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diff --git a/jython-tosca-parser/src/main/resources/Lib/site-packages/requests-2.10.0-py2.7.egg/requests/packages/chardet/sbcharsetprober.py b/jython-tosca-parser/src/main/resources/Lib/site-packages/requests-2.10.0-py2.7.egg/requests/packages/chardet/sbcharsetprober.py
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+######################## BEGIN LICENSE BLOCK ########################
+# The Original Code is Mozilla Universal charset detector code.
+#
+# The Initial Developer of the Original Code is
+# Netscape Communications Corporation.
+# Portions created by the Initial Developer are Copyright (C) 2001
+# the Initial Developer. All Rights Reserved.
+#
+# Contributor(s):
+# Mark Pilgrim - port to Python
+# Shy Shalom - original C code
+#
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
+# 02110-1301 USA
+######################### END LICENSE BLOCK #########################
+
+import sys
+from . import constants
+from .charsetprober import CharSetProber
+from .compat import wrap_ord
+
+SAMPLE_SIZE = 64
+SB_ENOUGH_REL_THRESHOLD = 1024
+POSITIVE_SHORTCUT_THRESHOLD = 0.95
+NEGATIVE_SHORTCUT_THRESHOLD = 0.05
+SYMBOL_CAT_ORDER = 250
+NUMBER_OF_SEQ_CAT = 4
+POSITIVE_CAT = NUMBER_OF_SEQ_CAT - 1
+#NEGATIVE_CAT = 0
+
+
+class SingleByteCharSetProber(CharSetProber):
+ def __init__(self, model, reversed=False, nameProber=None):
+ CharSetProber.__init__(self)
+ self._mModel = model
+ # TRUE if we need to reverse every pair in the model lookup
+ self._mReversed = reversed
+ # Optional auxiliary prober for name decision
+ self._mNameProber = nameProber
+ self.reset()
+
+ def reset(self):
+ CharSetProber.reset(self)
+ # char order of last character
+ self._mLastOrder = 255
+ self._mSeqCounters = [0] * NUMBER_OF_SEQ_CAT
+ self._mTotalSeqs = 0
+ self._mTotalChar = 0
+ # characters that fall in our sampling range
+ self._mFreqChar = 0
+
+ def get_charset_name(self):
+ if self._mNameProber:
+ return self._mNameProber.get_charset_name()
+ else:
+ return self._mModel['charsetName']
+
+ def feed(self, aBuf):
+ if not self._mModel['keepEnglishLetter']:
+ aBuf = self.filter_without_english_letters(aBuf)
+ aLen = len(aBuf)
+ if not aLen:
+ return self.get_state()
+ for c in aBuf:
+ order = self._mModel['charToOrderMap'][wrap_ord(c)]
+ if order < SYMBOL_CAT_ORDER:
+ self._mTotalChar += 1
+ if order < SAMPLE_SIZE:
+ self._mFreqChar += 1
+ if self._mLastOrder < SAMPLE_SIZE:
+ self._mTotalSeqs += 1
+ if not self._mReversed:
+ i = (self._mLastOrder * SAMPLE_SIZE) + order
+ model = self._mModel['precedenceMatrix'][i]
+ else: # reverse the order of the letters in the lookup
+ i = (order * SAMPLE_SIZE) + self._mLastOrder
+ model = self._mModel['precedenceMatrix'][i]
+ self._mSeqCounters[model] += 1
+ self._mLastOrder = order
+
+ if self.get_state() == constants.eDetecting:
+ if self._mTotalSeqs > SB_ENOUGH_REL_THRESHOLD:
+ cf = self.get_confidence()
+ if cf > POSITIVE_SHORTCUT_THRESHOLD:
+ if constants._debug:
+ sys.stderr.write('%s confidence = %s, we have a'
+ 'winner\n' %
+ (self._mModel['charsetName'], cf))
+ self._mState = constants.eFoundIt
+ elif cf < NEGATIVE_SHORTCUT_THRESHOLD:
+ if constants._debug:
+ sys.stderr.write('%s confidence = %s, below negative'
+ 'shortcut threshhold %s\n' %
+ (self._mModel['charsetName'], cf,
+ NEGATIVE_SHORTCUT_THRESHOLD))
+ self._mState = constants.eNotMe
+
+ return self.get_state()
+
+ def get_confidence(self):
+ r = 0.01
+ if self._mTotalSeqs > 0:
+ r = ((1.0 * self._mSeqCounters[POSITIVE_CAT]) / self._mTotalSeqs
+ / self._mModel['mTypicalPositiveRatio'])
+ r = r * self._mFreqChar / self._mTotalChar
+ if r >= 1.0:
+ r = 0.99
+ return r