normalizing.py

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#  Copyright 2008-2015 Nokia Networks
#  Copyright 2016-     Robot Framework Foundation
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
 
from collections.abc import MutableMapping
import re
 
from .robottypes import is_dict_like, is_string
 
 
 
def normalize(string, ignore=(), caseless=True, spaceless=True):
    empty = '' if is_string(string) else b''
    if isinstance(ignore, bytes):
        # Iterating bytes in Python3 yields integers.
        ignore = [bytes([i]) for i in ignore]
    if spaceless:
        string = empty.join(string.split())
    if caseless:
        string = string.lower()
        ignore = [i.lower() for i in ignore]
    # both if statements below enhance performance a little
    if ignore:
        for ign in ignore:
            if ign in string:
                string = string.replace(ign, empty)
    return string
 
 
def normalize_whitespace(string):
    return re.sub(r'\s', ' ', string, flags=re.UNICODE)
 
 
 
class NormalizedDict(MutableMapping):
 
    
    def __init__(self, initial=None, ignore=(), caseless=True, spaceless=True):
        self._data_data = {}
        self._keys_keys = {}
        self._normalize_normalize = lambda s: normalize(s, ignore, caseless, spaceless)
        if initial:
            self._add_initial_add_initial(initial)
 
    def _add_initial(self, initial):
        items = initial.items() if hasattr(initial, 'items') else initial
        for key, value in items:
            self[key] = value
 
    def __getitem__(self, key):
        return self._data_data[self._normalize_normalize(key)]
 
    def __setitem__(self, key, value):
        norm_key = self._normalize_normalize(key)
        self._data_data[norm_key] = value
        self._keys_keys.setdefault(norm_key, key)
 
    def __delitem__(self, key):
        norm_key = self._normalize_normalize(key)
        del self._data_data[norm_key]
        del self._keys_keys[norm_key]
 
    def __iter__(self):
        return (self._keys_keys[norm_key] for norm_key in sorted(self._keys_keys))
 
    def __len__(self):
        return len(self._data_data)
 
    def __str__(self):
        return '{%s}' % ', '.join('%r: %r' % (key, self[key]) for key in self)
 
    def __eq__(self, other):
        if not is_dict_like(other):
            return False
        if not isinstance(other, NormalizedDict):
            other = NormalizedDict(other)
        return self._data_data == other._data
 
    def copy(self):
        copy = NormalizedDict()
        copy._data = self._data_data.copy()
        copy._keys = self._keys_keys.copy()
        copy._normalize = self._normalize_normalize
        return copy
 
    # Speed-ups. Following methods are faster than default implementations.
 
    def __contains__(self, key):
        return self._normalize_normalize(key) in self._data_data
 
    def clear(self):
        self._data_data.clear()
        self._keys_keys.clear()