<commit_before># -*- coding: utf-8 -*-
from __future__ import print_function

import sys
import string
import numbers

try:
    from deep_neural_network.face_classifier import detect_and_predict_face_emotion
except ImportError:
    detect_and_predict_face_emotion = None

try:
    basestring
except NameError:
    basestring = (str, bytes)


TRUE_STRINGS = {
    'true', 'yes', 'on', 'yeah', 'yup', 'yarp',
    'oui',  # French
    'ja',   # German, Danish, Dutch, Afrikaans, Swedish, Norwegian
    'sim',  # Portuguese
    'sea',  # Irish
    'jes',  # Esperanto
    u'ﻦﻌﻣ', # Arabic
}
FALSE_STRINGS = {
    'false', 'no', 'off', 'nope', 'nah', 'narp',
    'non',   # French
    'nein',  # German
    'nej',   # Danish
    'nee',   # Dutch
    u'ﻷ',    # Arabic
}
STRIP_CHARS = string.whitespace + string.punctuation

HAPPY_STRINGS = {
    'happy',
    'happy-face',
    'joyful',
}
ANGRY_STRINGS = {
    'angry',
    'anger',
    'grrr',
}
NEUTRAL_STRINGS = {
    'neutral',
}


class UnIshable(Exception):
    def __init__(self, value):
        super(UnIshable, self).__init__('{!r} can not be ished!'.format(value))

class Maybe(ValueError):
    def __init__(self, value):
        super(Maybe, self).__init__('Maybe! ({!r} is not recognised)'.format(value))


def normalize_string(s):
    if isinstance(s, bytes):
        s = s.decode('utf-8', 'replace')
    return s.strip(STRIP_CHARS).lower()


class BaseIsh(object):
    def __init__(self, value):
        self._value = value

    def __repr__(self):
        return '{!r}-{!r}'.format(self._value, ish)


class BoolIsh(BaseIsh):
    def _check_string(self, s):
        normalized = normalize_string(s)

        try:
            return bool(int(normalized))
        except ValueError:
            pass

        if normalized in TRUE_STRINGS:
            return True
        if normalized in FALSE_STRINGS:
            return False

        raise Maybe(s)

    def __eq__(self, other):
        result = bool(other)

        if result and isinstance(other, basestring):
            result = self._check_string(other)

        return result == self._value

trueish = BoolIsh(True)
falseish = BoolIsh(False)


class NumberIsh(BaseIsh):
    def __init__(self, value, precision=0.2):
        super(NumberIsh, self).__init__(value)

        self._min = value * (1 - precision)
        self._max = value * (1 + precision)

    def _to_number(self, obj):
        try:
            return float(obj)
        except (TypeError, ValueError):
            try:
                return int(obj)
            except (TypeError, ValueError):
                raise Maybe(obj)
   
    def __cmp__(self, other):
        if not isinstance(other, numbers.Real):
            other = self._to_number(other)

        if other < self._min:
            return 1
        if other > self._max:
            return -1
        return 0


class EmotionIsh(BaseIsh):
    def __init__(self, value):
        if detect_and_predict_face_emotion:
            super(EmotionIsh, self).__init__(value)
            normalized = normalize_string(value)
            for (type, keywords) in ((3, HAPPY_STRINGS),<commit_msg>Fixed Python 3 compatibility for <number>-ish<commit_after># -*- coding: utf-8 -*-
from __future__ import print_function

import sys
import string
import numbers
import functools

try:
    from deep_neural_network.face_classifier import detect_and_predict_face_emotion
except ImportError:
    detect_and_predict_face_emotion = None

try:
    basestring
except NameError:
    basestring = (str, bytes)


TRUE_STRINGS = {
    'true', 'yes', 'on', 'yeah', 'yup', 'yarp',
    'oui',  # French
    'ja',   # German, Danish, Dutch, Afrikaans, Swedish, Norwegian
    'sim',  # Portuguese
    'sea',  # Irish
    'jes',  # Esperanto
    u'ﻦﻌﻣ', # Arabic
}
FALSE_STRINGS = {
    'false', 'no', 'off', 'nope', 'nah', 'narp',
    'non',   # French
    'nein',  # German
    'nej',   # Danish
    'nee',   # Dutch
    u'ﻷ',    # Arabic
}
STRIP_CHARS = string.whitespace + string.punctuation

HAPPY_STRINGS = {
    'happy',
    'happy-face',
    'joyful',
}
ANGRY_STRINGS = {
    'angry',
    'anger',
    'grrr',
}
NEUTRAL_STRINGS = {
    'neutral',
}


class UnIshable(Exception):
    def __init__(self, value):
        super(UnIshable, self).__init__('{!r} can not be ished!'.format(value))

class Maybe(ValueError):
    def __init__(self, value):
        super(Maybe, self).__init__('Maybe! ({!r} is not recognised)'.format(value))


def normalize_string(s):
    if isinstance(s, bytes):
        s = s.decode('utf-8', 'replace')
    return s.strip(STRIP_CHARS).lower()


class BaseIsh(object):
    def __init__(self, value):
        self._value = value

    def __repr__(self):
        return '{!r}-{!r}'.format(self._value, ish)


class BoolIsh(BaseIsh):
    def _check_string(self, s):
        normalized = normalize_string(s)

        try:
            return bool(int(normalized))
        except ValueError:
            pass

        if normalized in TRUE_STRINGS:
            return True
        if normalized in FALSE_STRINGS:
            return False

        raise Maybe(s)

    def __eq__(self, other):
        result = bool(other)

        if result and isinstance(other, basestring):
            result = self._check_string(other)

        return result == self._value

trueish = BoolIsh(True)
falseish = BoolIsh(False)


@functools.total_ordering
class NumberIsh(BaseIsh):
    def __init__(self, value, precision=0.2):
        super(NumberIsh, self).__init__(value)

        self._min = value * (1 - precision)
        self._max = value * (1 + precision)

    def _to_number(self, obj):
        if isinstance(obj, numbers.Real):
            return obj

        try:
            return float(obj)
        except (TypeError, ValueError):
            try:
                return int(obj)
            except (TypeError, ValueError):
                raise Maybe(obj)

    def __eq__(self, other):
        return self._min <= self._to_number(other) <= self._max

    def __lt__(self, other):
        return self._min < self._to_number(other)


class EmotionIsh(BaseIsh):
    def __init__(self, value):
        if detect_and_predict_face_emotion:
            super(EmotionIsh, self).__init__(value)
            normalized = normalize_string(value)
            for (type, keywords) in ((3, HAPPY_STRINGS),
{'fraction_non_alphanumeric': 0.12197819560752093, 'fraction_numerical': 0.0028440511929214724, 'mean_word_length': 2.5069252077562325, 'pattern_counts': {'":': 0, '<': 8, '<?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': '6012475', 'n_tokens_mistral': 2132, 'n_tokens_neox': 2064, 'n_words': 492}