<commit_before>import sys
import pylibvw

class SearchTask():
    def __init__(self, vw, sch, num_actions):
        self.vw = vw
        self.sch = sch
        self.blank_line = self.vw.example("")
        self.blank_line.finish()
        self.bogus_example = self.vw.example("1 | x")

    def __del__(self):
        self.bogus_example.finish()
        pass

    def _run(self, your_own_input_example):
        pass

    def _call_vw(self, fn, isTest):
        self.bogus_example.set_test_only(isTest)
        self.sch.set_structured_predict_hook(fn)
        self.vw.learn(self.bogus_example)
        self.vw.learn(self.blank_line) # this will cause our ._run hook to get called
        
    def learn(self, data_iterator):
        for my_example in data_iterator():
            self._call_vw(lambda: self._run(my_example), isTest=False)

    def predict(self, my_example):
        self._output = None
        def f(): self._output = self._run(my_example)
        self._call_vw(f, isTest=True)
        #if self._output is None:
        #    raise Exception('structured predict hook failed to return anything')
        # don't raise this exception because your _run code legitimately
        # _could_ return None!
        return self._output

class vw(pylibvw.vw):
    """The pyvw.vw object is a (trivial) wrapper around the pylibvw.vw
    object; you're probably best off using this directly and ignoring
    the pylibvw.vw structure entirely."""
    
    def __init__(self, argString=""):
        """Initialize the vw object. The (optional) argString is the
        same as the command line arguments you'd use to run vw (eg,"--audit")"""
        pylibvw.vw.__init__(self,argString)
        self.finished = False

    def get_weight(self, index, offset=0):
        """Given an (integer) index (and an optional offset), return
        the weight for that position in the (learned) weight vector."""
        return pylibvw.vw.get_weight(self, index, offset)

    def learn(self, example):
        """Perform an online update; example can either be an example
        object or a string (in which case it is parsed and then
        learned on)."""
        if isinstance(example, str):
            self.learn_string(example)
        else:
            pylibvw.vw.learn(self, example)

    def finish(self):
        """stop VW by calling finish (and, eg, write weights to disk)"""
        if not self.finished:
            pylibvw.vw.finish(self)
            self.finished = True

    def example(self, stringOrDict=None, labelType=pylibvw.vw.lDefault):
        """TODO: document"""
        return example(self, stringOrDict, labelType)

    def __del__(self):
        self.finish()

    def init_search_task(self, search_task):
        sch = self.get_search_ptr()

        def predict(examples, my_tag, oracle, condition=None, allowed=None, learner_id=0):
            """The basic (via-reduction) prediction mechanism. Several
            variants are supported through this overloaded function:
            
              'examples' can be a single example (interpreted as non-LDF
                 mode) or a list of examples (interpreted as LDF mode)

              'my_tag' should be an integer id, specifying this prediction
                 
              'oracle' can be a single label (or in LDF mode a single
                 array index in 'examples') or a list of such labels if
                 the oracle policy is indecisive; if it is None, then
                 the oracle doesn't care

              'condition' should be either: (1) a (tag,char) pair, indicating
                 to condition on the given tag with identifier from the char;
                 or (2) a (tag,len,char) triple, indicating to condition on
                 tag, tag-1, tag-2, ..., tag-len with identifiers char,
                 char+1, char+2, ..., char+len. or it can be a (heterogenous)
                 list of such things.

              'allowed' can be None, in which case all actions are allowed;
                 or it can be list of valid actions (in LDF mode, this should
                 be None and you should encode the valid actions in 'examples')

              'learner_id' specifies the underlying learner id

            Returns a single prediction.
            """
            if (isinstance(examples, list) and all([isinstance(ex, example) or isinstance(ex, pylibvw.example) for ex in examples])) or \
               isinstance(examples, example) or isinstance(examples, pylibvw.example):
                P = sch.get_predictor(my_tag)
                if isinstance(examples, list): # LDF
                    P.set_input_length(len(examples))
                    for n in range(len(examples)):
                        P.set_input_at(n, examples[n])
                else: # non-LDF
                    P.set_input(examples)
                
                if isinstance(oracle, list): P.set_oracles(oracle)
                elif isinstance(oracle, int): P.set_oracle(oracle)
                else: raise TypeError('expecting oracle to be a list or an integer')

                if condition is not None:
                    if not isinstance(condition, list): condition = [condition]
                    for c in condition:
                        if not isinstance(c, tuple): raise TypeError('item ' + str(c) + ' in condition list is malformed')
                        if   len(c) == 2 and isinstance(c[0], int) and isinstance(c[1], str) and len(c[1]) == 1:
                            P.add_condition(max(0, c[0]), c[1])
                        elif len(c) == 3 and isinstance(c[0], int) and isinstance(c[1], int) and isinstance(c[2], str) and len(c[2]) == 1:
                            P.add_condition_range(max(0,c[0]), max(0,c[1]), c[2])
                        else:
                            raise TypeError('item ' + str(c) + ' in condition list malformed')

                if allowed is None: pass
                elif isinstance(allowed, list):
                    P.set_alloweds(allowed)
                else: raise TypeError('allowed argument wrong type')

                if learner_id != 0: P.set_learner_id(learner_id)

                return P.predict()
            else:
                raise TypeError("'examples' should be a pyvw example (or a pylibvw example), or a list of said things")

        sch.predict = predict
        num_actions = sch.get_num_actions()
        return search_task(self, sch, num_actions)        

class namespace_id():
    """The namespace_id class is simply a wrapper to convert between
    hash spaces referred to by character (eg 'x') versus their index
    in a particular example. Mostly used internally, you shouldn't
    really need to touch this."""

    def __init__(self, ex, id):
        """Given an example and an id, construct a namespace_id. The
        id can either be an integer (in which case we take it to be an
        index into ex.indices[]) or a string (in which case we take
        the first character as the namespace id)."""
        if isinstance(id, int):  # you've specified a namespace by index
            if id < 0 or id >= ex.num_namespaces():
                raise Exception('namespace ' + str(id) + ' out of bounds')
            self.id = id
            self.ord_ns = ex.namespace(id)
            self.ns = chr(self.ord_ns)
        elif isinstance(id, str):   # you've specified a namespace by string
            if len(id) == 0:
                id = ' '
            self.id = None  # we don't know and we don't want to do the linear search requered to find it
            self.ns = id[0]
            self.ord_ns = ord(self.ns)
        else:
            raise Exception("ns_to_characterord failed because id type is unknown: " + str(type(id)))

class example_namespace():
    """The example_namespace class is a helper class that allows you
    to extract namespaces from examples and operate at a namespace<commit_msg>minor tweaks to pyvw<commit_after>import sys
import pylibvw

class SearchTask():
    def __init__(self, vw, sch, num_actions):
        self.vw = vw
        self.sch = sch
        self.blank_line = self.vw.example("")
        self.blank_line.finish()
        self.bogus_example = self.vw.example("1 | x")

    def __del__(self):
        self.bogus_example.finish()
        pass

    def _run(self, your_own_input_example):
        pass

    def _call_vw(self, fn, isTest):
        self.bogus_example.set_test_only(isTest)
        self.sch.set_structured_predict_hook(fn)
        self.vw.learn(self.bogus_example)
        self.vw.learn(self.blank_line) # this will cause our ._run hook to get called
        
    def learn(self, data_iterator):
        for my_example in data_iterator():
            self._call_vw(lambda: self._run(my_example), isTest=False)

    def predict(self, my_example):
        self._output = None
        def f(): self._output = self._run(my_example)
        self._call_vw(f, isTest=True)
        #if self._output is None:
        #    raise Exception('structured predict hook failed to return anything')
        # don't raise this exception because your _run code legitimately
        # _could_ return None!
        return self._output

class vw(pylibvw.vw):
    """The pyvw.vw object is a (trivial) wrapper around the pylibvw.vw
    object; you're probably best off using this directly and ignoring
    the pylibvw.vw structure entirely."""
    
    def __init__(self, argString=""):
        """Initialize the vw object. The (optional) argString is the
        same as the command line arguments you'd use to run vw (eg,"--audit")"""
        pylibvw.vw.__init__(self,argString)
        self.finished = False

    def get_weight(self, index, offset=0):
        """Given an (integer) index (and an optional offset), return
        the weight for that position in the (learned) weight vector."""
        return pylibvw.vw.get_weight(self, index, offset)

    def learn(self, example):
        """Perform an online update; example can either be an example
        object or a string (in which case it is parsed and then
        learned on)."""
        if isinstance(example, str):
            self.learn_string(example)
        else:
            pylibvw.vw.learn(self, example)

    def finish(self):
        """stop VW by calling finish (and, eg, write weights to disk)"""
        if not self.finished:
            pylibvw.vw.finish(self)
            self.finished = True

    def example(self, stringOrDict=None, labelType=pylibvw.vw.lDefault):
        """TODO: document"""
        return example(self, stringOrDict, labelType)

    def __del__(self):
        self.finish()

    def init_search_task(self, search_task, task_data=None):
        sch = self.get_search_ptr()

        def predict(examples, my_tag, oracle, condition=None, allowed=None, learner_id=0):
            """The basic (via-reduction) prediction mechanism. Several
            variants are supported through this overloaded function:
            
              'examples' can be a single example (interpreted as non-LDF
                 mode) or a list of examples (interpreted as LDF mode)

              'my_tag' should be an integer id, specifying this prediction
                 
              'oracle' can be a single label (or in LDF mode a single
                 array index in 'examples') or a list of such labels if
                 the oracle policy is indecisive; if it is None, then
                 the oracle doesn't care

              'condition' should be either: (1) a (tag,char) pair, indicating
                 to condition on the given tag with identifier from the char;
                 or (2) a (tag,len,char) triple, indicating to condition on
                 tag, tag-1, tag-2, ..., tag-len with identifiers char,
                 char+1, char+2, ..., char+len. or it can be a (heterogenous)
                 list of such things.

              'allowed' can be None, in which case all actions are allowed;
                 or it can be list of valid actions (in LDF mode, this should
                 be None and you should encode the valid actions in 'examples')

              'learner_id' specifies the underlying learner id

            Returns a single prediction.
            """
            if (isinstance(examples, list) and all([isinstance(ex, example) or isinstance(ex, pylibvw.example) for ex in examples])) or \
               isinstance(examples, example) or isinstance(examples, pylibvw.example):
                P = sch.get_predictor(my_tag)
                if isinstance(examples, list): # LDF
                    P.set_input_length(len(examples))
                    for n in range(len(examples)):
                        P.set_input_at(n, examples[n])
                else: # non-LDF
                    P.set_input(examples)
                
                if isinstance(oracle, list): P.set_oracles(oracle)
                elif isinstance(oracle, int): P.set_oracle(oracle)
                else: raise TypeError('expecting oracle to be a list or an integer')

                if condition is not None:
                    if not isinstance(condition, list): condition = [condition]
                    for c in condition:
                        if not isinstance(c, tuple): raise TypeError('item ' + str(c) + ' in condition list is malformed')
                        if   len(c) == 2 and isinstance(c[0], int) and isinstance(c[1], str) and len(c[1]) == 1:
                            P.add_condition(max(0, c[0]), c[1])
                        elif len(c) == 3 and isinstance(c[0], int) and isinstance(c[1], int) and isinstance(c[2], str) and len(c[2]) == 1:
                            P.add_condition_range(max(0,c[0]), max(0,c[1]), c[2])
                        else:
                            raise TypeError('item ' + str(c) + ' in condition list malformed')

                if allowed is None: pass
                elif isinstance(allowed, list):
                    P.set_alloweds(allowed)
                else: raise TypeError('allowed argument wrong type')

                if learner_id != 0: P.set_learner_id(learner_id)

                return P.predict()
            else:
                raise TypeError("'examples' should be a pyvw example (or a pylibvw example), or a list of said things")

        sch.predict = predict
        num_actions = sch.get_num_actions()
        return search_task(self, sch, num_actions) if task_data is None else search_task(self, sch, num_actions, task_data)

class namespace_id():
    """The namespace_id class is simply a wrapper to convert between
    hash spaces referred to by character (eg 'x') versus their index
    in a particular example. Mostly used internally, you shouldn't
    really need to touch this."""

    def __init__(self, ex, id):
        """Given an example and an id, construct a namespace_id. The
        id can either be an integer (in which case we take it to be an
        index into ex.indices[]) or a string (in which case we take
        the first character as the namespace id)."""
        if isinstance(id, int):  # you've specified a namespace by index
            if id < 0 or id >= ex.num_namespaces():
                raise Exception('namespace ' + str(id) + ' out of bounds')
            self.id = id
            self.ord_ns = ex.namespace(id)
            self.ns = chr(self.ord_ns)
        elif isinstance(id, str):   # you've specified a namespace by string
            if len(id) == 0:
                id = ' '
            self.id = None  # we don't know and we don't want to do the linear search requered to find it
            self.ns = id[0]
            self.ord_ns = ord(self.ns)
        else:
            raise Exception("ns_to_characterord failed because id type is unknown: " + str(type(id)))

class example_namespace():
    """The example_namespace class is a helper class that allows you
    to extract namespaces from examples and operate at a namespace
{'fraction_non_alphanumeric': 0.09042352645469398, 'fraction_numerical': 0.004021616187005153, 'mean_word_length': 2.0360549408622663, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7079619', 'n_tokens_mistral': 4510, 'n_tokens_neox': 4302, 'n_words': 1552}