_form())
+        grammar2 = grammar2.chomsky_normal_form()
+        self.assertTrue(grammar2.is_flexible_chomsky_normal_form())
+        self.assertTrue(grammar2.is_chomsky_normal_form())
+
+    def test_complex(self):
+        grammar = nltk.data.load("grammars/large_grammars/atis.cfg")
+        self.assertFalse(grammar.is_flexible_chomsky_normal_form())
+        self.assertFalse(grammar.is_chomsky_normal_form())
+        grammar = grammar.chomsky_normal_form(flexible=True)
+        self.assertTrue(grammar.is_flexible_chomsky_normal_form())
+        self.assertFalse(grammar.is_chomsky_normal_form())

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_chunk.py

@@ -0,0 +1,85 @@
+import unittest
+
+from nltk import RegexpParser
+
+
+class TestChunkRule(unittest.TestCase):
+    def test_tag_pattern2re_pattern_quantifier(self):
+        """Test for bug https://github.com/nltk/nltk/issues/1597
+
+        Ensures that curly bracket quantifiers can be used inside a chunk rule.
+        This type of quantifier has been used for the supplementary example
+        in https://www.nltk.org/book/ch07.html#exploring-text-corpora.
+        """
+        sent = [
+            ("The", "AT"),
+            ("September-October", "NP"),
+            ("term", "NN"),
+            ("jury", "NN"),
+            ("had", "HVD"),
+            ("been", "BEN"),
+            ("charged", "VBN"),
+            ("by", "IN"),
+            ("Fulton", "NP-TL"),
+            ("Superior", "JJ-TL"),
+            ("Court", "NN-TL"),
+            ("Judge", "NN-TL"),
+            ("Durwood", "NP"),
+            ("Pye", "NP"),
+            ("to", "TO"),
+            ("investigate", "VB"),
+            ("reports", "NNS"),
+            ("of", "IN"),
+            ("possible", "JJ"),
+            ("``", "``"),
+            ("irregularities", "NNS"),
+            ("''", "''"),
+            ("in", "IN"),
+            ("the", "AT"),
+            ("hard-fought", "JJ"),
+            ("primary", "NN"),
+            ("which", "WDT"),
+            ("was", "BEDZ"),
+            ("won", "VBN"),
+            ("by", "IN"),
+            ("Mayor-nominate", "NN-TL"),
+            ("Ivan", "NP"),
+            ("Allen", "NP"),
+            ("Jr.", "NP"),
+            (".", "."),
+        ]  # source: brown corpus
+        cp = RegexpParser("CHUNK: {<N.*>{4,}}")
+        tree = cp.parse(sent)
+        assert (
+            tree.pformat()
+            == """(S
+  The/AT
+  September-October/NP
+  term/NN
+  jury/NN
+  had/HVD
+  been/BEN
+  charged/VBN
+  by/IN
+  Fulton/NP-TL
+  Superior/JJ-TL
+  (CHUNK Court/NN-TL Judge/NN-TL Durwood/NP Pye/NP)
+  to/TO
+  investigate/VB
+  reports/NNS
+  of/IN
+  possible/JJ
+  ``/``
+  irregularities/NNS
+  ''/''
+  in/IN
+  the/AT
+  hard-fought/JJ
+  primary/NN
+  which/WDT
+  was/BEDZ
+  won/VBN
+  by/IN
+  (CHUNK Mayor-nominate/NN-TL Ivan/NP Allen/NP Jr./NP)
+  ./.)"""
+        )

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_classify.py

@@ -0,0 +1,49 @@
+"""
+Unit tests for nltk.classify. See also: nltk/test/classify.doctest
+"""
+import pytest
+
+from nltk import classify
+
+TRAIN = [
+    (dict(a=1, b=1, c=1), "y"),
+    (dict(a=1, b=1, c=1), "x"),
+    (dict(a=1, b=1, c=0), "y"),
+    (dict(a=0, b=1, c=1), "x"),
+    (dict(a=0, b=1, c=1), "y"),
+    (dict(a=0, b=0, c=1), "y"),
+    (dict(a=0, b=1, c=0), "x"),
+    (dict(a=0, b=0, c=0), "x"),
+    (dict(a=0, b=1, c=1), "y"),
+]
+
+TEST = [
+    (dict(a=1, b=0, c=1)),  # unseen
+    (dict(a=1, b=0, c=0)),  # unseen
+    (dict(a=0, b=1, c=1)),  # seen 3 times, labels=y,y,x
+    (dict(a=0, b=1, c=0)),  # seen 1 time, label=x
+]
+
+RESULTS = [(0.16, 0.84), (0.46, 0.54), (0.41, 0.59), (0.76, 0.24)]
+
+
+def assert_classifier_correct(algorithm):
+    try:
+        classifier = classify.MaxentClassifier.train(
+            TRAIN, algorithm, trace=0, max_iter=1000
+        )
+    except (LookupError, AttributeError) as e:
+        pytest.skip(str(e))
+
+    for (px, py), featureset in zip(RESULTS, TEST):
+        pdist = classifier.prob_classify(featureset)
+        assert abs(pdist.prob("x") - px) < 1e-2, (pdist.prob("x"), px)
+        assert abs(pdist.prob("y") - py) < 1e-2, (pdist.prob("y"), py)
+
+
+def test_megam():
+    assert_classifier_correct("MEGAM")
+
+
+def test_tadm():
+    assert_classifier_correct("TADM")

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_collocations.py

@@ -0,0 +1,120 @@
+from nltk.collocations import BigramCollocationFinder
+from nltk.metrics import BigramAssocMeasures
+
+## Test bigram counters with discontinuous bigrams and repeated words
+
+_EPSILON = 1e-8
+SENT = "this this is is a a test test".split()
+
+
+def close_enough(x, y):
+    """Verify that two sequences of n-gram association values are within
+    _EPSILON of each other.
+    """
+
+    return all(abs(x1[1] - y1[1]) <= _EPSILON for x1, y1 in zip(x, y))
+
+
+def test_bigram2():
+    b = BigramCollocationFinder.from_words(SENT)
+
+    assert sorted(b.ngram_fd.items()) == [
+        (("a", "a"), 1),
+        (("a", "test"), 1),
+        (("is", "a"), 1),
+        (("is", "is"), 1),
+        (("test", "test"), 1),
+        (("this", "is"), 1),
+        (("this", "this"), 1),
+    ]
+    assert sorted(b.word_fd.items()) == [("a", 2), ("is", 2), ("test", 2), ("this", 2)]
+
+    assert len(SENT) == sum(b.word_fd.values()) == sum(b.ngram_fd.values()) + 1
+    assert close_enough(
+        sorted(b.score_ngrams(BigramAssocMeasures.pmi)),
+        [
+            (("a", "a"), 1.0),
+            (("a", "test"), 1.0),
+            (("is", "a"), 1.0),
+            (("is", "is"), 1.0),
+            (("test", "test"), 1.0),
+            (("this", "is"), 1.0),
+            (("this", "this"), 1.0),
+        ],
+    )
+
+
+def test_bigram3():
+    b = BigramCollocationFinder.from_words(SENT, window_size=3)
+    assert sorted(b.ngram_fd.items()) == sorted(
+        [
+            (("a", "test"), 3),
+            (("is", "a"), 3),
+            (("this", "is"), 3),
+            (("a", "a"), 1),
+            (("is", "is"), 1),
+            (("test", "test"), 1),
+            (("this", "this"), 1),
+        ]
+    )
+
+    assert sorted(b.word_fd.items()) == sorted(
+        [("a", 2), ("is", 2), ("test", 2), ("this", 2)]
+    )
+
+    assert (
+        len(SENT) == sum(b.word_fd.values()) == (sum(b.ngram_fd.values()) + 2 + 1) / 2.0
+    )
+    assert close_enough(
+        sorted(b.score_ngrams(BigramAssocMeasures.pmi)),
+        sorted(
+            [
+                (("a", "test"), 1.584962500721156),
+                (("is", "a"), 1.584962500721156),
+                (("this", "is"), 1.584962500721156),
+                (("a", "a"), 0.0),
+                (("is", "is"), 0.0),
+                (("test", "test"), 0.0),
+                (("this", "this"), 0.0),
+            ]
+        ),
+    )
+
+
+def test_bigram5():
+    b = BigramCollocationFinder.from_words(SENT, window_size=5)
+    assert sorted(b.ngram_fd.items()) == sorted(
+        [
+            (("a", "test"), 4),
+            (("is", "a"), 4),
+            (("this", "is"), 4),
+            (("is", "test"), 3),
+            (("this", "a"), 3),
+            (("a", "a"), 1),
+            (("is", "is"), 1),
+            (("test", "test"), 1),
+            (("this", "this"), 1),
+        ]
+    )
+    assert sorted(b.word_fd.items()) == sorted(
+        [("a", 2), ("is", 2), ("test", 2), ("this", 2)]
+    )
+    n_word_fd = sum(b.word_fd.values())
+    n_ngram_fd = (sum(b.ngram_fd.values()) + 4 + 3 + 2 + 1) / 4.0
+    assert len(SENT) == n_word_fd == n_ngram_fd
+    assert close_enough(
+        sorted(b.score_ngrams(BigramAssocMeasures.pmi)),
+        sorted(
+            [
+                (("a", "test"), 1.0),
+                (("is", "a"), 1.0),
+                (("this", "is"), 1.0),
+                (("is", "test"), 0.5849625007211562),
+                (("this", "a"), 0.5849625007211562),
+                (("a", "a"), -1.0),
+                (("is", "is"), -1.0),
+                (("test", "test"), -1.0),
+                (("this", "this"), -1.0),
+            ]
+        ),
+    )

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_concordance.py

@@ -0,0 +1,98 @@
+import contextlib
+import sys
+import unittest
+from io import StringIO
+
+from nltk.corpus import gutenberg
+from nltk.text import Text
+
+
+@contextlib.contextmanager
+def stdout_redirect(where):
+    sys.stdout = where
+    try:
+        yield where
+    finally:
+        sys.stdout = sys.__stdout__
+
+
+class TestConcordance(unittest.TestCase):
+    """Text constructed using: https://www.nltk.org/book/ch01.html"""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.corpus = gutenberg.words("melville-moby_dick.txt")
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def setUp(self):
+        self.text = Text(TestConcordance.corpus)
+        self.query = "monstrous"
+        self.maxDiff = None
+        self.list_out = [
+            "ong the former , one was of a most monstrous size . ... This came towards us , ",
+            'ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r',
+            "ll over with a heathenish array of monstrous clubs and spears . Some were thick",
+            "d as you gazed , and wondered what monstrous cannibal and savage could ever hav",
+            "that has survived the flood ; most monstrous and most mountainous ! That Himmal",
+            "they might scout at Moby Dick as a monstrous fable , or still worse and more de",
+            "th of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l",
+            "ing Scenes . In connexion with the monstrous pictures of whales , I am strongly",
+            "ere to enter upon those still more monstrous stories of them which are to be fo",
+            "ght have been rummaged out of this monstrous cabinet there is no telling . But ",
+            "of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u",
+        ]
+
+    def tearDown(self):
+        pass
+
+    def test_concordance_list(self):
+        concordance_out = self.text.concordance_list(self.query)
+        self.assertEqual(self.list_out, [c.line for c in concordance_out])
+
+    def test_concordance_width(self):
+        list_out = [
+            "monstrous",
+            "monstrous",
+            "monstrous",
+            "monstrous",
+            "monstrous",
+            "monstrous",
+            "Monstrous",
+            "monstrous",
+            "monstrous",
+            "monstrous",
+            "monstrous",
+        ]
+
+        concordance_out = self.text.concordance_list(self.query, width=0)
+        self.assertEqual(list_out, [c.query for c in concordance_out])
+
+    def test_concordance_lines(self):
+        concordance_out = self.text.concordance_list(self.query, lines=3)
+        self.assertEqual(self.list_out[:3], [c.line for c in concordance_out])
+
+    def test_concordance_print(self):
+        print_out = """Displaying 11 of 11 matches:
+        ong the former , one was of a most monstrous size . ... This came towards us ,
+        ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r
+        ll over with a heathenish array of monstrous clubs and spears . Some were thick
+        d as you gazed , and wondered what monstrous cannibal and savage could ever hav
+        that has survived the flood ; most monstrous and most mountainous ! That Himmal
+        they might scout at Moby Dick as a monstrous fable , or still worse and more de
+        th of Radney .'" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l
+        ing Scenes . In connexion with the monstrous pictures of whales , I am strongly
+        ere to enter upon those still more monstrous stories of them which are to be fo
+        ght have been rummaged out of this monstrous cabinet there is no telling . But
+        of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u
+        """
+
+        with stdout_redirect(StringIO()) as stdout:
+            self.text.concordance(self.query)
+
+        def strip_space(raw_str):
+            return raw_str.replace(" ", "")
+
+        self.assertEqual(strip_space(print_out), strip_space(stdout.getvalue()))

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_corenlp.py

@@ -0,0 +1,1436 @@
+"""
+Mock test for Stanford CoreNLP wrappers.
+"""
+
+from unittest import TestCase
+from unittest.mock import MagicMock
+
+import pytest
+
+from nltk.parse import corenlp
+from nltk.tree import Tree
+
+
+def setup_module(module):
+    global server
+
+    try:
+        server = corenlp.CoreNLPServer(port=9000)
+    except LookupError:
+        pytest.skip("Could not instantiate CoreNLPServer.")
+
+    try:
+        server.start()
+    except corenlp.CoreNLPServerError as e:
+        pytest.skip(
+            "Skipping CoreNLP tests because the server could not be started. "
+            "Make sure that the 9000 port is free. "
+            "{}".format(e.strerror)
+        )
+
+
+def teardown_module(module):
+    server.stop()
+
+
+class TestTokenizerAPI(TestCase):
+    def test_tokenize(self):
+        corenlp_tokenizer = corenlp.CoreNLPParser()
+
+        api_return_value = {
+            "sentences": [
+                {
+                    "index": 0,
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "",
+                            "characterOffsetBegin": 0,
+                            "characterOffsetEnd": 4,
+                            "index": 1,
+                            "originalText": "Good",
+                            "word": "Good",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 5,
+                            "characterOffsetEnd": 12,
+                            "index": 2,
+                            "originalText": "muffins",
+                            "word": "muffins",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 13,
+                            "characterOffsetEnd": 17,
+                            "index": 3,
+                            "originalText": "cost",
+                            "word": "cost",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 18,
+                            "characterOffsetEnd": 19,
+                            "index": 4,
+                            "originalText": "$",
+                            "word": "$",
+                        },
+                        {
+                            "after": "\n",
+                            "before": "",
+                            "characterOffsetBegin": 19,
+                            "characterOffsetEnd": 23,
+                            "index": 5,
+                            "originalText": "3.88",
+                            "word": "3.88",
+                        },
+                        {
+                            "after": " ",
+                            "before": "\n",
+                            "characterOffsetBegin": 24,
+                            "characterOffsetEnd": 26,
+                            "index": 6,
+                            "originalText": "in",
+                            "word": "in",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 27,
+                            "characterOffsetEnd": 30,
+                            "index": 7,
+                            "originalText": "New",
+                            "word": "New",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 31,
+                            "characterOffsetEnd": 35,
+                            "index": 8,
+                            "originalText": "York",
+                            "word": "York",
+                        },
+                        {
+                            "after": "  ",
+                            "before": "",
+                            "characterOffsetBegin": 35,
+                            "characterOffsetEnd": 36,
+                            "index": 9,
+                            "originalText": ".",
+                            "word": ".",
+                        },
+                    ],
+                },
+                {
+                    "index": 1,
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "  ",
+                            "characterOffsetBegin": 38,
+                            "characterOffsetEnd": 44,
+                            "index": 1,
+                            "originalText": "Please",
+                            "word": "Please",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 45,
+                            "characterOffsetEnd": 48,
+                            "index": 2,
+                            "originalText": "buy",
+                            "word": "buy",
+                        },
+                        {
+                            "after": "\n",
+                            "before": " ",
+                            "characterOffsetBegin": 49,
+                            "characterOffsetEnd": 51,
+                            "index": 3,
+                            "originalText": "me",
+                            "word": "me",
+                        },
+                        {
+                            "after": " ",
+                            "before": "\n",
+                            "characterOffsetBegin": 52,
+                            "characterOffsetEnd": 55,
+                            "index": 4,
+                            "originalText": "two",
+                            "word": "two",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 56,
+                            "characterOffsetEnd": 58,
+                            "index": 5,
+                            "originalText": "of",
+                            "word": "of",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 59,
+                            "characterOffsetEnd": 63,
+                            "index": 6,
+                            "originalText": "them",
+                            "word": "them",
+                        },
+                        {
+                            "after": "\n",
+                            "before": "",
+                            "characterOffsetBegin": 63,
+                            "characterOffsetEnd": 64,
+                            "index": 7,
+                            "originalText": ".",
+                            "word": ".",
+                        },
+                    ],
+                },
+                {
+                    "index": 2,
+                    "tokens": [
+                        {
+                            "after": "",
+                            "before": "\n",
+                            "characterOffsetBegin": 65,
+                            "characterOffsetEnd": 71,
+                            "index": 1,
+                            "originalText": "Thanks",
+                            "word": "Thanks",
+                        },
+                        {
+                            "after": "",
+                            "before": "",
+                            "characterOffsetBegin": 71,
+                            "characterOffsetEnd": 72,
+                            "index": 2,
+                            "originalText": ".",
+                            "word": ".",
+                        },
+                    ],
+                },
+            ]
+        }
+        corenlp_tokenizer.api_call = MagicMock(return_value=api_return_value)
+
+        input_string = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\nThanks."
+
+        expected_output = [
+            "Good",
+            "muffins",
+            "cost",
+            "$",
+            "3.88",
+            "in",
+            "New",
+            "York",
+            ".",
+            "Please",
+            "buy",
+            "me",
+            "two",
+            "of",
+            "them",
+            ".",
+            "Thanks",
+            ".",
+        ]
+
+        tokenized_output = list(corenlp_tokenizer.tokenize(input_string))
+
+        corenlp_tokenizer.api_call.assert_called_once_with(
+            "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\nThanks.",
+            properties={"annotators": "tokenize,ssplit"},
+        )
+        self.assertEqual(expected_output, tokenized_output)
+
+
+class TestTaggerAPI(TestCase):
+    def test_pos_tagger(self):
+        corenlp_tagger = corenlp.CoreNLPParser(tagtype="pos")
+
+        api_return_value = {
+            "sentences": [
+                {
+                    "basicDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 1,
+                            "dependentGloss": "What",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "cop",
+                            "dependent": 2,
+                            "dependentGloss": "is",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 3,
+                            "dependentGloss": "the",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "airspeed",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 5,
+                            "dependentGloss": "of",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 6,
+                            "dependentGloss": "an",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "compound",
+                            "dependent": 7,
+                            "dependentGloss": "unladen",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "nmod",
+                            "dependent": 8,
+                            "dependentGloss": "swallow",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "punct",
+                            "dependent": 9,
+                            "dependentGloss": "?",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                    ],
+                    "enhancedDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 1,
+                            "dependentGloss": "What",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "cop",
+                            "dependent": 2,
+                            "dependentGloss": "is",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 3,
+                            "dependentGloss": "the",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "airspeed",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 5,
+                            "dependentGloss": "of",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 6,
+                            "dependentGloss": "an",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "compound",
+                            "dependent": 7,
+                            "dependentGloss": "unladen",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "nmod:of",
+                            "dependent": 8,
+                            "dependentGloss": "swallow",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "punct",
+                            "dependent": 9,
+                            "dependentGloss": "?",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                    ],
+                    "enhancedPlusPlusDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 1,
+                            "dependentGloss": "What",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "cop",
+                            "dependent": 2,
+                            "dependentGloss": "is",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 3,
+                            "dependentGloss": "the",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "airspeed",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 5,
+                            "dependentGloss": "of",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 6,
+                            "dependentGloss": "an",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "compound",
+                            "dependent": 7,
+                            "dependentGloss": "unladen",
+                            "governor": 8,
+                            "governorGloss": "swallow",
+                        },
+                        {
+                            "dep": "nmod:of",
+                            "dependent": 8,
+                            "dependentGloss": "swallow",
+                            "governor": 4,
+                            "governorGloss": "airspeed",
+                        },
+                        {
+                            "dep": "punct",
+                            "dependent": 9,
+                            "dependentGloss": "?",
+                            "governor": 1,
+                            "governorGloss": "What",
+                        },
+                    ],
+                    "index": 0,
+                    "parse": "(ROOT\n  (SBARQ\n    (WHNP (WP What))\n    (SQ (VBZ is)\n      (NP\n        (NP (DT the) (NN airspeed))\n        (PP (IN of)\n          (NP (DT an) (NN unladen) (NN swallow)))))\n    (. ?)))",
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "",
+                            "characterOffsetBegin": 0,
+                            "characterOffsetEnd": 4,
+                            "index": 1,
+                            "lemma": "what",
+                            "originalText": "What",
+                            "pos": "WP",
+                            "word": "What",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 5,
+                            "characterOffsetEnd": 7,
+                            "index": 2,
+                            "lemma": "be",
+                            "originalText": "is",
+                            "pos": "VBZ",
+                            "word": "is",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 8,
+                            "characterOffsetEnd": 11,
+                            "index": 3,
+                            "lemma": "the",
+                            "originalText": "the",
+                            "pos": "DT",
+                            "word": "the",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 12,
+                            "characterOffsetEnd": 20,
+                            "index": 4,
+                            "lemma": "airspeed",
+                            "originalText": "airspeed",
+                            "pos": "NN",
+                            "word": "airspeed",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 21,
+                            "characterOffsetEnd": 23,
+                            "index": 5,
+                            "lemma": "of",
+                            "originalText": "of",
+                            "pos": "IN",
+                            "word": "of",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 24,
+                            "characterOffsetEnd": 26,
+                            "index": 6,
+                            "lemma": "a",
+                            "originalText": "an",
+                            "pos": "DT",
+                            "word": "an",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 27,
+                            "characterOffsetEnd": 34,
+                            "index": 7,
+                            "lemma": "unladen",
+                            "originalText": "unladen",
+                            "pos": "JJ",
+                            "word": "unladen",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 35,
+                            "characterOffsetEnd": 42,
+                            "index": 8,
+                            "lemma": "swallow",
+                            "originalText": "swallow",
+                            "pos": "VB",
+                            "word": "swallow",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 43,
+                            "characterOffsetEnd": 44,
+                            "index": 9,
+                            "lemma": "?",
+                            "originalText": "?",
+                            "pos": ".",
+                            "word": "?",
+                        },
+                    ],
+                }
+            ]
+        }
+        corenlp_tagger.api_call = MagicMock(return_value=api_return_value)
+
+        input_tokens = "What is the airspeed of an unladen swallow ?".split()
+        expected_output = [
+            ("What", "WP"),
+            ("is", "VBZ"),
+            ("the", "DT"),
+            ("airspeed", "NN"),
+            ("of", "IN"),
+            ("an", "DT"),
+            ("unladen", "JJ"),
+            ("swallow", "VB"),
+            ("?", "."),
+        ]
+        tagged_output = corenlp_tagger.tag(input_tokens)
+
+        corenlp_tagger.api_call.assert_called_once_with(
+            "What is the airspeed of an unladen swallow ?",
+            properties={
+                "ssplit.isOneSentence": "true",
+                "annotators": "tokenize,ssplit,pos",
+            },
+        )
+        self.assertEqual(expected_output, tagged_output)
+
+    def test_ner_tagger(self):
+        corenlp_tagger = corenlp.CoreNLPParser(tagtype="ner")
+
+        api_return_value = {
+            "sentences": [
+                {
+                    "index": 0,
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "",
+                            "characterOffsetBegin": 0,
+                            "characterOffsetEnd": 4,
+                            "index": 1,
+                            "lemma": "Rami",
+                            "ner": "PERSON",
+                            "originalText": "Rami",
+                            "pos": "NNP",
+                            "word": "Rami",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 5,
+                            "characterOffsetEnd": 8,
+                            "index": 2,
+                            "lemma": "Eid",
+                            "ner": "PERSON",
+                            "originalText": "Eid",
+                            "pos": "NNP",
+                            "word": "Eid",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 9,
+                            "characterOffsetEnd": 11,
+                            "index": 3,
+                            "lemma": "be",
+                            "ner": "O",
+                            "originalText": "is",
+                            "pos": "VBZ",
+                            "word": "is",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 12,
+                            "characterOffsetEnd": 20,
+                            "index": 4,
+                            "lemma": "study",
+                            "ner": "O",
+                            "originalText": "studying",
+                            "pos": "VBG",
+                            "word": "studying",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 21,
+                            "characterOffsetEnd": 23,
+                            "index": 5,
+                            "lemma": "at",
+                            "ner": "O",
+                            "originalText": "at",
+                            "pos": "IN",
+                            "word": "at",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 24,
+                            "characterOffsetEnd": 29,
+                            "index": 6,
+                            "lemma": "Stony",
+                            "ner": "ORGANIZATION",
+                            "originalText": "Stony",
+                            "pos": "NNP",
+                            "word": "Stony",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 30,
+                            "characterOffsetEnd": 35,
+                            "index": 7,
+                            "lemma": "Brook",
+                            "ner": "ORGANIZATION",
+                            "originalText": "Brook",
+                            "pos": "NNP",
+                            "word": "Brook",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 36,
+                            "characterOffsetEnd": 46,
+                            "index": 8,
+                            "lemma": "University",
+                            "ner": "ORGANIZATION",
+                            "originalText": "University",
+                            "pos": "NNP",
+                            "word": "University",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 47,
+                            "characterOffsetEnd": 49,
+                            "index": 9,
+                            "lemma": "in",
+                            "ner": "O",
+                            "originalText": "in",
+                            "pos": "IN",
+                            "word": "in",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 50,
+                            "characterOffsetEnd": 52,
+                            "index": 10,
+                            "lemma": "NY",
+                            "ner": "O",
+                            "originalText": "NY",
+                            "pos": "NNP",
+                            "word": "NY",
+                        },
+                    ],
+                }
+            ]
+        }
+
+        corenlp_tagger.api_call = MagicMock(return_value=api_return_value)
+
+        input_tokens = "Rami Eid is studying at Stony Brook University in NY".split()
+        expected_output = [
+            ("Rami", "PERSON"),
+            ("Eid", "PERSON"),
+            ("is", "O"),
+            ("studying", "O"),
+            ("at", "O"),
+            ("Stony", "ORGANIZATION"),
+            ("Brook", "ORGANIZATION"),
+            ("University", "ORGANIZATION"),
+            ("in", "O"),
+            ("NY", "O"),
+        ]
+        tagged_output = corenlp_tagger.tag(input_tokens)
+
+        corenlp_tagger.api_call.assert_called_once_with(
+            "Rami Eid is studying at Stony Brook University in NY",
+            properties={
+                "ssplit.isOneSentence": "true",
+                "annotators": "tokenize,ssplit,ner",
+            },
+        )
+        self.assertEqual(expected_output, tagged_output)
+
+    def test_unexpected_tagtype(self):
+        with self.assertRaises(ValueError):
+            corenlp_tagger = corenlp.CoreNLPParser(tagtype="test")
+
+
+class TestParserAPI(TestCase):
+    def test_parse(self):
+        corenlp_parser = corenlp.CoreNLPParser()
+
+        api_return_value = {
+            "sentences": [
+                {
+                    "basicDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "dep",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "enhancedDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "dep",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod:over",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "enhancedPlusPlusDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "dep",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod:over",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "index": 0,
+                    "parse": "(ROOT\n  (NP\n    (NP (DT The) (JJ quick) (JJ brown) (NN fox))\n    (NP\n      (NP (NNS jumps))\n      (PP (IN over)\n        (NP (DT the) (JJ lazy) (NN dog))))))",
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "",
+                            "characterOffsetBegin": 0,
+                            "characterOffsetEnd": 3,
+                            "index": 1,
+                            "lemma": "the",
+                            "originalText": "The",
+                            "pos": "DT",
+                            "word": "The",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 4,
+                            "characterOffsetEnd": 9,
+                            "index": 2,
+                            "lemma": "quick",
+                            "originalText": "quick",
+                            "pos": "JJ",
+                            "word": "quick",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 10,
+                            "characterOffsetEnd": 15,
+                            "index": 3,
+                            "lemma": "brown",
+                            "originalText": "brown",
+                            "pos": "JJ",
+                            "word": "brown",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 16,
+                            "characterOffsetEnd": 19,
+                            "index": 4,
+                            "lemma": "fox",
+                            "originalText": "fox",
+                            "pos": "NN",
+                            "word": "fox",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 20,
+                            "characterOffsetEnd": 25,
+                            "index": 5,
+                            "lemma": "jump",
+                            "originalText": "jumps",
+                            "pos": "VBZ",
+                            "word": "jumps",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 26,
+                            "characterOffsetEnd": 30,
+                            "index": 6,
+                            "lemma": "over",
+                            "originalText": "over",
+                            "pos": "IN",
+                            "word": "over",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 31,
+                            "characterOffsetEnd": 34,
+                            "index": 7,
+                            "lemma": "the",
+                            "originalText": "the",
+                            "pos": "DT",
+                            "word": "the",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 35,
+                            "characterOffsetEnd": 39,
+                            "index": 8,
+                            "lemma": "lazy",
+                            "originalText": "lazy",
+                            "pos": "JJ",
+                            "word": "lazy",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 40,
+                            "characterOffsetEnd": 43,
+                            "index": 9,
+                            "lemma": "dog",
+                            "originalText": "dog",
+                            "pos": "NN",
+                            "word": "dog",
+                        },
+                    ],
+                }
+            ]
+        }
+
+        corenlp_parser.api_call = MagicMock(return_value=api_return_value)
+
+        input_string = "The quick brown fox jumps over the lazy dog".split()
+        expected_output = Tree(
+            "ROOT",
+            [
+                Tree(
+                    "NP",
+                    [
+                        Tree(
+                            "NP",
+                            [
+                                Tree("DT", ["The"]),
+                                Tree("JJ", ["quick"]),
+                                Tree("JJ", ["brown"]),
+                                Tree("NN", ["fox"]),
+                            ],
+                        ),
+                        Tree(
+                            "NP",
+                            [
+                                Tree("NP", [Tree("NNS", ["jumps"])]),
+                                Tree(
+                                    "PP",
+                                    [
+                                        Tree("IN", ["over"]),
+                                        Tree(
+                                            "NP",
+                                            [
+                                                Tree("DT", ["the"]),
+                                                Tree("JJ", ["lazy"]),
+                                                Tree("NN", ["dog"]),
+                                            ],
+                                        ),
+                                    ],
+                                ),
+                            ],
+                        ),
+                    ],
+                )
+            ],
+        )
+
+        parsed_data = next(corenlp_parser.parse(input_string))
+
+        corenlp_parser.api_call.assert_called_once_with(
+            "The quick brown fox jumps over the lazy dog",
+            properties={"ssplit.eolonly": "true"},
+        )
+        self.assertEqual(expected_output, parsed_data)
+
+    def test_dependency_parser(self):
+        corenlp_parser = corenlp.CoreNLPDependencyParser()
+
+        api_return_value = {
+            "sentences": [
+                {
+                    "basicDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "enhancedDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod:over",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "enhancedPlusPlusDependencies": [
+                        {
+                            "dep": "ROOT",
+                            "dependent": 5,
+                            "dependentGloss": "jumps",
+                            "governor": 0,
+                            "governorGloss": "ROOT",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 1,
+                            "dependentGloss": "The",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 2,
+                            "dependentGloss": "quick",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 3,
+                            "dependentGloss": "brown",
+                            "governor": 4,
+                            "governorGloss": "fox",
+                        },
+                        {
+                            "dep": "nsubj",
+                            "dependent": 4,
+                            "dependentGloss": "fox",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                        {
+                            "dep": "case",
+                            "dependent": 6,
+                            "dependentGloss": "over",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "det",
+                            "dependent": 7,
+                            "dependentGloss": "the",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "amod",
+                            "dependent": 8,
+                            "dependentGloss": "lazy",
+                            "governor": 9,
+                            "governorGloss": "dog",
+                        },
+                        {
+                            "dep": "nmod:over",
+                            "dependent": 9,
+                            "dependentGloss": "dog",
+                            "governor": 5,
+                            "governorGloss": "jumps",
+                        },
+                    ],
+                    "index": 0,
+                    "tokens": [
+                        {
+                            "after": " ",
+                            "before": "",
+                            "characterOffsetBegin": 0,
+                            "characterOffsetEnd": 3,
+                            "index": 1,
+                            "lemma": "the",
+                            "originalText": "The",
+                            "pos": "DT",
+                            "word": "The",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 4,
+                            "characterOffsetEnd": 9,
+                            "index": 2,
+                            "lemma": "quick",
+                            "originalText": "quick",
+                            "pos": "JJ",
+                            "word": "quick",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 10,
+                            "characterOffsetEnd": 15,
+                            "index": 3,
+                            "lemma": "brown",
+                            "originalText": "brown",
+                            "pos": "JJ",
+                            "word": "brown",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 16,
+                            "characterOffsetEnd": 19,
+                            "index": 4,
+                            "lemma": "fox",
+                            "originalText": "fox",
+                            "pos": "NN",
+                            "word": "fox",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 20,
+                            "characterOffsetEnd": 25,
+                            "index": 5,
+                            "lemma": "jump",
+                            "originalText": "jumps",
+                            "pos": "VBZ",
+                            "word": "jumps",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 26,
+                            "characterOffsetEnd": 30,
+                            "index": 6,
+                            "lemma": "over",
+                            "originalText": "over",
+                            "pos": "IN",
+                            "word": "over",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 31,
+                            "characterOffsetEnd": 34,
+                            "index": 7,
+                            "lemma": "the",
+                            "originalText": "the",
+                            "pos": "DT",
+                            "word": "the",
+                        },
+                        {
+                            "after": " ",
+                            "before": " ",
+                            "characterOffsetBegin": 35,
+                            "characterOffsetEnd": 39,
+                            "index": 8,
+                            "lemma": "lazy",
+                            "originalText": "lazy",
+                            "pos": "JJ",
+                            "word": "lazy",
+                        },
+                        {
+                            "after": "",
+                            "before": " ",
+                            "characterOffsetBegin": 40,
+                            "characterOffsetEnd": 43,
+                            "index": 9,
+                            "lemma": "dog",
+                            "originalText": "dog",
+                            "pos": "NN",
+                            "word": "dog",
+                        },
+                    ],
+                }
+            ]
+        }
+
+        corenlp_parser.api_call = MagicMock(return_value=api_return_value)
+
+        input_string = "The quick brown fox jumps over the lazy dog".split()
+        expected_output = Tree(
+            "jumps",
+            [
+                Tree("fox", ["The", "quick", "brown"]),
+                Tree("dog", ["over", "the", "lazy"]),
+            ],
+        )
+
+        parsed_data = next(corenlp_parser.parse(input_string))
+
+        corenlp_parser.api_call.assert_called_once_with(
+            "The quick brown fox jumps over the lazy dog",
+            properties={"ssplit.eolonly": "true"},
+        )
+        self.assertEqual(expected_output, parsed_data.tree())

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_corpora.py

@@ -0,0 +1,274 @@
+import unittest
+
+import pytest
+
+from nltk.corpus import (  # mwa_ppdb
+    cess_cat,
+    cess_esp,
+    conll2007,
+    floresta,
+    indian,
+    ptb,
+    sinica_treebank,
+    udhr,
+)
+from nltk.tree import Tree
+
+
+class TestUdhr(unittest.TestCase):
+    def test_words(self):
+        for name in udhr.fileids():
+            words = list(udhr.words(name))
+            self.assertTrue(words)
+
+    def test_raw_unicode(self):
+        for name in udhr.fileids():
+            txt = udhr.raw(name)
+            assert not isinstance(txt, bytes), name
+
+    def test_polish_encoding(self):
+        text_pl = udhr.raw("Polish-Latin2")[:164]
+        text_ppl = udhr.raw("Polish_Polski-Latin2")[:164]
+        expected = """POWSZECHNA DEKLARACJA PRAW CZŁOWIEKA
+[Preamble]
+Trzecia Sesja Ogólnego Zgromadzenia ONZ, obradująca w Paryżu, \
+uchwaliła 10 grudnia 1948 roku jednomyślnie Powszechną"""
+        assert text_pl == expected, "Polish-Latin2"
+        assert text_ppl == expected, "Polish_Polski-Latin2"
+
+
+class TestIndian(unittest.TestCase):
+    def test_words(self):
+        words = indian.words()[:3]
+        self.assertEqual(words, ["মহিষের", "সন্তান", ":"])
+
+    def test_tagged_words(self):
+        tagged_words = indian.tagged_words()[:3]
+        self.assertEqual(
+            tagged_words, [("মহিষের", "NN"), ("সন্তান", "NN"), (":", "SYM")]
+        )
+
+
+class TestCess(unittest.TestCase):
+    def test_catalan(self):
+        words = cess_cat.words()[:15]
+        txt = "El Tribunal_Suprem -Fpa- TS -Fpt- ha confirmat la condemna a quatre anys d' inhabilitació especial"
+        self.assertEqual(words, txt.split())
+        self.assertEqual(cess_cat.tagged_sents()[0][34][0], "càrrecs")
+
+    def test_esp(self):
+        words = cess_esp.words()[:15]
+        txt = "El grupo estatal Electricité_de_France -Fpa- EDF -Fpt- anunció hoy , jueves , la compra del"
+        self.assertEqual(words, txt.split())
+        self.assertEqual(cess_esp.words()[115], "años")
+
+
+class TestFloresta(unittest.TestCase):
+    def test_words(self):
+        words = floresta.words()[:10]
+        txt = "Um revivalismo refrescante O 7_e_Meio é um ex-libris de a"
+        self.assertEqual(words, txt.split())
+
+
+class TestSinicaTreebank(unittest.TestCase):
+    def test_sents(self):
+        first_3_sents = sinica_treebank.sents()[:3]
+        self.assertEqual(
+            first_3_sents, [["一"], ["友情"], ["嘉珍", "和", "我", "住在", "同一條", "巷子"]]
+        )
+
+    def test_parsed_sents(self):
+        parsed_sents = sinica_treebank.parsed_sents()[25]
+        self.assertEqual(
+            parsed_sents,
+            Tree(
+                "S",
+                [
+                    Tree("NP", [Tree("Nba", ["嘉珍"])]),
+                    Tree("V‧地", [Tree("VA11", ["不停"]), Tree("DE", ["的"])]),
+                    Tree("VA4", ["哭泣"]),
+                ],
+            ),
+        )
+
+
+class TestCoNLL2007(unittest.TestCase):
+    # Reading the CoNLL 2007 Dependency Treebanks
+
+    def test_sents(self):
+        sents = conll2007.sents("esp.train")[0]
+        self.assertEqual(
+            sents[:6], ["El", "aumento", "del", "índice", "de", "desempleo"]
+        )
+
+    def test_parsed_sents(self):
+
+        parsed_sents = conll2007.parsed_sents("esp.train")[0]
+
+        self.assertEqual(
+            parsed_sents.tree(),
+            Tree(
+                "fortaleció",
+                [
+                    Tree(
+                        "aumento",
+                        [
+                            "El",
+                            Tree(
+                                "del",
+                                [
+                                    Tree(
+                                        "índice",
+                                        [
+                                            Tree(
+                                                "de",
+                                                [Tree("desempleo", ["estadounidense"])],
+                                            )
+                                        ],
+                                    )
+                                ],
+                            ),
+                        ],
+                    ),
+                    "hoy",
+                    "considerablemente",
+                    Tree(
+                        "al",
+                        [
+                            Tree(
+                                "euro",
+                                [
+                                    Tree(
+                                        "cotizaba",
+                                        [
+                                            ",",
+                                            "que",
+                                            Tree("a", [Tree("15.35", ["las", "GMT"])]),
+                                            "se",
+                                            Tree(
+                                                "en",
+                                                [
+                                                    Tree(
+                                                        "mercado",
+                                                        [
+                                                            "el",
+                                                            Tree("de", ["divisas"]),
+                                                            Tree("de", ["Fráncfort"]),
+                                                        ],
+                                                    )
+                                                ],
+                                            ),
+                                            Tree("a", ["0,9452_dólares"]),
+                                            Tree(
+                                                "frente_a",
+                                                [
+                                                    ",",
+                                                    Tree(
+                                                        "0,9349_dólares",
+                                                        [
+                                                            "los",
+                                                            Tree(
+                                                                "de",
+                                                                [
+                                                                    Tree(
+                                                                        "mañana",
+                                                                        ["esta"],
+                                                                    )
+                                                                ],
+                                                            ),
+                                                        ],
+                                                    ),
+                                                ],
+                                            ),
+                                        ],
+                                    )
+                                ],
+                            )
+                        ],
+                    ),
+                    ".",
+                ],
+            ),
+        )
+
+
+@pytest.mark.skipif(
+    not ptb.fileids(),
+    reason="A full installation of the Penn Treebank is not available",
+)
+class TestPTB(unittest.TestCase):
+    def test_fileids(self):
+        self.assertEqual(
+            ptb.fileids()[:4],
+            [
+                "BROWN/CF/CF01.MRG",
+                "BROWN/CF/CF02.MRG",
+                "BROWN/CF/CF03.MRG",
+                "BROWN/CF/CF04.MRG",
+            ],
+        )
+
+    def test_words(self):
+        self.assertEqual(
+            ptb.words("WSJ/00/WSJ_0003.MRG")[:7],
+            ["A", "form", "of", "asbestos", "once", "used", "*"],
+        )
+
+    def test_tagged_words(self):
+        self.assertEqual(
+            ptb.tagged_words("WSJ/00/WSJ_0003.MRG")[:3],
+            [("A", "DT"), ("form", "NN"), ("of", "IN")],
+        )
+
+    def test_categories(self):
+        self.assertEqual(
+            ptb.categories(),
+            [
+                "adventure",
+                "belles_lettres",
+                "fiction",
+                "humor",
+                "lore",
+                "mystery",
+                "news",
+                "romance",
+                "science_fiction",
+            ],
+        )
+
+    def test_news_fileids(self):
+        self.assertEqual(
+            ptb.fileids("news")[:3],
+            ["WSJ/00/WSJ_0001.MRG", "WSJ/00/WSJ_0002.MRG", "WSJ/00/WSJ_0003.MRG"],
+        )
+
+    def test_category_words(self):
+        self.assertEqual(
+            ptb.words(categories=["humor", "fiction"])[:6],
+            ["Thirty-three", "Scotty", "did", "not", "go", "back"],
+        )
+
+
+@pytest.mark.skip("Skipping test for mwa_ppdb.")
+class TestMWAPPDB(unittest.TestCase):
+    def test_fileids(self):
+        self.assertEqual(
+            mwa_ppdb.fileids(), ["ppdb-1.0-xxxl-lexical.extended.synonyms.uniquepairs"]
+        )
+
+    def test_entries(self):
+        self.assertEqual(
+            mwa_ppdb.entries()[:10],
+            [
+                ("10/17/01", "17/10/2001"),
+                ("102,70", "102.70"),
+                ("13,53", "13.53"),
+                ("3.2.5.3.2.1", "3.2.5.3.2.1."),
+                ("53,76", "53.76"),
+                ("6.9.5", "6.9.5."),
+                ("7.7.6.3", "7.7.6.3."),
+                ("76,20", "76.20"),
+                ("79,85", "79.85"),
+                ("93,65", "93.65"),
+            ],
+        )

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_corpus_views.py

@@ -0,0 +1,48 @@
+"""
+Corpus View Regression Tests
+"""
+import unittest
+
+import nltk.data
+from nltk.corpus.reader.util import (
+    StreamBackedCorpusView,
+    read_line_block,
+    read_whitespace_block,
+)
+
+
+class TestCorpusViews(unittest.TestCase):
+
+    linetok = nltk.LineTokenizer(blanklines="keep")
+    names = [
+        "corpora/inaugural/README",  # A very short file (160 chars)
+        "corpora/inaugural/1793-Washington.txt",  # A relatively short file (791 chars)
+        "corpora/inaugural/1909-Taft.txt",  # A longer file (32k chars)
+    ]
+
+    def data(self):
+        for name in self.names:
+            f = nltk.data.find(name)
+            with f.open() as fp:
+                file_data = fp.read().decode("utf8")
+            yield f, file_data
+
+    def test_correct_values(self):
+        # Check that corpus views produce the correct sequence of values.
+
+        for f, file_data in self.data():
+            v = StreamBackedCorpusView(f, read_whitespace_block)
+            self.assertEqual(list(v), file_data.split())
+
+            v = StreamBackedCorpusView(f, read_line_block)
+            self.assertEqual(list(v), self.linetok.tokenize(file_data))
+
+    def test_correct_length(self):
+        # Check that the corpus views report the correct lengths:
+
+        for f, file_data in self.data():
+            v = StreamBackedCorpusView(f, read_whitespace_block)
+            self.assertEqual(len(v), len(file_data.split()))
+
+            v = StreamBackedCorpusView(f, read_line_block)
+            self.assertEqual(len(v), len(self.linetok.tokenize(file_data)))

env-llmeval/lib/python3.10/site-packages/nltk/test/unit/test_data.py

@@ -0,0 +1,15 @@
+import pytest
+
+import nltk.data
+
+
+def test_find_raises_exception():
+    with pytest.raises(LookupError):
+        nltk.data.find("no_such_resource/foo")
+
+
+def test_find_raises_exception_with_full_resource_name():
+    no_such_thing = "no_such_thing/bar"
+    with pytest.raises(LookupError) as exc:
+        nltk.data.find(no_such_thing)
+        assert no_such_thing in str(exc)