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iamilyasedunoviamilyasedunov
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benchmarks/ added, knuth_morris_pratt implemented
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+685
-88
lines changed

‎StringSearching.ipynb

+436-39
Large diffs are not rendered by default.

‎algorithm/boyer_moore_horspool.py

+50-49
Original file line numberDiff line numberDiff line change
@@ -1,49 +1,50 @@
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from collections import defaultdict
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from algorithm.base import Algorithm
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class BoyerMooreHorspool(Algorithm):
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def __init__(self, reference):
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spec_characters = '\tEmCGH7'
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reference = reference.translate(reference.maketrans('', '', spec_characters))
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self.reference = reference
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@property
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def name(self):
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return 'Boyer-Moore-Horspool'
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def set_candidate(self, candidate, **params):
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self.candidate = candidate
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def set_skip_table(self):
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len_reference = len(self.reference)
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len_candidate = len(self.candidate)
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if len_candidate > len_reference:
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print("Error: len candidate > len references {} > {}.".format(len_candidate, len_reference))
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return -1
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self.table_skip = defaultdict(lambda: len_candidate)
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for offset in range(len_candidate - 1):
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self.table_skip[ord(self.candidate[offset])] = len_candidate - offset - 1
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def search(self, multiple_search=False) -> list:
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self.set_skip_table()
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len_reference = len(self.reference)
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len_candidate = len(self.candidate)
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offset_lst = []
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offset = len_candidate - 1
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while offset < len_reference:
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j = len_candidate - 1
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i = offset
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while j >= 0 and self.reference[i] == self.candidate[j]:
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j -= 1
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i -= 1
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if j == -1:
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offset_lst.append(i + 1)
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offset += self.table_skip[ord(self.reference[offset])]
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return offset_lst
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from collections import defaultdict
2+
from algorithm.base import Algorithm
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from string import ascii_letters
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class BoyerMooreHorspool(Algorithm):
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def __init__(self, reference):
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# reference = reference.translate(reference.maketrans('', '', ascii_letters))
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self.reference = reference
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@property
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def name(self):
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return 'Boyer-Moore-Horspool'
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def set_candidate(self, candidate, **params):
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self.candidate = candidate
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def set_skip_table(self):
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len_reference = len(self.reference)
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len_candidate = len(self.candidate)
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if len_candidate > len_reference:
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print("Error: len candidate > len references {} > {}.".format(len_candidate, len_reference))
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return -1
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self.table_skip = defaultdict(lambda: len_candidate)
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for offset in range(len_candidate - 1):
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self.table_skip[ord(self.candidate[offset])] = len_candidate - offset - 1
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def search(self, multiple_search=False) -> list:
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self.set_skip_table()
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len_reference = len(self.reference)
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len_candidate = len(self.candidate)
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offset_lst = []
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offset = len_candidate - 1
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while offset < len_reference:
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j = len_candidate - 1
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i = offset
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while j >= 0 and self.reference[i] == self.candidate[j]:
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j -= 1
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i -= 1
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if j == -1:
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offset_lst.append(i + 1)
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offset += self.table_skip[ord(self.reference[offset])]
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return offset_lst

‎algorithm/knuth_morris_pratt.py

+80
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,80 @@
1+
from collections import defaultdict
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from algorithm.base import Algorithm
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from string import ascii_letters
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class KnuthMorrisPratt(Algorithm):
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def __init__(self, reference):
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# reference = reference.translate(reference.maketrans('', '', ascii_letters))
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self.reference = reference
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@property
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def name(self):
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return 'Knuth-Morris-Pratt'
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def set_candidate(self, candidate, **params):
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self.candidate = candidate
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def search(self, multiple_search=False) -> list:
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"""Return the lowest index of T at which substring P begins (or else -1)."""
21+
len_reference = len(self.reference)
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len_candidate = len(self.candidate)
23+
if len_candidate > len_reference:
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print("Error: len candidate > len references {} > {}.".format(len_candidate, len_reference))
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return -1
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# create lps[] that will hold the longest prefix suffix
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# values for pattern
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self.lps = [0] * len_candidate
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j = 0 # index for pat[]
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# Preprocess the pattern (calculate lps[] array)
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self.computeLPSArray()
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offset_lst = []
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i = 0 # index for txt[]
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while i < len_reference:
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if self.candidate[j] == self.reference[i]:
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i += 1
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j += 1
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if j == len_candidate:
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offset_lst.append(i - j)
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j = self.lps[j - 1]
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if (not multiple_search):
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return offset_lst
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# mismatch after j matches
48+
elif i < len_reference and self.candidate[j] != self.reference[i]:
49+
# Do not match lps[0..lps[j-1]] characters,
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# they will match anyway
51+
if j != 0:
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j = self.lps[j - 1]
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else:
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i += 1
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return offset_lst
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def computeLPSArray(self):
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len_candidate = len(self.candidate)
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length = 0 # length of the previous longest prefix suffix
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self.lps[0] = 0 # lps[0] is always 0
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i = 1
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# the loop calculates lps[i] for i = 1 to M-1
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while i < len_candidate:
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if self.candidate[i] == self.candidate[length]:
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length += 1
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self.lps[i] = length
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i += 1
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else:
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if length != 0:
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# This is tricky. Consier the example AAACAAAA
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# and i = 7
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length = self.lps[length - 1]
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# Also, note that we do not increment i here
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else:
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self.lps[i] = 0
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i += 1

‎benchmarks/bad_t_1.txt

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aaaaaaaaab

‎benchmarks/bad_t_2.txt

+1
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aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab

‎benchmarks/bad_t_3.txt

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aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab

‎benchmarks/bad_t_4.txt

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aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab

‎benchmarks/bad_w_1.txt

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ab

‎benchmarks/bad_w_2.txt

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aaaaaaaaab

‎benchmarks/bad_w_3.txt

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aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab

‎benchmarks/bad_w_4.txt

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aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab

‎benchmarks/good_t_1.txt

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Гостиная Анны Павловны начала понемногу наполняться. Приехала высшая знать Петербурга, люди самые разнородные по возрастам и характерам, но одинаковые по обществу, в каком все жили; приехала дочь князя Василия, красавица Элен, заехавшая за отцом, чтобы с ним вместе ехать на праздник посланника. Она была в шифре и бальном платье. Приехала и известная, как la femme la plus séduisante de Pétersbourg 1, молодая, маленькая княгиня Болконская, прошлую зиму вышедшая замуж и теперь не выезжавшая в большой свет по причине своей беременности, но ездившая еще на небольшие вечера. Приехал князь Ипполит, сын князя Василия, с Мортемаром, которого он представил; приехал и аббат Морио и многие другие.

‎benchmarks/good_t_2.txt

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Маленькая княгиня, переваливаясь, маленькими быстрыми шажками обошла стол с рабочею сумочкой на руке и, весело оправляя платье, села на диван, около серебряного самовара, как будто все, что она ни делала, было partie de plaisir 3 для нее и для всех ее окружавших.
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— J'ai apporté mon ouvrage 4, — сказала она, развертывая свой ридикюль и обращаясь ко всем вместе.
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— Смотрите, Annette, ne me jouez pas un mauvais tour, — обратилась она к хозяйке. — Vous m'avez écrit que c'était une toute petite soirée; voyez comme je suis attifée 5.
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— Вы не знаете аббата Морио? Он очень интересный человек... — сказала она.
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— Да, я слышал про его план вечного мира, и это очень интересно, но едва ли возможно...
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— Вы думаете?.. — сказала Анна Павловна, чтобы сказать что-нибудь и вновь обратиться к своим занятиям хозяйки дома, но Пьер сделал обратную неучтивость. Прежде он, не дослушав слов собеседницы, ушел; теперь он остановил своим разговором собеседницу, которой нужно было от него уйти. Он, нагнув голову и расставив большие ноги, стал доказывать Анне Павловне, почему он полагал, что план аббата был химера.
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— Мы после поговорим, — сказала Анна Павловна, улыбаясь.

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