content
stringlengths 7
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| fixed_cases
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|---|---|
STATS = [
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}
]
num_timeouts = 142
num_timeouts = 0
num_problems = 172
|
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num_timeouts = 142
num_timeouts = 0
num_problems = 172
|
'''
This program will do the following:
1. Ask for input of the amount of organisms
2. Ask for input of the daily population increase in percent
3. Ask for input of the maximum days the organism multiplies
4. Calculate the total population per day
5. Print the results for each day
'''
# ===Begin Loop
while True:
ORGANISMS = int(input("How many organisms do you want to start with?: "))
if ORGANISMS == 0:
print("You must have atleast 1 organism")
quit()
else:
# ===Input
INCREASE = float(input("At what percent (10% = 0.10) should they increase at?: "))
DAYS = int(input("How many days should the be left to multiply?: "))
population = ORGANISMS
print()
print('Days\tPopulation')
print('------------------')
break
# ===Process
for currentDay in range(1, DAYS + 1):
print(currentDay, "\t", format(population, ',.3f'))
population = population + (INCREASE * population)
|
"""
This program will do the following:
1. Ask for input of the amount of organisms
2. Ask for input of the daily population increase in percent
3. Ask for input of the maximum days the organism multiplies
4. Calculate the total population per day
5. Print the results for each day
"""
while True:
organisms = int(input('How many organisms do you want to start with?: '))
if ORGANISMS == 0:
print('You must have atleast 1 organism')
quit()
else:
increase = float(input('At what percent (10% = 0.10) should they increase at?: '))
days = int(input('How many days should the be left to multiply?: '))
population = ORGANISMS
print()
print('Days\tPopulation')
print('------------------')
break
for current_day in range(1, DAYS + 1):
print(currentDay, '\t', format(population, ',.3f'))
population = population + INCREASE * population
|
def bool_strings():
bools = []
for b in ['T', 'true', 'True', 'TRUE', 'F', 'false', 'False', 'FALSE']:
bools.append((b.lower().startswith('t'), b))
return bools
def test_bool_scalars(tmp_path, helpers):
helpers.do_test_scalar(tmp_path, bool_strings())
def test_bool_one_d_arrays(tmp_path, helpers):
helpers.do_test_one_d_array(tmp_path, bool_strings())
def test_bool_two_d_arrays(tmp_path, helpers):
helpers.do_test_two_d_array(tmp_path, bool_strings(), use_fortran=False)
|
def bool_strings():
bools = []
for b in ['T', 'true', 'True', 'TRUE', 'F', 'false', 'False', 'FALSE']:
bools.append((b.lower().startswith('t'), b))
return bools
def test_bool_scalars(tmp_path, helpers):
helpers.do_test_scalar(tmp_path, bool_strings())
def test_bool_one_d_arrays(tmp_path, helpers):
helpers.do_test_one_d_array(tmp_path, bool_strings())
def test_bool_two_d_arrays(tmp_path, helpers):
helpers.do_test_two_d_array(tmp_path, bool_strings(), use_fortran=False)
|
"""Additional Commandline option for pytest."""
MPI_SESSION_ARGUMENT = "--in_mpi_session"
"""
Argument added to the command line arguments useable with pytest.
/cf :any:`pytest_addoption`
"""
_IN_MPI_SESSION = False
"""Indicates whether the current test is run with the mpi session argument"""
def in_mpi_session():
"""
Return if the current test is run with the mpi session argument.
Returns
-------
bool
True if current test is run with the mpi session argument,
False otherwise.
"""
return _IN_MPI_SESSION
def pytest_addoption(parser):
"""
Add option `--in_mpi_session` to commandline options of pytest.
The default value is set to `False` so that it is
not activated by accident.
Arguments
---------
parser : ArgumentParser
The parser to which to add the option `in_mpi_session`
"""
parser.addoption(
MPI_SESSION_ARGUMENT,
action="store_true",
help="variable used by mpitest, to indicate that the current test is "
"run via mpirun etc. Should not be called directly.",
)
def pytest_configure(config):
"""
Set `IN_MPI_SESSION` module variable based on command line options.
Arguments
---------
config : Config fixture
The configuration from which to read the command line argument
"""
global _IN_MPI_SESSION
_IN_MPI_SESSION = config.getoption(MPI_SESSION_ARGUMENT)
|
"""Additional Commandline option for pytest."""
mpi_session_argument = '--in_mpi_session'
'\nArgument added to the command line arguments useable with pytest.\n/cf :any:`pytest_addoption`\n'
_in_mpi_session = False
'Indicates whether the current test is run with the mpi session argument'
def in_mpi_session():
"""
Return if the current test is run with the mpi session argument.
Returns
-------
bool
True if current test is run with the mpi session argument,
False otherwise.
"""
return _IN_MPI_SESSION
def pytest_addoption(parser):
"""
Add option `--in_mpi_session` to commandline options of pytest.
The default value is set to `False` so that it is
not activated by accident.
Arguments
---------
parser : ArgumentParser
The parser to which to add the option `in_mpi_session`
"""
parser.addoption(MPI_SESSION_ARGUMENT, action='store_true', help='variable used by mpitest, to indicate that the current test is run via mpirun etc. Should not be called directly.')
def pytest_configure(config):
"""
Set `IN_MPI_SESSION` module variable based on command line options.
Arguments
---------
config : Config fixture
The configuration from which to read the command line argument
"""
global _IN_MPI_SESSION
_in_mpi_session = config.getoption(MPI_SESSION_ARGUMENT)
|
class Solution:
def containsCycle(self, grid: List[List[str]]) -> bool:
rows, cols, visited, directions = len(grid), len(grid[0]), set(), [(0, 1), (1, 0), (0, -1), (-1, 0)]
def dfs(row, col, parentX, parentY, length):
visited.add((row, col))
for dirX, dirY in directions:
newRow, newCol = row + dirX, col + dirY
if newRow < 0 or newCol < 0 or newRow >= rows or newCol >= cols or (parentX == newRow and parentY == newCol):
continue
if (newRow, newCol) in visited and length >= 4 and grid[row][col] == grid[newRow][newCol]:
return True
elif grid[row][col] == grid[newRow][newCol]:
if dfs(newRow, newCol, row, col, length + 1):
return True
return False
for row in range(rows):
for col in range(cols):
if (row, col) in visited:
continue
if dfs(row, col, None, None, 1):
return True
return False
|
class Solution:
def contains_cycle(self, grid: List[List[str]]) -> bool:
(rows, cols, visited, directions) = (len(grid), len(grid[0]), set(), [(0, 1), (1, 0), (0, -1), (-1, 0)])
def dfs(row, col, parentX, parentY, length):
visited.add((row, col))
for (dir_x, dir_y) in directions:
(new_row, new_col) = (row + dirX, col + dirY)
if newRow < 0 or newCol < 0 or newRow >= rows or (newCol >= cols) or (parentX == newRow and parentY == newCol):
continue
if (newRow, newCol) in visited and length >= 4 and (grid[row][col] == grid[newRow][newCol]):
return True
elif grid[row][col] == grid[newRow][newCol]:
if dfs(newRow, newCol, row, col, length + 1):
return True
return False
for row in range(rows):
for col in range(cols):
if (row, col) in visited:
continue
if dfs(row, col, None, None, 1):
return True
return False
|
print(
min(
filter(
lambda x: x % 2 != 0,
map(
int,
input().split()
)
)
)
)
|
print(min(filter(lambda x: x % 2 != 0, map(int, input().split()))))
|
X = 1
def nester():
X = 2
print(X)
class C:
X = 3
print(X)
def method1(self):
print(X)
print(self.X)
def method2(self):
X = 4
print(X)
self.X = 5
print(self.X)
I = C()
I.method1()
I.method2()
print(X)
nester()
print('-'*40)
|
x = 1
def nester():
x = 2
print(X)
class C:
x = 3
print(X)
def method1(self):
print(X)
print(self.X)
def method2(self):
x = 4
print(X)
self.X = 5
print(self.X)
i = c()
I.method1()
I.method2()
print(X)
nester()
print('-' * 40)
|
animals = { 'a': ['aardvark'], 'b': ['baboon'], 'c': ['coati']}
animals['d'] = ['donkey']
animals['d'].append('dog')
animals['d'].append('dingo')
"""
def howMany(aDict):
i = 0
result = []
values = aDict.values()
print (values)
for w in values:
result.append(w)
for word in result:
i += len(word)
return i
print (howMany (animals))
"""
def largest(aDict):
larg_item = ['',0]
if len(aDict) ==0:
return None
for (k,v) in aDict.items():
if len(v)>larg_item[1]:
larg_item[0]=k
larg_item[1]=len(v)
return larg_item[0]
print (largest(animals))
|
animals = {'a': ['aardvark'], 'b': ['baboon'], 'c': ['coati']}
animals['d'] = ['donkey']
animals['d'].append('dog')
animals['d'].append('dingo')
'\ndef howMany(aDict):\n i = 0\n result = []\n values = aDict.values()\n print (values)\n for w in values:\n result.append(w)\n for word in result:\n i += len(word)\n return i\n\nprint (howMany (animals))\n'
def largest(aDict):
larg_item = ['', 0]
if len(aDict) == 0:
return None
for (k, v) in aDict.items():
if len(v) > larg_item[1]:
larg_item[0] = k
larg_item[1] = len(v)
return larg_item[0]
print(largest(animals))
|
#from django import http
# adapted from http://djangosnippets.org/snippets/2472/
class CloudMiddleware(object):
def process_request(self, request):
if 'HTTP_X_FORWARDED_PROTO' in request.META:
if request.META['HTTP_X_FORWARDED_PROTO'] == 'https':
request.is_secure = lambda: True
return None
|
class Cloudmiddleware(object):
def process_request(self, request):
if 'HTTP_X_FORWARDED_PROTO' in request.META:
if request.META['HTTP_X_FORWARDED_PROTO'] == 'https':
request.is_secure = lambda : True
return None
|
'''
A simple script for printing numbers
'''
def func1():
'''
func1 is simple methdo which shows the number which are entered inside.
'''
first_num = 1
second_num = 2
print(first_num)
print(second_num)
func1()
# When we run this program using pylint then we can get our code evaluted.
# It will be used when we'll be working with big projects to generate reports
# For execution: pylint filename.py
|
"""
A simple script for printing numbers
"""
def func1():
"""
func1 is simple methdo which shows the number which are entered inside.
"""
first_num = 1
second_num = 2
print(first_num)
print(second_num)
func1()
|
class Solution:
def dfs(self, image: List[List[int]], sr: int, sc: int, newColor: int, DIR: List[List[int]], startColor: int) -> List[List[int]]:
image[sr][sc] = newColor
visited = set()
visited.add((sr,sc))
for direction in DIR:
newRow, newCol = sr + direction[0], sc + direction[1]
if not self.checkSize(newRow, newCol, len(image), len(image[0])) or (newRow, newCol) in visited or image[newRow][newCol] != startColor:
continue
self.dfs(image, newRow, newCol, newColor, DIR, startColor)
return image
def checkSize(self, row: int, col: int, rowSize: int, colSize: int) -> bool:
return True if 0 <= row < rowSize and 0 <= col < colSize else False
def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:
if image[sr][sc] == newColor:
return image
startColor = image[sr][sc]
image[sr][sc] = newColor
DIR = [[0,1], [1,0], [0,-1], [-1,0]]
image = self.dfs(image, sr, sc, newColor, DIR, startColor)
return image
|
class Solution:
def dfs(self, image: List[List[int]], sr: int, sc: int, newColor: int, DIR: List[List[int]], startColor: int) -> List[List[int]]:
image[sr][sc] = newColor
visited = set()
visited.add((sr, sc))
for direction in DIR:
(new_row, new_col) = (sr + direction[0], sc + direction[1])
if not self.checkSize(newRow, newCol, len(image), len(image[0])) or (newRow, newCol) in visited or image[newRow][newCol] != startColor:
continue
self.dfs(image, newRow, newCol, newColor, DIR, startColor)
return image
def check_size(self, row: int, col: int, rowSize: int, colSize: int) -> bool:
return True if 0 <= row < rowSize and 0 <= col < colSize else False
def flood_fill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:
if image[sr][sc] == newColor:
return image
start_color = image[sr][sc]
image[sr][sc] = newColor
dir = [[0, 1], [1, 0], [0, -1], [-1, 0]]
image = self.dfs(image, sr, sc, newColor, DIR, startColor)
return image
|
# Author : Babu Baskaran
# Date : 05/04/2019 Time : 17:00 pm
# Solution for problem number 1
# taking user input
user1=int (input("Please Enter a Positive Integer : "))
# assigning user input into a variable
start = user1
# set i start value into 1
i = 1
# set value of variable ans to zero
ans = 0
# check the i value is equal or less than variable start
while i<= start:
# increase the value of variable ans by 1
ans = ans+i
# increase the value of i by 1
i = i + 1
# print the output sum of integer to the user
print(ans)
|
user1 = int(input('Please Enter a Positive Integer : '))
start = user1
i = 1
ans = 0
while i <= start:
ans = ans + i
i = i + 1
print(ans)
|
"""
https://leetcode.com/problems/implement-strstr/
Return the first occurence of needle in haystack, or -1 if not found. Return 0 when needle is an empty string.
haystack and needle are both strings
"""
class Solution:
def strStr(self, haystack: str, needle: str) -> int:
if len(needle) == 0:
return 0
else:
return haystack.find(needle)
# Another way, if using .find() was not allowed:
def strStr2(self, haystack, needle):
if len(needle) == 0:
return 0
substring_length = len(needle)
for i in range(len(haystack) - substring_length):
print(haystack[i:i+substring_length])
if haystack[i:i+substring_length] == needle:
return i
return -1
s = Solution()
print(s.strStr2('hello', 'll'))
|
"""
https://leetcode.com/problems/implement-strstr/
Return the first occurence of needle in haystack, or -1 if not found. Return 0 when needle is an empty string.
haystack and needle are both strings
"""
class Solution:
def str_str(self, haystack: str, needle: str) -> int:
if len(needle) == 0:
return 0
else:
return haystack.find(needle)
def str_str2(self, haystack, needle):
if len(needle) == 0:
return 0
substring_length = len(needle)
for i in range(len(haystack) - substring_length):
print(haystack[i:i + substring_length])
if haystack[i:i + substring_length] == needle:
return i
return -1
s = solution()
print(s.strStr2('hello', 'll'))
|
units = {
"DecimalDegrees": "degrees",
"WGS_1984": "WGS84",
"Meters": "meters",
"": "",
"Unknown": "Unknown",
}
|
units = {'DecimalDegrees': 'degrees', 'WGS_1984': 'WGS84', 'Meters': 'meters', '': '', 'Unknown': 'Unknown'}
|
class CommandResponse(object):
def __init__(self):
self.messages = []
self.named = {}
self.errors = []
def __getitem__(self, name):
return self.get_named_data(name)
@property
def active_user(self):
return self.get_named_data('active_user')
@property
def log_key(self):
return self.get_named_data('log_key')
def add(self, messages):
if not isinstance(messages, (list, tuple)):
messages = [messages]
for message in messages:
self.messages.append(message)
if message.name:
self.named[message.name] = message
if message.is_error():
self.errors.append(message)
@property
def aborted(self):
return len(self.errors) > 0
@property
def error(self):
return self.aborted
def error_message(self):
messages = []
for message in self.errors:
messages.append(message.format())
return "\n\n".join(messages)
def get_named_data(self, name):
message = self.named.get(name, None)
if message:
try:
return message.data
except AttributeError:
return message.message
return None
|
class Commandresponse(object):
def __init__(self):
self.messages = []
self.named = {}
self.errors = []
def __getitem__(self, name):
return self.get_named_data(name)
@property
def active_user(self):
return self.get_named_data('active_user')
@property
def log_key(self):
return self.get_named_data('log_key')
def add(self, messages):
if not isinstance(messages, (list, tuple)):
messages = [messages]
for message in messages:
self.messages.append(message)
if message.name:
self.named[message.name] = message
if message.is_error():
self.errors.append(message)
@property
def aborted(self):
return len(self.errors) > 0
@property
def error(self):
return self.aborted
def error_message(self):
messages = []
for message in self.errors:
messages.append(message.format())
return '\n\n'.join(messages)
def get_named_data(self, name):
message = self.named.get(name, None)
if message:
try:
return message.data
except AttributeError:
return message.message
return None
|
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def findTilt(self, root: TreeNode) -> int:
sum_tree, tilt_tree = self.sum_and_tilt(root)
return tilt_tree
def sum_and_tilt(self, root):
if not root:
return 0, 0
sum_left, tilt_left = self.sum_and_tilt(root.left)
sum_right, tilt_right = self.sum_and_tilt(root.right)
return sum_left + sum_right + root.val, abs(sum_left - sum_right) + tilt_left + tilt_right
|
class Solution:
def find_tilt(self, root: TreeNode) -> int:
(sum_tree, tilt_tree) = self.sum_and_tilt(root)
return tilt_tree
def sum_and_tilt(self, root):
if not root:
return (0, 0)
(sum_left, tilt_left) = self.sum_and_tilt(root.left)
(sum_right, tilt_right) = self.sum_and_tilt(root.right)
return (sum_left + sum_right + root.val, abs(sum_left - sum_right) + tilt_left + tilt_right)
|
'''
Comparison data as seen here,
http://www.nand2tetris.org/
'''
'''------------------------- Arithmetic gates -------------------------'''
k_halfAdder = [
# [ a, b, ( sum, carry ) ]
[ 0, 0, ( 0, 0 ) ],
[ 0, 1, ( 1, 0 ) ],
[ 1, 0, ( 1, 0 ) ],
[ 1, 1, ( 0, 1 ) ],
]
k_fullAdder = [
# [ a, b, c, ( sum, carry ) ]
[ 0, 0, 0, ( 0, 0 ) ],
[ 0, 0, 1, ( 1, 0 ) ],
[ 0, 1, 0, ( 1, 0 ) ],
[ 0, 1, 1, ( 0, 1 ) ],
[ 1, 0, 0, ( 1, 0 ) ],
[ 1, 0, 1, ( 0, 1 ) ],
[ 1, 1, 0, ( 0, 1 ) ],
[ 1, 1, 1, ( 1, 1 ) ],
]
k_add16 = [
# [ a, b, out ]
[ '0000000000000000', '0000000000000000', '0000000000000000' ],
[ '0000000000000000', '1111111111111111', '1111111111111111' ],
[ '1111111111111111', '1111111111111111', '1111111111111110' ],
[ '1010101010101010', '0101010101010101', '1111111111111111' ],
[ '0011110011000011', '0000111111110000', '0100110010110011' ],
[ '0001001000110100', '1001100001110110', '1010101010101010' ],
]
k_subtract16 = [
# [ a, b, out ]
[ '0000000000000111', '0000000000001001', '1111111111111110' ],
[ '0000000000011000', '0000000000110111', '1111111111100001' ],
[ '0000000000001111', '0000000000000111', '0000000000001000' ],
[ '0000000000111000', '0000000000000001', '0000000000110111' ],
[ '1000000000000000', '0000000000000010', '0111111111111110' ],
]
k_increment16 = [
# [ in, out ]
[ '0000000000000000', '0000000000000001' ],
[ '1111111111111111', '0000000000000000' ],
[ '0000000000000101', '0000000000000110' ],
[ '1111111111111011', '1111111111111100' ],
]
k_shiftLeft16 = [
[ '0110011111001010', '0000000000001010', '0010100000000000' ],
[ '0001010000101011', '0000000000000011', '1010000101011000' ],
[ '0011010100011101', '0000000000000100', '0101000111010000' ],
[ '1100111001000011', '0000000000000001', '1001110010000110' ],
[ '1111101111111000', '0000000000000110', '1111111000000000' ],
]
k_shiftRight16 = [
[ '0011100000111110', '0000000000000100', '0000001110000011' ],
[ '0011010001101111', '0000000000000011', '0000011010001101' ],
[ '1000010011010110', '0000000000001100', '0000000000001000' ],
[ '1110100101100001', '0000000000001000', '0000000011101001' ],
[ '0010101000011110', '0000000000000001', '0001010100001111' ],
]
k_ALU16 = [
# [ x, y, fsel, zx|nx|zy|ny|no, out, zr, ng ]
[ '0000000000000000', '1111111111111111', '0000', '10100', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '11111', '0000000000000001', 0, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '11100', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0001', '00110', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0001', '11000', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0001', '00111', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0001', '11001', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '00111', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '11001', '0000000000000001', 0, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '01111', '0000000000000001', 0, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '11011', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '00110', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0000', '11000', '1111111111111110', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0000', '00000', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0000', '01001', '0000000000000001', 0, 0 ],
[ '0000000000000000', '1111111111111111', '0000', '00011', '1111111111111111', 0, 1 ],
[ '0000000000000000', '1111111111111111', '0001', '00000', '0000000000000000', 1, 0 ],
[ '0000000000000000', '1111111111111111', '0001', '01011', '1111111111111111', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0000', '10100', '0000000000000000', 1, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '11111', '0000000000000001', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '11100', '1111111111111111', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0001', '00110', '0000000000010001', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0001', '11000', '0000000000000011', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0001', '00111', '1111111111101110', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0001', '11001', '1111111111111100', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0000', '00111', '1111111111101111', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0000', '11001', '1111111111111101', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0000', '01111', '0000000000010010', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '11011', '0000000000000100', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '00110', '0000000000010000', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '11000', '0000000000000010', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '00000', '0000000000010100', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '01001', '0000000000001110', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0000', '00011', '1111111111110010', 0, 1 ],
[ '0000000000010001', '0000000000000011', '0001', '00000', '0000000000000001', 0, 0 ],
[ '0000000000010001', '0000000000000011', '0001', '01011', '0000000000010011', 0, 0 ],
[ '0001000010100010', '1110110111010001', '0010', '00000', '1111110101110011', 0, 1 ],
[ '0000001001110011', '0000001010010101', '0010', '00000', '0000000011100110', 0, 0 ],
[ '1001001101110110', '0110100010001100', '0010', '00000', '1111101111111010', 0, 1 ],
[ '1111100011101101', '0000001010111011', '0010', '00000', '1111101001010110', 0, 1 ],
[ '0010111100101110', '1010111011111101', '0010', '00000', '1000000111010011', 0, 1 ],
[ '0011100000111110', '0000000000000100', '0011', '00000', '0000001110000011', 0, 0 ],
[ '0011010001101111', '0000000000000011', '0011', '00000', '0000011010001101', 0, 0 ],
[ '1000010011010110', '0000000000001100', '0011', '00000', '0000000000001000', 0, 0 ],
[ '1110100101100001', '0000000000001000', '0011', '00000', '0000000011101001', 0, 0 ],
[ '0010101000011110', '0000000000000001', '0011', '00000', '0001010100001111', 0, 0 ],
[ '0110011111001010', '0000000000001010', '0100', '00000', '0010100000000000', 0, 0 ],
[ '0001010000101011', '0000000000000011', '0100', '00000', '1010000101011000', 0, 1 ],
[ '0011010100011101', '0000000000000100', '0100', '00000', '0101000111010000', 0, 0 ],
[ '1100111001000011', '0000000000000001', '0100', '00000', '1001110010000110', 0, 1 ],
[ '1111101111111000', '0000000000000110', '0100', '00000', '1111111000000000', 0, 1 ],
]
|
"""
Comparison data as seen here,
http://www.nand2tetris.org/
"""
'------------------------- Arithmetic gates -------------------------'
k_half_adder = [[0, 0, (0, 0)], [0, 1, (1, 0)], [1, 0, (1, 0)], [1, 1, (0, 1)]]
k_full_adder = [[0, 0, 0, (0, 0)], [0, 0, 1, (1, 0)], [0, 1, 0, (1, 0)], [0, 1, 1, (0, 1)], [1, 0, 0, (1, 0)], [1, 0, 1, (0, 1)], [1, 1, 0, (0, 1)], [1, 1, 1, (1, 1)]]
k_add16 = [['0000000000000000', '0000000000000000', '0000000000000000'], ['0000000000000000', '1111111111111111', '1111111111111111'], ['1111111111111111', '1111111111111111', '1111111111111110'], ['1010101010101010', '0101010101010101', '1111111111111111'], ['0011110011000011', '0000111111110000', '0100110010110011'], ['0001001000110100', '1001100001110110', '1010101010101010']]
k_subtract16 = [['0000000000000111', '0000000000001001', '1111111111111110'], ['0000000000011000', '0000000000110111', '1111111111100001'], ['0000000000001111', '0000000000000111', '0000000000001000'], ['0000000000111000', '0000000000000001', '0000000000110111'], ['1000000000000000', '0000000000000010', '0111111111111110']]
k_increment16 = [['0000000000000000', '0000000000000001'], ['1111111111111111', '0000000000000000'], ['0000000000000101', '0000000000000110'], ['1111111111111011', '1111111111111100']]
k_shift_left16 = [['0110011111001010', '0000000000001010', '0010100000000000'], ['0001010000101011', '0000000000000011', '1010000101011000'], ['0011010100011101', '0000000000000100', '0101000111010000'], ['1100111001000011', '0000000000000001', '1001110010000110'], ['1111101111111000', '0000000000000110', '1111111000000000']]
k_shift_right16 = [['0011100000111110', '0000000000000100', '0000001110000011'], ['0011010001101111', '0000000000000011', '0000011010001101'], ['1000010011010110', '0000000000001100', '0000000000001000'], ['1110100101100001', '0000000000001000', '0000000011101001'], ['0010101000011110', '0000000000000001', '0001010100001111']]
k_alu16 = [['0000000000000000', '1111111111111111', '0000', '10100', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0000', '11111', '0000000000000001', 0, 0], ['0000000000000000', '1111111111111111', '0000', '11100', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0001', '00110', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0001', '11000', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0001', '00111', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0001', '11001', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0000', '00111', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0000', '11001', '0000000000000001', 0, 0], ['0000000000000000', '1111111111111111', '0000', '01111', '0000000000000001', 0, 0], ['0000000000000000', '1111111111111111', '0000', '11011', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0000', '00110', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0000', '11000', '1111111111111110', 0, 1], ['0000000000000000', '1111111111111111', '0000', '00000', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0000', '01001', '0000000000000001', 0, 0], ['0000000000000000', '1111111111111111', '0000', '00011', '1111111111111111', 0, 1], ['0000000000000000', '1111111111111111', '0001', '00000', '0000000000000000', 1, 0], ['0000000000000000', '1111111111111111', '0001', '01011', '1111111111111111', 0, 1], ['0000000000010001', '0000000000000011', '0000', '10100', '0000000000000000', 1, 0], ['0000000000010001', '0000000000000011', '0000', '11111', '0000000000000001', 0, 0], ['0000000000010001', '0000000000000011', '0000', '11100', '1111111111111111', 0, 1], ['0000000000010001', '0000000000000011', '0001', '00110', '0000000000010001', 0, 0], ['0000000000010001', '0000000000000011', '0001', '11000', '0000000000000011', 0, 0], ['0000000000010001', '0000000000000011', '0001', '00111', '1111111111101110', 0, 1], ['0000000000010001', '0000000000000011', '0001', '11001', '1111111111111100', 0, 1], ['0000000000010001', '0000000000000011', '0000', '00111', '1111111111101111', 0, 1], ['0000000000010001', '0000000000000011', '0000', '11001', '1111111111111101', 0, 1], ['0000000000010001', '0000000000000011', '0000', '01111', '0000000000010010', 0, 0], ['0000000000010001', '0000000000000011', '0000', '11011', '0000000000000100', 0, 0], ['0000000000010001', '0000000000000011', '0000', '00110', '0000000000010000', 0, 0], ['0000000000010001', '0000000000000011', '0000', '11000', '0000000000000010', 0, 0], ['0000000000010001', '0000000000000011', '0000', '00000', '0000000000010100', 0, 0], ['0000000000010001', '0000000000000011', '0000', '01001', '0000000000001110', 0, 0], ['0000000000010001', '0000000000000011', '0000', '00011', '1111111111110010', 0, 1], ['0000000000010001', '0000000000000011', '0001', '00000', '0000000000000001', 0, 0], ['0000000000010001', '0000000000000011', '0001', '01011', '0000000000010011', 0, 0], ['0001000010100010', '1110110111010001', '0010', '00000', '1111110101110011', 0, 1], ['0000001001110011', '0000001010010101', '0010', '00000', '0000000011100110', 0, 0], ['1001001101110110', '0110100010001100', '0010', '00000', '1111101111111010', 0, 1], ['1111100011101101', '0000001010111011', '0010', '00000', '1111101001010110', 0, 1], ['0010111100101110', '1010111011111101', '0010', '00000', '1000000111010011', 0, 1], ['0011100000111110', '0000000000000100', '0011', '00000', '0000001110000011', 0, 0], ['0011010001101111', '0000000000000011', '0011', '00000', '0000011010001101', 0, 0], ['1000010011010110', '0000000000001100', '0011', '00000', '0000000000001000', 0, 0], ['1110100101100001', '0000000000001000', '0011', '00000', '0000000011101001', 0, 0], ['0010101000011110', '0000000000000001', '0011', '00000', '0001010100001111', 0, 0], ['0110011111001010', '0000000000001010', '0100', '00000', '0010100000000000', 0, 0], ['0001010000101011', '0000000000000011', '0100', '00000', '1010000101011000', 0, 1], ['0011010100011101', '0000000000000100', '0100', '00000', '0101000111010000', 0, 0], ['1100111001000011', '0000000000000001', '0100', '00000', '1001110010000110', 0, 1], ['1111101111111000', '0000000000000110', '0100', '00000', '1111111000000000', 0, 1]]
|
class QuizBrain:
def __init__(self,question_list):
self.question_number = 0
self.score = 0
self.question_list = question_list
def next_question(self):
self.guess = input(f"Q{self.question_number + 1}: {self.question_list[self.question_number].question} True or False: ")
self.check_answer(self.guess, self.question_list[self.question_number].answer)
self.question_number += 1
def still_has_questions(self):
return self.question_number < len(self.question_list)
def check_answer(self, guess, answer):
if guess.lower() == answer.lower():
self.score += 1
print(f"Your score is {self.score}/{self.question_number+1}")
print("\n")
# def play_game():
# print("Let's play the game!")
# correct = 0
# for i in range(len(Self.question_list)):
# guess = input(f"{self.question_list[i].question} True or False: ")
# if guess == question_bank[i].answer:
# correct += 1
# print(f"You have guessed {correct} correct out of {i+1} questions. {100*correct/(i+1)}%\n")
|
class Quizbrain:
def __init__(self, question_list):
self.question_number = 0
self.score = 0
self.question_list = question_list
def next_question(self):
self.guess = input(f'Q{self.question_number + 1}: {self.question_list[self.question_number].question} True or False: ')
self.check_answer(self.guess, self.question_list[self.question_number].answer)
self.question_number += 1
def still_has_questions(self):
return self.question_number < len(self.question_list)
def check_answer(self, guess, answer):
if guess.lower() == answer.lower():
self.score += 1
print(f'Your score is {self.score}/{self.question_number + 1}')
print('\n')
|
mooniswap_abi = [
{
"inputs": [
{"internalType": "contract IERC20", "name": "_token0", "type": "address"},
{"internalType": "contract IERC20", "name": "_token1", "type": "address"},
{"internalType": "string", "name": "name", "type": "string"},
{"internalType": "string", "name": "symbol", "type": "string"},
{
"internalType": "contract IMooniswapFactoryGovernance",
"name": "_mooniswapFactoryGovernance",
"type": "address",
},
],
"stateMutability": "nonpayable",
"type": "constructor",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "owner",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "spender",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "value",
"type": "uint256",
},
],
"name": "Approval",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "user",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "decayPeriod",
"type": "uint256",
},
{
"indexed": False,
"internalType": "bool",
"name": "isDefault",
"type": "bool",
},
{
"indexed": False,
"internalType": "uint256",
"name": "amount",
"type": "uint256",
},
],
"name": "DecayPeriodVoteUpdate",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "sender",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "receiver",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "share",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "token0Amount",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "token1Amount",
"type": "uint256",
},
],
"name": "Deposited",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": False,
"internalType": "string",
"name": "reason",
"type": "string",
}
],
"name": "Error",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "user",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "fee",
"type": "uint256",
},
{
"indexed": False,
"internalType": "bool",
"name": "isDefault",
"type": "bool",
},
{
"indexed": False,
"internalType": "uint256",
"name": "amount",
"type": "uint256",
},
],
"name": "FeeVoteUpdate",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "previousOwner",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "newOwner",
"type": "address",
},
],
"name": "OwnershipTransferred",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "user",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "slippageFee",
"type": "uint256",
},
{
"indexed": False,
"internalType": "bool",
"name": "isDefault",
"type": "bool",
},
{
"indexed": False,
"internalType": "uint256",
"name": "amount",
"type": "uint256",
},
],
"name": "SlippageFeeVoteUpdate",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "sender",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "receiver",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "srcToken",
"type": "address",
},
{
"indexed": False,
"internalType": "address",
"name": "dstToken",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "amount",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "result",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "srcAdditionBalance",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "dstRemovalBalance",
"type": "uint256",
},
{
"indexed": False,
"internalType": "address",
"name": "referral",
"type": "address",
},
],
"name": "Swapped",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": False,
"internalType": "uint256",
"name": "srcBalance",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "dstBalance",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "fee",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "slippageFee",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "referralShare",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "governanceShare",
"type": "uint256",
},
],
"name": "Sync",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "from",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "to",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "value",
"type": "uint256",
},
],
"name": "Transfer",
"type": "event",
},
{
"anonymous": False,
"inputs": [
{
"indexed": True,
"internalType": "address",
"name": "sender",
"type": "address",
},
{
"indexed": True,
"internalType": "address",
"name": "receiver",
"type": "address",
},
{
"indexed": False,
"internalType": "uint256",
"name": "share",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "token0Amount",
"type": "uint256",
},
{
"indexed": False,
"internalType": "uint256",
"name": "token1Amount",
"type": "uint256",
},
],
"name": "Withdrawn",
"type": "event",
},
{
"inputs": [
{"internalType": "address", "name": "owner", "type": "address"},
{"internalType": "address", "name": "spender", "type": "address"},
],
"name": "allowance",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "address", "name": "spender", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
],
"name": "approve",
"outputs": [{"internalType": "bool", "name": "", "type": "bool"}],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "address", "name": "account", "type": "address"}],
"name": "balanceOf",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "decayPeriod",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "uint256", "name": "vote", "type": "uint256"}],
"name": "decayPeriodVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "address", "name": "user", "type": "address"}],
"name": "decayPeriodVotes",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "decimals",
"outputs": [{"internalType": "uint8", "name": "", "type": "uint8"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "address", "name": "spender", "type": "address"},
{"internalType": "uint256", "name": "subtractedValue", "type": "uint256"},
],
"name": "decreaseAllowance",
"outputs": [{"internalType": "bool", "name": "", "type": "bool"}],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [
{"internalType": "uint256[2]", "name": "maxAmounts", "type": "uint256[2]"},
{"internalType": "uint256[2]", "name": "minAmounts", "type": "uint256[2]"},
],
"name": "deposit",
"outputs": [
{"internalType": "uint256", "name": "fairSupply", "type": "uint256"},
{
"internalType": "uint256[2]",
"name": "receivedAmounts",
"type": "uint256[2]",
},
],
"stateMutability": "payable",
"type": "function",
},
{
"inputs": [
{"internalType": "uint256[2]", "name": "maxAmounts", "type": "uint256[2]"},
{"internalType": "uint256[2]", "name": "minAmounts", "type": "uint256[2]"},
{"internalType": "address", "name": "target", "type": "address"},
],
"name": "depositFor",
"outputs": [
{"internalType": "uint256", "name": "fairSupply", "type": "uint256"},
{
"internalType": "uint256[2]",
"name": "receivedAmounts",
"type": "uint256[2]",
},
],
"stateMutability": "payable",
"type": "function",
},
{
"inputs": [],
"name": "discardDecayPeriodVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [],
"name": "discardFeeVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [],
"name": "discardSlippageFeeVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [],
"name": "fee",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "uint256", "name": "vote", "type": "uint256"}],
"name": "feeVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "address", "name": "user", "type": "address"}],
"name": "feeVotes",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "token", "type": "address"}
],
"name": "getBalanceForAddition",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "token", "type": "address"}
],
"name": "getBalanceForRemoval",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "src", "type": "address"},
{"internalType": "contract IERC20", "name": "dst", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
],
"name": "getReturn",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "getTokens",
"outputs": [
{"internalType": "contract IERC20[]", "name": "tokens", "type": "address[]"}
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "address", "name": "spender", "type": "address"},
{"internalType": "uint256", "name": "addedValue", "type": "uint256"},
],
"name": "increaseAllowance",
"outputs": [{"internalType": "bool", "name": "", "type": "bool"}],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [],
"name": "mooniswapFactoryGovernance",
"outputs": [
{
"internalType": "contract IMooniswapFactoryGovernance",
"name": "",
"type": "address",
}
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "name",
"outputs": [{"internalType": "string", "name": "", "type": "string"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "owner",
"outputs": [{"internalType": "address", "name": "", "type": "address"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "renounceOwnership",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "token", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
],
"name": "rescueFunds",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [
{
"internalType": "contract IMooniswapFactoryGovernance",
"name": "newMooniswapFactoryGovernance",
"type": "address",
}
],
"name": "setMooniswapFactoryGovernance",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [],
"name": "slippageFee",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "uint256", "name": "vote", "type": "uint256"}],
"name": "slippageFeeVote",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "address", "name": "user", "type": "address"}],
"name": "slippageFeeVotes",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "src", "type": "address"},
{"internalType": "contract IERC20", "name": "dst", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
{"internalType": "uint256", "name": "minReturn", "type": "uint256"},
{"internalType": "address", "name": "referral", "type": "address"},
],
"name": "swap",
"outputs": [{"internalType": "uint256", "name": "result", "type": "uint256"}],
"stateMutability": "payable",
"type": "function",
},
{
"inputs": [
{"internalType": "contract IERC20", "name": "src", "type": "address"},
{"internalType": "contract IERC20", "name": "dst", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
{"internalType": "uint256", "name": "minReturn", "type": "uint256"},
{"internalType": "address", "name": "referral", "type": "address"},
{"internalType": "address payable", "name": "receiver", "type": "address"},
],
"name": "swapFor",
"outputs": [{"internalType": "uint256", "name": "result", "type": "uint256"}],
"stateMutability": "payable",
"type": "function",
},
{
"inputs": [],
"name": "symbol",
"outputs": [{"internalType": "string", "name": "", "type": "string"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "token0",
"outputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "token1",
"outputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "uint256", "name": "i", "type": "uint256"}],
"name": "tokens",
"outputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "totalSupply",
"outputs": [{"internalType": "uint256", "name": "", "type": "uint256"}],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "address", "name": "recipient", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
],
"name": "transfer",
"outputs": [{"internalType": "bool", "name": "", "type": "bool"}],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [
{"internalType": "address", "name": "sender", "type": "address"},
{"internalType": "address", "name": "recipient", "type": "address"},
{"internalType": "uint256", "name": "amount", "type": "uint256"},
],
"name": "transferFrom",
"outputs": [{"internalType": "bool", "name": "", "type": "bool"}],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "address", "name": "newOwner", "type": "address"}],
"name": "transferOwnership",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"name": "virtualBalancesForAddition",
"outputs": [
{"internalType": "uint216", "name": "balance", "type": "uint216"},
{"internalType": "uint40", "name": "time", "type": "uint40"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"name": "virtualBalancesForRemoval",
"outputs": [
{"internalType": "uint216", "name": "balance", "type": "uint216"},
{"internalType": "uint40", "name": "time", "type": "uint40"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "virtualDecayPeriod",
"outputs": [
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint48", "name": "", "type": "uint48"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "virtualFee",
"outputs": [
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint48", "name": "", "type": "uint48"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [],
"name": "virtualSlippageFee",
"outputs": [
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint104", "name": "", "type": "uint104"},
{"internalType": "uint48", "name": "", "type": "uint48"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [{"internalType": "contract IERC20", "name": "", "type": "address"}],
"name": "volumes",
"outputs": [
{"internalType": "uint128", "name": "confirmed", "type": "uint128"},
{"internalType": "uint128", "name": "result", "type": "uint128"},
],
"stateMutability": "view",
"type": "function",
},
{
"inputs": [
{"internalType": "uint256", "name": "amount", "type": "uint256"},
{"internalType": "uint256[]", "name": "minReturns", "type": "uint256[]"},
],
"name": "withdraw",
"outputs": [
{
"internalType": "uint256[2]",
"name": "withdrawnAmounts",
"type": "uint256[2]",
}
],
"stateMutability": "nonpayable",
"type": "function",
},
{
"inputs": [
{"internalType": "uint256", "name": "amount", "type": "uint256"},
{"internalType": "uint256[]", "name": "minReturns", "type": "uint256[]"},
{"internalType": "address payable", "name": "target", "type": "address"},
],
"name": "withdrawFor",
"outputs": [
{
"internalType": "uint256[2]",
"name": "withdrawnAmounts",
"type": "uint256[2]",
}
],
"stateMutability": "nonpayable",
"type": "function",
},
]
|
mooniswap_abi = [{'inputs': [{'internalType': 'contract IERC20', 'name': '_token0', 'type': 'address'}, {'internalType': 'contract IERC20', 'name': '_token1', 'type': 'address'}, {'internalType': 'string', 'name': 'name', 'type': 'string'}, {'internalType': 'string', 'name': 'symbol', 'type': 'string'}, {'internalType': 'contract IMooniswapFactoryGovernance', 'name': '_mooniswapFactoryGovernance', 'type': 'address'}], 'stateMutability': 'nonpayable', 'type': 'constructor'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'owner', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'spender', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'value', 'type': 'uint256'}], 'name': 'Approval', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'user', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'decayPeriod', 'type': 'uint256'}, {'indexed': False, 'internalType': 'bool', 'name': 'isDefault', 'type': 'bool'}, {'indexed': False, 'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'DecayPeriodVoteUpdate', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'sender', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'receiver', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'share', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'token0Amount', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'token1Amount', 'type': 'uint256'}], 'name': 'Deposited', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': False, 'internalType': 'string', 'name': 'reason', 'type': 'string'}], 'name': 'Error', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'user', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'fee', 'type': 'uint256'}, {'indexed': False, 'internalType': 'bool', 'name': 'isDefault', 'type': 'bool'}, {'indexed': False, 'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'FeeVoteUpdate', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'previousOwner', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'newOwner', 'type': 'address'}], 'name': 'OwnershipTransferred', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'user', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'slippageFee', 'type': 'uint256'}, {'indexed': False, 'internalType': 'bool', 'name': 'isDefault', 'type': 'bool'}, {'indexed': False, 'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'SlippageFeeVoteUpdate', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'sender', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'receiver', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'srcToken', 'type': 'address'}, {'indexed': False, 'internalType': 'address', 'name': 'dstToken', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'result', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'srcAdditionBalance', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'dstRemovalBalance', 'type': 'uint256'}, {'indexed': False, 'internalType': 'address', 'name': 'referral', 'type': 'address'}], 'name': 'Swapped', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': False, 'internalType': 'uint256', 'name': 'srcBalance', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'dstBalance', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'fee', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'slippageFee', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'referralShare', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'governanceShare', 'type': 'uint256'}], 'name': 'Sync', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'from', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'to', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'value', 'type': 'uint256'}], 'name': 'Transfer', 'type': 'event'}, {'anonymous': False, 'inputs': [{'indexed': True, 'internalType': 'address', 'name': 'sender', 'type': 'address'}, {'indexed': True, 'internalType': 'address', 'name': 'receiver', 'type': 'address'}, {'indexed': False, 'internalType': 'uint256', 'name': 'share', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'token0Amount', 'type': 'uint256'}, {'indexed': False, 'internalType': 'uint256', 'name': 'token1Amount', 'type': 'uint256'}], 'name': 'Withdrawn', 'type': 'event'}, {'inputs': [{'internalType': 'address', 'name': 'owner', 'type': 'address'}, {'internalType': 'address', 'name': 'spender', 'type': 'address'}], 'name': 'allowance', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'spender', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'approve', 'outputs': [{'internalType': 'bool', 'name': '', 'type': 'bool'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'account', 'type': 'address'}], 'name': 'balanceOf', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'decayPeriod', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'vote', 'type': 'uint256'}], 'name': 'decayPeriodVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'user', 'type': 'address'}], 'name': 'decayPeriodVotes', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'decimals', 'outputs': [{'internalType': 'uint8', 'name': '', 'type': 'uint8'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'spender', 'type': 'address'}, {'internalType': 'uint256', 'name': 'subtractedValue', 'type': 'uint256'}], 'name': 'decreaseAllowance', 'outputs': [{'internalType': 'bool', 'name': '', 'type': 'bool'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'uint256[2]', 'name': 'maxAmounts', 'type': 'uint256[2]'}, {'internalType': 'uint256[2]', 'name': 'minAmounts', 'type': 'uint256[2]'}], 'name': 'deposit', 'outputs': [{'internalType': 'uint256', 'name': 'fairSupply', 'type': 'uint256'}, {'internalType': 'uint256[2]', 'name': 'receivedAmounts', 'type': 'uint256[2]'}], 'stateMutability': 'payable', 'type': 'function'}, {'inputs': [{'internalType': 'uint256[2]', 'name': 'maxAmounts', 'type': 'uint256[2]'}, {'internalType': 'uint256[2]', 'name': 'minAmounts', 'type': 'uint256[2]'}, {'internalType': 'address', 'name': 'target', 'type': 'address'}], 'name': 'depositFor', 'outputs': [{'internalType': 'uint256', 'name': 'fairSupply', 'type': 'uint256'}, {'internalType': 'uint256[2]', 'name': 'receivedAmounts', 'type': 'uint256[2]'}], 'stateMutability': 'payable', 'type': 'function'}, {'inputs': [], 'name': 'discardDecayPeriodVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [], 'name': 'discardFeeVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [], 'name': 'discardSlippageFeeVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [], 'name': 'fee', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'vote', 'type': 'uint256'}], 'name': 'feeVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'user', 'type': 'address'}], 'name': 'feeVotes', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'token', 'type': 'address'}], 'name': 'getBalanceForAddition', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'token', 'type': 'address'}], 'name': 'getBalanceForRemoval', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'src', 'type': 'address'}, {'internalType': 'contract IERC20', 'name': 'dst', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'getReturn', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'getTokens', 'outputs': [{'internalType': 'contract IERC20[]', 'name': 'tokens', 'type': 'address[]'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'spender', 'type': 'address'}, {'internalType': 'uint256', 'name': 'addedValue', 'type': 'uint256'}], 'name': 'increaseAllowance', 'outputs': [{'internalType': 'bool', 'name': '', 'type': 'bool'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [], 'name': 'mooniswapFactoryGovernance', 'outputs': [{'internalType': 'contract IMooniswapFactoryGovernance', 'name': '', 'type': 'address'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'name', 'outputs': [{'internalType': 'string', 'name': '', 'type': 'string'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'owner', 'outputs': [{'internalType': 'address', 'name': '', 'type': 'address'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'renounceOwnership', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'token', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'rescueFunds', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'contract IMooniswapFactoryGovernance', 'name': 'newMooniswapFactoryGovernance', 'type': 'address'}], 'name': 'setMooniswapFactoryGovernance', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [], 'name': 'slippageFee', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'vote', 'type': 'uint256'}], 'name': 'slippageFeeVote', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'user', 'type': 'address'}], 'name': 'slippageFeeVotes', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'src', 'type': 'address'}, {'internalType': 'contract IERC20', 'name': 'dst', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}, {'internalType': 'uint256', 'name': 'minReturn', 'type': 'uint256'}, {'internalType': 'address', 'name': 'referral', 'type': 'address'}], 'name': 'swap', 'outputs': [{'internalType': 'uint256', 'name': 'result', 'type': 'uint256'}], 'stateMutability': 'payable', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': 'src', 'type': 'address'}, {'internalType': 'contract IERC20', 'name': 'dst', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}, {'internalType': 'uint256', 'name': 'minReturn', 'type': 'uint256'}, {'internalType': 'address', 'name': 'referral', 'type': 'address'}, {'internalType': 'address payable', 'name': 'receiver', 'type': 'address'}], 'name': 'swapFor', 'outputs': [{'internalType': 'uint256', 'name': 'result', 'type': 'uint256'}], 'stateMutability': 'payable', 'type': 'function'}, {'inputs': [], 'name': 'symbol', 'outputs': [{'internalType': 'string', 'name': '', 'type': 'string'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'token0', 'outputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'token1', 'outputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'i', 'type': 'uint256'}], 'name': 'tokens', 'outputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'totalSupply', 'outputs': [{'internalType': 'uint256', 'name': '', 'type': 'uint256'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'recipient', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'transfer', 'outputs': [{'internalType': 'bool', 'name': '', 'type': 'bool'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'sender', 'type': 'address'}, {'internalType': 'address', 'name': 'recipient', 'type': 'address'}, {'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}], 'name': 'transferFrom', 'outputs': [{'internalType': 'bool', 'name': '', 'type': 'bool'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'address', 'name': 'newOwner', 'type': 'address'}], 'name': 'transferOwnership', 'outputs': [], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'name': 'virtualBalancesForAddition', 'outputs': [{'internalType': 'uint216', 'name': 'balance', 'type': 'uint216'}, {'internalType': 'uint40', 'name': 'time', 'type': 'uint40'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'name': 'virtualBalancesForRemoval', 'outputs': [{'internalType': 'uint216', 'name': 'balance', 'type': 'uint216'}, {'internalType': 'uint40', 'name': 'time', 'type': 'uint40'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'virtualDecayPeriod', 'outputs': [{'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint48', 'name': '', 'type': 'uint48'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'virtualFee', 'outputs': [{'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint48', 'name': '', 'type': 'uint48'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [], 'name': 'virtualSlippageFee', 'outputs': [{'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint104', 'name': '', 'type': 'uint104'}, {'internalType': 'uint48', 'name': '', 'type': 'uint48'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'contract IERC20', 'name': '', 'type': 'address'}], 'name': 'volumes', 'outputs': [{'internalType': 'uint128', 'name': 'confirmed', 'type': 'uint128'}, {'internalType': 'uint128', 'name': 'result', 'type': 'uint128'}], 'stateMutability': 'view', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}, {'internalType': 'uint256[]', 'name': 'minReturns', 'type': 'uint256[]'}], 'name': 'withdraw', 'outputs': [{'internalType': 'uint256[2]', 'name': 'withdrawnAmounts', 'type': 'uint256[2]'}], 'stateMutability': 'nonpayable', 'type': 'function'}, {'inputs': [{'internalType': 'uint256', 'name': 'amount', 'type': 'uint256'}, {'internalType': 'uint256[]', 'name': 'minReturns', 'type': 'uint256[]'}, {'internalType': 'address payable', 'name': 'target', 'type': 'address'}], 'name': 'withdrawFor', 'outputs': [{'internalType': 'uint256[2]', 'name': 'withdrawnAmounts', 'type': 'uint256[2]'}], 'stateMutability': 'nonpayable', 'type': 'function'}]
|
msg = None
if msg:
print(msg)
|
msg = None
if msg:
print(msg)
|
class NoTasksError(Exception):
"""Exception raised when there are no tasks passed"""
pass
class TaskResultKeyAlreadyExists(Exception):
"""Exception raised when two tasks produce same key-ed result"""
pass
class TaskResultObjectMissing(Exception):
"""Exception raised when one or more expected input results could not be retrieved from predecessor tasks"""
pass
class TaskNameError(Exception):
"""Exception raised when an other object with the name of a task exists.
Task names have to be unique for pickling.
"""
pass
class GridSearchExpansionError(Exception):
"""Exception raised when Grid Search expansion fails."""
pass
class CyclicGraphError(Exception):
"""Exception raised when task spec graph contains circular dependencies."""
pass
|
class Notaskserror(Exception):
"""Exception raised when there are no tasks passed"""
pass
class Taskresultkeyalreadyexists(Exception):
"""Exception raised when two tasks produce same key-ed result"""
pass
class Taskresultobjectmissing(Exception):
"""Exception raised when one or more expected input results could not be retrieved from predecessor tasks"""
pass
class Tasknameerror(Exception):
"""Exception raised when an other object with the name of a task exists.
Task names have to be unique for pickling.
"""
pass
class Gridsearchexpansionerror(Exception):
"""Exception raised when Grid Search expansion fails."""
pass
class Cyclicgrapherror(Exception):
"""Exception raised when task spec graph contains circular dependencies."""
pass
|
def counter(c_var):
while True:
try:
c_var = c_var + 1
except KeyboardInterrupt:
print('Counter now at: ' + str(c_var))
def counter_p(c_var):
while True:
c_var = c_var + 1
print(c_var)
|
def counter(c_var):
while True:
try:
c_var = c_var + 1
except KeyboardInterrupt:
print('Counter now at: ' + str(c_var))
def counter_p(c_var):
while True:
c_var = c_var + 1
print(c_var)
|
# Type definiton for (type, data) tuples representing a value
# See http://androidxref.com/9.0.0_r3/xref/frameworks/base/libs/androidfw/include/androidfw/ResourceTypes.h#262
# The 'data' is either 0 or 1, specifying this resource is either
# undefined or empty, respectively.
TYPE_NULL = 0x00
# The 'data' holds a ResTable_ref, a reference to another resource
# table entry.
TYPE_REFERENCE = 0x01
# The 'data' holds an attribute resource identifier.
TYPE_ATTRIBUTE = 0x02
# The 'data' holds an index into the containing resource table's
# global value string pool.
TYPE_STRING = 0x03
# The 'data' holds a single-precision floating point number.
TYPE_FLOAT = 0x04
# The 'data' holds a complex number encoding a dimension value
# such as "100in".
TYPE_DIMENSION = 0x05
# The 'data' holds a complex number encoding a fraction of a
# container.
TYPE_FRACTION = 0x06
# The 'data' holds a dynamic ResTable_ref, which needs to be
# resolved before it can be used like a TYPE_REFERENCE.
TYPE_DYNAMIC_REFERENCE = 0x07
# The 'data' holds an attribute resource identifier, which needs to be resolved
# before it can be used like a TYPE_ATTRIBUTE.
TYPE_DYNAMIC_ATTRIBUTE = 0x08
# Beginning of integer flavors...
TYPE_FIRST_INT = 0x10
# The 'data' is a raw integer value of the form n..n.
TYPE_INT_DEC = 0x10
# The 'data' is a raw integer value of the form 0xn..n.
TYPE_INT_HEX = 0x11
# The 'data' is either 0 or 1, for input "false" or "true" respectively.
TYPE_INT_BOOLEAN = 0x12
# Beginning of color integer flavors...
TYPE_FIRST_COLOR_INT = 0x1c
# The 'data' is a raw integer value of the form #aarrggbb.
TYPE_INT_COLOR_ARGB8 = 0x1c
# The 'data' is a raw integer value of the form #rrggbb.
TYPE_INT_COLOR_RGB8 = 0x1d
# The 'data' is a raw integer value of the form #argb.
TYPE_INT_COLOR_ARGB4 = 0x1e
# The 'data' is a raw integer value of the form #rgb.
TYPE_INT_COLOR_RGB4 = 0x1f
# ...end of integer flavors.
TYPE_LAST_COLOR_INT = 0x1f
# ...end of integer flavors.
TYPE_LAST_INT = 0x1f
|
type_null = 0
type_reference = 1
type_attribute = 2
type_string = 3
type_float = 4
type_dimension = 5
type_fraction = 6
type_dynamic_reference = 7
type_dynamic_attribute = 8
type_first_int = 16
type_int_dec = 16
type_int_hex = 17
type_int_boolean = 18
type_first_color_int = 28
type_int_color_argb8 = 28
type_int_color_rgb8 = 29
type_int_color_argb4 = 30
type_int_color_rgb4 = 31
type_last_color_int = 31
type_last_int = 31
|
words=['Aaron',
'Ab',
'Abba',
'Abbe',
'Abbey',
'Abbie',
'Abbot',
'Abbott',
'Abby',
'Abdel',
'Abdul',
'Abe',
'Abel',
'Abelard',
'Abeu',
'Abey',
'Abie',
'Abner',
'Abraham',
'Abrahan',
'Abram',
'Abramo',
'Abran',
'Ad',
'Adair',
'Adam',
'Adamo',
'Adams',
'Adan',
'Addie',
'Addison',
'Addy',
'Ade',
'Adelbert',
'Adham',
'Adlai',
'Adler',
'Ado',
'Adolf',
'Adolph',
'Adolphe',
'Adolpho',
'Adolphus',
'Adrian',
'Adriano',
'Adrien',
'Agosto',
'Aguie',
'Aguistin',
'Aguste',
'Agustin',
'Aharon',
'Ahmad',
'Ahmed',
'Ailbert',
'Akim',
'Aksel',
'Al',
'Alain',
'Alair',
'Alan',
'Aland',
'Alano',
'Alanson',
'Alard',
'Alaric',
'Alasdair',
'Alastair',
'Alasteir',
'Alaster',
'Alberik',
'Albert',
'Alberto',
'Albie',
'Albrecht',
'Alden',
'Aldin',
'Aldis',
'Aldo',
'Aldon',
'Aldous',
'Aldric',
'Aldrich',
'Aldridge',
'Aldus',
'Aldwin',
'Alec',
'Alejandro',
'Alejoa',
'Aleksandr',
'Alessandro',
'Alex',
'Alexander',
'Alexandr',
'Alexandre',
'Alexandro',
'Alexandros',
'Alexei',
'Alexio',
'Alexis',
'Alf',
'Alfie',
'Alfons',
'Alfonse',
'Alfonso',
'Alford',
'Alfred',
'Alfredo',
'Alfy',
'Algernon',
'Ali',
'Alic',
'Alick',
'Alisander',
'Alistair',
'Alister',
'Alix',
'Allan',
'Allard',
'Allayne',
'Allen',
'Alley',
'Alleyn',
'Allie',
'Allin',
'Allister',
'Allistir',
'Allyn',
'Aloin',
'Alon',
'Alonso',
'Alonzo',
'Aloysius',
'Alphard',
'Alphonse',
'Alphonso',
'Alric',
'Aluin',
'Aluino',
'Alva',
'Alvan',
'Alvie',
'Alvin',
'Alvis',
'Alvy',
'Alwin',
'Alwyn',
'Alyosha',
'Amble',
'Ambros',
'Ambrose',
'Ambrosi',
'Ambrosio',
'Ambrosius',
'Amby',
'Amerigo',
'Amery',
'Amory',
'Amos',
'Anatol',
'Anatole',
'Anatollo',
'Ancell',
'Anders',
'Anderson',
'Andie',
'Andonis',
'Andras',
'Andre',
'Andrea',
'Andreas',
'Andrej',
'Andres',
'Andrew',
'Andrey',
'Andris',
'Andros',
'Andrus',
'Andy',
'Ange',
'Angel',
'Angeli',
'Angelico',
'Angelo',
'Angie',
'Angus',
'Ansel',
'Ansell',
'Anselm',
'Anson',
'Anthony',
'Antin',
'Antoine',
'Anton',
'Antone',
'Antoni',
'Antonin',
'Antonino',
'Antonio',
'Antonius',
'Antons',
'Antony',
'Any',
'Ara',
'Araldo',
'Arch',
'Archaimbaud',
'Archambault',
'Archer',
'Archibald',
'Archibaldo',
'Archibold',
'Archie',
'Archy',
'Arel',
'Ari',
'Arie',
'Ariel',
'Arin',
'Ario',
'Aristotle',
'Arlan',
'Arlen',
'Arley',
'Arlin',
'Arman',
'Armand',
'Armando',
'Armin',
'Armstrong',
'Arnaldo',
'Arne',
'Arney',
'Arni',
'Arnie',
'Arnold',
'Arnoldo',
'Arnuad',
'Arny',
'Aron',
'Arri',
'Arron',
'Art',
'Artair',
'Arte',
'Artemas',
'Artemis',
'Artemus',
'Arther',
'Arthur',
'Artie',
'Artur',
'Arturo',
'Artus',
'Arty',
'Arv',
'Arvie',
'Arvin',
'Arvy',
'Asa',
'Ase',
'Ash',
'Ashbey',
'Ashby',
'Asher',
'Ashley',
'Ashlin',
'Ashton',
'Aube',
'Auberon',
'Aubert',
'Aubrey',
'Augie',
'August',
'Augustin',
'Augustine',
'Augusto',
'Augustus',
'Augy',
'Aurthur',
'Austen',
'Austin',
'Ave',
'Averell',
'Averil',
'Averill',
'Avery',
'Avictor',
'Avigdor',
'Avram',
'Avrom',
'Ax',
'Axe',
'Axel',
'Aylmar',
'Aylmer',
'Aymer',
'Bail',
'Bailey',
'Bailie',
'Baillie',
'Baily',
'Baird',
'Bald',
'Balduin',
'Baldwin',
'Bale',
'Ban',
'Bancroft',
'Bank',
'Banky',
'Bar',
'Barbabas',
'Barclay',
'Bard',
'Barde',
'Barn',
'Barnabas',
'Barnabe',
'Barnaby',
'Barnard',
'Barnebas',
'Barnett',
'Barney',
'Barnie',
'Barny',
'Baron',
'Barr',
'Barret',
'Barrett',
'Barri',
'Barrie',
'Barris',
'Barron',
'Barry',
'Bart',
'Bartel',
'Barth',
'Barthel',
'Bartholemy',
'Bartholomeo',
'Bartholomeus',
'Bartholomew',
'Bartie',
'Bartlet',
'Bartlett',
'Bartolemo',
'Bartolomeo',
'Barton',
'Bartram',
'Barty',
'Bary',
'Baryram',
'Base',
'Basil',
'Basile',
'Basilio',
'Basilius',
'Bastian',
'Bastien',
'Bat',
'Batholomew',
'Baudoin',
'Bax',
'Baxie',
'Baxter',
'Baxy',
'Bay',
'Bayard',
'Beale',
'Bealle',
'Bear',
'Bearnard',
'Beau',
'Beaufort',
'Beauregard',
'Beck',
'Beltran',
'Ben',
'Bendick',
'Bendicty',
'Bendix',
'Benedetto',
'Benedick',
'Benedict',
'Benedicto',
'Benedikt',
'Bengt',
'Beniamino',
'Benito',
'Benjamen',
'Benjamin',
'Benji',
'Benjie',
'Benjy',
'Benn',
'Bennett',
'Bennie',
'Benny',
'Benoit',
'Benson',
'Bent',
'Bentlee',
'Bentley',
'Benton',
'Benyamin',
'Ber',
'Berk',
'Berke',
'Berkeley',
'Berkie',
'Berkley',
'Berkly',
'Berky',
'Bern',
'Bernard',
'Bernardo',
'Bernarr',
'Berne',
'Bernhard',
'Bernie',
'Berny',
'Bert',
'Berti',
'Bertie',
'Berton',
'Bertram',
'Bertrand',
'Bertrando',
'Berty',
'Bev',
'Bevan',
'Bevin',
'Bevon',
'Bil',
'Bill',
'Billie',
'Billy',
'Bing',
'Bink',
'Binky',
'Birch',
'Birk',
'Biron',
'Bjorn',
'Blaine',
'Blair',
'Blake',
'Blane',
'Blayne',
'Bo',
'Bob',
'Bobbie',
'Bobby',
'Bogart',
'Bogey',
'Boigie',
'Bond',
'Bondie',
'Bondon',
'Bondy',
'Bone',
'Boniface',
'Boone',
'Boonie',
'Boony',
'Boot',
'Boote',
'Booth',
'Boothe',
'Bord',
'Borden',
'Bordie',
'Bordy',
'Borg',
'Boris',
'Bourke',
'Bowie',
'Boy',
'Boyce',
'Boycey',
'Boycie',
'Boyd',
'Brad',
'Bradan',
'Brade',
'Braden',
'Bradford',
'Bradley',
'Bradly',
'Bradney',
'Brady',
'Bram',
'Bran',
'Brand',
'Branden',
'Brander',
'Brandon',
'Brandtr',
'Brandy',
'Brandyn',
'Brannon',
'Brant',
'Brantley',
'Bren',
'Brendan',
'Brenden',
'Brendin',
'Brendis',
'Brendon',
'Brennan',
'Brennen',
'Brent',
'Bret',
'Brett',
'Brew',
'Brewer',
'Brewster',
'Brian',
'Briano',
'Briant',
'Brice',
'Brien',
'Brig',
'Brigg',
'Briggs',
'Brigham',
'Brion',
'Brit',
'Britt',
'Brnaba',
'Brnaby',
'Brock',
'Brockie',
'Brocky',
'Brod',
'Broddie',
'Broddy',
'Broderic',
'Broderick',
'Brodie',
'Brody',
'Brok',
'Bron',
'Bronnie',
'Bronny',
'Bronson',
'Brook',
'Brooke',
'Brooks',
'Brose',
'Bruce',
'Brucie',
'Bruis',
'Bruno',
'Bryan',
'Bryant',
'Bryanty',
'Bryce',
'Bryn',
'Bryon',
'Buck',
'Buckie',
'Bucky',
'Bud',
'Budd',
'Buddie',
'Buddy',
'Buiron',
'Burch',
'Burg',
'Burgess',
'Burk',
'Burke',
'Burl',
'Burlie',
'Burnaby',
'Burnard',
'Burr',
'Burt',
'Burtie',
'Burton',
'Burty',
'Butch',
'Byram',
'Byran',
'Byrann',
'Byrle',
'Byrom',
'Byron',
'Cad',
'Caddric',
'Caesar',
'Cal',
'Caldwell',
'Cale',
'Caleb',
'Calhoun',
'Callean',
'Calv',
'Calvin',
'Cam',
'Cameron',
'Camey',
'Cammy',
'Car',
'Carce',
'Care',
'Carey',
'Carl',
'Carleton',
'Carlie',
'Carlin',
'Carling',
'Carlo',
'Carlos',
'Carly',
'Carlyle',
'Carmine',
'Carney',
'Carny',
'Carolus',
'Carr',
'Carrol',
'Carroll',
'Carson',
'Cart',
'Carter',
'Carver',
'Cary',
'Caryl',
'Casar',
'Case',
'Casey',
'Cash',
'Caspar',
'Casper',
'Cass',
'Cassie',
'Cassius',
'Caz',
'Cazzie',
'Cchaddie',
'Cece',
'Cecil',
'Cecilio',
'Cecilius',
'Ced',
'Cedric',
'Cello',
'Cesar',
'Cesare',
'Cesaro',
'Chad',
'Chadd',
'Chaddie',
'Chaddy',
'Chadwick',
'Chaim',
'Chalmers',
'Chan',
'Chance',
'Chancey',
'Chandler',
'Chane',
'Chariot',
'Charles',
'Charley',
'Charlie',
'Charlton',
'Chas',
'Chase',
'Chaunce',
'Chauncey',
'Che',
'Chen',
'Ches',
'Chester',
'Cheston',
'Chet',
'Chev',
'Chevalier',
'Chevy',
'Chic',
'Chick',
'Chickie',
'Chicky',
'Chico',
'Chilton',
'Chip',
'Chris',
'Chrisse',
'Chrissie',
'Chrissy',
'Christian',
'Christiano',
'Christie',
'Christoffer',
'Christoforo',
'Christoper',
'Christoph',
'Christophe',
'Christopher',
'Christophorus',
'Christos',
'Christy',
'Chrisy',
'Chrotoem',
'Chucho',
'Chuck',
'Cirillo',
'Cirilo',
'Ciro',
'Claiborn',
'Claiborne',
'Clair',
'Claire',
'Clarance',
'Clare',
'Clarence',
'Clark',
'Clarke',
'Claudell',
'Claudian',
'Claudianus',
'Claudio',
'Claudius',
'Claus',
'Clay',
'Clayborn',
'Clayborne',
'Claybourne',
'Clayson',
'Clayton',
'Cleavland',
'Clem',
'Clemens',
'Clement',
'Clemente',
'Clementius',
'Clemmie',
'Clemmy',
'Cleon',
'Clerc',
'Cletis',
'Cletus',
'Cleve',
'Cleveland',
'Clevey',
'Clevie',
'Cliff',
'Clifford',
'Clim',
'Clint',
'Clive',
'Cly',
'Clyde',
'Clyve',
'Clywd',
'Cob',
'Cobb',
'Cobbie',
'Cobby',
'Codi',
'Codie',
'Cody',
'Cointon',
'Colan',
'Colas',
'Colby',
'Cole',
'Coleman',
'Colet',
'Colin',
'Collin',
'Colman',
'Colver',
'Con',
'Conan',
'Conant',
'Conn',
'Conney',
'Connie',
'Connor',
'Conny',
'Conrad',
'Conrade',
'Conrado',
'Conroy',
'Consalve',
'Constantin',
'Constantine',
'Constantino',
'Conway',
'Coop',
'Cooper',
'Corbet',
'Corbett',
'Corbie',
'Corbin',
'Corby',
'Cord',
'Cordell',
'Cordie',
'Cordy',
'Corey',
'Cori',
'Cornall',
'Cornelius',
'Cornell',
'Corney',
'Cornie',
'Corny',
'Correy',
'Corrie',
'Cort',
'Cortie',
'Corty',
'Cory',
'Cos',
'Cosimo',
'Cosme',
'Cosmo',
'Costa',
'Court',
'Courtnay',
'Courtney',
'Cozmo',
'Craggie',
'Craggy',
'Craig',
'Crawford',
'Creigh',
'Creight',
'Creighton',
'Crichton',
'Cris',
'Cristian',
'Cristiano',
'Cristobal',
'Crosby',
'Cross',
'Cull',
'Cullan',
'Cullen',
'Culley',
'Cullie',
'Cullin',
'Cully',
'Culver',
'Curcio',
'Curr',
'Curran',
'Currey',
'Currie',
'Curry',
'Curt',
'Curtice',
'Curtis',
'Cy',
'Cyril',
'Cyrill',
'Cyrille',
'Cyrillus',
'Cyrus',
"D'Arcy",
'Dael',
'Dag',
'Dagny',
'Dal',
'Dale',
'Dalis',
'Dall',
'Dallas',
'Dalli',
'Dallis',
'Dallon',
'Dalston',
'Dalt',
'Dalton',
'Dame',
'Damian',
'Damiano',
'Damien',
'Damon',
'Dan',
'Dana',
'Dane',
'Dani',
'Danie',
'Daniel',
'Dannel',
'Dannie',
'Danny',
'Dante',
'Danya',
'Dar',
'Darb',
'Darbee',
'Darby',
'Darcy',
'Dare',
'Daren',
'Darill',
'Darin',
'Dario',
'Darius',
'Darn',
'Darnall',
'Darnell',
'Daron',
'Darrel',
'Darrell',
'Darren',
'Darrick',
'Darrin',
'Darryl',
'Darwin',
'Daryl',
'Daryle',
'Dav',
'Dave',
'Daven',
'Davey',
'David',
'Davidde',
'Davide',
'Davidson',
'Davie',
'Davin',
'Davis',
'Davon',
'Davy',
'De Witt',
'Dean',
'Deane',
'Decca',
'Deck',
'Del',
'Delainey',
'Delaney',
'Delano',
'Delbert',
'Dell',
'Delmar',
'Delmer',
'Delmor',
'Delmore',
'Demetre',
'Demetri',
'Demetris',
'Demetrius',
'Demott',
'Den',
'Dene',
'Denis',
'Dennet',
'Denney',
'Dennie',
'Dennis',
'Dennison',
'Denny',
'Denver',
'Denys',
'Der',
'Derby',
'Derek',
'Derick',
'Derk',
'Dermot',
'Derrek',
'Derrick',
'Derrik',
'Derril',
'Derron',
'Derry',
'Derward',
'Derwin',
'Des',
'Desi',
'Desmond',
'Desmund',
'Dev',
'Devin',
'Devland',
'Devlen',
'Devlin',
'Devy',
'Dew',
'Dewain',
'Dewey',
'Dewie',
'Dewitt',
'Dex',
'Dexter',
'Diarmid',
'Dick',
'Dickie',
'Dicky',
'Diego',
'Dieter',
'Dietrich',
'Dilan',
'Dill',
'Dillie',
'Dillon',
'Dilly',
'Dimitri',
'Dimitry',
'Dino',
'Dion',
'Dionisio',
'Dionysus',
'Dirk',
'Dmitri',
'Dolf',
'Dolph',
'Dom',
'Domenic',
'Domenico',
'Domingo',
'Dominic',
'Dominick',
'Dominik',
'Dominique',
'Don',
'Donal',
'Donall',
'Donalt',
'Donaugh',
'Donavon',
'Donn',
'Donnell',
'Donnie',
'Donny',
'Donovan',
'Dore',
'Dorey',
'Dorian',
'Dorie',
'Dory',
'Doug',
'Dougie',
'Douglas',
'Douglass',
'Dougy',
'Dov',
'Doy',
'Doyle',
'Drake',
'Drew',
'Dru',
'Drud',
'Drugi',
'Duane',
'Dud',
'Dudley',
'Duff',
'Duffie',
'Duffy',
'Dugald',
'Duke',
'Dukey',
'Dukie',
'Duky',
'Dun',
'Dunc',
'Duncan',
'Dunn',
'Dunstan',
'Dur',
'Durand',
'Durant',
'Durante',
'Durward',
'Dwain',
'Dwayne',
'Dwight',
'Dylan',
'Eadmund',
'Eal',
'Eamon',
'Earl',
'Earle',
'Earlie',
'Early',
'Earvin',
'Eb',
'Eben',
'Ebeneser',
'Ebenezer',
'Eberhard',
'Eberto',
'Ed',
'Edan',
'Edd',
'Eddie',
'Eddy',
'Edgar',
'Edgard',
'Edgardo',
'Edik',
'Edlin',
'Edmon',
'Edmund',
'Edouard',
'Edsel',
'Eduard',
'Eduardo',
'Eduino',
'Edvard',
'Edward',
'Edwin',
'Efrem',
'Efren',
'Egan',
'Egbert',
'Egon',
'Egor',
'El',
'Elbert',
'Elden',
'Eldin',
'Eldon',
'Eldredge',
'Eldridge',
'Eli',
'Elia',
'Elias',
'Elihu',
'Elijah',
'Eliot',
'Elisha',
'Ellary',
'Ellerey',
'Ellery',
'Elliot',
'Elliott',
'Ellis',
'Ellswerth',
'Ellsworth',
'Ellwood',
'Elmer',
'Elmo',
'Elmore',
'Elnar',
'Elroy',
'Elston',
'Elsworth',
'Elton',
'Elvin',
'Elvis',
'Elvyn',
'Elwin',
'Elwood',
'Elwyn',
'Ely',
'Em',
'Emanuel',
'Emanuele',
'Emelen',
'Emerson',
'Emery',
'Emile',
'Emilio',
'Emlen',
'Emlyn',
'Emmanuel',
'Emmerich',
'Emmery',
'Emmet',
'Emmett',
'Emmit',
'Emmott',
'Emmy',
'Emory',
'Engelbert',
'Englebert',
'Ennis',
'Enoch',
'Enos',
'Enrico',
'Enrique',
'Ephraim',
'Ephrayim',
'Ephrem',
'Erasmus',
'Erastus',
'Erek',
'Erhard',
'Erhart',
'Eric',
'Erich',
'Erick',
'Erie',
'Erik',
'Erin',
'Erl',
'Ermanno',
'Ermin',
'Ernest',
'Ernesto',
'Ernestus',
'Ernie',
'Ernst',
'Erny',
'Errick',
'Errol',
'Erroll',
'Erskine',
'Erv',
'ErvIn',
'Erwin',
'Esdras',
'Esme',
'Esra',
'Esteban',
'Estevan',
'Etan',
'Ethan',
'Ethe',
'Ethelbert',
'Ethelred',
'Etienne',
'Ettore',
'Euell',
'Eugen',
'Eugene',
'Eugenio',
'Eugenius',
'Eustace',
'Ev',
'Evan',
'Evelin',
'Evelyn',
'Even',
'Everard',
'Evered',
'Everett',
'Evin',
'Evyn',
'Ewan',
'Eward',
'Ewart',
'Ewell',
'Ewen',
'Ezechiel',
'Ezekiel',
'Ezequiel',
'Eziechiele',
'Ezra',
'Ezri',
'Fabe',
'Faber',
'Fabian',
'Fabiano',
'Fabien',
'Fabio',
'Fair',
'Fairfax',
'Fairleigh',
'Fairlie',
'Falito',
'Falkner',
'Far',
'Farlay',
'Farlee',
'Farleigh',
'Farley',
'Farlie',
'Farly',
'Farr',
'Farrel',
'Farrell',
'Farris',
'Faulkner',
'Fax',
'Federico',
'Fee',
'Felic',
'Felice',
'Felicio',
'Felike',
'Feliks',
'Felipe',
'Felix',
'Felizio',
'Feodor',
'Ferd',
'Ferdie',
'Ferdinand',
'Ferdy',
'Fergus',
'Ferguson',
'Fernando',
'Ferrel',
'Ferrell',
'Ferris',
'Fidel',
'Fidelio',
'Fidole',
'Field',
'Fielding',
'Fields',
'Filbert',
'Filberte',
'Filberto',
'Filip',
'Filippo',
'Filmer',
'Filmore',
'Fin',
'Findlay',
'Findley',
'Finlay',
'Finley',
'Finn',
'Fitz',
'Fitzgerald',
'Flem',
'Fleming',
'Flemming',
'Fletch',
'Fletcher',
'Flin',
'Flinn',
'Flint',
'Florian',
'Flory',
'Floyd',
'Flynn',
'Fons',
'Fonsie',
'Fonz',
'Fonzie',
'Forbes',
'Ford',
'Forest',
'Forester',
'Forrest',
'Forrester',
'Forster',
'Foss',
'Foster',
'Fowler',
'Fran',
'Francesco',
'Franchot',
'Francis',
'Francisco',
'Franciskus',
'Francklin',
'Francklyn',
'Francois',
'Frank',
'Frankie',
'Franklin',
'Franklyn',
'Franky',
'Frannie',
'Franny',
'Frans',
'Fransisco',
'Frants',
'Franz',
'Franzen',
'Frasco',
'Fraser',
'Frasier',
'Frasquito',
'Fraze',
'Frazer',
'Frazier',
'Fred',
'Freddie',
'Freddy',
'Fredek',
'Frederic',
'Frederich',
'Frederick',
'Frederico',
'Frederigo',
'Frederik',
'Fredric',
'Fredrick',
'Free',
'Freedman',
'Freeland',
'Freeman',
'Freemon',
'Fremont',
'Friedrich',
'Friedrick',
'Fritz',
'Fulton',
'Gabbie',
'Gabby',
'Gabe',
'Gabi',
'Gabie',
'Gabriel',
'Gabriele',
'Gabriello',
'Gaby',
'Gael',
'Gaelan',
'Gage',
'Gail',
'Gaile',
'Gal',
'Gale',
'Galen',
'Gallagher',
'Gallard',
'Galvan',
'Galven',
'Galvin',
'Gamaliel',
'Gan',
'Gannie',
'Gannon',
'Ganny',
'Gar',
'Garald',
'Gard',
'Gardener',
'Gardie',
'Gardiner',
'Gardner',
'Gardy',
'Gare',
'Garek',
'Gareth',
'Garey',
'Garfield',
'Garik',
'Garner',
'Garold',
'Garrard',
'Garrek',
'Garret',
'Garreth',
'Garrett',
'Garrick',
'Garrik',
'Garrot',
'Garrott',
'Garry',
'Garth',
'Garv',
'Garvey',
'Garvin',
'Garvy',
'Garwin',
'Garwood',
'Gary',
'Gaspar',
'Gaspard',
'Gasparo',
'Gasper',
'Gaston',
'Gaultiero',
'Gauthier',
'Gav',
'Gavan',
'Gaven',
'Gavin',
'Gawain',
'Gawen',
'Gay',
'Gayelord',
'Gayle',
'Gayler',
'Gaylor',
'Gaylord',
'Gearalt',
'Gearard',
'Gene',
'Geno',
'Geoff',
'Geoffrey',
'Geoffry',
'Georas',
'Geordie',
'Georg',
'George',
'Georges',
'Georgi',
'Georgie',
'Georgy',
'Gerald',
'Gerard',
'Gerardo',
'Gerek',
'Gerhard',
'Gerhardt',
'Geri',
'Gerick',
'Gerik',
'Germain',
'Germaine',
'Germayne',
'Gerome',
'Gerrard',
'Gerri',
'Gerrie',
'Gerry',
'Gery',
'Gherardo',
'Giacobo',
'Giacomo',
'Giacopo',
'Gian',
'Gianni',
'Giavani',
'Gib',
'Gibb',
'Gibbie',
'Gibby',
'Gideon',
'Giff',
'Giffard',
'Giffer',
'Giffie',
'Gifford',
'Giffy',
'Gil',
'Gilbert',
'Gilberto',
'Gilburt',
'Giles',
'Gill',
'Gilles',
'Ginger',
'Gino',
'Giordano',
'Giorgi',
'Giorgio',
'Giovanni',
'Giraldo',
'Giraud',
'Giselbert',
'Giulio',
'Giuseppe',
'Giustino',
'Giusto',
'Glen',
'Glenden',
'Glendon',
'Glenn',
'Glyn',
'Glynn',
'Godard',
'Godart',
'Goddard',
'Goddart',
'Godfree',
'Godfrey',
'Godfry',
'Godwin',
'Gonzales',
'Gonzalo',
'Goober',
'Goran',
'Goraud',
'Gordan',
'Gorden',
'Gordie',
'Gordon',
'Gordy',
'Gothart',
'Gottfried',
'Grace',
'Gradeigh',
'Gradey',
'Grady',
'Graehme',
'Graeme',
'Graham',
'Graig',
'Gram',
'Gran',
'Grange',
'Granger',
'Grannie',
'Granny',
'Grant',
'Grantham',
'Granthem',
'Grantley',
'Granville',
'Gray',
'Greg',
'Gregg',
'Greggory',
'Gregoire',
'Gregoor',
'Gregor',
'Gregorio',
'Gregorius',
'Gregory',
'Grenville',
'Griff',
'Griffie',
'Griffin',
'Griffith',
'Griffy',
'Gris',
'Griswold',
'Griz',
'Grove',
'Grover',
'Gualterio',
'Guglielmo',
'Guido',
'Guilbert',
'Guillaume',
'Guillermo',
'Gun',
'Gunar',
'Gunner',
'Guntar',
'Gunter',
'Gunther',
'Gus',
'Guss',
'Gustaf',
'Gustav',
'Gustave',
'Gustavo',
'Gustavus',
'Guthrey',
'Guthrie',
'Guthry',
'Guy',
'Had',
'Hadlee',
'Hadleigh',
'Hadley',
'Hadrian',
'Hagan',
'Hagen',
'Hailey',
'Haily',
'Hakeem',
'Hakim',
'Hal',
'Hale',
'Haleigh',
'Haley',
'Hall',
'Hallsy',
'Halsey',
'Halsy',
'Ham',
'Hamel',
'Hamid',
'Hamil',
'Hamilton',
'Hamish',
'Hamlen',
'Hamlin',
'Hammad',
'Hamnet',
'Hanan',
'Hank',
'Hans',
'Hansiain',
'Hanson',
'Harald',
'Harbert',
'Harcourt',
'Hardy',
'Harlan',
'Harland',
'Harlen',
'Harley',
'Harlin',
'Harman',
'Harmon',
'Harold',
'Haroun',
'Harp',
'Harper',
'Harris',
'Harrison',
'Harry',
'Hart',
'Hartley',
'Hartwell',
'Harv',
'Harvey',
'Harwell',
'Harwilll',
'Hasheem',
'Hashim',
'Haskel',
'Haskell',
'Haslett',
'Hastie',
'Hastings',
'Hasty',
'Haven',
'Hayden',
'Haydon',
'Hayes',
'Hayward',
'Haywood',
'Hayyim',
'Haze',
'Hazel',
'Hazlett',
'Heall',
'Heath',
'Hebert',
'Hector',
'Heindrick',
'Heinrick',
'Heinrik',
'Henderson',
'Hendrick',
'Hendrik',
'Henri',
'Henrik',
'Henry',
'Herb',
'Herbert',
'Herbie',
'Herby',
'Herc',
'Hercule',
'Hercules',
'Herculie',
'Heriberto',
'Herman',
'Hermann',
'Hermie',
'Hermon',
'Hermy',
'Hernando',
'Herold',
'Herrick',
'Hersch',
'Herschel',
'Hersh',
'Hershel',
'Herve',
'Hervey',
'Hew',
'Hewe',
'Hewet',
'Hewett',
'Hewie',
'Hewitt',
'Heywood',
'Hi',
'Hieronymus',
'Hilario',
'Hilarius',
'Hilary',
'Hill',
'Hillard',
'Hillary',
'Hillel',
'Hillery',
'Hilliard',
'Hillie',
'Hillier',
'Hilly',
'Hillyer',
'Hilton',
'Hinze',
'Hiram',
'Hirsch',
'Hobard',
'Hobart',
'Hobey',
'Hobie',
'Hodge',
'Hoebart',
'Hogan',
'Holden',
'Hollis',
'Holly',
'Holmes',
'Holt',
'Homer',
'Homere',
'Homerus',
'Horace',
'Horacio',
'Horatio',
'Horatius',
'Horst',
'Hort',
'Horten',
'Horton',
'Howard',
'Howey',
'Howie',
'Hoyt',
'Hube',
'Hubert',
'Huberto',
'Hubey',
'Hubie',
'Huey',
'Hugh',
'Hughie',
'Hugibert',
'Hugo',
'Hugues',
'Humbert',
'Humberto',
'Humfrey',
'Humfrid',
'Humfried',
'Humphrey',
'Hunfredo',
'Hunt',
'Hunter',
'Huntington',
'Huntlee',
'Huntley',
'Hurlee',
'Hurleigh',
'Hurley',
'Husain',
'Husein',
'Hussein',
'Hy',
'Hyatt',
'Hyman',
'Hymie',
'Iago',
'Iain',
'Ian',
'Ibrahim',
'Ichabod',
'Iggie',
'Iggy',
'Ignace',
'Ignacio',
'Ignacius',
'Ignatius',
'Ignaz',
'Ignazio',
'Igor',
'Ike',
'Ikey',
'Ilaire',
'Ilario',
'Immanuel',
'Ingamar',
'Ingar',
'Ingelbert',
'Ingemar',
'Inger',
'Inglebert',
'Inglis',
'Ingmar',
'Ingra',
'Ingram',
'Ingrim',
'Inigo',
'Inness',
'Innis',
'Iorgo',
'Iorgos',
'Iosep',
'Ira',
'Irv',
'Irvin',
'Irvine',
'Irving',
'Irwin',
'Irwinn',
'Isa',
'Isaac',
'Isaak',
'Isac',
'Isacco',
'Isador',
'Isadore',
'Isaiah',
'Isak',
'Isiahi',
'Isidor',
'Isidore',
'Isidoro',
'Isidro',
'Israel',
'Issiah',
'Itch',
'Ivan',
'Ivar',
'Ive',
'Iver',
'Ives',
'Ivor',
'Izaak',
'Izak',
'Izzy',
'Jabez',
'Jack',
'Jackie',
'Jackson',
'Jacky',
'Jacob',
'Jacobo',
'Jacques',
'Jae',
'Jaime',
'Jaimie',
'Jake',
'Jakie',
'Jakob',
'Jamaal',
'Jamal',
'James',
'Jameson',
'Jamesy',
'Jamey',
'Jamie',
'Jamil',
'Jamill',
'Jamison',
'Jammal',
'Jan',
'Janek',
'Janos',
'Jarad',
'Jard',
'Jareb',
'Jared',
'Jarib',
'Jarid',
'Jarrad',
'Jarred',
'Jarret',
'Jarrett',
'Jarrid',
'Jarrod',
'Jarvis',
'Jase',
'Jasen',
'Jason',
'Jasper',
'Jasun',
'Javier',
'Jay',
'Jaye',
'Jayme',
'Jaymie',
'Jayson',
'Jdavie',
'Jean',
'Jecho',
'Jed',
'Jedd',
'Jeddy',
'Jedediah',
'Jedidiah',
'Jeff',
'Jefferey',
'Jefferson',
'Jeffie',
'Jeffrey',
'Jeffry',
'Jeffy',
'Jehu',
'Jeno',
'Jens',
'Jephthah',
'Jerad',
'Jerald',
'Jeramey',
'Jeramie',
'Jere',
'Jereme',
'Jeremiah',
'Jeremias',
'Jeremie',
'Jeremy',
'Jermain',
'Jermaine',
'Jermayne',
'Jerome',
'Jeromy',
'Jerri',
'Jerrie',
'Jerrold',
'Jerrome',
'Jerry',
'Jervis',
'Jess',
'Jesse',
'Jessee',
'Jessey',
'Jessie',
'Jesus',
'Jeth',
'Jethro',
'Jim',
'Jimmie',
'Jimmy',
'Jo',
'Joachim',
'Joaquin',
'Job',
'Jock',
'Jocko',
'Jodi',
'Jodie',
'Jody',
'Joe',
'Joel',
'Joey',
'Johan',
'Johann',
'Johannes',
'John',
'Johnathan',
'Johnathon',
'Johnnie',
'Johnny',
'Johny',
'Jon',
'Jonah',
'Jonas',
'Jonathan',
'Jonathon',
'Jone',
'Jordan',
'Jordon',
'Jorgan',
'Jorge',
'Jory',
'Jose',
'Joseito',
'Joseph',
'Josh',
'Joshia',
'Joshua',
'Joshuah',
'Josiah',
'Josias',
'Jourdain',
'Jozef',
'Juan',
'Jud',
'Judah',
'Judas',
'Judd',
'Jude',
'Judon',
'Jule',
'Jules',
'Julian',
'Julie',
'Julio',
'Julius',
'Justen',
'Justin',
'Justinian',
'Justino',
'Justis',
'Justus',
'Kahaleel',
'Kahlil',
'Kain',
'Kaine',
'Kaiser',
'Kale',
'Kaleb',
'Kalil',
'Kalle',
'Kalvin',
'Kane',
'Kareem',
'Karel',
'Karim',
'Karl',
'Karlan',
'Karlens',
'Karlik',
'Karlis',
'Karney',
'Karoly',
'Kaspar',
'Kasper',
'Kayne',
'Kean',
'Keane',
'Kearney',
'Keary',
'Keefe',
'Keefer',
'Keelby',
'Keen',
'Keenan',
'Keene',
'Keir',
'Keith',
'Kelbee',
'Kelby',
'Kele',
'Kellby',
'Kellen',
'Kelley',
'Kelly',
'Kelsey',
'Kelvin',
'Kelwin',
'Ken',
'Kendal',
'Kendall',
'Kendell',
'Kendrick',
'Kendricks',
'Kenn',
'Kennan',
'Kennedy',
'Kenneth',
'Kennett',
'Kennie',
'Kennith',
'Kenny',
'Kenon',
'Kent',
'Kenton',
'Kenyon',
'Ker',
'Kerby',
'Kerk',
'Kermie',
'Kermit',
'Kermy',
'Kerr',
'Kerry',
'Kerwin',
'Kerwinn',
'Kev',
'Kevan',
'Keven',
'Kevin',
'Kevon',
'Khalil',
'Kiel',
'Kienan',
'Kile',
'Kiley',
'Kilian',
'Killian',
'Killie',
'Killy',
'Kim',
'Kimball',
'Kimbell',
'Kimble',
'Kin',
'Kincaid',
'King',
'Kingsley',
'Kingsly',
'Kingston',
'Kinnie',
'Kinny',
'Kinsley',
'Kip',
'Kipp',
'Kippar',
'Kipper',
'Kippie',
'Kippy',
'Kirby',
'Kirk',
'Kit',
'Klaus',
'Klemens',
'Klement',
'Kleon',
'Kliment',
'Knox',
'Koenraad',
'Konrad',
'Konstantin',
'Konstantine',
'Korey',
'Kort',
'Kory',
'Kris',
'Krisha',
'Krishna',
'Krishnah',
'Krispin',
'Kristian',
'Kristo',
'Kristofer',
'Kristoffer',
'Kristofor',
'Kristoforo',
'Kristopher',
'Kristos',
'Kurt',
'Kurtis',
'Ky',
'Kyle',
'Kylie',
'Laird',
'Lalo',
'Lamar',
'Lambert',
'Lammond',
'Lamond',
'Lamont',
'Lance',
'Lancelot',
'Land',
'Lane',
'Laney',
'Langsdon',
'Langston',
'Lanie',
'Lannie',
'Lanny',
'Larry',
'Lars',
'Laughton',
'Launce',
'Lauren',
'Laurence',
'Laurens',
'Laurent',
'Laurie',
'Lauritz',
'Law',
'Lawrence',
'Lawry',
'Lawton',
'Lay',
'Layton',
'Lazar',
'Lazare',
'Lazaro',
'Lazarus',
'Lee',
'Leeland',
'Lefty',
'Leicester',
'Leif',
'Leigh',
'Leighton',
'Lek',
'Leland',
'Lem',
'Lemar',
'Lemmie',
'Lemmy',
'Lemuel',
'Lenard',
'Lenci',
'Lennard',
'Lennie',
'Leo',
'Leon',
'Leonard',
'Leonardo',
'Leonerd',
'Leonhard',
'Leonid',
'Leonidas',
'Leopold',
'Leroi',
'Leroy',
'Les',
'Lesley',
'Leslie',
'Lester',
'Leupold',
'Lev',
'Levey',
'Levi',
'Levin',
'Levon',
'Levy',
'Lew',
'Lewes',
'Lewie',
'Lewiss',
'Lezley',
'Liam',
'Lief',
'Lin',
'Linc',
'Lincoln',
'Lind',
'Lindon',
'Lindsay',
'Lindsey',
'Lindy',
'Link',
'Linn',
'Linoel',
'Linus',
'Lion',
'Lionel',
'Lionello',
'Lisle',
'Llewellyn',
'Lloyd',
'Llywellyn',
'Lock',
'Locke',
'Lockwood',
'Lodovico',
'Logan',
'Lombard',
'Lon',
'Lonnard',
'Lonnie',
'Lonny',
'Lorant',
'Loren',
'Lorens',
'Lorenzo',
'Lorin',
'Lorne',
'Lorrie',
'Lorry',
'Lothaire',
'Lothario',
'Lou',
'Louie',
'Louis',
'Lovell',
'Lowe',
'Lowell',
'Lowrance',
'Loy',
'Loydie',
'Luca',
'Lucais',
'Lucas',
'Luce',
'Lucho',
'Lucian',
'Luciano',
'Lucias',
'Lucien',
'Lucio',
'Lucius',
'Ludovico',
'Ludvig',
'Ludwig',
'Luigi',
'Luis',
'Lukas',
'Luke',
'Lutero',
'Luther',
'Ly',
'Lydon',
'Lyell',
'Lyle',
'Lyman',
'Lyn',
'Lynn',
'Lyon',
'Mac',
'Mace',
'Mack',
'Mackenzie',
'Maddie',
'Maddy',
'Madison',
'Magnum',
'Mahmoud',
'Mahmud',
'Maison',
'Maje',
'Major',
'Mal',
'Malachi',
'Malchy',
'Malcolm',
'Mallory',
'Malvin',
'Man',
'Mandel',
'Manfred',
'Mannie',
'Manny',
'Mano',
'Manolo',
'Manuel',
'Mar',
'Marc',
'Marcel',
'Marcello',
'Marcellus',
'Marcelo',
'Marchall',
'Marco',
'Marcos',
'Marcus',
'Marijn',
'Mario',
'Marion',
'Marius',
'Mark',
'Markos',
'Markus',
'Marlin',
'Marlo',
'Marlon',
'Marlow',
'Marlowe',
'Marmaduke',
'Marsh',
'Marshal',
'Marshall',
'Mart',
'Martainn',
'Marten',
'Martie',
'Martin',
'Martino',
'Marty',
'Martyn',
'Marv',
'Marve',
'Marven',
'Marvin',
'Marwin',
'Mason',
'Massimiliano',
'Massimo',
'Mata',
'Mateo',
'Mathe',
'Mathew',
'Mathian',
'Mathias',
'Matias',
'Matt',
'Matteo',
'Matthaeus',
'Mattheus',
'Matthew',
'Matthias',
'Matthieu',
'Matthiew',
'Matthus',
'Mattias',
'Mattie',
'Matty',
'Maurice',
'Mauricio',
'Maurie',
'Maurise',
'Maurits',
'Maurizio',
'Maury',
'Max',
'Maxie',
'Maxim',
'Maximilian',
'Maximilianus',
'Maximilien',
'Maximo',
'Maxwell',
'Maxy',
'Mayer',
'Maynard',
'Mayne',
'Maynord',
'Mayor',
'Mead',
'Meade',
'Meier',
'Meir',
'Mel',
'Melvin',
'Melvyn',
'Menard',
'Mendel',
'Mendie',
'Mendy',
'Meredeth',
'Meredith',
'Merell',
'Merill',
'Merle',
'Merrel',
'Merrick',
'Merrill',
'Merry',
'Merv',
'Mervin',
'Merwin',
'Merwyn',
'Meryl',
'Meyer',
'Mic',
'Micah',
'Michael',
'Michail',
'Michal',
'Michale',
'Micheal',
'Micheil',
'Michel',
'Michele',
'Mick',
'Mickey',
'Mickie',
'Micky',
'Miguel',
'Mikael',
'Mike',
'Mikel',
'Mikey',
'Mikkel',
'Mikol',
'Mile',
'Miles',
'Mill',
'Millard',
'Miller',
'Milo',
'Milt',
'Miltie',
'Milton',
'Milty',
'Miner',
'Minor',
'Mischa',
'Mitch',
'Mitchael',
'Mitchel',
'Mitchell',
'Moe',
'Mohammed',
'Mohandas',
'Mohandis',
'Moise',
'Moises',
'Moishe',
'Monro',
'Monroe',
'Montague',
'Monte',
'Montgomery',
'Monti',
'Monty',
'Moore',
'Mord',
'Mordecai',
'Mordy',
'Morey',
'Morgan',
'Morgen',
'Morgun',
'Morie',
'Moritz',
'Morlee',
'Morley',
'Morly',
'Morrie',
'Morris',
'Morry',
'Morse',
'Mort',
'Morten',
'Mortie',
'Mortimer',
'Morton',
'Morty',
'Mose',
'Moses',
'Moshe',
'Moss',
'Mozes',
'Muffin',
'Muhammad',
'Munmro',
'Munroe',
'Murdoch',
'Murdock',
'Murray',
'Murry',
'Murvyn',
'My',
'Myca',
'Mycah',
'Mychal',
'Myer',
'Myles',
'Mylo',
'Myron',
'Myrvyn',
'Myrwyn',
'Nahum',
'Nap',
'Napoleon',
'Nappie',
'Nappy',
'Nat',
'Natal',
'Natale',
'Nataniel',
'Nate',
'Nathan',
'Nathanael',
'Nathanial',
'Nathaniel',
'Nathanil',
'Natty',
'Neal',
'Neale',
'Neall',
'Nealon',
'Nealson',
'Nealy',
'Ned',
'Neddie',
'Neddy',
'Neel',
'Nefen',
'Nehemiah',
'Neil',
'Neill',
'Neils',
'Nels',
'Nelson',
'Nero',
'Neron',
'Nester',
'Nestor',
'Nev',
'Nevil',
'Nevile',
'Neville',
'Nevin',
'Nevins',
'Newton',
'Nial',
'Niall',
'Niccolo',
'Nicholas',
'Nichole',
'Nichols',
'Nick',
'Nickey',
'Nickie',
'Nicko',
'Nickola',
'Nickolai',
'Nickolas',
'Nickolaus',
'Nicky',
'Nico',
'Nicol',
'Nicola',
'Nicolai',
'Nicolais',
'Nicolas',
'Nicolis',
'Niel',
'Niels',
'Nigel',
'Niki',
'Nikita',
'Nikki',
'Niko',
'Nikola',
'Nikolai',
'Nikolaos',
'Nikolas',
'Nikolaus',
'Nikolos',
'Nikos',
'Nil',
'Niles',
'Nils',
'Nilson',
'Niven',
'Noach',
'Noah',
'Noak',
'Noam',
'Nobe',
'Nobie',
'Noble',
'Noby',
'Noe',
'Noel',
'Nolan',
'Noland',
'Noll',
'Nollie',
'Nolly',
'Norbert',
'Norbie',
'Norby',
'Norman',
'Normand',
'Normie',
'Normy',
'Norrie',
'Norris',
'Norry',
'North',
'Northrop',
'Northrup',
'Norton',
'Nowell',
'Nye',
'Oates',
'Obadiah',
'Obadias',
'Obed',
'Obediah',
'Oberon',
'Obidiah',
'Obie',
'Oby',
'Octavius',
'Ode',
'Odell',
'Odey',
'Odie',
'Odo',
'Ody',
'Ogdan',
'Ogden',
'Ogdon',
'Olag',
'Olav',
'Ole',
'Olenolin',
'Olin',
'Oliver',
'Olivero',
'Olivier',
'Oliviero',
'Ollie',
'Olly',
'Olvan',
'Omar',
'Omero',
'Onfre',
'Onfroi',
'Onofredo',
'Oran',
'Orazio',
'Orbadiah',
'Oren',
'Orin',
'Orion',
'Orlan',
'Orland',
'Orlando',
'Orran',
'Orren',
'Orrin',
'Orson',
'Orton',
'Orv',
'Orville',
'Osbert',
'Osborn',
'Osborne',
'Osbourn',
'Osbourne',
'Osgood',
'Osmond',
'Osmund',
'Ossie',
'Oswald',
'Oswell',
'Otes',
'Othello',
'Otho',
'Otis',
'Otto',
'Owen',
'Ozzie',
'Ozzy',
'Pablo',
'Pace',
'Packston',
'Paco',
'Pacorro',
'Paddie',
'Paddy',
'Padget',
'Padgett',
'Padraic',
'Padraig',
'Padriac',
'Page',
'Paige',
'Pail',
'Pall',
'Palm',
'Palmer',
'Panchito',
'Pancho',
'Paolo',
'Papageno',
'Paquito',
'Park',
'Parke',
'Parker',
'Parnell',
'Parrnell',
'Parry',
'Parsifal',
'Pascal',
'Pascale',
'Pasquale',
'Pat',
'Pate',
'Paten',
'Patin',
'Paton',
'Patric',
'Patrice',
'Patricio',
'Patrick',
'Patrizio',
'Patrizius',
'Patsy',
'Patten',
'Pattie',
'Pattin',
'Patton',
'Patty',
'Paul',
'Paulie',
'Paulo',
'Pauly',
'Pavel',
'Pavlov',
'Paxon',
'Paxton',
'Payton',
'Peadar',
'Pearce',
'Pebrook',
'Peder',
'Pedro',
'Peirce',
'Pembroke',
'Pen',
'Penn',
'Pennie',
'Penny',
'Penrod',
'Pepe',
'Pepillo',
'Pepito',
'Perceval',
'Percival',
'Percy',
'Perice',
'Perkin',
'Pernell',
'Perren',
'Perry',
'Pete',
'Peter',
'Peterus',
'Petey',
'Petr',
'Peyter',
'Peyton',
'Phil',
'Philbert',
'Philip',
'Phillip',
'Phillipe',
'Phillipp',
'Phineas',
'Phip',
'Pierce',
'Pierre',
'Pierson',
'Pieter',
'Pietrek',
'Pietro',
'Piggy',
'Pincas',
'Pinchas',
'Pincus',
'Piotr',
'Pip',
'Pippo',
'Pooh',
'Port',
'Porter',
'Portie',
'Porty',
'Poul',
'Powell',
'Pren',
'Prent',
'Prentice',
'Prentiss',
'Prescott',
'Preston',
'Price',
'Prince',
'Prinz',
'Pryce',
'Puff',
'Purcell',
'Putnam',
'Putnem',
'Pyotr',
'Quent',
'Quentin',
'Quill',
'Quillan',
'Quincey',
'Quincy',
'Quinlan',
'Quinn',
'Quint',
'Quintin',
'Quinton',
'Quintus',
'Rab',
'Rabbi',
'Rabi',
'Rad',
'Radcliffe',
'Raddie',
'Raddy',
'Rafael',
'Rafaellle',
'Rafaello',
'Rafe',
'Raff',
'Raffaello',
'Raffarty',
'Rafferty',
'Rafi',
'Ragnar',
'Raimondo',
'Raimund',
'Raimundo',
'Rainer',
'Raleigh',
'Ralf',
'Ralph',
'Ram',
'Ramon',
'Ramsay',
'Ramsey',
'Rance',
'Rancell',
'Rand',
'Randal',
'Randall',
'Randell',
'Randi',
'Randie',
'Randolf',
'Randolph',
'Randy',
'Ransell',
'Ransom',
'Raoul',
'Raphael',
'Raul',
'Ravi',
'Ravid',
'Raviv',
'Rawley',
'Ray',
'Raymond',
'Raymund',
'Raynard',
'Rayner',
'Raynor',
'Read',
'Reade',
'Reagan',
'Reagen',
'Reamonn',
'Red',
'Redd',
'Redford',
'Reece',
'Reed',
'Rees',
'Reese',
'Reg',
'Regan',
'Regen',
'Reggie',
'Reggis',
'Reggy',
'Reginald',
'Reginauld',
'Reid',
'Reidar',
'Reider',
'Reilly',
'Reinald',
'Reinaldo',
'Reinaldos',
'Reinhard',
'Reinhold',
'Reinold',
'Reinwald',
'Rem',
'Remington',
'Remus',
'Renado',
'Renaldo',
'Renard',
'Renato',
'Renaud',
'Renault',
'Rene',
'Reube',
'Reuben',
'Reuven',
'Rex',
'Rey',
'Reynard',
'Reynold',
'Reynolds',
'Rhett',
'Rhys',
'Ric',
'Ricard',
'Ricardo',
'Riccardo',
'Rice',
'Rich',
'Richard',
'Richardo',
'Richart',
'Richie',
'Richmond',
'Richmound',
'Richy',
'Rick',
'Rickard',
'Rickert',
'Rickey',
'Ricki',
'Rickie',
'Ricky',
'Ricoriki',
'Rik',
'Rikki',
'Riley',
'Rinaldo',
'Ring',
'Ringo',
'Riobard',
'Riordan',
'Rip',
'Ripley',
'Ritchie',
'Roarke',
'Rob',
'Robb',
'Robbert',
'Robbie',
'Robby',
'Robers',
'Robert',
'Roberto',
'Robin',
'Robinet',
'Robinson',
'Rochester',
'Rock',
'Rockey',
'Rockie',
'Rockwell',
'Rocky',
'Rod',
'Rodd',
'Roddie',
'Roddy',
'Roderic',
'Roderich',
'Roderick',
'Roderigo',
'Rodge',
'Rodger',
'Rodney',
'Rodolfo',
'Rodolph',
'Rodolphe',
'Rodrick',
'Rodrigo',
'Rodrique',
'Rog',
'Roger',
'Rogerio',
'Rogers',
'Roi',
'Roland',
'Rolando',
'Roldan',
'Roley',
'Rolf',
'Rolfe',
'Rolland',
'Rollie',
'Rollin',
'Rollins',
'Rollo',
'Rolph',
'Roma',
'Romain',
'Roman',
'Romeo',
'Ron',
'Ronald',
'Ronnie',
'Ronny',
'Rooney',
'Roosevelt',
'Rorke',
'Rory',
'Rosco',
'Roscoe',
'Ross',
'Rossie',
'Rossy',
'Roth',
'Rourke',
'Rouvin',
'Rowan',
'Rowen',
'Rowland',
'Rowney',
'Roy',
'Royal',
'Royall',
'Royce',
'Rriocard',
'Rube',
'Ruben',
'Rubin',
'Ruby',
'Rudd',
'Ruddie',
'Ruddy',
'Rudie',
'Rudiger',
'Rudolf',
'Rudolfo',
'Rudolph',
'Rudy',
'Rudyard',
'Rufe',
'Rufus',
'Ruggiero',
'Rupert',
'Ruperto',
'Ruprecht',
'Rurik',
'Russ',
'Russell',
'Rustie',
'Rustin',
'Rusty',
'Rutger',
'Rutherford',
'Rutledge',
'Rutter',
'Ruttger',
'Ruy',
'Ryan',
'Ryley',
'Ryon',
'Ryun',
'Sal',
'Saleem',
'Salem',
'Salim',
'Salmon',
'Salomo',
'Salomon',
'Salomone',
'Salvador',
'Salvatore',
'Salvidor',
'Sam',
'Sammie',
'Sammy',
'Sampson',
'Samson',
'Samuel',
'Samuele',
'Sancho',
'Sander',
'Sanders',
'Sanderson',
'Sandor',
'Sandro',
'Sandy',
'Sanford',
'Sanson',
'Sansone',
'Sarge',
'Sargent',
'Sascha',
'Sasha',
'Saul',
'Sauncho',
'Saunder',
'Saunders',
'Saunderson',
'Saundra',
'Sauveur',
'Saw',
'Sawyer',
'Sawyere',
'Sax',
'Saxe',
'Saxon',
'Say',
'Sayer',
'Sayers',
'Sayre',
'Sayres',
'Scarface',
'Schuyler',
'Scot',
'Scott',
'Scotti',
'Scottie',
'Scotty',
'Seamus',
'Sean',
'Sebastian',
'Sebastiano',
'Sebastien',
'See',
'Selby',
'Selig',
'Serge',
'Sergeant',
'Sergei',
'Sergent',
'Sergio',
'Seth',
'Seumas',
'Seward',
'Seymour',
'Shadow',
'Shae',
'Shaine',
'Shalom',
'Shamus',
'Shanan',
'Shane',
'Shannan',
'Shannon',
'Shaughn',
'Shaun',
'Shaw',
'Shawn',
'Shay',
'Shayne',
'Shea',
'Sheff',
'Sheffie',
'Sheffield',
'Sheffy',
'Shelby',
'Shelden',
'Shell',
'Shelley',
'Shelton',
'Shem',
'Shep',
'Shepard',
'Shepherd',
'Sheppard',
'Shepperd',
'Sheridan',
'Sherlock',
'Sherlocke',
'Sherm',
'Sherman',
'Shermie',
'Shermy',
'Sherwin',
'Sherwood',
'Sherwynd',
'Sholom',
'Shurlock',
'Shurlocke',
'Shurwood',
'Si',
'Sibyl',
'Sid',
'Sidnee',
'Sidney',
'Siegfried',
'Siffre',
'Sig',
'Sigfrid',
'Sigfried',
'Sigismond',
'Sigismondo',
'Sigismund',
'Sigismundo',
'Sigmund',
'Sigvard',
'Silas',
'Silvain',
'Silvan',
'Silvano',
'Silvanus',
'Silvester',
'Silvio',
'Sim',
'Simeon',
'Simmonds',
'Simon',
'Simone',
'Sinclair',
'Sinclare',
'Siward',
'Skell',
'Skelly',
'Skip',
'Skipp',
'Skipper',
'Skippie',
'Skippy',
'Skipton',
'Sky',
'Skye',
'Skylar',
'Skyler',
'Slade',
'Sloan',
'Sloane',
'Sly',
'Smith',
'Smitty',
'Sol',
'Sollie',
'Solly',
'Solomon',
'Somerset',
'Son',
'Sonnie',
'Sonny',
'Spence',
'Spencer',
'Spense',
'Spenser',
'Spike',
'Stacee',
'Stacy',
'Staffard',
'Stafford',
'Staford',
'Stan',
'Standford',
'Stanfield',
'Stanford',
'Stanislas',
'Stanislaus',
'Stanislaw',
'Stanleigh',
'Stanley',
'Stanly',
'Stanton',
'Stanwood',
'Stavro',
'Stavros',
'Stearn',
'Stearne',
'Stefan',
'Stefano',
'Steffen',
'Stephan',
'Stephanus',
'Stephen',
'Sterling',
'Stern',
'Sterne',
'Steve',
'Steven',
'Stevie',
'Stevy',
'Steward',
'Stewart',
'Stillman',
'Stillmann',
'Stinky',
'Stirling',
'Stu',
'Stuart',
'Sullivan',
'Sully',
'Sumner',
'Sunny',
'Sutherlan',
'Sutherland',
'Sutton',
'Sven',
'Svend',
'Swen',
'Syd',
'Sydney',
'Sylas',
'Sylvan',
'Sylvester',
'Syman',
'Symon',
'Tab',
'Tabb',
'Tabbie',
'Tabby',
'Taber',
'Tabor',
'Tad',
'Tadd',
'Taddeo',
'Taddeusz',
'Tadeas',
'Tadeo',
'Tades',
'Tadio',
'Tailor',
'Tait',
'Taite',
'Talbert',
'Talbot',
'Tallie',
'Tally',
'Tam',
'Tamas',
'Tammie',
'Tammy',
'Tan',
'Tann',
'Tanner',
'Tanney',
'Tannie',
'Tanny',
'Tarrance',
'Tate',
'Taylor',
'Teador',
'Ted',
'Tedd',
'Teddie',
'Teddy',
'Tedie',
'Tedman',
'Tedmund',
'Temp',
'Temple',
'Templeton',
'Teodoor',
'Teodor',
'Teodorico',
'Teodoro',
'Terence',
'Terencio',
'Terrance',
'Terrel',
'Terrell',
'Terrence',
'Terri',
'Terrill',
'Terry',
'Thacher',
'Thaddeus',
'Thaddus',
'Thadeus',
'Thain',
'Thaine',
'Thane',
'Thatch',
'Thatcher',
'Thaxter',
'Thayne',
'Thebault',
'Thedric',
'Thedrick',
'Theo',
'Theobald',
'Theodor',
'Theodore',
'Theodoric',
'Thibaud',
'Thibaut',
'Thom',
'Thoma',
'Thomas',
'Thor',
'Thorin',
'Thorn',
'Thorndike',
'Thornie',
'Thornton',
'Thorny',
'Thorpe',
'Thorstein',
'Thorsten',
'Thorvald',
'Thurstan',
'Thurston',
'Tibold',
'Tiebold',
'Tiebout',
'Tiler',
'Tim',
'Timmie',
'Timmy',
'Timofei',
'Timoteo',
'Timothee',
'Timotheus',
'Timothy',
'Tirrell',
'Tito',
'Titos',
'Titus',
'Tobe',
'Tobiah',
'Tobias',
'Tobie',
'Tobin',
'Tobit',
'Toby',
'Tod',
'Todd',
'Toddie',
'Toddy',
'Toiboid',
'Tom',
'Tomas',
'Tomaso',
'Tome',
'Tomkin',
'Tomlin',
'Tommie',
'Tommy',
'Tonnie',
'Tony',
'Tore',
'Torey',
'Torin',
'Torr',
'Torrance',
'Torre',
'Torrence',
'Torrey',
'Torrin',
'Torry',
'Town',
'Towney',
'Townie',
'Townsend',
'Towny',
'Trace',
'Tracey',
'Tracie',
'Tracy',
'Traver',
'Travers',
'Travis',
'Travus',
'Trefor',
'Tremain',
'Tremaine',
'Tremayne',
'Trent',
'Trenton',
'Trev',
'Trevar',
'Trever',
'Trevor',
'Trey',
'Trip',
'Tripp',
'Tris',
'Tristam',
'Tristan',
'Troy',
'Trstram',
'Trueman',
'Trumaine',
'Truman',
'Trumann',
'Tuck',
'Tucker',
'Tuckie',
'Tucky',
'Tudor',
'Tull',
'Tulley',
'Tully',
'Turner',
'Ty',
'Tybalt',
'Tye',
'Tyler',
'Tymon',
'Tymothy',
'Tynan',
'Tyrone',
'Tyrus',
'Tyson',
'Udale',
'Udall',
'Udell',
'Ugo',
'Ulberto',
'Ulick',
'Ulises',
'Ulric',
'Ulrich',
'Ulrick',
'Ulysses',
'Umberto',
'Upton',
'Urbain',
'Urban',
'Urbano',
'Urbanus',
'Uri',
'Uriah',
'Uriel',
'Urson',
'Vachel',
'Vaclav',
'Vail',
'Val',
'Valdemar',
'Vale',
'Valentijn',
'Valentin',
'Valentine',
'Valentino',
'Valle',
'Van',
'Vance',
'Vanya',
'Vasili',
'Vasilis',
'Vasily',
'Vassili',
'Vassily',
'Vaughan',
'Vaughn',
'Verge',
'Vergil',
'Vern',
'Verne',
'Vernen',
'Verney',
'Vernon',
'Vernor',
'Vic',
'Vick',
'Victoir',
'Victor',
'Vidovic',
'Vidovik',
'Vin',
'Vince',
'Vincent',
'Vincents',
'Vincenty',
'Vincenz',
'Vinnie',
'Vinny',
'Vinson',
'Virge',
'Virgie',
'Virgil',
'Virgilio',
'Vite',
'Vito',
'Vittorio',
'Vlad',
'Vladamir',
'Vladimir',
'Von',
'Wade',
'Wadsworth',
'Wain',
'Wainwright',
'Wait',
'Waite',
'Waiter',
'Wake',
'Wakefield',
'Wald',
'Waldemar',
'Walden',
'Waldo',
'Waldon',
'Walker',
'Wallace',
'Wallache',
'Wallas',
'Wallie',
'Wallis',
'Wally',
'Walsh',
'Walt',
'Walther',
'Walton',
'Wang',
'Ward',
'Warde',
'Warden',
'Ware',
'Waring',
'Warner',
'Warren',
'Wash',
'Washington',
'Wat',
'Waverley',
'Waverly',
'Way',
'Waylan',
'Wayland',
'Waylen',
'Waylin',
'Waylon',
'Wayne',
'Web',
'Webb',
'Weber',
'Webster',
'Weidar',
'Weider',
'Welbie',
'Welby',
'Welch',
'Wells',
'Welsh',
'Wendall',
'Wendel',
'Wendell',
'Werner',
'Wernher',
'Wes',
'Wesley',
'West',
'Westbrook',
'Westbrooke',
'Westleigh',
'Westley',
'Weston',
'Weylin',
'Wheeler',
'Whit',
'Whitaker',
'Whitby',
'Whitman',
'Whitney',
'Whittaker',
'Wiatt',
'Wilbert',
'Wilbur',
'Wilburt',
'Wilden',
'Wildon',
'Wilek',
'Wiley',
'Wilfred',
'Wilfrid',
'Wilhelm',
'Will',
'Willard',
'Willdon',
'Willem',
'Willey',
'Willi',
'William',
'Willie',
'Willis',
'Willy',
'Wilmar',
'Wilmer',
'Wilt',
'Wilton',
'Win',
'Windham',
'Winfield',
'Winfred',
'Winifield',
'Winn',
'Winnie',
'Winny',
'Winslow',
'Winston',
'Winthrop',
'Wit',
'Wittie',
'Witty',
'Wolf',
'Wolfgang',
'Wolfie',
'Wolfy',
'Wood',
'Woodie',
'Woodman',
'Woodrow',
'Woody',
'Worden',
'Worth',
'Worthington',
'Worthy',
'Wright',
'Wyatan',
'Wyatt',
'Wye',
'Wylie',
'Wyn',
'Wyndham',
'Wynn',
'Xavier',
'Xenos',
'Xerxes',
'Xever',
'Ximenes',
'Ximenez',
'Xymenes',
'Yale',
'Yanaton',
'Yance',
'Yancey',
'Yancy',
'Yank',
'Yankee',
'Yard',
'Yardley',
'Yehudi',
'Yehudit',
'Yorgo',
'Yorgos',
'York',
'Yorke',
'Yorker',
'Yul',
'Yule',
'Yulma',
'Yuma',
'Yuri',
'Yurik',
'Yves',
'Yvon',
'Yvor',
'Zaccaria',
'Zach',
'Zacharia',
'Zachariah',
'Zacharias',
'Zacharie',
'Zachary',
'Zacherie',
'Zachery',
'Zack',
'Zackariah',
'Zak',
'Zane',
'Zared',
'Zeb',
'Zebadiah',
'Zebedee',
'Zebulen',
'Zebulon',
'Zechariah',
'Zed',
'Zedekiah',
'Zeke',
'Zelig',
'Zerk',
'Zollie',
'Zolly']
|
words = ['Aaron', 'Ab', 'Abba', 'Abbe', 'Abbey', 'Abbie', 'Abbot', 'Abbott', 'Abby', 'Abdel', 'Abdul', 'Abe', 'Abel', 'Abelard', 'Abeu', 'Abey', 'Abie', 'Abner', 'Abraham', 'Abrahan', 'Abram', 'Abramo', 'Abran', 'Ad', 'Adair', 'Adam', 'Adamo', 'Adams', 'Adan', 'Addie', 'Addison', 'Addy', 'Ade', 'Adelbert', 'Adham', 'Adlai', 'Adler', 'Ado', 'Adolf', 'Adolph', 'Adolphe', 'Adolpho', 'Adolphus', 'Adrian', 'Adriano', 'Adrien', 'Agosto', 'Aguie', 'Aguistin', 'Aguste', 'Agustin', 'Aharon', 'Ahmad', 'Ahmed', 'Ailbert', 'Akim', 'Aksel', 'Al', 'Alain', 'Alair', 'Alan', 'Aland', 'Alano', 'Alanson', 'Alard', 'Alaric', 'Alasdair', 'Alastair', 'Alasteir', 'Alaster', 'Alberik', 'Albert', 'Alberto', 'Albie', 'Albrecht', 'Alden', 'Aldin', 'Aldis', 'Aldo', 'Aldon', 'Aldous', 'Aldric', 'Aldrich', 'Aldridge', 'Aldus', 'Aldwin', 'Alec', 'Alejandro', 'Alejoa', 'Aleksandr', 'Alessandro', 'Alex', 'Alexander', 'Alexandr', 'Alexandre', 'Alexandro', 'Alexandros', 'Alexei', 'Alexio', 'Alexis', 'Alf', 'Alfie', 'Alfons', 'Alfonse', 'Alfonso', 'Alford', 'Alfred', 'Alfredo', 'Alfy', 'Algernon', 'Ali', 'Alic', 'Alick', 'Alisander', 'Alistair', 'Alister', 'Alix', 'Allan', 'Allard', 'Allayne', 'Allen', 'Alley', 'Alleyn', 'Allie', 'Allin', 'Allister', 'Allistir', 'Allyn', 'Aloin', 'Alon', 'Alonso', 'Alonzo', 'Aloysius', 'Alphard', 'Alphonse', 'Alphonso', 'Alric', 'Aluin', 'Aluino', 'Alva', 'Alvan', 'Alvie', 'Alvin', 'Alvis', 'Alvy', 'Alwin', 'Alwyn', 'Alyosha', 'Amble', 'Ambros', 'Ambrose', 'Ambrosi', 'Ambrosio', 'Ambrosius', 'Amby', 'Amerigo', 'Amery', 'Amory', 'Amos', 'Anatol', 'Anatole', 'Anatollo', 'Ancell', 'Anders', 'Anderson', 'Andie', 'Andonis', 'Andras', 'Andre', 'Andrea', 'Andreas', 'Andrej', 'Andres', 'Andrew', 'Andrey', 'Andris', 'Andros', 'Andrus', 'Andy', 'Ange', 'Angel', 'Angeli', 'Angelico', 'Angelo', 'Angie', 'Angus', 'Ansel', 'Ansell', 'Anselm', 'Anson', 'Anthony', 'Antin', 'Antoine', 'Anton', 'Antone', 'Antoni', 'Antonin', 'Antonino', 'Antonio', 'Antonius', 'Antons', 'Antony', 'Any', 'Ara', 'Araldo', 'Arch', 'Archaimbaud', 'Archambault', 'Archer', 'Archibald', 'Archibaldo', 'Archibold', 'Archie', 'Archy', 'Arel', 'Ari', 'Arie', 'Ariel', 'Arin', 'Ario', 'Aristotle', 'Arlan', 'Arlen', 'Arley', 'Arlin', 'Arman', 'Armand', 'Armando', 'Armin', 'Armstrong', 'Arnaldo', 'Arne', 'Arney', 'Arni', 'Arnie', 'Arnold', 'Arnoldo', 'Arnuad', 'Arny', 'Aron', 'Arri', 'Arron', 'Art', 'Artair', 'Arte', 'Artemas', 'Artemis', 'Artemus', 'Arther', 'Arthur', 'Artie', 'Artur', 'Arturo', 'Artus', 'Arty', 'Arv', 'Arvie', 'Arvin', 'Arvy', 'Asa', 'Ase', 'Ash', 'Ashbey', 'Ashby', 'Asher', 'Ashley', 'Ashlin', 'Ashton', 'Aube', 'Auberon', 'Aubert', 'Aubrey', 'Augie', 'August', 'Augustin', 'Augustine', 'Augusto', 'Augustus', 'Augy', 'Aurthur', 'Austen', 'Austin', 'Ave', 'Averell', 'Averil', 'Averill', 'Avery', 'Avictor', 'Avigdor', 'Avram', 'Avrom', 'Ax', 'Axe', 'Axel', 'Aylmar', 'Aylmer', 'Aymer', 'Bail', 'Bailey', 'Bailie', 'Baillie', 'Baily', 'Baird', 'Bald', 'Balduin', 'Baldwin', 'Bale', 'Ban', 'Bancroft', 'Bank', 'Banky', 'Bar', 'Barbabas', 'Barclay', 'Bard', 'Barde', 'Barn', 'Barnabas', 'Barnabe', 'Barnaby', 'Barnard', 'Barnebas', 'Barnett', 'Barney', 'Barnie', 'Barny', 'Baron', 'Barr', 'Barret', 'Barrett', 'Barri', 'Barrie', 'Barris', 'Barron', 'Barry', 'Bart', 'Bartel', 'Barth', 'Barthel', 'Bartholemy', 'Bartholomeo', 'Bartholomeus', 'Bartholomew', 'Bartie', 'Bartlet', 'Bartlett', 'Bartolemo', 'Bartolomeo', 'Barton', 'Bartram', 'Barty', 'Bary', 'Baryram', 'Base', 'Basil', 'Basile', 'Basilio', 'Basilius', 'Bastian', 'Bastien', 'Bat', 'Batholomew', 'Baudoin', 'Bax', 'Baxie', 'Baxter', 'Baxy', 'Bay', 'Bayard', 'Beale', 'Bealle', 'Bear', 'Bearnard', 'Beau', 'Beaufort', 'Beauregard', 'Beck', 'Beltran', 'Ben', 'Bendick', 'Bendicty', 'Bendix', 'Benedetto', 'Benedick', 'Benedict', 'Benedicto', 'Benedikt', 'Bengt', 'Beniamino', 'Benito', 'Benjamen', 'Benjamin', 'Benji', 'Benjie', 'Benjy', 'Benn', 'Bennett', 'Bennie', 'Benny', 'Benoit', 'Benson', 'Bent', 'Bentlee', 'Bentley', 'Benton', 'Benyamin', 'Ber', 'Berk', 'Berke', 'Berkeley', 'Berkie', 'Berkley', 'Berkly', 'Berky', 'Bern', 'Bernard', 'Bernardo', 'Bernarr', 'Berne', 'Bernhard', 'Bernie', 'Berny', 'Bert', 'Berti', 'Bertie', 'Berton', 'Bertram', 'Bertrand', 'Bertrando', 'Berty', 'Bev', 'Bevan', 'Bevin', 'Bevon', 'Bil', 'Bill', 'Billie', 'Billy', 'Bing', 'Bink', 'Binky', 'Birch', 'Birk', 'Biron', 'Bjorn', 'Blaine', 'Blair', 'Blake', 'Blane', 'Blayne', 'Bo', 'Bob', 'Bobbie', 'Bobby', 'Bogart', 'Bogey', 'Boigie', 'Bond', 'Bondie', 'Bondon', 'Bondy', 'Bone', 'Boniface', 'Boone', 'Boonie', 'Boony', 'Boot', 'Boote', 'Booth', 'Boothe', 'Bord', 'Borden', 'Bordie', 'Bordy', 'Borg', 'Boris', 'Bourke', 'Bowie', 'Boy', 'Boyce', 'Boycey', 'Boycie', 'Boyd', 'Brad', 'Bradan', 'Brade', 'Braden', 'Bradford', 'Bradley', 'Bradly', 'Bradney', 'Brady', 'Bram', 'Bran', 'Brand', 'Branden', 'Brander', 'Brandon', 'Brandtr', 'Brandy', 'Brandyn', 'Brannon', 'Brant', 'Brantley', 'Bren', 'Brendan', 'Brenden', 'Brendin', 'Brendis', 'Brendon', 'Brennan', 'Brennen', 'Brent', 'Bret', 'Brett', 'Brew', 'Brewer', 'Brewster', 'Brian', 'Briano', 'Briant', 'Brice', 'Brien', 'Brig', 'Brigg', 'Briggs', 'Brigham', 'Brion', 'Brit', 'Britt', 'Brnaba', 'Brnaby', 'Brock', 'Brockie', 'Brocky', 'Brod', 'Broddie', 'Broddy', 'Broderic', 'Broderick', 'Brodie', 'Brody', 'Brok', 'Bron', 'Bronnie', 'Bronny', 'Bronson', 'Brook', 'Brooke', 'Brooks', 'Brose', 'Bruce', 'Brucie', 'Bruis', 'Bruno', 'Bryan', 'Bryant', 'Bryanty', 'Bryce', 'Bryn', 'Bryon', 'Buck', 'Buckie', 'Bucky', 'Bud', 'Budd', 'Buddie', 'Buddy', 'Buiron', 'Burch', 'Burg', 'Burgess', 'Burk', 'Burke', 'Burl', 'Burlie', 'Burnaby', 'Burnard', 'Burr', 'Burt', 'Burtie', 'Burton', 'Burty', 'Butch', 'Byram', 'Byran', 'Byrann', 'Byrle', 'Byrom', 'Byron', 'Cad', 'Caddric', 'Caesar', 'Cal', 'Caldwell', 'Cale', 'Caleb', 'Calhoun', 'Callean', 'Calv', 'Calvin', 'Cam', 'Cameron', 'Camey', 'Cammy', 'Car', 'Carce', 'Care', 'Carey', 'Carl', 'Carleton', 'Carlie', 'Carlin', 'Carling', 'Carlo', 'Carlos', 'Carly', 'Carlyle', 'Carmine', 'Carney', 'Carny', 'Carolus', 'Carr', 'Carrol', 'Carroll', 'Carson', 'Cart', 'Carter', 'Carver', 'Cary', 'Caryl', 'Casar', 'Case', 'Casey', 'Cash', 'Caspar', 'Casper', 'Cass', 'Cassie', 'Cassius', 'Caz', 'Cazzie', 'Cchaddie', 'Cece', 'Cecil', 'Cecilio', 'Cecilius', 'Ced', 'Cedric', 'Cello', 'Cesar', 'Cesare', 'Cesaro', 'Chad', 'Chadd', 'Chaddie', 'Chaddy', 'Chadwick', 'Chaim', 'Chalmers', 'Chan', 'Chance', 'Chancey', 'Chandler', 'Chane', 'Chariot', 'Charles', 'Charley', 'Charlie', 'Charlton', 'Chas', 'Chase', 'Chaunce', 'Chauncey', 'Che', 'Chen', 'Ches', 'Chester', 'Cheston', 'Chet', 'Chev', 'Chevalier', 'Chevy', 'Chic', 'Chick', 'Chickie', 'Chicky', 'Chico', 'Chilton', 'Chip', 'Chris', 'Chrisse', 'Chrissie', 'Chrissy', 'Christian', 'Christiano', 'Christie', 'Christoffer', 'Christoforo', 'Christoper', 'Christoph', 'Christophe', 'Christopher', 'Christophorus', 'Christos', 'Christy', 'Chrisy', 'Chrotoem', 'Chucho', 'Chuck', 'Cirillo', 'Cirilo', 'Ciro', 'Claiborn', 'Claiborne', 'Clair', 'Claire', 'Clarance', 'Clare', 'Clarence', 'Clark', 'Clarke', 'Claudell', 'Claudian', 'Claudianus', 'Claudio', 'Claudius', 'Claus', 'Clay', 'Clayborn', 'Clayborne', 'Claybourne', 'Clayson', 'Clayton', 'Cleavland', 'Clem', 'Clemens', 'Clement', 'Clemente', 'Clementius', 'Clemmie', 'Clemmy', 'Cleon', 'Clerc', 'Cletis', 'Cletus', 'Cleve', 'Cleveland', 'Clevey', 'Clevie', 'Cliff', 'Clifford', 'Clim', 'Clint', 'Clive', 'Cly', 'Clyde', 'Clyve', 'Clywd', 'Cob', 'Cobb', 'Cobbie', 'Cobby', 'Codi', 'Codie', 'Cody', 'Cointon', 'Colan', 'Colas', 'Colby', 'Cole', 'Coleman', 'Colet', 'Colin', 'Collin', 'Colman', 'Colver', 'Con', 'Conan', 'Conant', 'Conn', 'Conney', 'Connie', 'Connor', 'Conny', 'Conrad', 'Conrade', 'Conrado', 'Conroy', 'Consalve', 'Constantin', 'Constantine', 'Constantino', 'Conway', 'Coop', 'Cooper', 'Corbet', 'Corbett', 'Corbie', 'Corbin', 'Corby', 'Cord', 'Cordell', 'Cordie', 'Cordy', 'Corey', 'Cori', 'Cornall', 'Cornelius', 'Cornell', 'Corney', 'Cornie', 'Corny', 'Correy', 'Corrie', 'Cort', 'Cortie', 'Corty', 'Cory', 'Cos', 'Cosimo', 'Cosme', 'Cosmo', 'Costa', 'Court', 'Courtnay', 'Courtney', 'Cozmo', 'Craggie', 'Craggy', 'Craig', 'Crawford', 'Creigh', 'Creight', 'Creighton', 'Crichton', 'Cris', 'Cristian', 'Cristiano', 'Cristobal', 'Crosby', 'Cross', 'Cull', 'Cullan', 'Cullen', 'Culley', 'Cullie', 'Cullin', 'Cully', 'Culver', 'Curcio', 'Curr', 'Curran', 'Currey', 'Currie', 'Curry', 'Curt', 'Curtice', 'Curtis', 'Cy', 'Cyril', 'Cyrill', 'Cyrille', 'Cyrillus', 'Cyrus', "D'Arcy", 'Dael', 'Dag', 'Dagny', 'Dal', 'Dale', 'Dalis', 'Dall', 'Dallas', 'Dalli', 'Dallis', 'Dallon', 'Dalston', 'Dalt', 'Dalton', 'Dame', 'Damian', 'Damiano', 'Damien', 'Damon', 'Dan', 'Dana', 'Dane', 'Dani', 'Danie', 'Daniel', 'Dannel', 'Dannie', 'Danny', 'Dante', 'Danya', 'Dar', 'Darb', 'Darbee', 'Darby', 'Darcy', 'Dare', 'Daren', 'Darill', 'Darin', 'Dario', 'Darius', 'Darn', 'Darnall', 'Darnell', 'Daron', 'Darrel', 'Darrell', 'Darren', 'Darrick', 'Darrin', 'Darryl', 'Darwin', 'Daryl', 'Daryle', 'Dav', 'Dave', 'Daven', 'Davey', 'David', 'Davidde', 'Davide', 'Davidson', 'Davie', 'Davin', 'Davis', 'Davon', 'Davy', 'De Witt', 'Dean', 'Deane', 'Decca', 'Deck', 'Del', 'Delainey', 'Delaney', 'Delano', 'Delbert', 'Dell', 'Delmar', 'Delmer', 'Delmor', 'Delmore', 'Demetre', 'Demetri', 'Demetris', 'Demetrius', 'Demott', 'Den', 'Dene', 'Denis', 'Dennet', 'Denney', 'Dennie', 'Dennis', 'Dennison', 'Denny', 'Denver', 'Denys', 'Der', 'Derby', 'Derek', 'Derick', 'Derk', 'Dermot', 'Derrek', 'Derrick', 'Derrik', 'Derril', 'Derron', 'Derry', 'Derward', 'Derwin', 'Des', 'Desi', 'Desmond', 'Desmund', 'Dev', 'Devin', 'Devland', 'Devlen', 'Devlin', 'Devy', 'Dew', 'Dewain', 'Dewey', 'Dewie', 'Dewitt', 'Dex', 'Dexter', 'Diarmid', 'Dick', 'Dickie', 'Dicky', 'Diego', 'Dieter', 'Dietrich', 'Dilan', 'Dill', 'Dillie', 'Dillon', 'Dilly', 'Dimitri', 'Dimitry', 'Dino', 'Dion', 'Dionisio', 'Dionysus', 'Dirk', 'Dmitri', 'Dolf', 'Dolph', 'Dom', 'Domenic', 'Domenico', 'Domingo', 'Dominic', 'Dominick', 'Dominik', 'Dominique', 'Don', 'Donal', 'Donall', 'Donalt', 'Donaugh', 'Donavon', 'Donn', 'Donnell', 'Donnie', 'Donny', 'Donovan', 'Dore', 'Dorey', 'Dorian', 'Dorie', 'Dory', 'Doug', 'Dougie', 'Douglas', 'Douglass', 'Dougy', 'Dov', 'Doy', 'Doyle', 'Drake', 'Drew', 'Dru', 'Drud', 'Drugi', 'Duane', 'Dud', 'Dudley', 'Duff', 'Duffie', 'Duffy', 'Dugald', 'Duke', 'Dukey', 'Dukie', 'Duky', 'Dun', 'Dunc', 'Duncan', 'Dunn', 'Dunstan', 'Dur', 'Durand', 'Durant', 'Durante', 'Durward', 'Dwain', 'Dwayne', 'Dwight', 'Dylan', 'Eadmund', 'Eal', 'Eamon', 'Earl', 'Earle', 'Earlie', 'Early', 'Earvin', 'Eb', 'Eben', 'Ebeneser', 'Ebenezer', 'Eberhard', 'Eberto', 'Ed', 'Edan', 'Edd', 'Eddie', 'Eddy', 'Edgar', 'Edgard', 'Edgardo', 'Edik', 'Edlin', 'Edmon', 'Edmund', 'Edouard', 'Edsel', 'Eduard', 'Eduardo', 'Eduino', 'Edvard', 'Edward', 'Edwin', 'Efrem', 'Efren', 'Egan', 'Egbert', 'Egon', 'Egor', 'El', 'Elbert', 'Elden', 'Eldin', 'Eldon', 'Eldredge', 'Eldridge', 'Eli', 'Elia', 'Elias', 'Elihu', 'Elijah', 'Eliot', 'Elisha', 'Ellary', 'Ellerey', 'Ellery', 'Elliot', 'Elliott', 'Ellis', 'Ellswerth', 'Ellsworth', 'Ellwood', 'Elmer', 'Elmo', 'Elmore', 'Elnar', 'Elroy', 'Elston', 'Elsworth', 'Elton', 'Elvin', 'Elvis', 'Elvyn', 'Elwin', 'Elwood', 'Elwyn', 'Ely', 'Em', 'Emanuel', 'Emanuele', 'Emelen', 'Emerson', 'Emery', 'Emile', 'Emilio', 'Emlen', 'Emlyn', 'Emmanuel', 'Emmerich', 'Emmery', 'Emmet', 'Emmett', 'Emmit', 'Emmott', 'Emmy', 'Emory', 'Engelbert', 'Englebert', 'Ennis', 'Enoch', 'Enos', 'Enrico', 'Enrique', 'Ephraim', 'Ephrayim', 'Ephrem', 'Erasmus', 'Erastus', 'Erek', 'Erhard', 'Erhart', 'Eric', 'Erich', 'Erick', 'Erie', 'Erik', 'Erin', 'Erl', 'Ermanno', 'Ermin', 'Ernest', 'Ernesto', 'Ernestus', 'Ernie', 'Ernst', 'Erny', 'Errick', 'Errol', 'Erroll', 'Erskine', 'Erv', 'ErvIn', 'Erwin', 'Esdras', 'Esme', 'Esra', 'Esteban', 'Estevan', 'Etan', 'Ethan', 'Ethe', 'Ethelbert', 'Ethelred', 'Etienne', 'Ettore', 'Euell', 'Eugen', 'Eugene', 'Eugenio', 'Eugenius', 'Eustace', 'Ev', 'Evan', 'Evelin', 'Evelyn', 'Even', 'Everard', 'Evered', 'Everett', 'Evin', 'Evyn', 'Ewan', 'Eward', 'Ewart', 'Ewell', 'Ewen', 'Ezechiel', 'Ezekiel', 'Ezequiel', 'Eziechiele', 'Ezra', 'Ezri', 'Fabe', 'Faber', 'Fabian', 'Fabiano', 'Fabien', 'Fabio', 'Fair', 'Fairfax', 'Fairleigh', 'Fairlie', 'Falito', 'Falkner', 'Far', 'Farlay', 'Farlee', 'Farleigh', 'Farley', 'Farlie', 'Farly', 'Farr', 'Farrel', 'Farrell', 'Farris', 'Faulkner', 'Fax', 'Federico', 'Fee', 'Felic', 'Felice', 'Felicio', 'Felike', 'Feliks', 'Felipe', 'Felix', 'Felizio', 'Feodor', 'Ferd', 'Ferdie', 'Ferdinand', 'Ferdy', 'Fergus', 'Ferguson', 'Fernando', 'Ferrel', 'Ferrell', 'Ferris', 'Fidel', 'Fidelio', 'Fidole', 'Field', 'Fielding', 'Fields', 'Filbert', 'Filberte', 'Filberto', 'Filip', 'Filippo', 'Filmer', 'Filmore', 'Fin', 'Findlay', 'Findley', 'Finlay', 'Finley', 'Finn', 'Fitz', 'Fitzgerald', 'Flem', 'Fleming', 'Flemming', 'Fletch', 'Fletcher', 'Flin', 'Flinn', 'Flint', 'Florian', 'Flory', 'Floyd', 'Flynn', 'Fons', 'Fonsie', 'Fonz', 'Fonzie', 'Forbes', 'Ford', 'Forest', 'Forester', 'Forrest', 'Forrester', 'Forster', 'Foss', 'Foster', 'Fowler', 'Fran', 'Francesco', 'Franchot', 'Francis', 'Francisco', 'Franciskus', 'Francklin', 'Francklyn', 'Francois', 'Frank', 'Frankie', 'Franklin', 'Franklyn', 'Franky', 'Frannie', 'Franny', 'Frans', 'Fransisco', 'Frants', 'Franz', 'Franzen', 'Frasco', 'Fraser', 'Frasier', 'Frasquito', 'Fraze', 'Frazer', 'Frazier', 'Fred', 'Freddie', 'Freddy', 'Fredek', 'Frederic', 'Frederich', 'Frederick', 'Frederico', 'Frederigo', 'Frederik', 'Fredric', 'Fredrick', 'Free', 'Freedman', 'Freeland', 'Freeman', 'Freemon', 'Fremont', 'Friedrich', 'Friedrick', 'Fritz', 'Fulton', 'Gabbie', 'Gabby', 'Gabe', 'Gabi', 'Gabie', 'Gabriel', 'Gabriele', 'Gabriello', 'Gaby', 'Gael', 'Gaelan', 'Gage', 'Gail', 'Gaile', 'Gal', 'Gale', 'Galen', 'Gallagher', 'Gallard', 'Galvan', 'Galven', 'Galvin', 'Gamaliel', 'Gan', 'Gannie', 'Gannon', 'Ganny', 'Gar', 'Garald', 'Gard', 'Gardener', 'Gardie', 'Gardiner', 'Gardner', 'Gardy', 'Gare', 'Garek', 'Gareth', 'Garey', 'Garfield', 'Garik', 'Garner', 'Garold', 'Garrard', 'Garrek', 'Garret', 'Garreth', 'Garrett', 'Garrick', 'Garrik', 'Garrot', 'Garrott', 'Garry', 'Garth', 'Garv', 'Garvey', 'Garvin', 'Garvy', 'Garwin', 'Garwood', 'Gary', 'Gaspar', 'Gaspard', 'Gasparo', 'Gasper', 'Gaston', 'Gaultiero', 'Gauthier', 'Gav', 'Gavan', 'Gaven', 'Gavin', 'Gawain', 'Gawen', 'Gay', 'Gayelord', 'Gayle', 'Gayler', 'Gaylor', 'Gaylord', 'Gearalt', 'Gearard', 'Gene', 'Geno', 'Geoff', 'Geoffrey', 'Geoffry', 'Georas', 'Geordie', 'Georg', 'George', 'Georges', 'Georgi', 'Georgie', 'Georgy', 'Gerald', 'Gerard', 'Gerardo', 'Gerek', 'Gerhard', 'Gerhardt', 'Geri', 'Gerick', 'Gerik', 'Germain', 'Germaine', 'Germayne', 'Gerome', 'Gerrard', 'Gerri', 'Gerrie', 'Gerry', 'Gery', 'Gherardo', 'Giacobo', 'Giacomo', 'Giacopo', 'Gian', 'Gianni', 'Giavani', 'Gib', 'Gibb', 'Gibbie', 'Gibby', 'Gideon', 'Giff', 'Giffard', 'Giffer', 'Giffie', 'Gifford', 'Giffy', 'Gil', 'Gilbert', 'Gilberto', 'Gilburt', 'Giles', 'Gill', 'Gilles', 'Ginger', 'Gino', 'Giordano', 'Giorgi', 'Giorgio', 'Giovanni', 'Giraldo', 'Giraud', 'Giselbert', 'Giulio', 'Giuseppe', 'Giustino', 'Giusto', 'Glen', 'Glenden', 'Glendon', 'Glenn', 'Glyn', 'Glynn', 'Godard', 'Godart', 'Goddard', 'Goddart', 'Godfree', 'Godfrey', 'Godfry', 'Godwin', 'Gonzales', 'Gonzalo', 'Goober', 'Goran', 'Goraud', 'Gordan', 'Gorden', 'Gordie', 'Gordon', 'Gordy', 'Gothart', 'Gottfried', 'Grace', 'Gradeigh', 'Gradey', 'Grady', 'Graehme', 'Graeme', 'Graham', 'Graig', 'Gram', 'Gran', 'Grange', 'Granger', 'Grannie', 'Granny', 'Grant', 'Grantham', 'Granthem', 'Grantley', 'Granville', 'Gray', 'Greg', 'Gregg', 'Greggory', 'Gregoire', 'Gregoor', 'Gregor', 'Gregorio', 'Gregorius', 'Gregory', 'Grenville', 'Griff', 'Griffie', 'Griffin', 'Griffith', 'Griffy', 'Gris', 'Griswold', 'Griz', 'Grove', 'Grover', 'Gualterio', 'Guglielmo', 'Guido', 'Guilbert', 'Guillaume', 'Guillermo', 'Gun', 'Gunar', 'Gunner', 'Guntar', 'Gunter', 'Gunther', 'Gus', 'Guss', 'Gustaf', 'Gustav', 'Gustave', 'Gustavo', 'Gustavus', 'Guthrey', 'Guthrie', 'Guthry', 'Guy', 'Had', 'Hadlee', 'Hadleigh', 'Hadley', 'Hadrian', 'Hagan', 'Hagen', 'Hailey', 'Haily', 'Hakeem', 'Hakim', 'Hal', 'Hale', 'Haleigh', 'Haley', 'Hall', 'Hallsy', 'Halsey', 'Halsy', 'Ham', 'Hamel', 'Hamid', 'Hamil', 'Hamilton', 'Hamish', 'Hamlen', 'Hamlin', 'Hammad', 'Hamnet', 'Hanan', 'Hank', 'Hans', 'Hansiain', 'Hanson', 'Harald', 'Harbert', 'Harcourt', 'Hardy', 'Harlan', 'Harland', 'Harlen', 'Harley', 'Harlin', 'Harman', 'Harmon', 'Harold', 'Haroun', 'Harp', 'Harper', 'Harris', 'Harrison', 'Harry', 'Hart', 'Hartley', 'Hartwell', 'Harv', 'Harvey', 'Harwell', 'Harwilll', 'Hasheem', 'Hashim', 'Haskel', 'Haskell', 'Haslett', 'Hastie', 'Hastings', 'Hasty', 'Haven', 'Hayden', 'Haydon', 'Hayes', 'Hayward', 'Haywood', 'Hayyim', 'Haze', 'Hazel', 'Hazlett', 'Heall', 'Heath', 'Hebert', 'Hector', 'Heindrick', 'Heinrick', 'Heinrik', 'Henderson', 'Hendrick', 'Hendrik', 'Henri', 'Henrik', 'Henry', 'Herb', 'Herbert', 'Herbie', 'Herby', 'Herc', 'Hercule', 'Hercules', 'Herculie', 'Heriberto', 'Herman', 'Hermann', 'Hermie', 'Hermon', 'Hermy', 'Hernando', 'Herold', 'Herrick', 'Hersch', 'Herschel', 'Hersh', 'Hershel', 'Herve', 'Hervey', 'Hew', 'Hewe', 'Hewet', 'Hewett', 'Hewie', 'Hewitt', 'Heywood', 'Hi', 'Hieronymus', 'Hilario', 'Hilarius', 'Hilary', 'Hill', 'Hillard', 'Hillary', 'Hillel', 'Hillery', 'Hilliard', 'Hillie', 'Hillier', 'Hilly', 'Hillyer', 'Hilton', 'Hinze', 'Hiram', 'Hirsch', 'Hobard', 'Hobart', 'Hobey', 'Hobie', 'Hodge', 'Hoebart', 'Hogan', 'Holden', 'Hollis', 'Holly', 'Holmes', 'Holt', 'Homer', 'Homere', 'Homerus', 'Horace', 'Horacio', 'Horatio', 'Horatius', 'Horst', 'Hort', 'Horten', 'Horton', 'Howard', 'Howey', 'Howie', 'Hoyt', 'Hube', 'Hubert', 'Huberto', 'Hubey', 'Hubie', 'Huey', 'Hugh', 'Hughie', 'Hugibert', 'Hugo', 'Hugues', 'Humbert', 'Humberto', 'Humfrey', 'Humfrid', 'Humfried', 'Humphrey', 'Hunfredo', 'Hunt', 'Hunter', 'Huntington', 'Huntlee', 'Huntley', 'Hurlee', 'Hurleigh', 'Hurley', 'Husain', 'Husein', 'Hussein', 'Hy', 'Hyatt', 'Hyman', 'Hymie', 'Iago', 'Iain', 'Ian', 'Ibrahim', 'Ichabod', 'Iggie', 'Iggy', 'Ignace', 'Ignacio', 'Ignacius', 'Ignatius', 'Ignaz', 'Ignazio', 'Igor', 'Ike', 'Ikey', 'Ilaire', 'Ilario', 'Immanuel', 'Ingamar', 'Ingar', 'Ingelbert', 'Ingemar', 'Inger', 'Inglebert', 'Inglis', 'Ingmar', 'Ingra', 'Ingram', 'Ingrim', 'Inigo', 'Inness', 'Innis', 'Iorgo', 'Iorgos', 'Iosep', 'Ira', 'Irv', 'Irvin', 'Irvine', 'Irving', 'Irwin', 'Irwinn', 'Isa', 'Isaac', 'Isaak', 'Isac', 'Isacco', 'Isador', 'Isadore', 'Isaiah', 'Isak', 'Isiahi', 'Isidor', 'Isidore', 'Isidoro', 'Isidro', 'Israel', 'Issiah', 'Itch', 'Ivan', 'Ivar', 'Ive', 'Iver', 'Ives', 'Ivor', 'Izaak', 'Izak', 'Izzy', 'Jabez', 'Jack', 'Jackie', 'Jackson', 'Jacky', 'Jacob', 'Jacobo', 'Jacques', 'Jae', 'Jaime', 'Jaimie', 'Jake', 'Jakie', 'Jakob', 'Jamaal', 'Jamal', 'James', 'Jameson', 'Jamesy', 'Jamey', 'Jamie', 'Jamil', 'Jamill', 'Jamison', 'Jammal', 'Jan', 'Janek', 'Janos', 'Jarad', 'Jard', 'Jareb', 'Jared', 'Jarib', 'Jarid', 'Jarrad', 'Jarred', 'Jarret', 'Jarrett', 'Jarrid', 'Jarrod', 'Jarvis', 'Jase', 'Jasen', 'Jason', 'Jasper', 'Jasun', 'Javier', 'Jay', 'Jaye', 'Jayme', 'Jaymie', 'Jayson', 'Jdavie', 'Jean', 'Jecho', 'Jed', 'Jedd', 'Jeddy', 'Jedediah', 'Jedidiah', 'Jeff', 'Jefferey', 'Jefferson', 'Jeffie', 'Jeffrey', 'Jeffry', 'Jeffy', 'Jehu', 'Jeno', 'Jens', 'Jephthah', 'Jerad', 'Jerald', 'Jeramey', 'Jeramie', 'Jere', 'Jereme', 'Jeremiah', 'Jeremias', 'Jeremie', 'Jeremy', 'Jermain', 'Jermaine', 'Jermayne', 'Jerome', 'Jeromy', 'Jerri', 'Jerrie', 'Jerrold', 'Jerrome', 'Jerry', 'Jervis', 'Jess', 'Jesse', 'Jessee', 'Jessey', 'Jessie', 'Jesus', 'Jeth', 'Jethro', 'Jim', 'Jimmie', 'Jimmy', 'Jo', 'Joachim', 'Joaquin', 'Job', 'Jock', 'Jocko', 'Jodi', 'Jodie', 'Jody', 'Joe', 'Joel', 'Joey', 'Johan', 'Johann', 'Johannes', 'John', 'Johnathan', 'Johnathon', 'Johnnie', 'Johnny', 'Johny', 'Jon', 'Jonah', 'Jonas', 'Jonathan', 'Jonathon', 'Jone', 'Jordan', 'Jordon', 'Jorgan', 'Jorge', 'Jory', 'Jose', 'Joseito', 'Joseph', 'Josh', 'Joshia', 'Joshua', 'Joshuah', 'Josiah', 'Josias', 'Jourdain', 'Jozef', 'Juan', 'Jud', 'Judah', 'Judas', 'Judd', 'Jude', 'Judon', 'Jule', 'Jules', 'Julian', 'Julie', 'Julio', 'Julius', 'Justen', 'Justin', 'Justinian', 'Justino', 'Justis', 'Justus', 'Kahaleel', 'Kahlil', 'Kain', 'Kaine', 'Kaiser', 'Kale', 'Kaleb', 'Kalil', 'Kalle', 'Kalvin', 'Kane', 'Kareem', 'Karel', 'Karim', 'Karl', 'Karlan', 'Karlens', 'Karlik', 'Karlis', 'Karney', 'Karoly', 'Kaspar', 'Kasper', 'Kayne', 'Kean', 'Keane', 'Kearney', 'Keary', 'Keefe', 'Keefer', 'Keelby', 'Keen', 'Keenan', 'Keene', 'Keir', 'Keith', 'Kelbee', 'Kelby', 'Kele', 'Kellby', 'Kellen', 'Kelley', 'Kelly', 'Kelsey', 'Kelvin', 'Kelwin', 'Ken', 'Kendal', 'Kendall', 'Kendell', 'Kendrick', 'Kendricks', 'Kenn', 'Kennan', 'Kennedy', 'Kenneth', 'Kennett', 'Kennie', 'Kennith', 'Kenny', 'Kenon', 'Kent', 'Kenton', 'Kenyon', 'Ker', 'Kerby', 'Kerk', 'Kermie', 'Kermit', 'Kermy', 'Kerr', 'Kerry', 'Kerwin', 'Kerwinn', 'Kev', 'Kevan', 'Keven', 'Kevin', 'Kevon', 'Khalil', 'Kiel', 'Kienan', 'Kile', 'Kiley', 'Kilian', 'Killian', 'Killie', 'Killy', 'Kim', 'Kimball', 'Kimbell', 'Kimble', 'Kin', 'Kincaid', 'King', 'Kingsley', 'Kingsly', 'Kingston', 'Kinnie', 'Kinny', 'Kinsley', 'Kip', 'Kipp', 'Kippar', 'Kipper', 'Kippie', 'Kippy', 'Kirby', 'Kirk', 'Kit', 'Klaus', 'Klemens', 'Klement', 'Kleon', 'Kliment', 'Knox', 'Koenraad', 'Konrad', 'Konstantin', 'Konstantine', 'Korey', 'Kort', 'Kory', 'Kris', 'Krisha', 'Krishna', 'Krishnah', 'Krispin', 'Kristian', 'Kristo', 'Kristofer', 'Kristoffer', 'Kristofor', 'Kristoforo', 'Kristopher', 'Kristos', 'Kurt', 'Kurtis', 'Ky', 'Kyle', 'Kylie', 'Laird', 'Lalo', 'Lamar', 'Lambert', 'Lammond', 'Lamond', 'Lamont', 'Lance', 'Lancelot', 'Land', 'Lane', 'Laney', 'Langsdon', 'Langston', 'Lanie', 'Lannie', 'Lanny', 'Larry', 'Lars', 'Laughton', 'Launce', 'Lauren', 'Laurence', 'Laurens', 'Laurent', 'Laurie', 'Lauritz', 'Law', 'Lawrence', 'Lawry', 'Lawton', 'Lay', 'Layton', 'Lazar', 'Lazare', 'Lazaro', 'Lazarus', 'Lee', 'Leeland', 'Lefty', 'Leicester', 'Leif', 'Leigh', 'Leighton', 'Lek', 'Leland', 'Lem', 'Lemar', 'Lemmie', 'Lemmy', 'Lemuel', 'Lenard', 'Lenci', 'Lennard', 'Lennie', 'Leo', 'Leon', 'Leonard', 'Leonardo', 'Leonerd', 'Leonhard', 'Leonid', 'Leonidas', 'Leopold', 'Leroi', 'Leroy', 'Les', 'Lesley', 'Leslie', 'Lester', 'Leupold', 'Lev', 'Levey', 'Levi', 'Levin', 'Levon', 'Levy', 'Lew', 'Lewes', 'Lewie', 'Lewiss', 'Lezley', 'Liam', 'Lief', 'Lin', 'Linc', 'Lincoln', 'Lind', 'Lindon', 'Lindsay', 'Lindsey', 'Lindy', 'Link', 'Linn', 'Linoel', 'Linus', 'Lion', 'Lionel', 'Lionello', 'Lisle', 'Llewellyn', 'Lloyd', 'Llywellyn', 'Lock', 'Locke', 'Lockwood', 'Lodovico', 'Logan', 'Lombard', 'Lon', 'Lonnard', 'Lonnie', 'Lonny', 'Lorant', 'Loren', 'Lorens', 'Lorenzo', 'Lorin', 'Lorne', 'Lorrie', 'Lorry', 'Lothaire', 'Lothario', 'Lou', 'Louie', 'Louis', 'Lovell', 'Lowe', 'Lowell', 'Lowrance', 'Loy', 'Loydie', 'Luca', 'Lucais', 'Lucas', 'Luce', 'Lucho', 'Lucian', 'Luciano', 'Lucias', 'Lucien', 'Lucio', 'Lucius', 'Ludovico', 'Ludvig', 'Ludwig', 'Luigi', 'Luis', 'Lukas', 'Luke', 'Lutero', 'Luther', 'Ly', 'Lydon', 'Lyell', 'Lyle', 'Lyman', 'Lyn', 'Lynn', 'Lyon', 'Mac', 'Mace', 'Mack', 'Mackenzie', 'Maddie', 'Maddy', 'Madison', 'Magnum', 'Mahmoud', 'Mahmud', 'Maison', 'Maje', 'Major', 'Mal', 'Malachi', 'Malchy', 'Malcolm', 'Mallory', 'Malvin', 'Man', 'Mandel', 'Manfred', 'Mannie', 'Manny', 'Mano', 'Manolo', 'Manuel', 'Mar', 'Marc', 'Marcel', 'Marcello', 'Marcellus', 'Marcelo', 'Marchall', 'Marco', 'Marcos', 'Marcus', 'Marijn', 'Mario', 'Marion', 'Marius', 'Mark', 'Markos', 'Markus', 'Marlin', 'Marlo', 'Marlon', 'Marlow', 'Marlowe', 'Marmaduke', 'Marsh', 'Marshal', 'Marshall', 'Mart', 'Martainn', 'Marten', 'Martie', 'Martin', 'Martino', 'Marty', 'Martyn', 'Marv', 'Marve', 'Marven', 'Marvin', 'Marwin', 'Mason', 'Massimiliano', 'Massimo', 'Mata', 'Mateo', 'Mathe', 'Mathew', 'Mathian', 'Mathias', 'Matias', 'Matt', 'Matteo', 'Matthaeus', 'Mattheus', 'Matthew', 'Matthias', 'Matthieu', 'Matthiew', 'Matthus', 'Mattias', 'Mattie', 'Matty', 'Maurice', 'Mauricio', 'Maurie', 'Maurise', 'Maurits', 'Maurizio', 'Maury', 'Max', 'Maxie', 'Maxim', 'Maximilian', 'Maximilianus', 'Maximilien', 'Maximo', 'Maxwell', 'Maxy', 'Mayer', 'Maynard', 'Mayne', 'Maynord', 'Mayor', 'Mead', 'Meade', 'Meier', 'Meir', 'Mel', 'Melvin', 'Melvyn', 'Menard', 'Mendel', 'Mendie', 'Mendy', 'Meredeth', 'Meredith', 'Merell', 'Merill', 'Merle', 'Merrel', 'Merrick', 'Merrill', 'Merry', 'Merv', 'Mervin', 'Merwin', 'Merwyn', 'Meryl', 'Meyer', 'Mic', 'Micah', 'Michael', 'Michail', 'Michal', 'Michale', 'Micheal', 'Micheil', 'Michel', 'Michele', 'Mick', 'Mickey', 'Mickie', 'Micky', 'Miguel', 'Mikael', 'Mike', 'Mikel', 'Mikey', 'Mikkel', 'Mikol', 'Mile', 'Miles', 'Mill', 'Millard', 'Miller', 'Milo', 'Milt', 'Miltie', 'Milton', 'Milty', 'Miner', 'Minor', 'Mischa', 'Mitch', 'Mitchael', 'Mitchel', 'Mitchell', 'Moe', 'Mohammed', 'Mohandas', 'Mohandis', 'Moise', 'Moises', 'Moishe', 'Monro', 'Monroe', 'Montague', 'Monte', 'Montgomery', 'Monti', 'Monty', 'Moore', 'Mord', 'Mordecai', 'Mordy', 'Morey', 'Morgan', 'Morgen', 'Morgun', 'Morie', 'Moritz', 'Morlee', 'Morley', 'Morly', 'Morrie', 'Morris', 'Morry', 'Morse', 'Mort', 'Morten', 'Mortie', 'Mortimer', 'Morton', 'Morty', 'Mose', 'Moses', 'Moshe', 'Moss', 'Mozes', 'Muffin', 'Muhammad', 'Munmro', 'Munroe', 'Murdoch', 'Murdock', 'Murray', 'Murry', 'Murvyn', 'My', 'Myca', 'Mycah', 'Mychal', 'Myer', 'Myles', 'Mylo', 'Myron', 'Myrvyn', 'Myrwyn', 'Nahum', 'Nap', 'Napoleon', 'Nappie', 'Nappy', 'Nat', 'Natal', 'Natale', 'Nataniel', 'Nate', 'Nathan', 'Nathanael', 'Nathanial', 'Nathaniel', 'Nathanil', 'Natty', 'Neal', 'Neale', 'Neall', 'Nealon', 'Nealson', 'Nealy', 'Ned', 'Neddie', 'Neddy', 'Neel', 'Nefen', 'Nehemiah', 'Neil', 'Neill', 'Neils', 'Nels', 'Nelson', 'Nero', 'Neron', 'Nester', 'Nestor', 'Nev', 'Nevil', 'Nevile', 'Neville', 'Nevin', 'Nevins', 'Newton', 'Nial', 'Niall', 'Niccolo', 'Nicholas', 'Nichole', 'Nichols', 'Nick', 'Nickey', 'Nickie', 'Nicko', 'Nickola', 'Nickolai', 'Nickolas', 'Nickolaus', 'Nicky', 'Nico', 'Nicol', 'Nicola', 'Nicolai', 'Nicolais', 'Nicolas', 'Nicolis', 'Niel', 'Niels', 'Nigel', 'Niki', 'Nikita', 'Nikki', 'Niko', 'Nikola', 'Nikolai', 'Nikolaos', 'Nikolas', 'Nikolaus', 'Nikolos', 'Nikos', 'Nil', 'Niles', 'Nils', 'Nilson', 'Niven', 'Noach', 'Noah', 'Noak', 'Noam', 'Nobe', 'Nobie', 'Noble', 'Noby', 'Noe', 'Noel', 'Nolan', 'Noland', 'Noll', 'Nollie', 'Nolly', 'Norbert', 'Norbie', 'Norby', 'Norman', 'Normand', 'Normie', 'Normy', 'Norrie', 'Norris', 'Norry', 'North', 'Northrop', 'Northrup', 'Norton', 'Nowell', 'Nye', 'Oates', 'Obadiah', 'Obadias', 'Obed', 'Obediah', 'Oberon', 'Obidiah', 'Obie', 'Oby', 'Octavius', 'Ode', 'Odell', 'Odey', 'Odie', 'Odo', 'Ody', 'Ogdan', 'Ogden', 'Ogdon', 'Olag', 'Olav', 'Ole', 'Olenolin', 'Olin', 'Oliver', 'Olivero', 'Olivier', 'Oliviero', 'Ollie', 'Olly', 'Olvan', 'Omar', 'Omero', 'Onfre', 'Onfroi', 'Onofredo', 'Oran', 'Orazio', 'Orbadiah', 'Oren', 'Orin', 'Orion', 'Orlan', 'Orland', 'Orlando', 'Orran', 'Orren', 'Orrin', 'Orson', 'Orton', 'Orv', 'Orville', 'Osbert', 'Osborn', 'Osborne', 'Osbourn', 'Osbourne', 'Osgood', 'Osmond', 'Osmund', 'Ossie', 'Oswald', 'Oswell', 'Otes', 'Othello', 'Otho', 'Otis', 'Otto', 'Owen', 'Ozzie', 'Ozzy', 'Pablo', 'Pace', 'Packston', 'Paco', 'Pacorro', 'Paddie', 'Paddy', 'Padget', 'Padgett', 'Padraic', 'Padraig', 'Padriac', 'Page', 'Paige', 'Pail', 'Pall', 'Palm', 'Palmer', 'Panchito', 'Pancho', 'Paolo', 'Papageno', 'Paquito', 'Park', 'Parke', 'Parker', 'Parnell', 'Parrnell', 'Parry', 'Parsifal', 'Pascal', 'Pascale', 'Pasquale', 'Pat', 'Pate', 'Paten', 'Patin', 'Paton', 'Patric', 'Patrice', 'Patricio', 'Patrick', 'Patrizio', 'Patrizius', 'Patsy', 'Patten', 'Pattie', 'Pattin', 'Patton', 'Patty', 'Paul', 'Paulie', 'Paulo', 'Pauly', 'Pavel', 'Pavlov', 'Paxon', 'Paxton', 'Payton', 'Peadar', 'Pearce', 'Pebrook', 'Peder', 'Pedro', 'Peirce', 'Pembroke', 'Pen', 'Penn', 'Pennie', 'Penny', 'Penrod', 'Pepe', 'Pepillo', 'Pepito', 'Perceval', 'Percival', 'Percy', 'Perice', 'Perkin', 'Pernell', 'Perren', 'Perry', 'Pete', 'Peter', 'Peterus', 'Petey', 'Petr', 'Peyter', 'Peyton', 'Phil', 'Philbert', 'Philip', 'Phillip', 'Phillipe', 'Phillipp', 'Phineas', 'Phip', 'Pierce', 'Pierre', 'Pierson', 'Pieter', 'Pietrek', 'Pietro', 'Piggy', 'Pincas', 'Pinchas', 'Pincus', 'Piotr', 'Pip', 'Pippo', 'Pooh', 'Port', 'Porter', 'Portie', 'Porty', 'Poul', 'Powell', 'Pren', 'Prent', 'Prentice', 'Prentiss', 'Prescott', 'Preston', 'Price', 'Prince', 'Prinz', 'Pryce', 'Puff', 'Purcell', 'Putnam', 'Putnem', 'Pyotr', 'Quent', 'Quentin', 'Quill', 'Quillan', 'Quincey', 'Quincy', 'Quinlan', 'Quinn', 'Quint', 'Quintin', 'Quinton', 'Quintus', 'Rab', 'Rabbi', 'Rabi', 'Rad', 'Radcliffe', 'Raddie', 'Raddy', 'Rafael', 'Rafaellle', 'Rafaello', 'Rafe', 'Raff', 'Raffaello', 'Raffarty', 'Rafferty', 'Rafi', 'Ragnar', 'Raimondo', 'Raimund', 'Raimundo', 'Rainer', 'Raleigh', 'Ralf', 'Ralph', 'Ram', 'Ramon', 'Ramsay', 'Ramsey', 'Rance', 'Rancell', 'Rand', 'Randal', 'Randall', 'Randell', 'Randi', 'Randie', 'Randolf', 'Randolph', 'Randy', 'Ransell', 'Ransom', 'Raoul', 'Raphael', 'Raul', 'Ravi', 'Ravid', 'Raviv', 'Rawley', 'Ray', 'Raymond', 'Raymund', 'Raynard', 'Rayner', 'Raynor', 'Read', 'Reade', 'Reagan', 'Reagen', 'Reamonn', 'Red', 'Redd', 'Redford', 'Reece', 'Reed', 'Rees', 'Reese', 'Reg', 'Regan', 'Regen', 'Reggie', 'Reggis', 'Reggy', 'Reginald', 'Reginauld', 'Reid', 'Reidar', 'Reider', 'Reilly', 'Reinald', 'Reinaldo', 'Reinaldos', 'Reinhard', 'Reinhold', 'Reinold', 'Reinwald', 'Rem', 'Remington', 'Remus', 'Renado', 'Renaldo', 'Renard', 'Renato', 'Renaud', 'Renault', 'Rene', 'Reube', 'Reuben', 'Reuven', 'Rex', 'Rey', 'Reynard', 'Reynold', 'Reynolds', 'Rhett', 'Rhys', 'Ric', 'Ricard', 'Ricardo', 'Riccardo', 'Rice', 'Rich', 'Richard', 'Richardo', 'Richart', 'Richie', 'Richmond', 'Richmound', 'Richy', 'Rick', 'Rickard', 'Rickert', 'Rickey', 'Ricki', 'Rickie', 'Ricky', 'Ricoriki', 'Rik', 'Rikki', 'Riley', 'Rinaldo', 'Ring', 'Ringo', 'Riobard', 'Riordan', 'Rip', 'Ripley', 'Ritchie', 'Roarke', 'Rob', 'Robb', 'Robbert', 'Robbie', 'Robby', 'Robers', 'Robert', 'Roberto', 'Robin', 'Robinet', 'Robinson', 'Rochester', 'Rock', 'Rockey', 'Rockie', 'Rockwell', 'Rocky', 'Rod', 'Rodd', 'Roddie', 'Roddy', 'Roderic', 'Roderich', 'Roderick', 'Roderigo', 'Rodge', 'Rodger', 'Rodney', 'Rodolfo', 'Rodolph', 'Rodolphe', 'Rodrick', 'Rodrigo', 'Rodrique', 'Rog', 'Roger', 'Rogerio', 'Rogers', 'Roi', 'Roland', 'Rolando', 'Roldan', 'Roley', 'Rolf', 'Rolfe', 'Rolland', 'Rollie', 'Rollin', 'Rollins', 'Rollo', 'Rolph', 'Roma', 'Romain', 'Roman', 'Romeo', 'Ron', 'Ronald', 'Ronnie', 'Ronny', 'Rooney', 'Roosevelt', 'Rorke', 'Rory', 'Rosco', 'Roscoe', 'Ross', 'Rossie', 'Rossy', 'Roth', 'Rourke', 'Rouvin', 'Rowan', 'Rowen', 'Rowland', 'Rowney', 'Roy', 'Royal', 'Royall', 'Royce', 'Rriocard', 'Rube', 'Ruben', 'Rubin', 'Ruby', 'Rudd', 'Ruddie', 'Ruddy', 'Rudie', 'Rudiger', 'Rudolf', 'Rudolfo', 'Rudolph', 'Rudy', 'Rudyard', 'Rufe', 'Rufus', 'Ruggiero', 'Rupert', 'Ruperto', 'Ruprecht', 'Rurik', 'Russ', 'Russell', 'Rustie', 'Rustin', 'Rusty', 'Rutger', 'Rutherford', 'Rutledge', 'Rutter', 'Ruttger', 'Ruy', 'Ryan', 'Ryley', 'Ryon', 'Ryun', 'Sal', 'Saleem', 'Salem', 'Salim', 'Salmon', 'Salomo', 'Salomon', 'Salomone', 'Salvador', 'Salvatore', 'Salvidor', 'Sam', 'Sammie', 'Sammy', 'Sampson', 'Samson', 'Samuel', 'Samuele', 'Sancho', 'Sander', 'Sanders', 'Sanderson', 'Sandor', 'Sandro', 'Sandy', 'Sanford', 'Sanson', 'Sansone', 'Sarge', 'Sargent', 'Sascha', 'Sasha', 'Saul', 'Sauncho', 'Saunder', 'Saunders', 'Saunderson', 'Saundra', 'Sauveur', 'Saw', 'Sawyer', 'Sawyere', 'Sax', 'Saxe', 'Saxon', 'Say', 'Sayer', 'Sayers', 'Sayre', 'Sayres', 'Scarface', 'Schuyler', 'Scot', 'Scott', 'Scotti', 'Scottie', 'Scotty', 'Seamus', 'Sean', 'Sebastian', 'Sebastiano', 'Sebastien', 'See', 'Selby', 'Selig', 'Serge', 'Sergeant', 'Sergei', 'Sergent', 'Sergio', 'Seth', 'Seumas', 'Seward', 'Seymour', 'Shadow', 'Shae', 'Shaine', 'Shalom', 'Shamus', 'Shanan', 'Shane', 'Shannan', 'Shannon', 'Shaughn', 'Shaun', 'Shaw', 'Shawn', 'Shay', 'Shayne', 'Shea', 'Sheff', 'Sheffie', 'Sheffield', 'Sheffy', 'Shelby', 'Shelden', 'Shell', 'Shelley', 'Shelton', 'Shem', 'Shep', 'Shepard', 'Shepherd', 'Sheppard', 'Shepperd', 'Sheridan', 'Sherlock', 'Sherlocke', 'Sherm', 'Sherman', 'Shermie', 'Shermy', 'Sherwin', 'Sherwood', 'Sherwynd', 'Sholom', 'Shurlock', 'Shurlocke', 'Shurwood', 'Si', 'Sibyl', 'Sid', 'Sidnee', 'Sidney', 'Siegfried', 'Siffre', 'Sig', 'Sigfrid', 'Sigfried', 'Sigismond', 'Sigismondo', 'Sigismund', 'Sigismundo', 'Sigmund', 'Sigvard', 'Silas', 'Silvain', 'Silvan', 'Silvano', 'Silvanus', 'Silvester', 'Silvio', 'Sim', 'Simeon', 'Simmonds', 'Simon', 'Simone', 'Sinclair', 'Sinclare', 'Siward', 'Skell', 'Skelly', 'Skip', 'Skipp', 'Skipper', 'Skippie', 'Skippy', 'Skipton', 'Sky', 'Skye', 'Skylar', 'Skyler', 'Slade', 'Sloan', 'Sloane', 'Sly', 'Smith', 'Smitty', 'Sol', 'Sollie', 'Solly', 'Solomon', 'Somerset', 'Son', 'Sonnie', 'Sonny', 'Spence', 'Spencer', 'Spense', 'Spenser', 'Spike', 'Stacee', 'Stacy', 'Staffard', 'Stafford', 'Staford', 'Stan', 'Standford', 'Stanfield', 'Stanford', 'Stanislas', 'Stanislaus', 'Stanislaw', 'Stanleigh', 'Stanley', 'Stanly', 'Stanton', 'Stanwood', 'Stavro', 'Stavros', 'Stearn', 'Stearne', 'Stefan', 'Stefano', 'Steffen', 'Stephan', 'Stephanus', 'Stephen', 'Sterling', 'Stern', 'Sterne', 'Steve', 'Steven', 'Stevie', 'Stevy', 'Steward', 'Stewart', 'Stillman', 'Stillmann', 'Stinky', 'Stirling', 'Stu', 'Stuart', 'Sullivan', 'Sully', 'Sumner', 'Sunny', 'Sutherlan', 'Sutherland', 'Sutton', 'Sven', 'Svend', 'Swen', 'Syd', 'Sydney', 'Sylas', 'Sylvan', 'Sylvester', 'Syman', 'Symon', 'Tab', 'Tabb', 'Tabbie', 'Tabby', 'Taber', 'Tabor', 'Tad', 'Tadd', 'Taddeo', 'Taddeusz', 'Tadeas', 'Tadeo', 'Tades', 'Tadio', 'Tailor', 'Tait', 'Taite', 'Talbert', 'Talbot', 'Tallie', 'Tally', 'Tam', 'Tamas', 'Tammie', 'Tammy', 'Tan', 'Tann', 'Tanner', 'Tanney', 'Tannie', 'Tanny', 'Tarrance', 'Tate', 'Taylor', 'Teador', 'Ted', 'Tedd', 'Teddie', 'Teddy', 'Tedie', 'Tedman', 'Tedmund', 'Temp', 'Temple', 'Templeton', 'Teodoor', 'Teodor', 'Teodorico', 'Teodoro', 'Terence', 'Terencio', 'Terrance', 'Terrel', 'Terrell', 'Terrence', 'Terri', 'Terrill', 'Terry', 'Thacher', 'Thaddeus', 'Thaddus', 'Thadeus', 'Thain', 'Thaine', 'Thane', 'Thatch', 'Thatcher', 'Thaxter', 'Thayne', 'Thebault', 'Thedric', 'Thedrick', 'Theo', 'Theobald', 'Theodor', 'Theodore', 'Theodoric', 'Thibaud', 'Thibaut', 'Thom', 'Thoma', 'Thomas', 'Thor', 'Thorin', 'Thorn', 'Thorndike', 'Thornie', 'Thornton', 'Thorny', 'Thorpe', 'Thorstein', 'Thorsten', 'Thorvald', 'Thurstan', 'Thurston', 'Tibold', 'Tiebold', 'Tiebout', 'Tiler', 'Tim', 'Timmie', 'Timmy', 'Timofei', 'Timoteo', 'Timothee', 'Timotheus', 'Timothy', 'Tirrell', 'Tito', 'Titos', 'Titus', 'Tobe', 'Tobiah', 'Tobias', 'Tobie', 'Tobin', 'Tobit', 'Toby', 'Tod', 'Todd', 'Toddie', 'Toddy', 'Toiboid', 'Tom', 'Tomas', 'Tomaso', 'Tome', 'Tomkin', 'Tomlin', 'Tommie', 'Tommy', 'Tonnie', 'Tony', 'Tore', 'Torey', 'Torin', 'Torr', 'Torrance', 'Torre', 'Torrence', 'Torrey', 'Torrin', 'Torry', 'Town', 'Towney', 'Townie', 'Townsend', 'Towny', 'Trace', 'Tracey', 'Tracie', 'Tracy', 'Traver', 'Travers', 'Travis', 'Travus', 'Trefor', 'Tremain', 'Tremaine', 'Tremayne', 'Trent', 'Trenton', 'Trev', 'Trevar', 'Trever', 'Trevor', 'Trey', 'Trip', 'Tripp', 'Tris', 'Tristam', 'Tristan', 'Troy', 'Trstram', 'Trueman', 'Trumaine', 'Truman', 'Trumann', 'Tuck', 'Tucker', 'Tuckie', 'Tucky', 'Tudor', 'Tull', 'Tulley', 'Tully', 'Turner', 'Ty', 'Tybalt', 'Tye', 'Tyler', 'Tymon', 'Tymothy', 'Tynan', 'Tyrone', 'Tyrus', 'Tyson', 'Udale', 'Udall', 'Udell', 'Ugo', 'Ulberto', 'Ulick', 'Ulises', 'Ulric', 'Ulrich', 'Ulrick', 'Ulysses', 'Umberto', 'Upton', 'Urbain', 'Urban', 'Urbano', 'Urbanus', 'Uri', 'Uriah', 'Uriel', 'Urson', 'Vachel', 'Vaclav', 'Vail', 'Val', 'Valdemar', 'Vale', 'Valentijn', 'Valentin', 'Valentine', 'Valentino', 'Valle', 'Van', 'Vance', 'Vanya', 'Vasili', 'Vasilis', 'Vasily', 'Vassili', 'Vassily', 'Vaughan', 'Vaughn', 'Verge', 'Vergil', 'Vern', 'Verne', 'Vernen', 'Verney', 'Vernon', 'Vernor', 'Vic', 'Vick', 'Victoir', 'Victor', 'Vidovic', 'Vidovik', 'Vin', 'Vince', 'Vincent', 'Vincents', 'Vincenty', 'Vincenz', 'Vinnie', 'Vinny', 'Vinson', 'Virge', 'Virgie', 'Virgil', 'Virgilio', 'Vite', 'Vito', 'Vittorio', 'Vlad', 'Vladamir', 'Vladimir', 'Von', 'Wade', 'Wadsworth', 'Wain', 'Wainwright', 'Wait', 'Waite', 'Waiter', 'Wake', 'Wakefield', 'Wald', 'Waldemar', 'Walden', 'Waldo', 'Waldon', 'Walker', 'Wallace', 'Wallache', 'Wallas', 'Wallie', 'Wallis', 'Wally', 'Walsh', 'Walt', 'Walther', 'Walton', 'Wang', 'Ward', 'Warde', 'Warden', 'Ware', 'Waring', 'Warner', 'Warren', 'Wash', 'Washington', 'Wat', 'Waverley', 'Waverly', 'Way', 'Waylan', 'Wayland', 'Waylen', 'Waylin', 'Waylon', 'Wayne', 'Web', 'Webb', 'Weber', 'Webster', 'Weidar', 'Weider', 'Welbie', 'Welby', 'Welch', 'Wells', 'Welsh', 'Wendall', 'Wendel', 'Wendell', 'Werner', 'Wernher', 'Wes', 'Wesley', 'West', 'Westbrook', 'Westbrooke', 'Westleigh', 'Westley', 'Weston', 'Weylin', 'Wheeler', 'Whit', 'Whitaker', 'Whitby', 'Whitman', 'Whitney', 'Whittaker', 'Wiatt', 'Wilbert', 'Wilbur', 'Wilburt', 'Wilden', 'Wildon', 'Wilek', 'Wiley', 'Wilfred', 'Wilfrid', 'Wilhelm', 'Will', 'Willard', 'Willdon', 'Willem', 'Willey', 'Willi', 'William', 'Willie', 'Willis', 'Willy', 'Wilmar', 'Wilmer', 'Wilt', 'Wilton', 'Win', 'Windham', 'Winfield', 'Winfred', 'Winifield', 'Winn', 'Winnie', 'Winny', 'Winslow', 'Winston', 'Winthrop', 'Wit', 'Wittie', 'Witty', 'Wolf', 'Wolfgang', 'Wolfie', 'Wolfy', 'Wood', 'Woodie', 'Woodman', 'Woodrow', 'Woody', 'Worden', 'Worth', 'Worthington', 'Worthy', 'Wright', 'Wyatan', 'Wyatt', 'Wye', 'Wylie', 'Wyn', 'Wyndham', 'Wynn', 'Xavier', 'Xenos', 'Xerxes', 'Xever', 'Ximenes', 'Ximenez', 'Xymenes', 'Yale', 'Yanaton', 'Yance', 'Yancey', 'Yancy', 'Yank', 'Yankee', 'Yard', 'Yardley', 'Yehudi', 'Yehudit', 'Yorgo', 'Yorgos', 'York', 'Yorke', 'Yorker', 'Yul', 'Yule', 'Yulma', 'Yuma', 'Yuri', 'Yurik', 'Yves', 'Yvon', 'Yvor', 'Zaccaria', 'Zach', 'Zacharia', 'Zachariah', 'Zacharias', 'Zacharie', 'Zachary', 'Zacherie', 'Zachery', 'Zack', 'Zackariah', 'Zak', 'Zane', 'Zared', 'Zeb', 'Zebadiah', 'Zebedee', 'Zebulen', 'Zebulon', 'Zechariah', 'Zed', 'Zedekiah', 'Zeke', 'Zelig', 'Zerk', 'Zollie', 'Zolly']
|
class Solution(object):
def removeDuplicates(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
pre,cur = 0,1
while cur < len(nums):
if nums[pre] == nums[cur]:
nums.pop(cur)
else:
pre,cur = pre + 1, cur + 1
return len(nums)
|
class Solution(object):
def remove_duplicates(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
(pre, cur) = (0, 1)
while cur < len(nums):
if nums[pre] == nums[cur]:
nums.pop(cur)
else:
(pre, cur) = (pre + 1, cur + 1)
return len(nums)
|
# by Kami Bigdely
# Extract class
class Actor:
def __init__(self, first_name, last_name, birth_year, movies, email) -> None:
self.first_name = first_name
self.last_name = last_name
self.birth_year = birth_year
self.movies = movies
self.email = email
def send_hiring_email(self):
print("Email sent to: ", self.email)
elizabeth_debicki = Actor("Elizabeth", "Debicki", 1990, \
['Tenet', 'Vita & Virgina', 'Guardians of the Galexy', 'The Great Gatsby'], '[email protected]')
jim_carrey = Actor("Jim", "Carrey", 1962, \
['Ace Ventura', 'The Mask', 'Dubm and Dumber', 'The Truman Show', 'Yes Man'], '[email protected]')
actors = [elizabeth_debicki, jim_carrey]
for actor in actors:
if actor.birth_year > 1985:
print(actor.first_name, actor.last_name)
print('Movies Played: ', end='')
for m in actor.movies:
print(m, end=', ')
print()
actor.send_hiring_email()
|
class Actor:
def __init__(self, first_name, last_name, birth_year, movies, email) -> None:
self.first_name = first_name
self.last_name = last_name
self.birth_year = birth_year
self.movies = movies
self.email = email
def send_hiring_email(self):
print('Email sent to: ', self.email)
elizabeth_debicki = actor('Elizabeth', 'Debicki', 1990, ['Tenet', 'Vita & Virgina', 'Guardians of the Galexy', 'The Great Gatsby'], '[email protected]')
jim_carrey = actor('Jim', 'Carrey', 1962, ['Ace Ventura', 'The Mask', 'Dubm and Dumber', 'The Truman Show', 'Yes Man'], '[email protected]')
actors = [elizabeth_debicki, jim_carrey]
for actor in actors:
if actor.birth_year > 1985:
print(actor.first_name, actor.last_name)
print('Movies Played: ', end='')
for m in actor.movies:
print(m, end=', ')
print()
actor.send_hiring_email()
|
#creating a function
def cal(one,two):
three=float(one)-float(two)
print(three)
return
cal(1,4)
|
def cal(one, two):
three = float(one) - float(two)
print(three)
return
cal(1, 4)
|
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def __init__(self):
self.plus1 = 0
def copyTheResults(self, node, result):
while node != None:
result.next = ListNode(node.val)
result = result.next
result.next = None
if self.plus1 == 1:
result.val = node.val + 1
if result.val >= 10:
result.val = result.val - 10
self.plus1 = 1
else:
self.plus1 = 0
node = node.next
if self.plus1 == 1:
result.next = ListNode(1)
result = result.next
result.next = None
def addTwoNumbers(self, l1, l2):
"""
:type l1: ListNode
:type l2: ListNode
:rtype: ListNode
"""
if l1 == None:
return l2
if l2 == None:
return l1
resultHead = result = ListNode(0)
while l1 != None and l2 != None:
result.next = ListNode(l1.val + l2.val)
result = result.next
result.next = None
if self.plus1 == 1:
result.val = result.val + 1
self.plus1 = 0
if result.val >= 10:
result.val = result.val - 10
self.plus1 = 1
l1 = l1.next
l2 = l2.next
if l1 == None:
self.copyTheResults(l2, result)
if l2 == None:
self.copyTheResults(l1, result)
return resultHead.next
|
class Solution(object):
def __init__(self):
self.plus1 = 0
def copy_the_results(self, node, result):
while node != None:
result.next = list_node(node.val)
result = result.next
result.next = None
if self.plus1 == 1:
result.val = node.val + 1
if result.val >= 10:
result.val = result.val - 10
self.plus1 = 1
else:
self.plus1 = 0
node = node.next
if self.plus1 == 1:
result.next = list_node(1)
result = result.next
result.next = None
def add_two_numbers(self, l1, l2):
"""
:type l1: ListNode
:type l2: ListNode
:rtype: ListNode
"""
if l1 == None:
return l2
if l2 == None:
return l1
result_head = result = list_node(0)
while l1 != None and l2 != None:
result.next = list_node(l1.val + l2.val)
result = result.next
result.next = None
if self.plus1 == 1:
result.val = result.val + 1
self.plus1 = 0
if result.val >= 10:
result.val = result.val - 10
self.plus1 = 1
l1 = l1.next
l2 = l2.next
if l1 == None:
self.copyTheResults(l2, result)
if l2 == None:
self.copyTheResults(l1, result)
return resultHead.next
|
class XYZfileWrongFormat(Exception):
pass
class XYZfileDidNotExist(Exception):
pass
|
class Xyzfilewrongformat(Exception):
pass
class Xyzfiledidnotexist(Exception):
pass
|
def number_length(a: int) -> int:
# your code here
# algorithm
# 1) Will recive an argument called "a" of datatype "int"
# 2) Convert the integer into a string
# 3) Calculate the length of the string
# 4) Return the Length of the string
return len(str(a))
def number_length_two(a: int) -> int:
mystring = str(a)
mylength = len(mystring)
return mylength
if __name__ == '__main__':
print("Example:")
print(number_length(10))
# These "asserts" are used for self-checking and not for an auto-testing
assert number_length(10) == 2
assert number_length(0) == 1
assert number_length(4) == 1
assert number_length(44) == 2
print("Coding complete? Click 'Check' to earn cool rewards!")
|
def number_length(a: int) -> int:
return len(str(a))
def number_length_two(a: int) -> int:
mystring = str(a)
mylength = len(mystring)
return mylength
if __name__ == '__main__':
print('Example:')
print(number_length(10))
assert number_length(10) == 2
assert number_length(0) == 1
assert number_length(4) == 1
assert number_length(44) == 2
print("Coding complete? Click 'Check' to earn cool rewards!")
|
# URI Online Judge 2152
N = int(input())
for n in range(N):
entrada = [int(i) for i in input().split()]
if entrada[2] == 0: estado = 'fechou'
else: estado = 'abriu'
print('{:02d}:{:02d} - A porta {}!'.format(entrada[0], entrada[1], estado))
|
n = int(input())
for n in range(N):
entrada = [int(i) for i in input().split()]
if entrada[2] == 0:
estado = 'fechou'
else:
estado = 'abriu'
print('{:02d}:{:02d} - A porta {}!'.format(entrada[0], entrada[1], estado))
|
# -*- coding: utf-8 -*-
"""
lswifi.ie
~~~~~~~~~
schema definition for information element
"""
class InformationElement:
"""Base class for Information Elements"""
def __init__(
self,
element,
element_id,
element_id_extension=None,
extensible=None,
fragmentable=None,
):
self.element = element
self.element_id = element_id
self.element_id_extension = element_id_extension
self.extensible = extensible
self.fragmentable = fragmentable
|
"""
lswifi.ie
~~~~~~~~~
schema definition for information element
"""
class Informationelement:
"""Base class for Information Elements"""
def __init__(self, element, element_id, element_id_extension=None, extensible=None, fragmentable=None):
self.element = element
self.element_id = element_id
self.element_id_extension = element_id_extension
self.extensible = extensible
self.fragmentable = fragmentable
|
# Count and display the number of vowels,
# consonants, uppercase, lowercase characters in string
def countCharacterType(s):
vowels = 0
consonant = 0
lowercase = 0
uppercase = 0
for i in range(0, len(s)):
ch = s[i]
if ((ch >= 'a' and ch <= 'z') or
(ch >= 'A' and ch <= 'Z')):
if ch.islower():
lowercase += 1
if ch.isupper():
uppercase += 1
ch = ch.lower()
if (ch == 'a' or ch == 'e' or ch == 'i'
or ch == 'o' or ch == 'u'):
vowels += 1
else:
consonant += 1
print("Vowels:", vowels)
print("Consonant:", consonant)
print("LowerCase:", lowercase)
print("UpperCase:", uppercase)
k = True
while k == True:
s = input('Enter a string: ')
countCharacterType(s)
option = input('Do you want to try again.(y/n): ').lower()
if option == 'y':
continue
else:
k = False
|
def count_character_type(s):
vowels = 0
consonant = 0
lowercase = 0
uppercase = 0
for i in range(0, len(s)):
ch = s[i]
if ch >= 'a' and ch <= 'z' or (ch >= 'A' and ch <= 'Z'):
if ch.islower():
lowercase += 1
if ch.isupper():
uppercase += 1
ch = ch.lower()
if ch == 'a' or ch == 'e' or ch == 'i' or (ch == 'o') or (ch == 'u'):
vowels += 1
else:
consonant += 1
print('Vowels:', vowels)
print('Consonant:', consonant)
print('LowerCase:', lowercase)
print('UpperCase:', uppercase)
k = True
while k == True:
s = input('Enter a string: ')
count_character_type(s)
option = input('Do you want to try again.(y/n): ').lower()
if option == 'y':
continue
else:
k = False
|
class Solution:
def __init__(self, N: int, blacklist: List[int]):
self.validRange = N - len(blacklist)
self.dict = {}
for b in blacklist:
self.dict[b] = -1
for b in blacklist:
if b < self.validRange:
while N - 1 in self.dict:
N -= 1
self.dict[b] = N - 1
N -= 1
def pick(self) -> int:
value = randint(0, self.validRange - 1)
if value in self.dict:
return self.dict[value]
return value
|
class Solution:
def __init__(self, N: int, blacklist: List[int]):
self.validRange = N - len(blacklist)
self.dict = {}
for b in blacklist:
self.dict[b] = -1
for b in blacklist:
if b < self.validRange:
while N - 1 in self.dict:
n -= 1
self.dict[b] = N - 1
n -= 1
def pick(self) -> int:
value = randint(0, self.validRange - 1)
if value in self.dict:
return self.dict[value]
return value
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Copyright 2020, Yutong Xie, UIUC.
Iteratively reverse a singly linked list
'''
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution(object):
def reverseList(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
prev = None
curr = head
while curr:
tmp = curr.next
curr.next = prev
prev = curr
curr = tmp
return prev
|
"""
Copyright 2020, Yutong Xie, UIUC.
Iteratively reverse a singly linked list
"""
class Solution(object):
def reverse_list(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
prev = None
curr = head
while curr:
tmp = curr.next
curr.next = prev
prev = curr
curr = tmp
return prev
|
totalelements=int(input())
numeratorele=[int(ele) for ele in input().split()]
denomele=[int(ele) for ele in input().split()]
resnumerator=0
if len(numeratorele) == len(denomele):
for i in range(totalelements):
cal=numeratorele[i]*denomele[i]
resnumerator+=cal
print(round(resnumerator/sum(denomele),1))
#weighted mean
|
totalelements = int(input())
numeratorele = [int(ele) for ele in input().split()]
denomele = [int(ele) for ele in input().split()]
resnumerator = 0
if len(numeratorele) == len(denomele):
for i in range(totalelements):
cal = numeratorele[i] * denomele[i]
resnumerator += cal
print(round(resnumerator / sum(denomele), 1))
|
def foo():
'''
>>> from mod import Good as Good
'''
pass # Ignore PyUnusedCodeBear
|
def foo():
"""
>>> from mod import Good as Good
"""
pass
|
class Node:
def __init__(self):
self.children = {}
self.endOfWord = False
class Trie:
def __init__(self):
"""
Initialize your data structure here.
"""
# Trie intializes with a Node
self.root = Node()
def insert(self, word: str) -> None:
"""
Inserts a word into the trie.
"""
# have a pointer to the root
cur = self.root
# for every letter in the word
for letter in word:
# if its not in the the roots children
if letter not in cur.children:
# have a the letter be the key and the node be the value
cur.children[letter] = Node()
# then curent points to the child's letter
cur = cur.children[letter]
# after all of the letters set end of word to true
cur.endOfWord = True
def search(self, word: str) -> bool:
"""
Returns if the word is in the trie.
"""
# have a pointer to the root
cur = self.root
# for every letter in the word
for letter in word:
# if the letter is not in the cur.children then it doesnt exist so return False
if letter not in cur.children:
return False
# then continue to point to the next child's letters
cur = cur.children[letter]
# then if the the end of word is true or false return it
return cur.endOfWord
def startsWith(self, prefix: str) -> bool:
"""
Returns if there is any word in the trie that starts with the given prefix.
"""
cur = self.root
# for every letter in the prefix
for letter in prefix:
# if its not in the children return False
if letter not in cur.children:
return False
cur = cur.children[letter]
# otherwise it exists so return true
return True
t = Trie()
t.insert("word")
t.insert("the")
t.insert("what")
t.startsWith("search")
print(t.search("the"))
print(t.root.children)
|
class Node:
def __init__(self):
self.children = {}
self.endOfWord = False
class Trie:
def __init__(self):
"""
Initialize your data structure here.
"""
self.root = node()
def insert(self, word: str) -> None:
"""
Inserts a word into the trie.
"""
cur = self.root
for letter in word:
if letter not in cur.children:
cur.children[letter] = node()
cur = cur.children[letter]
cur.endOfWord = True
def search(self, word: str) -> bool:
"""
Returns if the word is in the trie.
"""
cur = self.root
for letter in word:
if letter not in cur.children:
return False
cur = cur.children[letter]
return cur.endOfWord
def starts_with(self, prefix: str) -> bool:
"""
Returns if there is any word in the trie that starts with the given prefix.
"""
cur = self.root
for letter in prefix:
if letter not in cur.children:
return False
cur = cur.children[letter]
return True
t = trie()
t.insert('word')
t.insert('the')
t.insert('what')
t.startsWith('search')
print(t.search('the'))
print(t.root.children)
|
#!/usr/bin/env python3
# Conditional statements check for a condition,
# and act accordingly.
# Python uses the `if/elif/else` structure for this.
# Example 1
if 2 > 1:
print("2 greater than 1")
# Example 2
var1 = 1
var2 = 10
if var1 > var2:
print("var1 greater than var2")
else:
print("var2 greater than var1")
# Example 3
# Ask for an input
value = input("What is the price for that thing?")
value = int(value) # Try converting it to an int
if value < 10:
print("That's great!")
elif 10 <= value <= 20:
print("I would still buy it!")
else:
print("That's costly!")
|
if 2 > 1:
print('2 greater than 1')
var1 = 1
var2 = 10
if var1 > var2:
print('var1 greater than var2')
else:
print('var2 greater than var1')
value = input('What is the price for that thing?')
value = int(value)
if value < 10:
print("That's great!")
elif 10 <= value <= 20:
print('I would still buy it!')
else:
print("That's costly!")
|
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution(object):
def isBalanced(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
def _treeDepth(node):
if node is None:
return 0
else:
return 1 + max(_treeDepth(node.left), _treeDepth(node.right))
if root is None:
return True
else:
return abs(_treeDepth(root.left)-_treeDepth(root.right)) <= 1 and self.isBalanced(root.left) and self.isBalanced(root.right)
|
class Solution(object):
def is_balanced(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
def _tree_depth(node):
if node is None:
return 0
else:
return 1 + max(_tree_depth(node.left), _tree_depth(node.right))
if root is None:
return True
else:
return abs(_tree_depth(root.left) - _tree_depth(root.right)) <= 1 and self.isBalanced(root.left) and self.isBalanced(root.right)
|
class Rollout:
'''
Usage: rollout = Rollout(state)
'''
def __init__(self, state):
self.state_list = [state]
self.action_list = []
self.reward_list = []
self.act_val_list = []
self.done = False
def append(self, state, action, reward, done, act_val):
self.state_list.append(state)
self.action_list.append(action)
self.reward_list.append(reward)
self.act_val_list.append(act_val)
self.done = done
'''
Length of the rollout is (number of states - 1) or (number of rewards)
'''
def __len__(self):
return len(self.reward_list)
|
class Rollout:
"""
Usage: rollout = Rollout(state)
"""
def __init__(self, state):
self.state_list = [state]
self.action_list = []
self.reward_list = []
self.act_val_list = []
self.done = False
def append(self, state, action, reward, done, act_val):
self.state_list.append(state)
self.action_list.append(action)
self.reward_list.append(reward)
self.act_val_list.append(act_val)
self.done = done
'\n Length of the rollout is (number of states - 1) or (number of rewards)\n '
def __len__(self):
return len(self.reward_list)
|
#!/usr/bin/env python
TEST_PUBLIC_KEY = 5764801
CARD_PUBLIC_KEY = 18499292
DOOR_PUBLIC_KEY = 8790390
def find_loop_size(
target_key,
subject=7,
starting_value=1,
):
value = starting_value
loop_size = 0
while value != target_key:
value = value * subject
value = value % 20201227
loop_size += 1
return loop_size
def find_encryption_key(
public_key,
loop_size,
starting_value=1,
):
value = starting_value
loop_count = 0
while loop_count < loop_size:
value = value * public_key
value = value % 20201227
loop_count += 1
return value
def main():
test_loop_size = find_loop_size(TEST_PUBLIC_KEY)
assert(test_loop_size == 8)
card_loop_size = find_loop_size(CARD_PUBLIC_KEY)
door_loop_size = find_loop_size(DOOR_PUBLIC_KEY)
card_encryption_key = find_encryption_key(
DOOR_PUBLIC_KEY, card_loop_size
)
door_encryption_key = find_encryption_key(
CARD_PUBLIC_KEY, door_loop_size
)
assert(card_encryption_key == door_encryption_key)
part_1_result = card_encryption_key
print(f"Part 1: {part_1_result}")
# part_2_result = None
# print(f"Part 2: {part_2_result}")
if __name__ == "__main__":
main()
|
test_public_key = 5764801
card_public_key = 18499292
door_public_key = 8790390
def find_loop_size(target_key, subject=7, starting_value=1):
value = starting_value
loop_size = 0
while value != target_key:
value = value * subject
value = value % 20201227
loop_size += 1
return loop_size
def find_encryption_key(public_key, loop_size, starting_value=1):
value = starting_value
loop_count = 0
while loop_count < loop_size:
value = value * public_key
value = value % 20201227
loop_count += 1
return value
def main():
test_loop_size = find_loop_size(TEST_PUBLIC_KEY)
assert test_loop_size == 8
card_loop_size = find_loop_size(CARD_PUBLIC_KEY)
door_loop_size = find_loop_size(DOOR_PUBLIC_KEY)
card_encryption_key = find_encryption_key(DOOR_PUBLIC_KEY, card_loop_size)
door_encryption_key = find_encryption_key(CARD_PUBLIC_KEY, door_loop_size)
assert card_encryption_key == door_encryption_key
part_1_result = card_encryption_key
print(f'Part 1: {part_1_result}')
if __name__ == '__main__':
main()
|
class BaseDerivative:
def __init__(self, config, instance, *args, **kwargs):
self.config = config
self.instance = instance
|
class Basederivative:
def __init__(self, config, instance, *args, **kwargs):
self.config = config
self.instance = instance
|
# display characters
BLANK_STR = ' '
PLAYER_1_STR = ' X '
PLAYER_2_STR = ' O '
LEFT_PAD_STR = ' '
HORIZ_BOARD_STR = '-'
HORIZ_BOARD_STR_THREE = ''.join(HORIZ_BOARD_STR for x in xrange(3))
VERTICAL_BOARD_STR = '|'
assert len(BLANK_STR) == len(PLAYER_1_STR) == len(PLAYER_2_STR)
def get_board_display(board_state, debug_mode=False):
"""
The L{board_state} must be a 2-dimensional array (list of lists)
where each positional value is what should be printed in the
corresponding cell.
Returns a string representation of the board state. Always
has number labels on the columns.
If L{debug_mode} is C{True}, the column number labels will
be zero-based (0...NUM_COLS) and 0-based row labels
will be present. If C{debug_mode} is C{False}, the column labels
will be 1-based (1...NUM_COLS+1) and there will be no row labels.
Example (debug mode):
0 1 2 3 4 5 6 7 8 9
|---|---|---|---|---|---|---|---|---|---|
0 | | | | | | | | | | |
1 | | | | | | | | | | |
2 | | | | | | | | | | |
3 | | | | | | | | | | |
4 | | | | | | | | | | |
5 | | | | | | | | | | |
6 | | | | | | | | | | |
7 | | | | | | | | | | |
|---|---|---|---|---|---|---|---|---|---|
"""
assert isinstance(board_state, list) # better be a list
assert isinstance(debug_mode, bool) # better be a boolean
# detect the board dimensions
board_height = len(board_state)
assert board_height >= 1
# just asserted there is at least 1 element, grab it to detect width
board_width = len(board_state[0])
# make sure 2d array is rectangular
assert all(len(cols) == board_width for cols in board_state)
# make the base of the string that will represent the top and bottom of the board.
# the statement 'STR_1.join(iterable)'' will join each element from the iterable using STR_1.
# for example, 'm'.join(('1','2','3','4')) will create the string 1m2m3m4
# NOTE: it doesn't put one before or after, so we do it manually.
BASE_TOP_BOT = (LEFT_PAD_STR + # indent the top line by the pad
VERTICAL_BOARD_STR + # add one before
# this join uses a shortcut syntax for defining an iterable,
# the innards of the join(stuff to join) will yield an iterable
# the same length as the xrange (which we're using just to
# count iterations) but with every element as '---'
VERTICAL_BOARD_STR.join(HORIZ_BOARD_STR_THREE for x in xrange(board_width)) +
VERTICAL_BOARD_STR + # add one after
'\n')
# debug mode shows the 0-based indices of the columns, but
# when we're playing the game we use 1 to len(cols)
if debug_mode:
col_labels = xrange(0, board_width)
else:
col_labels = xrange(1, board_width + 1)
# the line with the numbers labelling the columns
HEADER = ('\n' +
LEFT_PAD_STR +
# this next one labels the columns
' '.join('%3d' % x for x in col_labels) +
'\n' +
BASE_TOP_BOT)
FOOTER = '' # just in case we want to add feet or something
board_display_string = '' # build this string as we go
board_display_string += HEADER
# for each row in the grid, print the grid lines
# with the contents of any cell injected into them.
for idx, row_cell_list in enumerate(board_state):
if debug_mode:
# add the row counter
board_display_string += '%2d ' % idx
else:
board_display_string += LEFT_PAD_STR
board_display_string += (VERTICAL_BOARD_STR +
VERTICAL_BOARD_STR.join(row_cell_list) +
VERTICAL_BOARD_STR +
'\n')
board_display_string += BASE_TOP_BOT
board_display_string += FOOTER
return board_display_string
# end of the function: get_board_state
def get_next_free_row_in_column(col_number, board_state):
"""
Calculate and return the row index.
indicating the next available cell in the given L{col_number}.
All row/col indices this deals with are zero-based.
DO NOT ALTER L{board_state} in this function!
"""
assert isinstance(col_number, int) # better be an integer
assert isinstance(board_state, list)
board_height = len(board_state)
assert board_height >= 1
# just asserted there is at least 1 element, grab it to detect width
board_width = len(board_state[0])
# make sure 2d array is rectangular
assert all(len(cols) == board_width for cols in board_state)
# from max row index (height - 1) to 0 (to -1, not inclusive!), by -1
for row_idx in xrange(board_height-1, -1, -1):
if board_state[row_idx][col_number] == BLANK_STR:
return row_idx # return as soon as we find an empty cell
# never found an empty cell
return None
def get_next_player(current_player):
assert isinstance(current_player, str)
if current_player == PLAYER_1_STR:
return PLAYER_2_STR
assert current_player == PLAYER_2_STR
return PLAYER_1_STR
def is_board_full(board_state):
assert isinstance(board_state, list) # better be a list
assert isinstance(debug_mode, bool) # better be a boolean
# detect the board dimensions
board_height = len(board_state)
assert board_height >= 1
# just asserted there is at least 1 element, grab it to detect width
board_width = len(board_state[0])
# make sure 2d array is rectangular
assert all(len(cols) == board_width for cols in board_state)
for row in board_state:
for col_value in row:
if col_value == BLANK_STR:
# as soon as you find any open spot, return
# that the board is NOT full
return False
# if we make it here, we never found an empty spot,
# so the board must be full
return True
# create the board state that will track th
board_state = []
BOARD_HEIGHT = 6 # standard is 6
BOARD_WIDTH = 7 # standard is 7
for x_idx in xrange(BOARD_HEIGHT):
board_state.append([BLANK_STR for x in xrange(BOARD_WIDTH)])
# # toss a few dummy values in here to demonstrate where they land
# board_state[0][0] = PLAYER_1_STR
# board_state[0][1] = PLAYER_1_STR
# board_state[3][5] = PLAYER_2_STR
# board_state[3][6] = PLAYER_2_STR
# show the board
print(get_board_display(board_state))
# player 1 goes first
current_player = PLAYER_1_STR
while not is_board_full(board_state):
# raw_input will not return a value until a user enters it
# into the terminal that called this script. The returned
# value is always a string, even if it's composed of numbers!
col_input = raw_input('Player%sPick a column, 1-%s: ' % (current_player, BOARD_WIDTH))
# this is a way of handling errors. If anything in the 'try' block
# goes BOOM, the 'except' block is executed. This is generally a bad
# idea, but when used carefully can be really helpful.
try:
# try to slam the input string into an integer. If
# not possible, BOOM
col_num = int(col_input) - 1 # display to user is offset by one!!!
except:
print('Invalid column number')
continue # stops executing this iteration of the loop and goes back to the top
assert isinstance(col_num, int)
# handle illegal column integers.
if col_num >= BOARD_WIDTH:
print('Column number must be between 1 and %s, you gave %s' % (BOARD_WIDTH, col_input))
# add something to the specified column
row_to_add = get_next_free_row_in_column(col_num, board_state) # won't work, just for example
if row_to_add is not None:
board_state[row_to_add][col_num] = current_player
else:
print('Column %s is full! pick again' % col_input)
continue
# update the current player.
current_player = get_next_player(current_player)
# finally, print the board state so that the next players
# turn reflects the latest state
print(get_board_display(board_state))
# end loop if the board is full
print("Are you sure no one won? If not, the board is full so it's a tie")
|
blank_str = ' '
player_1_str = ' X '
player_2_str = ' O '
left_pad_str = ' '
horiz_board_str = '-'
horiz_board_str_three = ''.join((HORIZ_BOARD_STR for x in xrange(3)))
vertical_board_str = '|'
assert len(BLANK_STR) == len(PLAYER_1_STR) == len(PLAYER_2_STR)
def get_board_display(board_state, debug_mode=False):
"""
The L{board_state} must be a 2-dimensional array (list of lists)
where each positional value is what should be printed in the
corresponding cell.
Returns a string representation of the board state. Always
has number labels on the columns.
If L{debug_mode} is C{True}, the column number labels will
be zero-based (0...NUM_COLS) and 0-based row labels
will be present. If C{debug_mode} is C{False}, the column labels
will be 1-based (1...NUM_COLS+1) and there will be no row labels.
Example (debug mode):
0 1 2 3 4 5 6 7 8 9
|---|---|---|---|---|---|---|---|---|---|
0 | | | | | | | | | | |
1 | | | | | | | | | | |
2 | | | | | | | | | | |
3 | | | | | | | | | | |
4 | | | | | | | | | | |
5 | | | | | | | | | | |
6 | | | | | | | | | | |
7 | | | | | | | | | | |
|---|---|---|---|---|---|---|---|---|---|
"""
assert isinstance(board_state, list)
assert isinstance(debug_mode, bool)
board_height = len(board_state)
assert board_height >= 1
board_width = len(board_state[0])
assert all((len(cols) == board_width for cols in board_state))
base_top_bot = LEFT_PAD_STR + VERTICAL_BOARD_STR + VERTICAL_BOARD_STR.join((HORIZ_BOARD_STR_THREE for x in xrange(board_width))) + VERTICAL_BOARD_STR + '\n'
if debug_mode:
col_labels = xrange(0, board_width)
else:
col_labels = xrange(1, board_width + 1)
header = '\n' + LEFT_PAD_STR + ' '.join(('%3d' % x for x in col_labels)) + '\n' + BASE_TOP_BOT
footer = ''
board_display_string = ''
board_display_string += HEADER
for (idx, row_cell_list) in enumerate(board_state):
if debug_mode:
board_display_string += '%2d ' % idx
else:
board_display_string += LEFT_PAD_STR
board_display_string += VERTICAL_BOARD_STR + VERTICAL_BOARD_STR.join(row_cell_list) + VERTICAL_BOARD_STR + '\n'
board_display_string += BASE_TOP_BOT
board_display_string += FOOTER
return board_display_string
def get_next_free_row_in_column(col_number, board_state):
"""
Calculate and return the row index.
indicating the next available cell in the given L{col_number}.
All row/col indices this deals with are zero-based.
DO NOT ALTER L{board_state} in this function!
"""
assert isinstance(col_number, int)
assert isinstance(board_state, list)
board_height = len(board_state)
assert board_height >= 1
board_width = len(board_state[0])
assert all((len(cols) == board_width for cols in board_state))
for row_idx in xrange(board_height - 1, -1, -1):
if board_state[row_idx][col_number] == BLANK_STR:
return row_idx
return None
def get_next_player(current_player):
assert isinstance(current_player, str)
if current_player == PLAYER_1_STR:
return PLAYER_2_STR
assert current_player == PLAYER_2_STR
return PLAYER_1_STR
def is_board_full(board_state):
assert isinstance(board_state, list)
assert isinstance(debug_mode, bool)
board_height = len(board_state)
assert board_height >= 1
board_width = len(board_state[0])
assert all((len(cols) == board_width for cols in board_state))
for row in board_state:
for col_value in row:
if col_value == BLANK_STR:
return False
return True
board_state = []
board_height = 6
board_width = 7
for x_idx in xrange(BOARD_HEIGHT):
board_state.append([BLANK_STR for x in xrange(BOARD_WIDTH)])
print(get_board_display(board_state))
current_player = PLAYER_1_STR
while not is_board_full(board_state):
col_input = raw_input('Player%sPick a column, 1-%s: ' % (current_player, BOARD_WIDTH))
try:
col_num = int(col_input) - 1
except:
print('Invalid column number')
continue
assert isinstance(col_num, int)
if col_num >= BOARD_WIDTH:
print('Column number must be between 1 and %s, you gave %s' % (BOARD_WIDTH, col_input))
row_to_add = get_next_free_row_in_column(col_num, board_state)
if row_to_add is not None:
board_state[row_to_add][col_num] = current_player
else:
print('Column %s is full! pick again' % col_input)
continue
current_player = get_next_player(current_player)
print(get_board_display(board_state))
print("Are you sure no one won? If not, the board is full so it's a tie")
|
class Solution:
def reorderedPowerOf2(self, N: int) -> bool:
x="".join(i for i in sorted(str(N)))
for i in range(30):
s="".join(i for i in sorted(str(2**i)))
if s==x:
print(s)
return True
return False
|
class Solution:
def reordered_power_of2(self, N: int) -> bool:
x = ''.join((i for i in sorted(str(N))))
for i in range(30):
s = ''.join((i for i in sorted(str(2 ** i))))
if s == x:
print(s)
return True
return False
|
#-----------------------------------------------------------------------------
# Copyright (c) 2019, PyInstaller Development Team.
#
# Distributed under the terms of the GNU General Public License with exception
# for distributing bootloader.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
# Hook for https://github.com/PyWavelets/pywt
hiddenimports = ['pywt._extensions._cwt']
# NOTE: There is another project `https://github.com/Knapstad/pywt installing
# a packagre `pywt`, too. This name clash is not much of a problem, even if
# this hook is picked up for the other package, since PyInstaller will simply
# skip any module added by this hook but acutally missing. If the other project
# requires a hook, too, simply add it to this file.
|
hiddenimports = ['pywt._extensions._cwt']
|
def bbox_mk(p1, p2, offset=(0, 0)):
x1 = min(p1[0], p2[0]) - offset[0]
x2 = max(p1[0], p2[0]) + offset[0]
y1 = min(p1[1], p2[1]) - offset[1]
y2 = max(p1[1], p2[1]) + offset[1]
return (x1, y1), (x2, y2)
def bbox_add_point(bbox, p, offset=(0, 0)):
x1 = min(bbox[0][0], p[0] - offset[0])
x2 = max(bbox[1][0], p[0] + offset[0])
y1 = min(bbox[0][1], p[1] - offset[1])
y2 = max(bbox[1][1], p[1] + offset[1])
return (x1, y1), (x2, y2)
def bbox_add_bbox(b1, b2):
bb = bbox_add_point(b1, b2[0])
bb = bbox_add_point(bb, b2[1])
return bb
|
def bbox_mk(p1, p2, offset=(0, 0)):
x1 = min(p1[0], p2[0]) - offset[0]
x2 = max(p1[0], p2[0]) + offset[0]
y1 = min(p1[1], p2[1]) - offset[1]
y2 = max(p1[1], p2[1]) + offset[1]
return ((x1, y1), (x2, y2))
def bbox_add_point(bbox, p, offset=(0, 0)):
x1 = min(bbox[0][0], p[0] - offset[0])
x2 = max(bbox[1][0], p[0] + offset[0])
y1 = min(bbox[0][1], p[1] - offset[1])
y2 = max(bbox[1][1], p[1] + offset[1])
return ((x1, y1), (x2, y2))
def bbox_add_bbox(b1, b2):
bb = bbox_add_point(b1, b2[0])
bb = bbox_add_point(bb, b2[1])
return bb
|
#!/usr/bin/python3
def isprime(n):
if n == 1:
return False
for x in range(2, n):
if n % x == 0:
return False
else:
return True
def primes(n = 1):
while(True):
if isprime(n): yield n
n += 1
for n in primes():
if n > 100: break
print(n)
|
def isprime(n):
if n == 1:
return False
for x in range(2, n):
if n % x == 0:
return False
else:
return True
def primes(n=1):
while True:
if isprime(n):
yield n
n += 1
for n in primes():
if n > 100:
break
print(n)
|
""" This problem was asked by Uber.
Given an array of integers, return a new array such that each element at index i
of the new array is the product of all the numbers in the original array except
the one at i.For example, if our input was [1, 2, 3, 4, 5], the expected output
would be [120, 60, 40, 30, 24]. If our input was [3, 2, 1], the expected output
would be [2, 3, 6]. Follow-up: what if you can't use division? """
# function to get the product of elements inside an array.
def get_array_product(array):
product = 1
for i in array:
product *= i
return product
# this function does the main job of the problem but with division.
def get_new_array_division(array):
# array holding the final result
new_array = []
# getting the product of the array elements.
product = get_array_product(array)
for i in array:
# for each number divide the product by the current element.
new_array.append(product / i)
# return the array
return new_array
# this function does the main job of the problem without division.(a to -1 power)
def get_new_array(array):
# array holding the final result
new_array = []
# getting the product of the array elements.
product = get_array_product(array)
for i in array:
# for each number product the product by the current element.
new_array.append(product * (i ** -1))
# return the array
return new_array
# This function does the main job of the problem without division.(a to -1 power)
def get_new_array_hard(array):
# array holding the final result
new_array = []
for i in range(len(array)):
p = 1
for j in range(len(array)):
if j != i:
p *= array[j]
new_array.append(p)
# return the array
return new_array
# test the functions.
# print(get_new_array_division([1, 2, 3, 4, 5]))
# print(get_new_array_division([3, 2, 1]))
# print(get_new_array([1, 2, 3, 4, 5]))
# print(get_new_array([3, 2, 1]))
print(get_new_array_hard([1, 2, 3, 4, 5]))
print(get_new_array_hard([3, 2, 1]))
|
""" This problem was asked by Uber.
Given an array of integers, return a new array such that each element at index i
of the new array is the product of all the numbers in the original array except
the one at i.For example, if our input was [1, 2, 3, 4, 5], the expected output
would be [120, 60, 40, 30, 24]. If our input was [3, 2, 1], the expected output
would be [2, 3, 6]. Follow-up: what if you can't use division? """
def get_array_product(array):
product = 1
for i in array:
product *= i
return product
def get_new_array_division(array):
new_array = []
product = get_array_product(array)
for i in array:
new_array.append(product / i)
return new_array
def get_new_array(array):
new_array = []
product = get_array_product(array)
for i in array:
new_array.append(product * i ** (-1))
return new_array
def get_new_array_hard(array):
new_array = []
for i in range(len(array)):
p = 1
for j in range(len(array)):
if j != i:
p *= array[j]
new_array.append(p)
return new_array
print(get_new_array_hard([1, 2, 3, 4, 5]))
print(get_new_array_hard([3, 2, 1]))
|
#!/usr/bin/env python
class Service(object):
pass
|
class Service(object):
pass
|
model_params = dict(
image_shape=(1, 256, 256),
n_part_caps=30,
n_obj_caps=16,
scae_regression_params=dict(
is_active=True,
loss='mse',
attention_hp=1,
),
scae_classification_params=dict(
is_active=False,
n_classes=1,
),
pcae_cnn_encoder_params=dict(
out_channels=[128] * 4,
kernel_sizes=[3, 3, 3, 3],
strides=[2, 2, 1, 1],
activate_final=True
),
pcae_encoder_params=dict(
n_poses=6,
n_special_features=16,
similarity_transform=False,
),
pcae_template_generator_params=dict(
template_size=(32, 32),
template_nonlin='sigmoid',
colorize_templates=True,
color_nonlin='sigmoid',
),
pcae_decoder_params=dict(
learn_output_scale=False,
use_alpha_channel=True,
background_value=True,
),
ocae_encoder_set_transformer_params=dict(
n_layers=3,
n_heads=1,
dim_hidden=16,
dim_out=256,
layer_norm=True,
),
obj_age_regressor_params=dict(
hidden_sizes=[128, 64, 1],
inner_activation='relu',
final_activation=None,
bias=True,
dropout=0,
),
ocae_decoder_capsule_params=dict(
dim_caps=32,
hidden_sizes=(128,),
caps_dropout_rate=0.0,
learn_vote_scale=True,
allow_deformations=True,
noise_type='uniform',
noise_scale=4.,
similarity_transform=False,
),
scae_params=dict(
vote_type='enc',
presence_type='enc',
stop_grad_caps_input=True,
stop_grad_caps_target=True,
caps_ll_weight=1.,
cpr_dynamic_reg_weight=10,
prior_sparsity_loss_type='l2',
prior_within_example_sparsity_weight=2.0,
prior_between_example_sparsity_weight=0.2, #from 0.35 to 0.2
posterior_sparsity_loss_type='entropy',
posterior_within_example_sparsity_weight=0.5, #from 0.7 to 0.5
posterior_between_example_sparsity_weight=0.2,
reconstruct_alternatives=False,
)
)
|
model_params = dict(image_shape=(1, 256, 256), n_part_caps=30, n_obj_caps=16, scae_regression_params=dict(is_active=True, loss='mse', attention_hp=1), scae_classification_params=dict(is_active=False, n_classes=1), pcae_cnn_encoder_params=dict(out_channels=[128] * 4, kernel_sizes=[3, 3, 3, 3], strides=[2, 2, 1, 1], activate_final=True), pcae_encoder_params=dict(n_poses=6, n_special_features=16, similarity_transform=False), pcae_template_generator_params=dict(template_size=(32, 32), template_nonlin='sigmoid', colorize_templates=True, color_nonlin='sigmoid'), pcae_decoder_params=dict(learn_output_scale=False, use_alpha_channel=True, background_value=True), ocae_encoder_set_transformer_params=dict(n_layers=3, n_heads=1, dim_hidden=16, dim_out=256, layer_norm=True), obj_age_regressor_params=dict(hidden_sizes=[128, 64, 1], inner_activation='relu', final_activation=None, bias=True, dropout=0), ocae_decoder_capsule_params=dict(dim_caps=32, hidden_sizes=(128,), caps_dropout_rate=0.0, learn_vote_scale=True, allow_deformations=True, noise_type='uniform', noise_scale=4.0, similarity_transform=False), scae_params=dict(vote_type='enc', presence_type='enc', stop_grad_caps_input=True, stop_grad_caps_target=True, caps_ll_weight=1.0, cpr_dynamic_reg_weight=10, prior_sparsity_loss_type='l2', prior_within_example_sparsity_weight=2.0, prior_between_example_sparsity_weight=0.2, posterior_sparsity_loss_type='entropy', posterior_within_example_sparsity_weight=0.5, posterior_between_example_sparsity_weight=0.2, reconstruct_alternatives=False))
|
# -*- coding: utf-8 -*-
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
def __eq__(self, other):
return (
other is not None and
self.val == other.val and
self.left == other.left and
self.right == other.right
)
class Solution:
def removeLeafNodes(self, root: TreeNode, target: int) -> TreeNode:
if root is None:
return None
return self._removeLeafNodes(root, target)
def _removeLeafNodes(self, root: TreeNode, target: int) -> TreeNode:
if root.left is not None:
root.left = self._removeLeafNodes(root.left, target)
if root.right is not None:
root.right = self._removeLeafNodes(root.right, target)
if root.left is None and root.val == target and root.right is None:
return None
return root
if __name__ == '__main__':
solution = Solution()
t0_0 = TreeNode(1)
t0_1 = TreeNode(2)
t0_2 = TreeNode(3)
t0_3 = TreeNode(2)
t0_4 = TreeNode(2)
t0_5 = TreeNode(4)
t0_2.right = t0_5
t0_2.left = t0_4
t0_1.left = t0_3
t0_0.right = t0_2
t0_0.left = t0_1
t1_0 = TreeNode(1)
t1_1 = TreeNode(3)
t1_2 = TreeNode(4)
t1_1.right = t1_2
t1_0.right = t1_1
assert t1_0 == solution.removeLeafNodes(t0_0, 2)
t2_0 = TreeNode(1)
t2_1 = TreeNode(3)
t2_2 = TreeNode(3)
t2_3 = TreeNode(3)
t2_4 = TreeNode(2)
t2_1.right = t2_4
t2_1.left = t2_3
t2_0.right = t2_2
t2_0.left = t2_1
t3_0 = TreeNode(1)
t3_1 = TreeNode(3)
t3_2 = TreeNode(2)
t3_1.right = t3_2
t3_0.left = t3_1
assert t3_0 == solution.removeLeafNodes(t2_0, 3)
t4_0 = TreeNode(1)
t4_1 = TreeNode(2)
t4_2 = TreeNode(2)
t4_3 = TreeNode(2)
t4_2.left = t4_3
t4_1.left = t4_2
t4_0.left = t4_1
t5_0 = TreeNode(1)
assert t5_0 == solution.removeLeafNodes(t4_0, 2)
t6_0 = TreeNode(1)
t6_1 = TreeNode(1)
t6_2 = TreeNode(1)
t6_0.right = t6_2
t6_0.left = t6_1
t7_0 = None
assert t7_0 == solution.removeLeafNodes(t6_0, 1)
t8_0 = TreeNode(1)
t8_1 = TreeNode(2)
t8_2 = TreeNode(3)
t8_0.right = t8_2
t8_0.left = t8_1
t9_0 = TreeNode(1)
t9_1 = TreeNode(2)
t9_2 = TreeNode(3)
t9_0.right = t9_2
t9_0.left = t9_1
assert t9_0 == solution.removeLeafNodes(t8_0, 1)
|
class Treenode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
def __eq__(self, other):
return other is not None and self.val == other.val and (self.left == other.left) and (self.right == other.right)
class Solution:
def remove_leaf_nodes(self, root: TreeNode, target: int) -> TreeNode:
if root is None:
return None
return self._removeLeafNodes(root, target)
def _remove_leaf_nodes(self, root: TreeNode, target: int) -> TreeNode:
if root.left is not None:
root.left = self._removeLeafNodes(root.left, target)
if root.right is not None:
root.right = self._removeLeafNodes(root.right, target)
if root.left is None and root.val == target and (root.right is None):
return None
return root
if __name__ == '__main__':
solution = solution()
t0_0 = tree_node(1)
t0_1 = tree_node(2)
t0_2 = tree_node(3)
t0_3 = tree_node(2)
t0_4 = tree_node(2)
t0_5 = tree_node(4)
t0_2.right = t0_5
t0_2.left = t0_4
t0_1.left = t0_3
t0_0.right = t0_2
t0_0.left = t0_1
t1_0 = tree_node(1)
t1_1 = tree_node(3)
t1_2 = tree_node(4)
t1_1.right = t1_2
t1_0.right = t1_1
assert t1_0 == solution.removeLeafNodes(t0_0, 2)
t2_0 = tree_node(1)
t2_1 = tree_node(3)
t2_2 = tree_node(3)
t2_3 = tree_node(3)
t2_4 = tree_node(2)
t2_1.right = t2_4
t2_1.left = t2_3
t2_0.right = t2_2
t2_0.left = t2_1
t3_0 = tree_node(1)
t3_1 = tree_node(3)
t3_2 = tree_node(2)
t3_1.right = t3_2
t3_0.left = t3_1
assert t3_0 == solution.removeLeafNodes(t2_0, 3)
t4_0 = tree_node(1)
t4_1 = tree_node(2)
t4_2 = tree_node(2)
t4_3 = tree_node(2)
t4_2.left = t4_3
t4_1.left = t4_2
t4_0.left = t4_1
t5_0 = tree_node(1)
assert t5_0 == solution.removeLeafNodes(t4_0, 2)
t6_0 = tree_node(1)
t6_1 = tree_node(1)
t6_2 = tree_node(1)
t6_0.right = t6_2
t6_0.left = t6_1
t7_0 = None
assert t7_0 == solution.removeLeafNodes(t6_0, 1)
t8_0 = tree_node(1)
t8_1 = tree_node(2)
t8_2 = tree_node(3)
t8_0.right = t8_2
t8_0.left = t8_1
t9_0 = tree_node(1)
t9_1 = tree_node(2)
t9_2 = tree_node(3)
t9_0.right = t9_2
t9_0.left = t9_1
assert t9_0 == solution.removeLeafNodes(t8_0, 1)
|
class Solution:
def longestPalindrome(self, s: str) -> int:
str_dict={}
for each in s:
if each not in str_dict:
str_dict[each]=0
str_dict[each]+=1
result=0
odd=0
for k, v in str_dict.items():
if v%2==0:
result+=v
else:
result+=v-1
odd=1
return result+odd
|
class Solution:
def longest_palindrome(self, s: str) -> int:
str_dict = {}
for each in s:
if each not in str_dict:
str_dict[each] = 0
str_dict[each] += 1
result = 0
odd = 0
for (k, v) in str_dict.items():
if v % 2 == 0:
result += v
else:
result += v - 1
odd = 1
return result + odd
|
for desi in Parameters.GetAllDesignPoints():
for message in GetMessages():
if (DateTime.Compare(message.DateTimeStamp, startTime) == 1) and (message.DesignPoint == desi.Name):
desi.Retained = False
break
|
for desi in Parameters.GetAllDesignPoints():
for message in get_messages():
if DateTime.Compare(message.DateTimeStamp, startTime) == 1 and message.DesignPoint == desi.Name:
desi.Retained = False
break
|
def solve(heads,legs):
for i in range(heads+1):
j=heads-i
if (2*i)+(4*j)==legs:
print (i,j)
return
print("No solution")
#Start writing your code here
#Populate the variables: chicken_count and rabbit_count
# Use the below given print statements to display the output
# Also, do not modify them for verification to work
#print(chicken_count,rabbit_count)
#print(error_msg)
#Provide different values for heads and legs and test your program
solve(38,131)
|
def solve(heads, legs):
for i in range(heads + 1):
j = heads - i
if 2 * i + 4 * j == legs:
print(i, j)
return
print('No solution')
solve(38, 131)
|
class Controller():
def __init__(self):
self.debugger = None
def get_cmd(self):
pass
|
class Controller:
def __init__(self):
self.debugger = None
def get_cmd(self):
pass
|
class Solution:
def countDigitOne(self, n: int) -> int:
if n <= 0:
return 0
ln = len(str(n))
if ln == 1:
return 1
tmp1 = 10 ** (ln - 1)
firstnum = n // tmp1
fone = n % tmp1 + 1 if firstnum == 1 else tmp1
other = firstnum * (ln - 1) * (tmp1 // 10)
return fone + other + self.countDigitOne(n % tmp1)
|
class Solution:
def count_digit_one(self, n: int) -> int:
if n <= 0:
return 0
ln = len(str(n))
if ln == 1:
return 1
tmp1 = 10 ** (ln - 1)
firstnum = n // tmp1
fone = n % tmp1 + 1 if firstnum == 1 else tmp1
other = firstnum * (ln - 1) * (tmp1 // 10)
return fone + other + self.countDigitOne(n % tmp1)
|
class PathNameChecker(object):
def check(self, pathname: str):
pass
|
class Pathnamechecker(object):
def check(self, pathname: str):
pass
|
total = 0
line = input()
while line != "NoMoreMoney":
current = float(line)
if current < 0:
print("Invalid operation!")
break
total += current
print(f"Increase: {current:.2f}")
line = input()
print(f"Total: {total:.2f}")
|
total = 0
line = input()
while line != 'NoMoreMoney':
current = float(line)
if current < 0:
print('Invalid operation!')
break
total += current
print(f'Increase: {current:.2f}')
line = input()
print(f'Total: {total:.2f}')
|
#
# PySNMP MIB module PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB
# Produced by pysmi-0.3.4 at Wed May 1 14:36:34 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion, ValueRangeConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "SingleValueConstraint", "ConstraintsUnion", "ValueRangeConstraint", "ValueSizeConstraint")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
pgDSLAMAlarmSeverity, pgDSLAMAlarm = mibBuilder.importSymbols("PAIRGAIN-COMMON-HD-MIB", "pgDSLAMAlarmSeverity", "pgDSLAMAlarm")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
Counter64, NotificationType, Unsigned32, MibScalar, MibTable, MibTableRow, MibTableColumn, Bits, Gauge32, Integer32, Counter32, iso, IpAddress, ObjectIdentity, TimeTicks, ModuleIdentity, MibIdentifier = mibBuilder.importSymbols("SNMPv2-SMI", "Counter64", "NotificationType", "Unsigned32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Bits", "Gauge32", "Integer32", "Counter32", "iso", "IpAddress", "ObjectIdentity", "TimeTicks", "ModuleIdentity", "MibIdentifier")
TextualConvention, DisplayString, RowStatus = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString", "RowStatus")
pgdsalsvMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1))
if mibBuilder.loadTexts: pgdsalsvMIB.setLastUpdated('9901141600Z')
if mibBuilder.loadTexts: pgdsalsvMIB.setOrganization('PairGain Technologies, INC.')
if mibBuilder.loadTexts: pgdsalsvMIB.setContactInfo(' Ken Huang Tel: +1 714-481-4543 Fax: +1 714-481-2114 E-mail: [email protected] ')
if mibBuilder.loadTexts: pgdsalsvMIB.setDescription('The MIB module defining objects for the alarm severity configuration and status management of a central DSLAM (Digital Subscriber Line Access Multiplexer), including from chassis power supply, fan status, to each channel/port related alarms in each HDSL/ADSL card inside the chassis. For HDSL alarm management: Please refer to Spec#157-1759-01 by Ken Huang for detail architecture model. For ADSL alarm management: Please refer to AdslLineMib(TR006) from adslForum for details architecture model. Naming Conventions: Atuc -- (ATU-C) ADSL modem at near (Central) end of line Atur -- (ATU-R) ADSL modem at Remote end of line ES -- Errored Second. Lof -- Loss of Frame Los -- Loss of Signal Lpr -- Loss of Power LOSW -- Loss of Sync Word UAS -- Unavailable Second ')
class PgDSLAMAlarmSeverity(Integer32):
subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))
namedValues = NamedValues(("disable", 1), ("minor", 2), ("major", 3), ("critical", 4))
class PgDSLAMAlarmStatus(Integer32):
subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))
namedValues = NamedValues(("noalarm", 1), ("minor", 2), ("major", 3), ("critical", 4), ("alarm", 5))
pgDSLAMChassisAlarmSeverity = MibIdentifier((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2))
pgDSLAMChassisPsAlarmSeverity = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 1), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMChassisPsAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisPsAlarmSeverity.setDescription('The Chassis Power failure Alarm Severity Setting.')
pgDSLAMChassisFanAlarmSeverity = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 2), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMChassisFanAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisFanAlarmSeverity.setDescription('The Chassis Fan failure Alarm Severity Setting.')
pgDSLAMChassisConfigChangeAlarmSeverity = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 3), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMChassisConfigChangeAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisConfigChangeAlarmSeverity.setDescription('The Chassis Config change Alarm Severity Setting.')
pgDSLAMChassisTempAlarmSeverity = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 4), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMChassisTempAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisTempAlarmSeverity.setDescription('The Chassis Temperature exceed threshold Alarm Severity Setting.')
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext.setDescription("This object contains an appropriate value to be used for pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex when creating entries in the alarmThresholdConfTable. The value '0' indicates that no unassigned entries are available. To obtain the pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext value for a new entry, the manager issues a management protocol retrieval operation to obtain the current value of this object. After each retrieval, the agent should modify the value to the next unassigned index.")
pgDSLAMHDSLSpanAlarmThresholdConfProfileTable = MibTable((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8), )
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileTable.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileTable.setDescription('The DSLAM HDSL Span Alarm Threshold Configuration Profile table.')
pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry = MibTableRow((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1), ).setIndexNames((0, "PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB", "pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex"))
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry.setDescription('Entry in the DSLAM HDSL Span Alarm Threshold Configuration Profile table.')
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255)))
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex.setDescription('This object is used by the line alarm Threshold configuration table in order to identify a row of this table')
pgDSLAMHDSLSpanMarginThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanMarginThreshold.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanMarginThreshold.setDescription('Sets the HDSL Margin threshold value.')
pgDSLAMHDSLSpanESThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanESThreshold.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanESThreshold.setDescription('Sets the HDSL Errored Seconds threshold value.')
pgDSLAMHDSLSpanUASThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanUASThreshold.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanUASThreshold.setDescription('Sets the HDSL Unavailable Seconds threshold value.')
pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 5), RowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus.setDescription('This object is used to create a new row or modify or delete an existing row in this table.')
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext = MibScalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 9), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext.setDescription("This object contains an appropriate value to be used for pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex when creating entries in the alarmSeverityConfTable. The value '0' indicates that no unassigned entries are available. To obtain the pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext value for a new entry, the manager issues a management protocol retrieval operation to obtain the current value of this object. After each retrieval, the agent should modify the value to the next unassigned index.")
pgDSLAMHDSLSpanAlarmSeverityConfProfileTable = MibTable((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10), )
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileTable.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileTable.setDescription('The DSLAM HDSL Span Alarm Severity Configuration Profile table.')
pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry = MibTableRow((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1), ).setIndexNames((0, "PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB", "pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex"))
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry.setDescription('Entry in the DSLAM HDSL Span Alarm Severity Configuration Profile table.')
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 255)))
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex.setDescription('This object is used by the line alarm severity configuration table in order to identify a row of this table')
pgDSLAMHDSLSpanLOSWAlarmSetting = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 2), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanLOSWAlarmSetting.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanLOSWAlarmSetting.setDescription('Sets the severity for Loss of Sync Word alarm.')
pgDSLAMHDSLSpanMarginAlarmSetting = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 3), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanMarginAlarmSetting.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanMarginAlarmSetting.setDescription('Sets the severity for Margin threshold exceeded alarm.')
pgDSLAMHDSLSpanESAlarmSetting = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 4), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanESAlarmSetting.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanESAlarmSetting.setDescription('Sets the severity for Errored Seconds threshold exceeded alarm.')
pgDSLAMHDSLSpanUASAlarmSetting = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 5), PgDSLAMAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanUASAlarmSetting.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanUASAlarmSetting.setDescription('Sets the severity for UAS threshold exceeded alarm.')
pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 6), RowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus.setDescription('This object is used to create a new row or modify or delete an existing row in this table.')
pgDSLAMADSLAtucAlarmTable = MibTable((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1), )
if mibBuilder.loadTexts: pgDSLAMADSLAtucAlarmTable.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucAlarmTable.setDescription('The DSLAM ADSL ATU-C Alarm indication table.')
pgDSLAMADSLAtucAlarmEntry = MibTableRow((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: pgDSLAMADSLAtucAlarmEntry.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucAlarmEntry.setDescription('Entry in the DSLAM ADSL ATU-C Alarm indication table.')
pgDSLAMADSLAtucLofAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 1), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucLofAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucLofAlarm.setDescription('ADSL loss of framing alarm on ATU-C ')
pgDSLAMADSLAtucLosAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 2), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucLosAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucLosAlarm.setDescription('ADSL loss of signal alarm on ATU-C ')
pgDSLAMADSLAtucLprAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 3), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucLprAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucLprAlarm.setDescription('ADSL loss of power alarm on ATU-C ')
pgDSLAMADSLAtucESAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 4), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucESAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucESAlarm.setDescription('ADSL Errored Second threshold exceeded alarm on ATU-C ')
pgDSLAMADSLAtucRateChangeAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 5), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucRateChangeAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucRateChangeAlarm.setDescription('ADSL Rate Changed alarm on ATU-C ')
pgDSLAMADSLAtucInitFailureAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 6), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAtucInitFailureAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAtucInitFailureAlarm.setDescription('ADSL initialization failed alarm on ATU-C ')
pgDSLAMADSLAturAlarmTable = MibTable((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2), )
if mibBuilder.loadTexts: pgDSLAMADSLAturAlarmTable.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturAlarmTable.setDescription('The DSLAM ADSL ATU-R Alarm indication table.')
pgDSLAMADSLAturAlarmEntry = MibTableRow((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: pgDSLAMADSLAturAlarmEntry.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturAlarmEntry.setDescription('Entry in the DSLAM ADSL ATU-R Alarm indication table.')
pgDSLAMADSLAturLofAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 1), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturLofAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturLofAlarm.setDescription('ADSL loss of framing alarm on ATU-R ')
pgDSLAMADSLAturLosAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 2), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturLosAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturLosAlarm.setDescription('ADSL loss of signal alarm on ATU-R ')
pgDSLAMADSLAturLprAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 3), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturLprAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturLprAlarm.setDescription('ADSL loss of power alarm on ATU-R ')
pgDSLAMADSLAturESAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 4), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturESAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturESAlarm.setDescription('ADSL Errored Second threshold exceeded alarm on ATU-R ')
pgDSLAMADSLAturRateChangeAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 5), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturRateChangeAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturRateChangeAlarm.setDescription('ADSL Rate Changed alarm on ATU-R ')
pgDSLAMADSLAturInitFailureAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 6), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMADSLAturInitFailureAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMADSLAturInitFailureAlarm.setDescription('ADSL initialization failed alarm on ATU-R ')
pgDSLAMChassisAlarmTable = MibTable((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3), )
if mibBuilder.loadTexts: pgDSLAMChassisAlarmTable.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisAlarmTable.setDescription('The DSLAM Chassis Alarm indication table.')
pgDSLAMChassisAlarmEntry = MibTableRow((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: pgDSLAMChassisAlarmEntry.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMChassisAlarmEntry.setDescription('Entry in the DSLAM Chassis Alarm indication table.')
pgDSLAMPSFailureAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 1), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMPSFailureAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMPSFailureAlarm.setDescription('chassis power supply failure alarm ')
pgDSLAMFanFailureAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 2), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMFanFailureAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMFanFailureAlarm.setDescription('chassis fan failure alarm ')
pgDSLAMConfigChangeAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 3), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMConfigChangeAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMConfigChangeAlarm.setDescription('chassis config changed alarm ')
pgDSLAMTempExceedThreshAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 4), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMTempExceedThreshAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMTempExceedThreshAlarm.setDescription('chassis temperature exceeded threshold ')
pgDSLAMLineCardDownAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 5), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMLineCardDownAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMLineCardDownAlarm.setDescription('the line card in the chassis is down ')
pgDSLAMCellBusDownAlarm = MibTableColumn((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 6), PgDSLAMAlarmStatus()).setMaxAccess("readonly")
if mibBuilder.loadTexts: pgDSLAMCellBusDownAlarm.setStatus('current')
if mibBuilder.loadTexts: pgDSLAMCellBusDownAlarm.setDescription('the cell bus in the chassis is down ')
mibBuilder.exportSymbols("PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB", pgDSLAMADSLAtucLprAlarm=pgDSLAMADSLAtucLprAlarm, pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry=pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry, pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext=pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext, pgDSLAMADSLAtucAlarmTable=pgDSLAMADSLAtucAlarmTable, pgDSLAMChassisFanAlarmSeverity=pgDSLAMChassisFanAlarmSeverity, pgDSLAMADSLAturAlarmEntry=pgDSLAMADSLAturAlarmEntry, PgDSLAMAlarmSeverity=PgDSLAMAlarmSeverity, PgDSLAMAlarmStatus=PgDSLAMAlarmStatus, pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext=pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext, pgDSLAMHDSLSpanAlarmSeverityConfProfileTable=pgDSLAMHDSLSpanAlarmSeverityConfProfileTable, pgDSLAMADSLAtucAlarmEntry=pgDSLAMADSLAtucAlarmEntry, pgDSLAMHDSLSpanESAlarmSetting=pgDSLAMHDSLSpanESAlarmSetting, pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry=pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry, pgDSLAMADSLAturLofAlarm=pgDSLAMADSLAturLofAlarm, pgDSLAMADSLAturLprAlarm=pgDSLAMADSLAturLprAlarm, pgDSLAMADSLAtucESAlarm=pgDSLAMADSLAtucESAlarm, pgDSLAMADSLAtucRateChangeAlarm=pgDSLAMADSLAtucRateChangeAlarm, pgDSLAMConfigChangeAlarm=pgDSLAMConfigChangeAlarm, pgDSLAMFanFailureAlarm=pgDSLAMFanFailureAlarm, pgDSLAMHDSLSpanAlarmThresholdConfProfileTable=pgDSLAMHDSLSpanAlarmThresholdConfProfileTable, pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus=pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus, pgdsalsvMIB=pgdsalsvMIB, pgDSLAMChassisAlarmSeverity=pgDSLAMChassisAlarmSeverity, pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex=pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex, pgDSLAMHDSLSpanUASAlarmSetting=pgDSLAMHDSLSpanUASAlarmSetting, pgDSLAMPSFailureAlarm=pgDSLAMPSFailureAlarm, pgDSLAMADSLAtucLofAlarm=pgDSLAMADSLAtucLofAlarm, pgDSLAMTempExceedThreshAlarm=pgDSLAMTempExceedThreshAlarm, pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex=pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex, pgDSLAMHDSLSpanLOSWAlarmSetting=pgDSLAMHDSLSpanLOSWAlarmSetting, pgDSLAMHDSLSpanMarginThreshold=pgDSLAMHDSLSpanMarginThreshold, pgDSLAMADSLAtucInitFailureAlarm=pgDSLAMADSLAtucInitFailureAlarm, pgDSLAMChassisAlarmTable=pgDSLAMChassisAlarmTable, pgDSLAMHDSLSpanESThreshold=pgDSLAMHDSLSpanESThreshold, pgDSLAMADSLAtucLosAlarm=pgDSLAMADSLAtucLosAlarm, pgDSLAMADSLAturRateChangeAlarm=pgDSLAMADSLAturRateChangeAlarm, pgDSLAMChassisAlarmEntry=pgDSLAMChassisAlarmEntry, pgDSLAMLineCardDownAlarm=pgDSLAMLineCardDownAlarm, pgDSLAMHDSLSpanMarginAlarmSetting=pgDSLAMHDSLSpanMarginAlarmSetting, pgDSLAMADSLAturInitFailureAlarm=pgDSLAMADSLAturInitFailureAlarm, pgDSLAMADSLAturLosAlarm=pgDSLAMADSLAturLosAlarm, pgDSLAMCellBusDownAlarm=pgDSLAMCellBusDownAlarm, pgDSLAMADSLAturESAlarm=pgDSLAMADSLAturESAlarm, pgDSLAMChassisPsAlarmSeverity=pgDSLAMChassisPsAlarmSeverity, pgDSLAMChassisTempAlarmSeverity=pgDSLAMChassisTempAlarmSeverity, pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus=pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus, pgDSLAMHDSLSpanUASThreshold=pgDSLAMHDSLSpanUASThreshold, pgDSLAMADSLAturAlarmTable=pgDSLAMADSLAturAlarmTable, pgDSLAMChassisConfigChangeAlarmSeverity=pgDSLAMChassisConfigChangeAlarmSeverity, PYSNMP_MODULE_ID=pgdsalsvMIB)
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(octet_string, integer, object_identifier) = mibBuilder.importSymbols('ASN1', 'OctetString', 'Integer', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_intersection, single_value_constraint, constraints_union, value_range_constraint, value_size_constraint) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsIntersection', 'SingleValueConstraint', 'ConstraintsUnion', 'ValueRangeConstraint', 'ValueSizeConstraint')
(if_index,) = mibBuilder.importSymbols('IF-MIB', 'ifIndex')
(pg_dslam_alarm_severity, pg_dslam_alarm) = mibBuilder.importSymbols('PAIRGAIN-COMMON-HD-MIB', 'pgDSLAMAlarmSeverity', 'pgDSLAMAlarm')
(module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup')
(counter64, notification_type, unsigned32, mib_scalar, mib_table, mib_table_row, mib_table_column, bits, gauge32, integer32, counter32, iso, ip_address, object_identity, time_ticks, module_identity, mib_identifier) = mibBuilder.importSymbols('SNMPv2-SMI', 'Counter64', 'NotificationType', 'Unsigned32', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'Bits', 'Gauge32', 'Integer32', 'Counter32', 'iso', 'IpAddress', 'ObjectIdentity', 'TimeTicks', 'ModuleIdentity', 'MibIdentifier')
(textual_convention, display_string, row_status) = mibBuilder.importSymbols('SNMPv2-TC', 'TextualConvention', 'DisplayString', 'RowStatus')
pgdsalsv_mib = module_identity((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1))
if mibBuilder.loadTexts:
pgdsalsvMIB.setLastUpdated('9901141600Z')
if mibBuilder.loadTexts:
pgdsalsvMIB.setOrganization('PairGain Technologies, INC.')
if mibBuilder.loadTexts:
pgdsalsvMIB.setContactInfo(' Ken Huang Tel: +1 714-481-4543 Fax: +1 714-481-2114 E-mail: [email protected] ')
if mibBuilder.loadTexts:
pgdsalsvMIB.setDescription('The MIB module defining objects for the alarm severity configuration and status management of a central DSLAM (Digital Subscriber Line Access Multiplexer), including from chassis power supply, fan status, to each channel/port related alarms in each HDSL/ADSL card inside the chassis. For HDSL alarm management: Please refer to Spec#157-1759-01 by Ken Huang for detail architecture model. For ADSL alarm management: Please refer to AdslLineMib(TR006) from adslForum for details architecture model. Naming Conventions: Atuc -- (ATU-C) ADSL modem at near (Central) end of line Atur -- (ATU-R) ADSL modem at Remote end of line ES -- Errored Second. Lof -- Loss of Frame Los -- Loss of Signal Lpr -- Loss of Power LOSW -- Loss of Sync Word UAS -- Unavailable Second ')
class Pgdslamalarmseverity(Integer32):
subtype_spec = Integer32.subtypeSpec + constraints_union(single_value_constraint(1, 2, 3, 4))
named_values = named_values(('disable', 1), ('minor', 2), ('major', 3), ('critical', 4))
class Pgdslamalarmstatus(Integer32):
subtype_spec = Integer32.subtypeSpec + constraints_union(single_value_constraint(1, 2, 3, 4, 5))
named_values = named_values(('noalarm', 1), ('minor', 2), ('major', 3), ('critical', 4), ('alarm', 5))
pg_dslam_chassis_alarm_severity = mib_identifier((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2))
pg_dslam_chassis_ps_alarm_severity = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 1), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMChassisPsAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisPsAlarmSeverity.setDescription('The Chassis Power failure Alarm Severity Setting.')
pg_dslam_chassis_fan_alarm_severity = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 2), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMChassisFanAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisFanAlarmSeverity.setDescription('The Chassis Fan failure Alarm Severity Setting.')
pg_dslam_chassis_config_change_alarm_severity = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 3), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMChassisConfigChangeAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisConfigChangeAlarmSeverity.setDescription('The Chassis Config change Alarm Severity Setting.')
pg_dslam_chassis_temp_alarm_severity = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 2, 4), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMChassisTempAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisTempAlarmSeverity.setDescription('The Chassis Temperature exceed threshold Alarm Severity Setting.')
pg_dslamhdsl_span_alarm_threshold_conf_profile_index_next = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 7), integer32().subtype(subtypeSpec=value_range_constraint(0, 255))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext.setDescription("This object contains an appropriate value to be used for pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex when creating entries in the alarmThresholdConfTable. The value '0' indicates that no unassigned entries are available. To obtain the pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext value for a new entry, the manager issues a management protocol retrieval operation to obtain the current value of this object. After each retrieval, the agent should modify the value to the next unassigned index.")
pg_dslamhdsl_span_alarm_threshold_conf_profile_table = mib_table((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileTable.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileTable.setDescription('The DSLAM HDSL Span Alarm Threshold Configuration Profile table.')
pg_dslamhdsl_span_alarm_threshold_conf_profile_entry = mib_table_row((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1)).setIndexNames((0, 'PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB', 'pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex'))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry.setDescription('Entry in the DSLAM HDSL Span Alarm Threshold Configuration Profile table.')
pg_dslamhdsl_span_alarm_threshold_conf_profile_index = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 1), integer32().subtype(subtypeSpec=value_range_constraint(0, 255)))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex.setDescription('This object is used by the line alarm Threshold configuration table in order to identify a row of this table')
pg_dslamhdsl_span_margin_threshold = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 2), integer32().subtype(subtypeSpec=value_range_constraint(0, 255))).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanMarginThreshold.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanMarginThreshold.setDescription('Sets the HDSL Margin threshold value.')
pg_dslamhdsl_span_es_threshold = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 3), integer32().subtype(subtypeSpec=value_range_constraint(0, 255))).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanESThreshold.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanESThreshold.setDescription('Sets the HDSL Errored Seconds threshold value.')
pg_dslamhdsl_span_uas_threshold = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 4), integer32().subtype(subtypeSpec=value_range_constraint(0, 255))).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanUASThreshold.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanUASThreshold.setDescription('Sets the HDSL Unavailable Seconds threshold value.')
pg_dslamhdsl_span_alarm_threshold_conf_profile_row_status = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 8, 1, 5), row_status()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus.setDescription('This object is used to create a new row or modify or delete an existing row in this table.')
pg_dslamhdsl_span_alarm_severity_conf_profile_index_next = mib_scalar((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 9), integer32().subtype(subtypeSpec=value_range_constraint(0, 255))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext.setDescription("This object contains an appropriate value to be used for pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex when creating entries in the alarmSeverityConfTable. The value '0' indicates that no unassigned entries are available. To obtain the pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext value for a new entry, the manager issues a management protocol retrieval operation to obtain the current value of this object. After each retrieval, the agent should modify the value to the next unassigned index.")
pg_dslamhdsl_span_alarm_severity_conf_profile_table = mib_table((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileTable.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileTable.setDescription('The DSLAM HDSL Span Alarm Severity Configuration Profile table.')
pg_dslamhdsl_span_alarm_severity_conf_profile_entry = mib_table_row((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1)).setIndexNames((0, 'PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB', 'pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex'))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry.setDescription('Entry in the DSLAM HDSL Span Alarm Severity Configuration Profile table.')
pg_dslamhdsl_span_alarm_severity_conf_profile_index = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 1), integer32().subtype(subtypeSpec=value_range_constraint(0, 255)))
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex.setDescription('This object is used by the line alarm severity configuration table in order to identify a row of this table')
pg_dslamhdsl_span_losw_alarm_setting = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 2), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanLOSWAlarmSetting.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanLOSWAlarmSetting.setDescription('Sets the severity for Loss of Sync Word alarm.')
pg_dslamhdsl_span_margin_alarm_setting = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 3), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanMarginAlarmSetting.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanMarginAlarmSetting.setDescription('Sets the severity for Margin threshold exceeded alarm.')
pg_dslamhdsl_span_es_alarm_setting = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 4), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanESAlarmSetting.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanESAlarmSetting.setDescription('Sets the severity for Errored Seconds threshold exceeded alarm.')
pg_dslamhdsl_span_uas_alarm_setting = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 5), pg_dslam_alarm_severity()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanUASAlarmSetting.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanUASAlarmSetting.setDescription('Sets the severity for UAS threshold exceeded alarm.')
pg_dslamhdsl_span_alarm_severity_conf_profile_row_status = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 3, 1, 10, 1, 6), row_status()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus.setDescription('This object is used to create a new row or modify or delete an existing row in this table.')
pg_dslamadsl_atuc_alarm_table = mib_table((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1))
if mibBuilder.loadTexts:
pgDSLAMADSLAtucAlarmTable.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucAlarmTable.setDescription('The DSLAM ADSL ATU-C Alarm indication table.')
pg_dslamadsl_atuc_alarm_entry = mib_table_row((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1)).setIndexNames((0, 'IF-MIB', 'ifIndex'))
if mibBuilder.loadTexts:
pgDSLAMADSLAtucAlarmEntry.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucAlarmEntry.setDescription('Entry in the DSLAM ADSL ATU-C Alarm indication table.')
pg_dslamadsl_atuc_lof_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 1), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLofAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLofAlarm.setDescription('ADSL loss of framing alarm on ATU-C ')
pg_dslamadsl_atuc_los_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 2), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLosAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLosAlarm.setDescription('ADSL loss of signal alarm on ATU-C ')
pg_dslamadsl_atuc_lpr_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 3), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLprAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucLprAlarm.setDescription('ADSL loss of power alarm on ATU-C ')
pg_dslamadsl_atuc_es_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 4), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucESAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucESAlarm.setDescription('ADSL Errored Second threshold exceeded alarm on ATU-C ')
pg_dslamadsl_atuc_rate_change_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 5), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucRateChangeAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucRateChangeAlarm.setDescription('ADSL Rate Changed alarm on ATU-C ')
pg_dslamadsl_atuc_init_failure_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 1, 1, 6), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucInitFailureAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAtucInitFailureAlarm.setDescription('ADSL initialization failed alarm on ATU-C ')
pg_dslamadsl_atur_alarm_table = mib_table((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2))
if mibBuilder.loadTexts:
pgDSLAMADSLAturAlarmTable.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturAlarmTable.setDescription('The DSLAM ADSL ATU-R Alarm indication table.')
pg_dslamadsl_atur_alarm_entry = mib_table_row((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1)).setIndexNames((0, 'IF-MIB', 'ifIndex'))
if mibBuilder.loadTexts:
pgDSLAMADSLAturAlarmEntry.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturAlarmEntry.setDescription('Entry in the DSLAM ADSL ATU-R Alarm indication table.')
pg_dslamadsl_atur_lof_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 1), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLofAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLofAlarm.setDescription('ADSL loss of framing alarm on ATU-R ')
pg_dslamadsl_atur_los_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 2), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLosAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLosAlarm.setDescription('ADSL loss of signal alarm on ATU-R ')
pg_dslamadsl_atur_lpr_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 3), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLprAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturLprAlarm.setDescription('ADSL loss of power alarm on ATU-R ')
pg_dslamadsl_atur_es_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 4), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturESAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturESAlarm.setDescription('ADSL Errored Second threshold exceeded alarm on ATU-R ')
pg_dslamadsl_atur_rate_change_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 5), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturRateChangeAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturRateChangeAlarm.setDescription('ADSL Rate Changed alarm on ATU-R ')
pg_dslamadsl_atur_init_failure_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 2, 1, 6), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMADSLAturInitFailureAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMADSLAturInitFailureAlarm.setDescription('ADSL initialization failed alarm on ATU-R ')
pg_dslam_chassis_alarm_table = mib_table((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3))
if mibBuilder.loadTexts:
pgDSLAMChassisAlarmTable.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisAlarmTable.setDescription('The DSLAM Chassis Alarm indication table.')
pg_dslam_chassis_alarm_entry = mib_table_row((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1)).setIndexNames((0, 'IF-MIB', 'ifIndex'))
if mibBuilder.loadTexts:
pgDSLAMChassisAlarmEntry.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMChassisAlarmEntry.setDescription('Entry in the DSLAM Chassis Alarm indication table.')
pg_dslamps_failure_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 1), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMPSFailureAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMPSFailureAlarm.setDescription('chassis power supply failure alarm ')
pg_dslam_fan_failure_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 2), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMFanFailureAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMFanFailureAlarm.setDescription('chassis fan failure alarm ')
pg_dslam_config_change_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 3), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMConfigChangeAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMConfigChangeAlarm.setDescription('chassis config changed alarm ')
pg_dslam_temp_exceed_thresh_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 4), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMTempExceedThreshAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMTempExceedThreshAlarm.setDescription('chassis temperature exceeded threshold ')
pg_dslam_line_card_down_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 5), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMLineCardDownAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMLineCardDownAlarm.setDescription('the line card in the chassis is down ')
pg_dslam_cell_bus_down_alarm = mib_table_column((1, 3, 6, 1, 4, 1, 927, 1, 9, 4, 3, 1, 6), pg_dslam_alarm_status()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
pgDSLAMCellBusDownAlarm.setStatus('current')
if mibBuilder.loadTexts:
pgDSLAMCellBusDownAlarm.setDescription('the cell bus in the chassis is down ')
mibBuilder.exportSymbols('PAIRGAIN-DSLAM-ALARM-SEVERITY-MIB', pgDSLAMADSLAtucLprAlarm=pgDSLAMADSLAtucLprAlarm, pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry=pgDSLAMHDSLSpanAlarmThresholdConfProfileEntry, pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext=pgDSLAMHDSLSpanAlarmThresholdConfProfileIndexNext, pgDSLAMADSLAtucAlarmTable=pgDSLAMADSLAtucAlarmTable, pgDSLAMChassisFanAlarmSeverity=pgDSLAMChassisFanAlarmSeverity, pgDSLAMADSLAturAlarmEntry=pgDSLAMADSLAturAlarmEntry, PgDSLAMAlarmSeverity=PgDSLAMAlarmSeverity, PgDSLAMAlarmStatus=PgDSLAMAlarmStatus, pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext=pgDSLAMHDSLSpanAlarmSeverityConfProfileIndexNext, pgDSLAMHDSLSpanAlarmSeverityConfProfileTable=pgDSLAMHDSLSpanAlarmSeverityConfProfileTable, pgDSLAMADSLAtucAlarmEntry=pgDSLAMADSLAtucAlarmEntry, pgDSLAMHDSLSpanESAlarmSetting=pgDSLAMHDSLSpanESAlarmSetting, pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry=pgDSLAMHDSLSpanAlarmSeverityConfProfileEntry, pgDSLAMADSLAturLofAlarm=pgDSLAMADSLAturLofAlarm, pgDSLAMADSLAturLprAlarm=pgDSLAMADSLAturLprAlarm, pgDSLAMADSLAtucESAlarm=pgDSLAMADSLAtucESAlarm, pgDSLAMADSLAtucRateChangeAlarm=pgDSLAMADSLAtucRateChangeAlarm, pgDSLAMConfigChangeAlarm=pgDSLAMConfigChangeAlarm, pgDSLAMFanFailureAlarm=pgDSLAMFanFailureAlarm, pgDSLAMHDSLSpanAlarmThresholdConfProfileTable=pgDSLAMHDSLSpanAlarmThresholdConfProfileTable, pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus=pgDSLAMHDSLSpanAlarmSeverityConfProfileRowStatus, pgdsalsvMIB=pgdsalsvMIB, pgDSLAMChassisAlarmSeverity=pgDSLAMChassisAlarmSeverity, pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex=pgDSLAMHDSLSpanAlarmThresholdConfProfileIndex, pgDSLAMHDSLSpanUASAlarmSetting=pgDSLAMHDSLSpanUASAlarmSetting, pgDSLAMPSFailureAlarm=pgDSLAMPSFailureAlarm, pgDSLAMADSLAtucLofAlarm=pgDSLAMADSLAtucLofAlarm, pgDSLAMTempExceedThreshAlarm=pgDSLAMTempExceedThreshAlarm, pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex=pgDSLAMHDSLSpanAlarmSeverityConfProfileIndex, pgDSLAMHDSLSpanLOSWAlarmSetting=pgDSLAMHDSLSpanLOSWAlarmSetting, pgDSLAMHDSLSpanMarginThreshold=pgDSLAMHDSLSpanMarginThreshold, pgDSLAMADSLAtucInitFailureAlarm=pgDSLAMADSLAtucInitFailureAlarm, pgDSLAMChassisAlarmTable=pgDSLAMChassisAlarmTable, pgDSLAMHDSLSpanESThreshold=pgDSLAMHDSLSpanESThreshold, pgDSLAMADSLAtucLosAlarm=pgDSLAMADSLAtucLosAlarm, pgDSLAMADSLAturRateChangeAlarm=pgDSLAMADSLAturRateChangeAlarm, pgDSLAMChassisAlarmEntry=pgDSLAMChassisAlarmEntry, pgDSLAMLineCardDownAlarm=pgDSLAMLineCardDownAlarm, pgDSLAMHDSLSpanMarginAlarmSetting=pgDSLAMHDSLSpanMarginAlarmSetting, pgDSLAMADSLAturInitFailureAlarm=pgDSLAMADSLAturInitFailureAlarm, pgDSLAMADSLAturLosAlarm=pgDSLAMADSLAturLosAlarm, pgDSLAMCellBusDownAlarm=pgDSLAMCellBusDownAlarm, pgDSLAMADSLAturESAlarm=pgDSLAMADSLAturESAlarm, pgDSLAMChassisPsAlarmSeverity=pgDSLAMChassisPsAlarmSeverity, pgDSLAMChassisTempAlarmSeverity=pgDSLAMChassisTempAlarmSeverity, pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus=pgDSLAMHDSLSpanAlarmThresholdConfProfileRowStatus, pgDSLAMHDSLSpanUASThreshold=pgDSLAMHDSLSpanUASThreshold, pgDSLAMADSLAturAlarmTable=pgDSLAMADSLAturAlarmTable, pgDSLAMChassisConfigChangeAlarmSeverity=pgDSLAMChassisConfigChangeAlarmSeverity, PYSNMP_MODULE_ID=pgdsalsvMIB)
|
"""
In Fractional Knapsack, we can break items for maximizing the total value of knapsack.
This problem in which we can break an item is also called the fractional knapsack problem.
Input:
Items as (value, weight) pairs
arr[] = {{60, 10}, {100, 20}, {120, 30}}
Knapsack Capacity, W = 50;
Output :
Maximum possible value = 240
By taking full items of 10 kg, 20 kg and
2/3rd of last item of 30 kg
SOLUTION:
Greedy approach:
Sort the items on the basis of value/weight ratio.
Then starting from the highest ratio, keep adding full items.
At the end, we might end up with remaining capacity < weight of item.
In that case, we'll just add as much of the item as we can.
That means we will add 'k' full items. (k+1)th item might be partial.
"""
def fractional_knapsack(items, capacity):
items.sort(reverse=True, key=lambda item: item[1] / item[0])
ans = 0
for item in items:
wt = item[0]
val = item[1]
if capacity >= wt:
capacity -= wt
ans += val
else:
fraction = capacity / wt
ans += val * fraction
break
return ans
def main():
weights = [10, 40, 20, 30]
values = [60, 40, 100, 120]
items = list(zip(weights, values))
capacity = 50
ans = fractional_knapsack(items, capacity)
print(ans)
main()
|
"""
In Fractional Knapsack, we can break items for maximizing the total value of knapsack.
This problem in which we can break an item is also called the fractional knapsack problem.
Input:
Items as (value, weight) pairs
arr[] = {{60, 10}, {100, 20}, {120, 30}}
Knapsack Capacity, W = 50;
Output :
Maximum possible value = 240
By taking full items of 10 kg, 20 kg and
2/3rd of last item of 30 kg
SOLUTION:
Greedy approach:
Sort the items on the basis of value/weight ratio.
Then starting from the highest ratio, keep adding full items.
At the end, we might end up with remaining capacity < weight of item.
In that case, we'll just add as much of the item as we can.
That means we will add 'k' full items. (k+1)th item might be partial.
"""
def fractional_knapsack(items, capacity):
items.sort(reverse=True, key=lambda item: item[1] / item[0])
ans = 0
for item in items:
wt = item[0]
val = item[1]
if capacity >= wt:
capacity -= wt
ans += val
else:
fraction = capacity / wt
ans += val * fraction
break
return ans
def main():
weights = [10, 40, 20, 30]
values = [60, 40, 100, 120]
items = list(zip(weights, values))
capacity = 50
ans = fractional_knapsack(items, capacity)
print(ans)
main()
|
"""
Profile ../profile-datasets-py/standard54lev_co2o3ref/004.py
file automaticaly created by prof_gen.py script
"""
self["ID"] = "../profile-datasets-py/standard54lev_co2o3ref/004.py"
self["Q"] = numpy.array([ 1.39778700e+00, 2.03491300e+00, 2.66180300e+00,
3.22654500e+00, 3.85401500e+00, 4.34052700e+00,
4.69179800e+00, 4.88755200e+00, 4.96474000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 4.96439400e+00, 4.92893600e+00,
4.88941100e+00, 4.83480500e+00, 4.77841800e+00,
4.64101000e+00, 4.51061600e+00, 4.26763700e+00,
4.03489800e+00, 4.00000000e+00, 4.00000000e+00,
4.21260500e+00, 5.10170300e+00, 8.35923200e+00,
1.84631700e+01, 4.19706800e+01, 1.09246300e+02,
2.69457900e+02, 4.97095800e+02, 7.75652800e+02,
1.12614500e+03, 1.56190000e+03, 2.11260500e+03,
2.76592300e+03, 3.41780100e+03, 4.17417700e+03,
4.89914400e+03, 5.79499100e+03, 6.62109200e+03,
7.39798300e+03, 8.10957300e+03, 8.78921900e+03,
9.76592500e+03, 1.06304400e+04, 1.13840900e+04,
1.20280100e+04, 1.25631900e+04, 1.29904100e+04])
self["P"] = numpy.array([ 5.00000000e-03, 1.31000000e-02, 3.04000000e-02,
6.44000000e-02, 1.26300000e-01, 2.32400000e-01,
4.05200000e-01, 6.74900000e-01, 1.08010000e+00,
1.66910000e+00, 2.50110000e+00, 3.64620000e+00,
5.18640000e+00, 7.21500000e+00, 9.83680000e+00,
1.31672000e+01, 1.73308000e+01, 2.24601000e+01,
2.86937000e+01, 3.61735000e+01, 4.50430000e+01,
5.54433000e+01, 6.75109000e+01, 8.13744000e+01,
9.71505000e+01, 1.14941500e+02, 1.34831800e+02,
1.56884600e+02, 1.81139400e+02, 2.07609200e+02,
2.36278400e+02, 2.67101200e+02, 3.00000000e+02,
3.34864800e+02, 3.71552900e+02, 4.09889300e+02,
4.49667700e+02, 4.90651600e+02, 5.32576900e+02,
5.75153800e+02, 6.18070600e+02, 6.60996500e+02,
7.03586300e+02, 7.45484100e+02, 7.86327800e+02,
8.25754600e+02, 8.63404700e+02, 8.98927500e+02,
9.31985300e+02, 9.62258700e+02, 9.89451000e+02,
1.01329200e+03, 1.03354400e+03, 1.05000000e+03])
self["CO2"] = numpy.array([ 374.361, 374.362, 374.364, 374.368, 374.374, 374.386,
374.404, 374.438, 374.512, 374.607, 374.67 , 374.737,
374.846, 374.995, 375.127, 375.218, 375.291, 375.41 ,
375.624, 375.952, 376.381, 376.895, 377.647, 378.552,
379.347, 379.9 , 380.384, 380.832, 381.22 , 381.611,
382.002, 382.357, 382.619, 382.773, 382.873, 382.951,
383.041, 383.136, 383.204, 383.246, 383.272, 383.299,
383.336, 383.368, 383.436, 383.542, 383.65 , 383.76 ,
383.854, 383.923, 383.971, 384.002, 384.02 , 384.029])
self["T"] = numpy.array([ 162.6005, 171.7471, 192.345 , 213.7842, 234.8825, 255.3642,
268.8923, 275.2017, 276.9052, 274.335 , 269.3013, 260.5016,
252.5294, 245.4093, 239.3723, 235.3422, 232.1803, 229.7783,
227.7823, 225.5364, 225.2 , 225.2 , 225.2 , 225.2 ,
225.2 , 225.2 , 225.2 , 225.2 , 225.2 , 225.2 ,
225.2 , 225.2 , 230.5416, 235.8209, 240.8924, 245.7277,
250.4222, 254.928 , 259.2692, 262.6463, 265.6418, 268.4782,
271.1214, 273.628 , 275.9393, 278.0935, 280.063 , 281.8498,
283.5083, 284.9763, 286.256 , 287.3495, 288.2582, 288.9836])
self["O3"] = numpy.array([ 0.296166 , 0.320791 , 0.380517 , 0.526745 , 0.769079 ,
1.074 , 1.47091 , 1.9911 , 2.78683 , 3.75638 ,
4.86419 , 5.95341 , 6.76255 , 7.10919 , 7.06019 ,
6.57373 , 5.68748 , 4.70472 , 3.86951 , 3.11078 ,
2.47791 , 1.90696 , 1.44011 , 1.02021 , 0.733271 ,
0.60395 , 0.489326 , 0.387625 , 0.28429 , 0.198038 ,
0.145005 , 0.109867 , 0.0862935, 0.0726348, 0.0631269,
0.0574107, 0.0537303, 0.0517329, 0.0502232, 0.049555 ,
0.0491254, 0.0475869, 0.0471438, 0.0461746, 0.0446271,
0.0430512, 0.0414805, 0.0399446, 0.037603 , 0.0343882,
0.0303838, 0.027844 , 0.0274868, 0.0273608])
self["CTP"] = 500.0
self["CFRACTION"] = 0.0
self["IDG"] = 4
self["ISH"] = 2
self["ELEVATION"] = 0.5
self["S2M"]["T"] = 287.2
self["S2M"]["Q"] = 12940.0
self["S2M"]["O"] = 0.02112
self["S2M"]["P"] = 1050.0
self["S2M"]["U"] = 4.0
self["S2M"]["V"] = 1.0
self["S2M"]["WFETC"] = 100000.0
self["SKIN"]["SURFTYPE"] = 0
self["SKIN"]["WATERTYPE"] = 0
self["SKIN"]["T"] = 295.2
self["SKIN"]["SALINITY"] = 35.0
self["SKIN"]["FOAM_FRACTION"] = 0.2
self["SKIN"]["FASTEM"] = numpy.array([ 3. , 5. , 15. , 0.1, 0.3])
self["ZENANGLE"] = 55.0
self["AZANGLE"] = 25.0
self["SUNZENANGLE"] = 15.0
self["SUNAZANGLE"] = 120.0
self["LATITUDE"] = 60.0
self["GAS_UNITS"] = 0
self["BE"] = 0.2
self["COSBK"] = 0.5
self["DATE"] = numpy.array([1966, 7, 1])
self["TIME"] = numpy.array([23, 0, 0])
|
"""
Profile ../profile-datasets-py/standard54lev_co2o3ref/004.py
file automaticaly created by prof_gen.py script
"""
self['ID'] = '../profile-datasets-py/standard54lev_co2o3ref/004.py'
self['Q'] = numpy.array([1.397787, 2.034913, 2.661803, 3.226545, 3.854015, 4.340527, 4.691798, 4.887552, 4.96474, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.964394, 4.928936, 4.889411, 4.834805, 4.778418, 4.64101, 4.510616, 4.267637, 4.034898, 4.0, 4.0, 4.212605, 5.101703, 8.359232, 18.46317, 41.97068, 109.2463, 269.4579, 497.0958, 775.6528, 1126.145, 1561.9, 2112.605, 2765.923, 3417.801, 4174.177, 4899.144, 5794.991, 6621.092, 7397.983, 8109.573, 8789.219, 9765.925, 10630.44, 11384.09, 12028.01, 12563.19, 12990.41])
self['P'] = numpy.array([0.005, 0.0131, 0.0304, 0.0644, 0.1263, 0.2324, 0.4052, 0.6749, 1.0801, 1.6691, 2.5011, 3.6462, 5.1864, 7.215, 9.8368, 13.1672, 17.3308, 22.4601, 28.6937, 36.1735, 45.043, 55.4433, 67.5109, 81.3744, 97.1505, 114.9415, 134.8318, 156.8846, 181.1394, 207.6092, 236.2784, 267.1012, 300.0, 334.8648, 371.5529, 409.8893, 449.6677, 490.6516, 532.5769, 575.1538, 618.0706, 660.9965, 703.5863, 745.4841, 786.3278, 825.7546, 863.4047, 898.9275, 931.9853, 962.2587, 989.451, 1013.292, 1033.544, 1050.0])
self['CO2'] = numpy.array([374.361, 374.362, 374.364, 374.368, 374.374, 374.386, 374.404, 374.438, 374.512, 374.607, 374.67, 374.737, 374.846, 374.995, 375.127, 375.218, 375.291, 375.41, 375.624, 375.952, 376.381, 376.895, 377.647, 378.552, 379.347, 379.9, 380.384, 380.832, 381.22, 381.611, 382.002, 382.357, 382.619, 382.773, 382.873, 382.951, 383.041, 383.136, 383.204, 383.246, 383.272, 383.299, 383.336, 383.368, 383.436, 383.542, 383.65, 383.76, 383.854, 383.923, 383.971, 384.002, 384.02, 384.029])
self['T'] = numpy.array([162.6005, 171.7471, 192.345, 213.7842, 234.8825, 255.3642, 268.8923, 275.2017, 276.9052, 274.335, 269.3013, 260.5016, 252.5294, 245.4093, 239.3723, 235.3422, 232.1803, 229.7783, 227.7823, 225.5364, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 225.2, 230.5416, 235.8209, 240.8924, 245.7277, 250.4222, 254.928, 259.2692, 262.6463, 265.6418, 268.4782, 271.1214, 273.628, 275.9393, 278.0935, 280.063, 281.8498, 283.5083, 284.9763, 286.256, 287.3495, 288.2582, 288.9836])
self['O3'] = numpy.array([0.296166, 0.320791, 0.380517, 0.526745, 0.769079, 1.074, 1.47091, 1.9911, 2.78683, 3.75638, 4.86419, 5.95341, 6.76255, 7.10919, 7.06019, 6.57373, 5.68748, 4.70472, 3.86951, 3.11078, 2.47791, 1.90696, 1.44011, 1.02021, 0.733271, 0.60395, 0.489326, 0.387625, 0.28429, 0.198038, 0.145005, 0.109867, 0.0862935, 0.0726348, 0.0631269, 0.0574107, 0.0537303, 0.0517329, 0.0502232, 0.049555, 0.0491254, 0.0475869, 0.0471438, 0.0461746, 0.0446271, 0.0430512, 0.0414805, 0.0399446, 0.037603, 0.0343882, 0.0303838, 0.027844, 0.0274868, 0.0273608])
self['CTP'] = 500.0
self['CFRACTION'] = 0.0
self['IDG'] = 4
self['ISH'] = 2
self['ELEVATION'] = 0.5
self['S2M']['T'] = 287.2
self['S2M']['Q'] = 12940.0
self['S2M']['O'] = 0.02112
self['S2M']['P'] = 1050.0
self['S2M']['U'] = 4.0
self['S2M']['V'] = 1.0
self['S2M']['WFETC'] = 100000.0
self['SKIN']['SURFTYPE'] = 0
self['SKIN']['WATERTYPE'] = 0
self['SKIN']['T'] = 295.2
self['SKIN']['SALINITY'] = 35.0
self['SKIN']['FOAM_FRACTION'] = 0.2
self['SKIN']['FASTEM'] = numpy.array([3.0, 5.0, 15.0, 0.1, 0.3])
self['ZENANGLE'] = 55.0
self['AZANGLE'] = 25.0
self['SUNZENANGLE'] = 15.0
self['SUNAZANGLE'] = 120.0
self['LATITUDE'] = 60.0
self['GAS_UNITS'] = 0
self['BE'] = 0.2
self['COSBK'] = 0.5
self['DATE'] = numpy.array([1966, 7, 1])
self['TIME'] = numpy.array([23, 0, 0])
|
# -*- coding: utf-8 -*-
def main():
n, m = map(int, input().split())
a = [int(input()) for _ in range(m)][::-1]
memo = [0 for _ in range(n + 1)]
for ai in a:
if memo[ai] == 1:
continue
else:
memo[ai] = 1
print(ai)
for index, m in enumerate(memo):
if index == 0:
continue
if m == 0:
print(index)
if __name__ == '__main__':
main()
|
def main():
(n, m) = map(int, input().split())
a = [int(input()) for _ in range(m)][::-1]
memo = [0 for _ in range(n + 1)]
for ai in a:
if memo[ai] == 1:
continue
else:
memo[ai] = 1
print(ai)
for (index, m) in enumerate(memo):
if index == 0:
continue
if m == 0:
print(index)
if __name__ == '__main__':
main()
|
#!/usr/bin/env python
# Parse key mapping
keymap = {}
with open('reverb.keymap', 'r') as mapping:
content = mapping.read()
for line in content.split("\n"):
if len(line) > 0:
fields = line.split("\t")
keymap[fields[0]] = fields[1] + "\t" + fields[2] + "\t" + fields[3]
# Training data
train = []
with open('reverb_correct_train.keys', 'r') as f:
keys = f.read()
for line in keys.split("\n"):
if len(line) > 0:
train.append("true\t" + keymap[line.replace('\\','\\\\')])
with open('reverb_wrong_train.keys', 'r') as f:
keys = f.read()
for line in keys.split("\n"):
if len(line) > 0:
train.append("false\t" + keymap[line.replace('\\','\\\\')])
with open("reverb_train.tab", "w") as f:
f.write("\n".join(train))
# Test data
test = []
with open('reverb_correct_test.keys', 'r') as f:
keys = f.read()
for line in keys.split("\n"):
if len(line) > 0:
test.append("true\t" + keymap[line.replace('\\','\\\\')])
with open('reverb_wrong_test.keys', 'r') as f:
keys = f.read()
for line in keys.split("\n"):
if len(line) > 0:
test.append("false\t" + keymap[line.replace('\\','\\\\')])
with open("reverb_test.tab", "w") as f:
f.write("\n".join(test))
|
keymap = {}
with open('reverb.keymap', 'r') as mapping:
content = mapping.read()
for line in content.split('\n'):
if len(line) > 0:
fields = line.split('\t')
keymap[fields[0]] = fields[1] + '\t' + fields[2] + '\t' + fields[3]
train = []
with open('reverb_correct_train.keys', 'r') as f:
keys = f.read()
for line in keys.split('\n'):
if len(line) > 0:
train.append('true\t' + keymap[line.replace('\\', '\\\\')])
with open('reverb_wrong_train.keys', 'r') as f:
keys = f.read()
for line in keys.split('\n'):
if len(line) > 0:
train.append('false\t' + keymap[line.replace('\\', '\\\\')])
with open('reverb_train.tab', 'w') as f:
f.write('\n'.join(train))
test = []
with open('reverb_correct_test.keys', 'r') as f:
keys = f.read()
for line in keys.split('\n'):
if len(line) > 0:
test.append('true\t' + keymap[line.replace('\\', '\\\\')])
with open('reverb_wrong_test.keys', 'r') as f:
keys = f.read()
for line in keys.split('\n'):
if len(line) > 0:
test.append('false\t' + keymap[line.replace('\\', '\\\\')])
with open('reverb_test.tab', 'w') as f:
f.write('\n'.join(test))
|
# AUTHOR: Akash Rajak
# Python3 Concept: Matrix Transpose
# GITHUB: https://github.com/akash435
# Add your python3 concept below
def matrix_transpose():
for i in range(len(A)):
for j in range(len(A[0])):
res[i][j] = A[j][i]
A=[]
print("Enter N:")
n = int(input())
print("Enter Matrix A:")
for i in range(0, n):
temp = []
for j in range(0, n):
x = int(input())
temp.append(x)
A.append(temp)
res = []
for i in range(0, n):
temp = []
for j in range(0, n):
temp.append(0)
res.append(temp)
matrix_transpose()
for r in res:
print(r)
|
def matrix_transpose():
for i in range(len(A)):
for j in range(len(A[0])):
res[i][j] = A[j][i]
a = []
print('Enter N:')
n = int(input())
print('Enter Matrix A:')
for i in range(0, n):
temp = []
for j in range(0, n):
x = int(input())
temp.append(x)
A.append(temp)
res = []
for i in range(0, n):
temp = []
for j in range(0, n):
temp.append(0)
res.append(temp)
matrix_transpose()
for r in res:
print(r)
|
first_name = "Bob"
last_name = "Daily"
#first_name[0] = "R"
fixed_first_name = "R" + first_name[-2:]
print(fixed_first_name)
|
first_name = 'Bob'
last_name = 'Daily'
fixed_first_name = 'R' + first_name[-2:]
print(fixed_first_name)
|
class Solution(object):
def uniquePathsWithObstacles(self, grid):
"""
:type obstacleGrid: List[List[int]]
:rtype: int
"""
m = len(grid)
n = len(grid[0])
dp = [[0 for _ in range(n)] for _ in range(m)]
for i in range(n):
if grid[0][i] == 1:
break
dp[0][i] = 1
for i in range(m):
if grid[i][0] == 1:
break
dp[i][0] = 1
for i in range(1, m):
for j in range(1, n):
if grid[i][j] != 1:
dp[i][j] = dp[i-1][j] + dp[i][j-1]
return dp[-1][-1]
|
class Solution(object):
def unique_paths_with_obstacles(self, grid):
"""
:type obstacleGrid: List[List[int]]
:rtype: int
"""
m = len(grid)
n = len(grid[0])
dp = [[0 for _ in range(n)] for _ in range(m)]
for i in range(n):
if grid[0][i] == 1:
break
dp[0][i] = 1
for i in range(m):
if grid[i][0] == 1:
break
dp[i][0] = 1
for i in range(1, m):
for j in range(1, n):
if grid[i][j] != 1:
dp[i][j] = dp[i - 1][j] + dp[i][j - 1]
return dp[-1][-1]
|
del_items(0x80127F74)
SetType(0x80127F74, "struct Creds CreditsTitle[6]")
del_items(0x8012811C)
SetType(0x8012811C, "struct Creds CreditsSubTitle[28]")
del_items(0x801285B8)
SetType(0x801285B8, "struct Creds CreditsText[35]")
del_items(0x801286D0)
SetType(0x801286D0, "int CreditsTable[224]")
del_items(0x80129900)
SetType(0x80129900, "struct DIRENTRY card_dir[16][2]")
del_items(0x80129E00)
SetType(0x80129E00, "struct file_header card_header[16][2]")
del_items(0x80129824)
SetType(0x80129824, "struct sjis sjis_table[37]")
del_items(0x8012ECEC)
SetType(0x8012ECEC, "unsigned char save_buffer[106496]")
del_items(0x8012EC68)
SetType(0x8012EC68, "struct FeTable McLoadGameMenu")
del_items(0x8012EC48)
SetType(0x8012EC48, "char *CharFileList[5]")
del_items(0x8012EC5C)
SetType(0x8012EC5C, "char *Classes[3]")
del_items(0x8012EC84)
SetType(0x8012EC84, "struct FeTable McLoadCharMenu")
del_items(0x8012ECA0)
SetType(0x8012ECA0, "struct FeTable McLoadCard1Menu")
del_items(0x8012ECBC)
SetType(0x8012ECBC, "struct FeTable McLoadCard2Menu")
|
del_items(2148695924)
set_type(2148695924, 'struct Creds CreditsTitle[6]')
del_items(2148696348)
set_type(2148696348, 'struct Creds CreditsSubTitle[28]')
del_items(2148697528)
set_type(2148697528, 'struct Creds CreditsText[35]')
del_items(2148697808)
set_type(2148697808, 'int CreditsTable[224]')
del_items(2148702464)
set_type(2148702464, 'struct DIRENTRY card_dir[16][2]')
del_items(2148703744)
set_type(2148703744, 'struct file_header card_header[16][2]')
del_items(2148702244)
set_type(2148702244, 'struct sjis sjis_table[37]')
del_items(2148723948)
set_type(2148723948, 'unsigned char save_buffer[106496]')
del_items(2148723816)
set_type(2148723816, 'struct FeTable McLoadGameMenu')
del_items(2148723784)
set_type(2148723784, 'char *CharFileList[5]')
del_items(2148723804)
set_type(2148723804, 'char *Classes[3]')
del_items(2148723844)
set_type(2148723844, 'struct FeTable McLoadCharMenu')
del_items(2148723872)
set_type(2148723872, 'struct FeTable McLoadCard1Menu')
del_items(2148723900)
set_type(2148723900, 'struct FeTable McLoadCard2Menu')
|
# MIT OC - CS600 - Introduction to Computer Science and Programming
# Problem Set 1: 3 Simple Problems - Problem 2 Pay off debt in 1 year
# Name: Luke Young
# Collaborators: None
# Time Spent: 01:00 (hr:min)
# 2018 04 22 20:27
# Program: Finding the minimum payment required to pay off the debt
#
# Write a program that does the following:
# Use raw_input() to ask for the following three floating point numbers:
# 1. the outstanding balance on the credit card
# 2. annual interest rate
#
#
# Print out the fixed minimum monthly payment, number of months
##(at most 12 and possibly less than 12) it takes to pay off the debt,
##and the balance (likely to be a negative number).
##Assume that the interest is compounded monthly according to the balance
##at the start of the month (before the payment for that month is made).
##The monthly payment must be a multiple of $10 and is the same for all months.
##Notice that it is possible for the balance to become negative
##using this payment scheme. In short:
##Monthly interest rate = Annual interest rate / 12.0
##Updated balance each month = Previous balance * (1 + Monthly interest rate)
## - Minimum monthly payment .
balance = 0.0
apr = 0.0
balance = float(raw_input("What is your current Balance? "))
apr = float(raw_input("What is the annual interest rate as a decimal? "))
minPay = 0.0
principle = 0.0
endBalance = 0.0
paid = False
# main algorithm
while paid == False:
tempBalance = balance
minPay += 10
month = 1
for month in range(1, 13): #runs 12 times
principle = round((minPay - (apr / 12 * tempBalance)), 2)
tempBalance = round((tempBalance - principle), 2)
if tempBalance < 0:
endBalance = tempBalance
paid = True
break
# end for loop
# print("Ending balance was: " + str(tempBalance))
# end while loop
# print results
print("RESULT")
print("Monthly payment to pay off debt in 1 year: " + str(minPay))
print("Number of months needed: " + str(month))
print("Balance " + str(endBalance))
|
balance = 0.0
apr = 0.0
balance = float(raw_input('What is your current Balance? '))
apr = float(raw_input('What is the annual interest rate as a decimal? '))
min_pay = 0.0
principle = 0.0
end_balance = 0.0
paid = False
while paid == False:
temp_balance = balance
min_pay += 10
month = 1
for month in range(1, 13):
principle = round(minPay - apr / 12 * tempBalance, 2)
temp_balance = round(tempBalance - principle, 2)
if tempBalance < 0:
end_balance = tempBalance
paid = True
break
print('RESULT')
print('Monthly payment to pay off debt in 1 year: ' + str(minPay))
print('Number of months needed: ' + str(month))
print('Balance ' + str(endBalance))
|
"""
Othello game written in Python using Textual as TUI in Bash for Linux.
"""
# Setup game
# Ask player name
# Ask white or black
# Initial board
# Check if player can make a move
# Take player input for placing piece
# Check if move is valid input
# Place piece and flip opponents pieces affected
# Update score
# Keep track on remaining possible turns
# Skip players turn if no move is possible
# Declare winner when all pieces are placed or no more moves possible
# Play again?
def main():
print('Lets play Tothello')
if __name__ == "__main__":
main()
|
"""
Othello game written in Python using Textual as TUI in Bash for Linux.
"""
def main():
print('Lets play Tothello')
if __name__ == '__main__':
main()
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
def get_func(tag):
def func(s):
group = tag, s
return group
return func
|
def get_func(tag):
def func(s):
group = (tag, s)
return group
return func
|
"""Make an infinite loop.
Write a loop which has no end clause.
Source: programming-idioms.org
"""
# Implementation author: JackStouffer
# Created on 2016-02-18T16:57:59.907374Z
# Last modified on 2016-02-18T16:57:59.907374Z
# Version 1
while True:
pass
|
"""Make an infinite loop.
Write a loop which has no end clause.
Source: programming-idioms.org
"""
while True:
pass
|
# container-service-extension
# Copyright (c) 2019 VMware, Inc. All Rights Reserved.
# SPDX-License-Identifier: BSD-2-Clause
# End point of Vmware Analytics staging server
# TODO() : This URL should reflect production server during release
VAC_URL = "https://vcsa.vmware.com/ph-stg/api/hyper/send/"
# Value of collector id that is required as part of HTTP request
# to post sample data to analytics server
COLLECTOR_ID = "CSE.2_6"
|
vac_url = 'https://vcsa.vmware.com/ph-stg/api/hyper/send/'
collector_id = 'CSE.2_6'
|
quit = False
flag = False
while not quit :
num = int(input(""))
if num == 42:
quit = True;
else:
print(num)
|
quit = False
flag = False
while not quit:
num = int(input(''))
if num == 42:
quit = True
else:
print(num)
|
# -*- coding: utf-8 -*-
'''
File name: code\47smooth_triangular_numbers\sol_581.py
Author: Vaidic Joshi
Date created: Oct 20, 2018
Python Version: 3.x
'''
# Solution to Project Euler Problem #581 :: 47-smooth triangular numbers
#
# For more information see:
# https://projecteuler.net/problem=581
# Problem Statement
'''
A number is p-smooth if it has no prime factors larger than p.
Let T be the sequence of triangular numbers, ie T(n)=n(n+1)/2.
Find the sum of all indices n such that T(n) is 47-smooth.
'''
# Solution
# Solution Approach
'''
'''
|
"""
File name: code'smooth_triangular_numbers\\sol_581.py
Author: Vaidic Joshi
Date created: Oct 20, 2018
Python Version: 3.x
"""
'\nA number is p-smooth if it has no prime factors larger than p.\nLet T be the sequence of triangular numbers, ie T(n)=n(n+1)/2.\nFind the sum of all indices n such that T(n) is 47-smooth.\n'
'\n'
|
# throws KeyError
students = {'John': 18, 'Jack': 19}
print(students['Joe'])
# try/catch KeyError
students = {'John': 18, 'Jack': 19}
try:
print(students['Joe'])
except KeyError:
print('you tried to access an entry that does not exists')
|
students = {'John': 18, 'Jack': 19}
print(students['Joe'])
students = {'John': 18, 'Jack': 19}
try:
print(students['Joe'])
except KeyError:
print('you tried to access an entry that does not exists')
|
"""
Write a function that reverses characters in (possibly nested) parentheses in the input string.
Input strings will always be well-formed with matching ()s.
Example
For inputString = "(bar)", the output should be
reverseInParentheses(inputString) = "rab";
For inputString = "foo(bar)baz", the output should be
reverseInParentheses(inputString) = "foorabbaz";
For inputString = "foo(bar)baz(blim)", the output should be
reverseInParentheses(inputString) = "foorabbazmilb";
For inputString = "foo(bar(baz))blim", the output should be
reverseInParentheses(inputString) = "foobazrabblim".
Because "foo(bar(baz))blim" becomes "foo(barzab)blim" and then "foobazrabblim".
"""
def reverseInParentheses(inputString):
char = list(inputString)
#stack
open_bracket = []
for i, c in enumerate(inputString):
if c == '(':
open_bracket.append(i)
elif c == ')':
j = open_bracket.pop()
char[j:i] = char[i:j:-1]
# review this stuff
return ''.join(c for c in char if c not in '()')
inputString = "(bar)"
print(reverseInParentheses(inputString))# = "rab";
inputString = "foo(bar)baz"
print(reverseInParentheses(inputString))# = "foorabbaz";
inputString = "foo(bar)baz(blim)"
print(reverseInParentheses(inputString))# = "foorabbazmilb";
inputString = "foo(bar(baz))blim"
print(reverseInParentheses(inputString))# = "foobazrabblim".
# Because "foo(bar(baz))blim" becomes "foo(barzab)blim" and then "foobazrabblim".
|
"""
Write a function that reverses characters in (possibly nested) parentheses in the input string.
Input strings will always be well-formed with matching ()s.
Example
For inputString = "(bar)", the output should be
reverseInParentheses(inputString) = "rab";
For inputString = "foo(bar)baz", the output should be
reverseInParentheses(inputString) = "foorabbaz";
For inputString = "foo(bar)baz(blim)", the output should be
reverseInParentheses(inputString) = "foorabbazmilb";
For inputString = "foo(bar(baz))blim", the output should be
reverseInParentheses(inputString) = "foobazrabblim".
Because "foo(bar(baz))blim" becomes "foo(barzab)blim" and then "foobazrabblim".
"""
def reverse_in_parentheses(inputString):
char = list(inputString)
open_bracket = []
for (i, c) in enumerate(inputString):
if c == '(':
open_bracket.append(i)
elif c == ')':
j = open_bracket.pop()
char[j:i] = char[i:j:-1]
return ''.join((c for c in char if c not in '()'))
input_string = '(bar)'
print(reverse_in_parentheses(inputString))
input_string = 'foo(bar)baz'
print(reverse_in_parentheses(inputString))
input_string = 'foo(bar)baz(blim)'
print(reverse_in_parentheses(inputString))
input_string = 'foo(bar(baz))blim'
print(reverse_in_parentheses(inputString))
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# file_client_args = dict(backend='disk',)
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(
backend='memcached',
server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf',
client_cfg='/mnt/lustre/share/memcached_client/client.conf',
sys_path='/mnt/lustre/share/pymc/py3',
)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True,
file_client_args=file_client_args),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/panoptic_train2017.json',
img_prefix=data_root + 'train2017/',
seg_prefix=data_root + 'annotations/panoptic_train2017/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/panoptic_val2017.json',
img_prefix=data_root + 'val2017/',
seg_prefix=data_root + 'annotations/panoptic_val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/panoptic_val2017.json',
img_prefix=data_root + 'val2017/',
seg_prefix=data_root + 'annotations/panoptic_val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric=['pq'])
# optimizer
# this is different from the original 1x schedule that use SGD
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.05,
paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.25)}))
optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
# learning policy
# Experiments show that using step=[9, 11] has higher performance
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
|
_base_ = '../_base_/default_runtime.py'
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
file_client_args = dict(backend='memcached', server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf', client_cfg='/mnt/lustre/share/memcached_client/client.conf', sys_path='/mnt/lustre/share/pymc/py3')
train_pipeline = [dict(type='LoadImageFromFile', file_client_args=file_client_args), dict(type='LoadPanopticAnnotations', with_bbox=True, with_mask=True, with_seg=True, file_client_args=file_client_args), dict(type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])]
test_pipeline = [dict(type='LoadImageFromFile', file_client_args=file_client_args), dict(type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img'])])]
data = dict(samples_per_gpu=2, workers_per_gpu=2, train=dict(type='RepeatDataset', times=3, dataset=dict(type=dataset_type, ann_file=data_root + 'annotations/panoptic_train2017.json', img_prefix=data_root + 'train2017/', seg_prefix=data_root + 'annotations/panoptic_train2017/', pipeline=train_pipeline)), val=dict(type=dataset_type, ann_file=data_root + 'annotations/panoptic_val2017.json', img_prefix=data_root + 'val2017/', seg_prefix=data_root + 'annotations/panoptic_val2017/', pipeline=test_pipeline), test=dict(type=dataset_type, ann_file=data_root + 'annotations/panoptic_val2017.json', img_prefix=data_root + 'val2017/', seg_prefix=data_root + 'annotations/panoptic_val2017/', pipeline=test_pipeline))
evaluation = dict(interval=1, metric=['pq'])
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.05, paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.25)}))
optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
lr_config = dict(policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.001, step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
|
STATUS_MAPPER = [
"Success",
"Unknown HCI Command",
"Unknown Connection Identifier",
"Hardware Failure",
"Page Timeout",
"Authentication Failure",
"PIN or Key Missing",
"Memory Capacity Exceeded",
"Connection Timeout",
"Connection Limit Exceeded",
"Synchronous Connection Limit to a Device Exceeded",
"ACL Connection Already Exists",
"Command Disallowed",
"Connection Rejected due to Limited Resources",
"Connection Rejected due to Security Reasons",
"Connection Rejected due to Unacceptable BD_ADDR",
"Connection Accept Timeout Exceeded",
"Unsupported Feature or Parameter Value",
"Invalid HCI Command Parameters",
"Remote User Terminated Connection",
"Remote Device Terminated due to Low Resources",
"Remote Device Terminated due to Power Off",
"Connection Terminated By Local Host",
"Repeated Attempts",
"Pairing Not Allowed",
"Unknown LMP PDU",
"Unsupported Remote Feature / Unsupported LMP Feature",
"SCO Offset Rejected",
"SCO Interval Rejected",
"SCO Air Mode Rejected",
"Invalid LMP Parameters / Invalid LL Parameters",
"Unspecified Error",
"Unsupported LMP Parameter Value / Unsupported LL Parameter Value",
"Role Change Not Allowed",
"LMP Response Timeout / LL Response Timeout",
"LMP Error Transaction Collision",
"LMP PDU Not Allowed",
"Encryption Mode Not Acceptable",
"Link Key cannot be Changed",
"Requested QoS Not Supported",
"Instant Passed",
"Pairing With Unit Key Not Supported",
"Different Transaction Collision",
"Reserved",
"QoS Unacceptable Parameter",
"QoS Rejected",
"Channel Classification Not Supported",
"Insufficient Security",
"Parameter Out Of Manadatory Range",
"Reserved",
"Role Switch Pending",
"Reserved",
"Reserved Slot Violation",
"Role Switch Failed",
"Extended Inquiry Response Too Large",
"Secure Simple Pairing Not Supported By Host",
"Host Busy - Pairing",
"Connection Rejected due to No Suitable Channel Found",
"Controller Busy",
"Unacceptable Connection Parameters" ,
"Directed Advertising Timeout",
"Connection Terminated due to MIC Failure",
"Connection Failed to be Established",
"MAC Connection Failed",
"Coarse Clock Adjustment Rejected but Will Try to Adjust Using Clock Dragging"
]
|
status_mapper = ['Success', 'Unknown HCI Command', 'Unknown Connection Identifier', 'Hardware Failure', 'Page Timeout', 'Authentication Failure', 'PIN or Key Missing', 'Memory Capacity Exceeded', 'Connection Timeout', 'Connection Limit Exceeded', 'Synchronous Connection Limit to a Device Exceeded', 'ACL Connection Already Exists', 'Command Disallowed', 'Connection Rejected due to Limited Resources', 'Connection Rejected due to Security Reasons', 'Connection Rejected due to Unacceptable BD_ADDR', 'Connection Accept Timeout Exceeded', 'Unsupported Feature or Parameter Value', 'Invalid HCI Command Parameters', 'Remote User Terminated Connection', 'Remote Device Terminated due to Low Resources', 'Remote Device Terminated due to Power Off', 'Connection Terminated By Local Host', 'Repeated Attempts', 'Pairing Not Allowed', 'Unknown LMP PDU', 'Unsupported Remote Feature / Unsupported LMP Feature', 'SCO Offset Rejected', 'SCO Interval Rejected', 'SCO Air Mode Rejected', 'Invalid LMP Parameters / Invalid LL Parameters', 'Unspecified Error', 'Unsupported LMP Parameter Value / Unsupported LL Parameter Value', 'Role Change Not Allowed', 'LMP Response Timeout / LL Response Timeout', 'LMP Error Transaction Collision', 'LMP PDU Not Allowed', 'Encryption Mode Not Acceptable', 'Link Key cannot be Changed', 'Requested QoS Not Supported', 'Instant Passed', 'Pairing With Unit Key Not Supported', 'Different Transaction Collision', 'Reserved', 'QoS Unacceptable Parameter', 'QoS Rejected', 'Channel Classification Not Supported', 'Insufficient Security', 'Parameter Out Of Manadatory Range', 'Reserved', 'Role Switch Pending', 'Reserved', 'Reserved Slot Violation', 'Role Switch Failed', 'Extended Inquiry Response Too Large', 'Secure Simple Pairing Not Supported By Host', 'Host Busy - Pairing', 'Connection Rejected due to No Suitable Channel Found', 'Controller Busy', 'Unacceptable Connection Parameters', 'Directed Advertising Timeout', 'Connection Terminated due to MIC Failure', 'Connection Failed to be Established', 'MAC Connection Failed', 'Coarse Clock Adjustment Rejected but Will Try to Adjust Using Clock Dragging']
|
# __init__
__version__ = '1.1.0'
|
__version__ = '1.1.0'
|
def test_get_public_key(cmd, button, model):
pub_key, address = cmd.get_public_key(
bip32_path="44'/5741565'/0'/0'/1'",
network_byte='V',
display=True,
button=button,
model=model
) # type: bytes, bytes
assert len(pub_key) == 32
assert len(address) == 35
|
def test_get_public_key(cmd, button, model):
(pub_key, address) = cmd.get_public_key(bip32_path="44'/5741565'/0'/0'/1'", network_byte='V', display=True, button=button, model=model)
assert len(pub_key) == 32
assert len(address) == 35
|
x = [1,2,3]
name = "/tests/fixtures/data/names/{name_id}.txt"
output = "/tests/fixtures/data/salutations/{name_id}-{x}.txt"
def main():
return "Hello {name} for the {x} time!".format(name=name, x=x)
|
x = [1, 2, 3]
name = '/tests/fixtures/data/names/{name_id}.txt'
output = '/tests/fixtures/data/salutations/{name_id}-{x}.txt'
def main():
return 'Hello {name} for the {x} time!'.format(name=name, x=x)
|
# Skin info and colours
theme_name = "Future Bloo"
theme_author = "Lucas."
theme_version = "1.0"
theme_bio = "Bloo" # A long bio will get cut off, keep it simple.
window_theme = "Black"
button_colour = "black"
attacks_theme = {"background": "Black", "button_colour": ('black', 'cyan')}
banner_size = (600, 100)
banner_padding = ((75, 15), 0)
# Command Line colours
menu1 = "cyan"
menu2 = "white"
# Button/Banner Images (Base64)
rtb_icon = b'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button_leaver = 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'
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'
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'
button_role_mass_mentioner = 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d14V1Zkz42/fvEpnd1FVyvLcHzzEP0ajr+eCVMPbNonUZl316950gkgnz5WeQFV9J+6TzaL51XkB+qqmatskLl/H/94wGXinIDewepgXvuFLp2bkZtrke6PSSOmYBRXlU0GCTdHpJHHo1RVmk9u3srIpXkcEcoFLKi0n97+sBBEo/HiqjOPM5a/sh9Ma8upnLO9IJayuv1Ys6dhWiqJzrjfOTA4T0Lyng8MO+CLm3x7uvZJaLUkKO+tAb2+XyYl59lmYyN9R0/blqH/8E7C5rFuTT6/X6Lzvtu77Ikw6Z1OJ+4n7I75/Z+Yll4l7XcE4vm1cd9tyOa6jHLq3pVp9/vR/7sKqufuXU+9wcrxpBb7u6buvansR7n4icI/tfPDl5hVlRUSNM00TQNKSWBQEfooPHa/0QPleHcv4c+Lz+Do7mxW39YujyEZ8wiPnEq0uXGvXkdgTdewFG319oUkiO8sROmED3xVKRQcDTWUvLi0zga9/c8QqwoyEKTQwEShZTp4FX3/nz4tHOIjZuMFJLqewszNxqNEg6Hs/5LZ8RiMdrb23E6nZSXl2dN2NbW1i7PhMNhotFo3gsPBoMHbKehoQEpJRUVFcTj8YJlCj3buS2fz4fL5aK1tRWXy0VZWVnRNuvr61FVNduf3D5lTNLctmOxWDbY43K58Pl8RSPshcz9WCyWZ/q6XC48Hg8+ny97T9M0mpqa8niWe9/j8VhLYOl3EolEssHDDE3hcBgpJVVVVd3W1/l+NBolGo1m6wwGgyQSCbRPWlCb66lMb1rp3J/MKkEuzwrxt/OE0dbWlg2OJRKJAiZ0wf23dGSy6EYARDKO/52l1sL+cSeSHH082pCjcO7agrO2BjQdI1SCdsQQ9D5lYBqojXUE33wRR1Ndj5erjPIqUsNGYfpDVrAqG8hK/9/soFUKBUdbE65dW1BiPVjCkN3wA/D7fPgzA6lAGZ/Xi8/rzfvd43ZT3bdvl2eCgQDBQKDgeyjWTmVFRbe0FLpfsC3Io6tYfVWVlV3uFetTXh10z88umtjtzkujWqwOp8NRsP1C9/PeSae2MuV6U582ZSbhXz+OuncnVddfiFK724pLPrjIWh/+ZHveuy/Yn2742x3vvnIfGEBtbSL018dx7t1B9DvnYJRVkhw7keSYE9LH99LH+ZIJPBvXEnh3aY+0e4d9qRKdMpP4hMlIlzu93VPv2DmWOeaYswSmxGME/v4a3vUf/VMDWTb+PaBpGk2LnkBcdyvGwGHUL/kgv0A8SvCPD/zT6XAcUPl0VsA9rFR6/Wj9B6NXVIOaPhiQa+pKmU4IkM6GccQQ0HUr+0VPZmjTwLVtI0aoBOkLpLd7pg9eGEbHv5lTVQ4namsTjv1fZHeV9RR2ij8bBQXH6bSW4C6bAb94AKae2WGFfryKkkfuRtn40Vc6fnq/EyvX3+xJA04nyXGTiJ1yFkZVf6TbbUWhdR0Rj6HEIqBrVoTa4UACySOPJjVgGCIWxfOPtfg+W43a2tStILv+8QnOrRsL0Ca79lSAMEyElrSzbtr4ylBSUkKkbg/xOWflBeECgUDBHM7/Eg1cVHi7kWH9iMFEzr4I7cijMQMhRCqBc8dm3Bs+xlmzDaW9FTLbGIVAur3o5VWkho4kNfhIjGCI+IRvkxo+Gt+H7+D5/BOEXnxDh9A12xS28bVCURRCoRChUOjrswS6kd7Mp9IOWCp19DiiMy4gNXw0KAru9R/hfe8VHF/UoETbraWhAppPrd+Ha8cmTF+AxDHjiR9/MnpFNZFp38MsKcP3wTvfiGUlGzb+dQKcK2hCQCb3cyYC3UkQtaEjiZw1C234aIRp4nttMd7330Rt2N+tuZrRoko0jG/12zi/2E3k1LPRK/sRPWkaIpnEu3ZlwQMI0uG0zherjhyzuXNy+k7mdPaYoLUEJfQUSqQ9b3mri7dhm9w2DhX0OgqdXmvtEoTKxJJ8AWJTZ6ANPgpME9/ri/G99bIlFL01RxIx3Fs3IPQU7TMuwCitJPrtM3A01OLataXLAYT45OkkjjsxvRdadux9NkzrEIaR+dtIR6KFFVATCjhUcLgQmob/7Zdwb15XQIht2DgMNbAsKMBq19+A5NgTSB15DEgT95qVeN95BRFp/1KRN+fOLfg+eIfIaedgBkLEx05EbahFSW8cz8DwhzBKK5Bub87po/QasNEpIm2a+X1xOJBON0oqgenxYSLyXHxZjB82bHydCrjXPrBQcvJD5Wtg6faSmDAZ0+NDRCN41ryHEmn7Sgj1rPuQxHEnog0cRmLcJDwb1+KKhvO0sHfNO7h2bEJmPofa2WzOPUmV+U1kzGcBimqlkq3d0+2BCxs2Dh8fOM+uVXJM6E6zgaoiYjFELIJ7w8c4vtj1lfmLQkvhWbcGM1SCWeQ0j1pfi1pfe1gxO3HJdYSvvJHK7ww7rOhuWrwazwfv4f/tfxT93bXxE4K/uPYbIRQtj7yId+UbeJ5eWFgT9h9Mw5IPCS28q2iZQ0OAs6lluvq/SjyK753/h3PrBpy7tvR8E0YP4d74MSCtVDy1Nd0fwj/EkUwmaR82Cuem9YcV3c3NzXhnHpu3/zjv9z6VGAOG4uzm4MbhgtbSKrRxJxF49r+LxJEkDZOtdV/3Wy9/7fQq3clvdimpS5IniXPXFnwrl+H8YtdXHgRS4lG8H/0dz2cf9DjNzqGE2LW30vD2DupX19F279NomoZSfQRmZd+8+5kZvfmp5dSvrqN+dR1Ni1cj+w9GmzqT+tV1tDzyIvWr62h4ewfa1JkAhO94JFu++anlND9lfQImde4lNC7dkC2fOtdKNNd279PZ8pl2c1GsreD3LyGyLozsPziPzoa3d6BNmYk+wsrwqO7ZQezaW2lavBpt6kxaHnmRpsWrs7R019foT39D49IN2fLhOx4h+tPfZNsyxn4LIHsv82zmfmfeF+JBMZpyhTP1qjXBts5fiOw/OMuPDJ9TqRQcMRgRjyL27abt3qdpfmo5sv/gorwv1q42dWZe+cy7Le4E9/ZAf3r9V2RTyx4Kbvs/HyKbA+zgMmVGR48nctk85DP/jXPBfJKTz0CfMBk5+EgMUyLPHo9j0RMkJ5+BlJKmG/4PumnCMIF6w2yMAUNJTJhMy/jJ1ot+4WnEtFGWgH7rVJpvuof4hMlw8TQcP7kEfcQYnDu3ED/mBFpvuBNzwR2I4QqupYsIX3odLRddS3L8JDhtJK47f0xy8hl5A1/X9YJtxb81lZa+A1GaGuCLXRadHh9i2ihEY72Vl/k4KxlbZOIUoqedTWjuLGIv/QVt5FjMSBhzYlW3fQ2Pm0RsyFGYSMx5F+JY9ASJKWcSbWlCTBuF9PpJTp5O69W3EJ14itXvEU5EQx2xTjmmIt8+k8hl8zBf+BNiuIK6aR3hS6+jra2tIE25aGhoyB4FrDh1KE233o9WXgWnjczyOXH5T6DfANQ9O2n845topqT0f55GorSyIO+LtavrOq1zb8N88yXEcAWlZgexcy7utTQcOLG7roM0Md1upMPBvwtMtxvZi8TuuTAMg+j5V6Ds2Umfh+5EW3AH5RMrMPbtQZZXUbrwLgKN+9BNE6WpgUgkgvrYf+FurMP51DKM+62zssa2zyEQwvHJ+1S+/jzs2mIJ295dGCdOxfvuK5Rt+RT9xWesbA+1ewmfd7mVCeLOhcjtJsnzL8f0+tGbm8DnRyxdS2rqTPr89HLU9R9mabaS63VtS3xRgxh1LGrNdsLl/TCnTCf40jOoe7bjPusYPHPPg34DLcE/fw7eD1fgrNtDqv8Q8PkpeeJ3BAIBdNNE3buzaF/NbZ9DZTXed14hsOFDDNNENNZT+eg92Q/Pm+vWoJ1+DgwaBn9ejr5FQx93UjolccdEFDt3Nura96l67NdW9pK1qzHLq4rSlItAIIDSbwDumu20jRqHOX4SpQvvoqy9Mctno/8gxNAR6CPGYI4cS5+lf8E0zYK8l95A0Xbb29ut5H4/vBKWfoZY9xF9br7kqzWhHbU1YBgYJRVog4b3ON3N4Qy9qj/6EUNAUVDbWnr9fDweR1QfgdpYh9vtzuZ/Mk4+DQDne1YyNRHsg1qzHffJp6H99xJ0KdG+qIHn/mC92DXvoQwfhXvHFit9y5ARSK8ffecW5KBhuDevx+FwoA4fjSyvwli3BoaNzMs7rB7loM8ZI5F/WoiYPQ3lo5Uow0bQ9tsnkf0Hd0xYplmwLXPXVkR1f9T6fSSGH22NiT07KCsrIxgMWmd9h4+Emh2oK5aRGjsBKSUy/T0j57tWX5VgH5SGuqJ9Nd9/K9unLG8a6yzijhlvTTIb1iIHDrPybKX75z3Glzfo4/E4YtgIPDutCcjlcmH2G4Bas7MoTZ0nXzl0BErdF6SOOzH7vnL5LDd+gkznbVaa6kl961Ti8XhB3pfPPKZou4qiYM44FnXR4ygNteiz5hC+45GvVoA9n7yf3ZQRm3QGqeGjMP1B6yPfQunYqfVlrrxI95d4tid1dCmbDtKpKtLlxiivIjZ5OlrfI6wgRedsED3UwEKAMWh41v9pfmo5HDMuO+ObpokYNhLHjk20nWQJtnfueXg/fBdOPwelqR5d15GDh6HW1uQJhfHSnxHxKNrIY5FSYqTTtpr/+BQ1FkX99mlUHj+R0lmXY65rw7ziRpQ1daj/4xLElWdjrnqrS1rXYm3x6iLk4CNRt2zo8JWPHp/1l1OzroJBw3F88C7i41XoI8aQ6jugS1/prq9nWgnsnR+twDRN5NgJuHZstiaI4yaiNDWgb92IiEdxT/8eVVVVBH7xAImPGvJcAU3TUBr2kzhxihU4PO8K+M5MXK8vKUiTa8fm/DiAplkpkL6ogZ1bsysHUkqMB59F2bMTo6nBEpxlL6CsWUHixCnoul6Q99HZ1xVt1/XzexEbIzifWIDxi3mIpnqUSO9jPQdUqa6tG/C9v5zYydMwyipp//7luLduxLX9c5T2loP3i3O/fCAOZORLuhbiy7UpukbWpduNfsQQki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button_reaction_adder = 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'
|
theme_name = 'Future Bloo'
theme_author = 'Lucas.'
theme_version = '1.0'
theme_bio = 'Bloo'
window_theme = 'Black'
button_colour = 'black'
attacks_theme = {'background': 'Black', 'button_colour': ('black', 'cyan')}
banner_size = (600, 100)
banner_padding = ((75, 15), 0)
menu1 = 'cyan'
menu2 = 'white'
rtb_icon = 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button_leaver = 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NqMtWMFU9irO/UbxY/GKjPwTXfhjl3RhHRaFRFo1FM00QIQV1dHZm3GCp4XQdWqgFn6gyd4ywkwnVIH/NpvPapYEeIrnga++UXwPcQnof94rMYA30U3vM+hs7+mg7HVKu0ox0Ovl9h59HsXnZgWbZeMKq92Z4Lrguug738SSJPPILIZRDFAjIzHISvhLt9Wg55nkc2mx1zvROJBPF4vCYGnMlkGBkZoaGhYQyIfN8nnU6TzWZrxhh9UzqOw/DwMI7jaC9rMolSKhi3eOVvyR59Ilx1GQ03/ZJsNkuxFJpJpVLb5H3u7e0dr/1phYWrklcyGe39LQPRtm3i8XjN+WWzWdLpdDBmeZ+RkRGUUrS1tdUuEv39OI5DXV0d8Xh8zPzbMmdvby+GYUxIRs5mszWL0w5RoWU2jXCKiFiiYgcbRsXRVA75WBbWmpUYfb2402biTp6uP/O9SreN8VIgpRFkXgUOLNOs2L+mVdqq5lKqJtRUnHsAeB6Rpx5F+B5eQwt+PIkfjWOt79wuhQ2GYZBKpbZp9U8kElsEkJSSuro66urqXtezPN5NWR539Bk1NTW94XMaDag3e06ByRGPjwvELWkCrwe6bZnzjZzDxLOBff9tb6kjXAdjsE8nXpQSNCoglpU+V/kcxmtdeI3NuFM6NLirc7XL3xOilDUlamuIA/u6CsBl4JpmZdEoA3gUkxf3nY9wHSLPPKYdI6aF2z4NUSxgdq/b9eqEawLDKuzuGTqxtjLxYB+YFs7k6TA6/ioEyjAwe15DeC5e+zSdTcWom6q62CEAv6wFcRDPNWudZGXwjk7woPYmLrzvYCgWiTz3OCKfQ0mJO3k6MpvGGNgU3kGhTCwAv1OZWPg+Rs9rUCyUbNqy3STAKWL09WCs78SPxHTYSUidRBCU+VUBs9r5JWQNi9ekUAaeaLMC+tGMXb4kSoGlQPkU5h+O0d2FufZlnbIZieK2tCN3MQCnvnsu4uufI5PJoOrrCfk3ZOCta2yWpdVkp1jTf0pm0xibupGZER0CCryJZaYs9cIK4rol1dgYZUOPVq+r48vl11SzdemvMsBUtapkeVwpEb5fagSw60kymSSZTIbICAG8FfKNJ/Hqm1CJJMqKIJyCrrUtGWLKjuJOnaGLFDy3ip1VLTCNUbatZdUy63g2crWqzjg2szTGqNH204sxBjdpB5jytYoSiYZ3TyjvMgBLidvUhtcySTuwSkwp3EjFawyoaAy/rgm/oRljQyfCKYBK1gKymklNs8LCpllRo6vt2vEYebTdW203l8T+51Iiyx5HFAolJ5pXSvCS4d0TygQE8NvlfRQCt3WyrkYxqmKv1fNWz++5eC1tmF2vIAf6dPsbxBZAbNbawlVlgLUgFVsHbmWlAQnWsieIPP53RC6rx/a8yryhlzaUdxMDu02tOG1TxqqooxePqtd+QzNeQxPGxvW6AqmuoYbNa51ZRpVta4wF5XjVJVuqOBECa8UzRJc8gMgM6zF9vyq2jH4MRCihTDQAvx1dKVUkits6uVQ6WMWEY5qya+Cq8l8Fzu7vwV7+lPZIl73Vo3OoR4N5vHjwG9AU7CcfxV5yP6RH9DGX56x+EoQi9NKG8u5gYLe5TXfbGIPXqr5T44HO91CxOH4yhdHbTeTpJXit7eQP/ggqkdR2rx0BQ6eBBsX98RREo8FC4Te1oraxiiby17uJ33wtfmMLfnNrBbxKhHdLKO9OAPupet3tQozDvjWOpSomdR3MjeswNnQh8lnNvlISXfIg9nNPanXajiDyOeTwZkRe5wGLQh5cB795El7HbLwpM8h+7it4k6a+rlptrFtD7PbfYq5ZibP3ftDQVMnSGmPvTgxAj1xxLX4iRf3Xzwjv5hDAb48o0y7FWKvBKyuPSBFCd5ksJV0I38PqXIW59mVULIE7bRZ+U0vQm1mUQ0tSQl0j3rRZKNtGeC5iZBijuwtjfSeyb6MO+zjFSvgHMS6QzVdXE3nwT3oBkBJRyCF8T3f2qGnrWdKd5cQAsDNrDix7kpGRkZ22oiaUNy/vTCxkTKF0NQOX4rFVZYRGT7fOgZ7SQeEDh+DO3AM/WR8wtDItVCSqNzui2V0plGHhN7XgzJtPcf4RYNmYq5YjRoZ0lZHnlQohvBpWNbpeIXLPbcj+TTplU6lR/bFLDQJ8HzwfUcwjB/omxA/oTZ8FG9ZiGAaD195J79Ieepf2MHDjQ3rxnDKDgRsfCv7ff/tS1JQZjFxxLYPX3hmMM3DjQ2S+8RO8uQfSf/vSYP/MN34CQPbcS8eMMVqy517Kpr+uCfbLnnvp2AXn8GOD8ftvX8qmv66h+PEzGLjxIfoWrQjmzJ57afC+vA/A4LV3MnLFtcG59S7twTn8WAavvXPc4w4BvN1EjB+LLavMvk85J1oZBu6sOTi7v6dUoF/r5Krd/CrAlUDmFPEbWyjuN1936CjkdD/nAMR+8D1jw6tE771dp0UaBiJgWV/v6xQR+RwiM6I7Ym5ch/nKSuznn97x4J17ICqWwLvrFvIL/4hKJInsHYUj98RtamPg1HMZvugHoMDeKwLnfxZv2iwG5h2ESI/gtkxiaGiI4sfPwJs+i+Kvf8rAlddjP/53zDkWnP9ZMp9aQKZ5MukF5xP9/oVw5J54TW30H/fZmv5Y3twDSS84n8hPv43YTcLTj5Gefzi5XK7y002ZwdA3f4JVGt/r6UbFEgw9tEgvRGtXY+xhkvnbvaQXnI/5599j7GGiXljG5jPOo1gs4nXshrOhi3Q6jbP/oRrUTz6G1zEbX4F5zD5w6/XBcYcq9NuC5dGPL5EYm/vxJk8HIcgfegyykNPMOW6staTGKl87l8rqsV9l1ypfN4OfuYcOKzlOJdykdCjL2LSB6KI/aFW7zOIljUAO9mt13ikismlENoPIjOgywgkS/3X2PVBXSSmFe+hHtMPwxXyN7yH225+TPetC3P+8Cw47WoPtpeeR6SHUpxbgeR7pT5+Fveg2ikefiGpuI/epM+FTZwbjZGbvjchlyH/n37GOOI7IbdeT+eHXyCeTxGK60YGx/Alid9xA7rCPIc+8ADV9FvLR+ykUCsE+hSNPQMXjOP96jv7fiqfJzNxdLwSxBNzwSyzTxDzlbLxn/0Hx+xeSSqVQK54m/ZmzSbdO1Y7FZU9gGAbOnvOQ6zrxAb+5DeOCj0DnKpoe/BMDp3yBTH0z8b7XdpoGdROXgcd7mmA1AxumbsruuoHXOH/Ih1GJ1BYdTqB0n+Zy8b7vV6nIFfb0Gls0KJ1iaXPAc5G93UT/8gfkpu6Kiq/8wBY3Nq7HeuFZzNXPY2xYixzs003jJ1Dyhje5A9m1BnGoBuaYRm0P3U164W0YQiB7NuiOFNkMLH0Y++XnNeC/fx1+8yT83/wMs2MWxqrlY5u+LbqFxkM74C+34bRMIr3gq9gXXB4As+xMK84/HEMKrJ9fpnsrv7aupilB+Xh93ycej+O1T0WufQV5wqn68q94hkQigTtzd3h5JZFIhFgspvfrWoN30BF6Ybrrv7EsCz+ZQmzaiDFvf2T/JrzHHqS+vh63tLCx9OGtNhcIGfgtaNPVoFSGifnqaqyXn9eF9KaFO2MPsg2tmGtexOjvrai/qrpeSpcdCiExejbojKmamlYf7KhWn4uFEvsq5GA/sfvvQPa8pp9uOCoLTE0YH/PWpTh7T0TnSxjDg7gAl/yMtt/8mKGf3oQ7c3diD91NBnAWHIP9idMpfvtnyP5elBCYG9ch+zfhn/IFzP+6Gu+VF4kISa5jNuYnTqdx7UsM/OJW7OefxU+kGG6bTNvnj2Lo4KMpXPU7Cn2bKBQKRCIRza77H4p8+G749pdwr18E8QSiZJvXRCRaJmHu8V6cffcnf9gxGH+7F6bN0Ez66ksYra3ITT24Bx1O1LIofvwM8ocdg/WfP8WbMRsPYOYc/A8fr79/9x9QhxyF39yKPOGzkB0h88kzsBbdhiPEmPlDAL9ZBq55PX78N/nf1zHcOhm3YzediVXfSPF9H9TAdV1d2OBVOaHKtrRhEFn2OPYLz2iGrbaXXafCvspCpoeJPngnsmdDCdBUFoSdLDXSb52EeHYp9gP/H7H4AZxzLqL3nIsw1ndiXnouec+Hz5yNesXHXd+J7OvBVyq4qY2uV/BRgaoavXkh+QMPw/35TWwCrGeX4px/Csbxp+L+4Dp6l/YAEL3jBvI3LURUNbiLPPUouZPPgpPPIrL4ATxAbR6oAVD85oXkDjwM597lDK/v1Pb7i8uQB3wIsWlj0NYpecuv2PzNH5N+bgSRy2j1/hdXkDz8o6Q/cTrq4VWMlPpfees6Md6zr14Arv49/UDkvj/iXfLFbWoBtLPLmJ5YiViMbJXjYXtIft6BOpFCGrWPLgnK/Ezk5gEiK56isN98Mqd8CXfGbiDNkqpcUonL4SPPq+3IISX2C89gL3tCh4GqFgzhFCnO3Z/CB49CZEaILb4f2ddTqR0O9lUgJMb6TqyXViAyIzvFDzgwOEgsFiMWjTIyMkKu9JAy0zSpL7XYGRgcRClV00upob4egKHhYVzXpbnUPkcpxeDmzUFD+EgkQn1dHa7rMrh5c+C4Spb6dlVLutRvCiAWjZLL52mor8e27ao1tTT+N38Mp30Zjn8/9QMbKRaL+EoFx+y6LkPDw3iep+/LUmsdz/PYPDSE53mYponneci/LEM895Rm/9Jxx+NxkrsggPOFwtZ7Yr0dBf1m9zr9BIVYEhWJ6KohTDAC6Oi5pURu7id27x04e7wXZ6+5+I2tQdtX7WX2akM8QqCsCLJ/U+2TA0s3mm/Z2M8uJbL4fuTwEN702dp2KnuwSx5nUSwihjcjN6yFXGanSZNsbGwMrmEylSI5Tix4vIdtl8+v3EdLVWlI5TGr9zVMc8w4o6/R6H5T5WNRJQ/04C//EDxJ0VjfifWzb1NYuxqruRm7pIqXxzRMc0xPLgVIwxjz/76O2Rj33EEkEqk59ndDqusYBo7FYjWu/+0qhqkfiZKs17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D9LEY4ssSwlBUjRhXHwS4oxiqvRt+5fViV3sfvUOLRtx8pNg5KbvgPun/4G2TjbPobZ8Pl38wdPHo6cb7+eXRdx+v1cqAR58GfXEtiwQk4jXPofGDlqHsmbroWEY0SuvsXqKd8DHv6bBK3/JHEyHIb19H361vw3vy7XOm+fhXeF58k9ZFziV57C97brjvg+t6JBRp44XFiZ10EiTjOjV/Fv5fnPnHJF13VGrBuuxtuu5sIEBlqRzJOxclT8hL4oAqnkjg+P3ZRKVI3QEqUeBRj61vYRaXETzqL+ElnYU6ahhKPou3akVXr7OJyMlNngBAElz2J763VCDPjrkYKF6J17sLTshFhWdgl5a50VDVQVdf2VZQRJB7x2TWUEY6NOWESzgFIYccfdFXuQxzkoa5fRemSWYjH7ndV0JF49D7k+cejrnrhbUmR8gsXoTy+l2392nLkFRejtjVnnT9lnzltdLlH70MOepS1MVTdwusvR/R0Yp95IbHiyoOq7+3Cf/cv3ce38gWEEDlTRQCpWUfuV/Hq7Oz8wBN41GKGcDhMfJwIl/4LvoBdWIJn20Z8q19G7et2lxFWVBP96AWkZ80HVUNv34Fv+d/xvvYSSiLmhl7OX0x88akokX5Cj9+L3tE66HWehFk3BalqSI+X9JwFOIEw+o6taLvbRkjhEWJ3LNVaul5t75qVaJ279pnVw6xrIHH8R0nNXkDFtZ/+p3RsNBolmUzm5PINBAIEAoEcezcejxONRnOmYEaeD4VCOWSJRqM5z8fr9RIKhUY5wfYuFwgECIVC+6yzq6sLKSWlpaXZNu6vPtM06enpybn/ASt0jkN3dzc+n2/Ma3t7e8f1Jwz5DcrLyz9QhE0kEvtZzDAUCz2WB3FXK1ZpJVZ5NY4/hNrbhbBMtF2tBJ57GKNlI6iau1po+xZX+kqJNDxYZZXg8YDHQ7pxLnZJOcb2zdiFpUjVDf4QgMikISRcT6huDE8PidGEHWau+1dIiV1YgtrTOe70ldQN7IJi7FAYJRb5p6XvCQWDhMYJ6BhZZ8DvJzAkfQ7g/Lj33et3jFlusMx49y4rLR1Vdn/16ZpGZUXFmG3Yr0YnBOVDEWljXFtcVHRgPpC8DXygUyVbSE+bhVVWhVVWid6+Pbtowdi2EW1XKwiBkkrkrAiyy6uwaiYNz+1qGnZRKcnC4kF12HVYSVVFIEA3XO/10BxwNmpKjsHgER5rx8EpLEZq+j4IrGMXluD4gqg9e/JekDzeXwTeVySWtqcdbU876dJKzLrJ6M0bXAfWYMCFSMZH0QwgU9swaCRqSE1HDu0EoWi5jinHcYM7QkWge7BSSZT+HpT4UBYOMWLEFbk1Df7r+ENI3UAmxzYDHN2DPUhybVdrPhVAHh8cCSzSKYzmja7dWtuAOXUm6urlIMf3+lqVNaAoKPEYqCpOuAjp9UEmM8KTPBis4fEggwXIQBA7FMYJF6L29aB27kLt6kAkY4hMBiwT4TggB1k7eL1UBMJMI1WVTONcHI8PZaAXrXv3oNdZIn0B7HAR2Bbarh35NyCPDw6BAYyWTWQmTyM1bxGZhiY861Yh0uMT2C6tdO3beBSRSmIXlmBX1CDMDEo6nY1zlrqBVT0Ju6wCkXFT6DihIqwJE7ErqlFiAyiDZFZ6u1BiA2DZg0SWQztBubHRioJdUIzjD+AUuPfQ9rRjNG/ACYZx/EGUZBy1a3f+Dcjjg0VgkUqgt7WQbjoCc8JErOqJaK3N4y4KkL7B+N54DLVzF04wjDl5OlLX0TvaEJk00uPFqqzBnNyI1A301ma09h044ULs8iqcwlKcgmLssirsqlrUPbtQejtR+ntRBnoRiRjCAVQVtacL6fWDrg97vhQFs7IWO1zkagC6jjrQhxrtz78BefxLQ9U07btDbmkhBIau73+dpZQ4RaXY1XVI3TP+ogVFwZwwCTQD4diIVNKN0CqtxCmrwikowgkXY9VMwpo0FXwB9B3N6M0bUAb6UGIR1O7dqD2dKLEIQkocfwC7uAyntAK7tALpDyKE4gZlSDC2rMMuLMUpLB4M73MdXMKxcYpKMWsnI31+jLYWjK1vuV7vPPL4F4FlWVmuvi0JDKD2duJ563Ws6jrSsxfg+8eLKDu2js5F5TgI00R6HXAESiyKvn0LSIlVPx2rqtYN1kCixCJoO7agb9/qTu+Aa+vaFqSSKJF+tI427OJSMjOOwAkVIj0+ZCCMXTEBkYih7WzBLirDCRe695VOjpPM8QewS8oRjo3avTtP3jzefyr0AeV1tCy0tma0HVsx6xpIzV6AiEVRuweXGCqKKyXDRTge73DguwARj6Jv24QSj+KUViL9AXeBRE8nameH64QaCtwY6eQyM256WTONmDrTTd8THRgM3SyCgmJkMIz55mrIZNy804jsFJUApDfghoGm0yiRPrDMvBc6jw+WDZy1heNRjOYNmPXTSM9d6GaF7O3E8frJTJ2FEyoYHhKyBB4Me8yk0dpboaPNDZEcGWmVJd7I/wc/S4n0BUGCtrMFZc9O7AkTsVQNYaZRd7W59nAygWMYSFXFnRhTQFFxAkGkx4eSiLvSW9XcWO088vjAEdiy0Ha24Fm3ivjpF5CZ3ITa0eYuIRyMkx43SmakZCU348ZwxNUYoVeKgkjG0DevQ+3pxAmEsMurURJRAn/5b4x1qxCppLu9y7yF2MWDSwyFdMkbKkCoKko66drPXh8ilidwHh9AAgMokT6MLW+SOrKDTONcpGGgDS7MH9ZNxwhcdiRKrBcxZKM6trtzw6gUmsNhktlzioKuvYmIRzAb54Giona1oe3Y6jrJAGGZeF5fjl1U5i4xVFXM2sluaKZ0EKkU0h/CCRUM29vjIHHZN0mefCZ2Tb3bYZvXU/ypkwHouf8VvCtfJHDz1952H8rqiXQ9+Crh22/Ae9ftB3Vt/Jof4wRDhL5z2bhlotffQWbmEZSct/BdfdFSl36J6OeuouzDk3PafSj6EMCefTT93/k5xV++ELHX/L6snkjkqh+QPu5U9/1Ixin46bcxHroLe/bR9Pz64VH3Czzwh1Ft6rvjfzGPyO3H4B/+E/8d33/X+vUdZXYTloW2px3/sieQho45ZSZ2eVXuaqGcZYDuX2Fb6FveRH9zNfqbqzHeegN9wxr0jUPH2hF/h75bi75pHfqGNRhrV6K1b0d6fTihAkR0AJFM7PVEbdS+btSuDtQ97UivD+nxIdJpRDrpauRZNX98giTOvAj9dz9Dm6bDFRdj1dTT89UfIqsnYtfUk1r2FIlE4m33YfqkswCIPHgX6fTBOdVSx5yAFR3Y504amZlHINatpre3910lsPeu2wkvmkBXV1e23dHiykPShwDm3KNxSsrpW/96TrIBWT2Rnl8/6mZRnemHyQL56kv0f+V7pMsnYM492o0g3CtpQfyaT49qk9U4G+227w6Xe/Q+YmdcSDKZ/NeUwOAukPe8vpzUvGMxG5owG5oQpunGGTsjAzOHSCyRukH6qONylwhmPU2Mtp33zv4gJU64EHPqDDfSaldrji1rl1ZiTW7ECRe4ziwpSc9diBMqQO3qyO7e4PhD+xzR4x9fiu+rnyLz0J8JhUIYq54ldu+dJH0BYvWNbrmrbiB2293Ek3EKv/sl9BceI3P2pUT+7VqckrKc0d48cQmRK76NXVOPurOF4i9fiF1Vh0jGkds3k/rjU8TLqyi69rM5EsPz0tMUXH1pTvt6//gMdk099nn1aCueI77oZOIfd9O9qjtbKLz+y6jrXsUpKUdEB9DPvIju627F/8hf8N/x/RxpMqRVxK/5MckPLUEkYzltBOj9+V+zWsjeUidz9qX0f+MWyhdWYJ64hL4f/S4rwQZ+cperdAVCaJ27UOunYY1o93h9mLjsmyTOvGhUH3Y9t43Qnbfivev2rFQv+Om3iXzJTZJg3fMC4vJzsm2LXPUD1NZmnAuOw9E0ysvLkT/7Fr1Xfp9IqAhfVR1K6zYcISgbmep3DE1J+gLY2zZRUFCAx+Ohd2oT4qE/uyuEPvrx7LMd+cz67vhfnLIKpC+IU1I25rN81yRwllyqjr5jC9qeduziMjLT52CXVbkhk4oKYmhN78gF+WIvO3iExp21h5XhhHYjDtc5JVAi/ai7WlF3toDlzkNbk6aRmX8sdmn5YFJ3AZrhTi0pwp1PTiUR0kF6PONLt4+eh9rWQvJ//kRBQUE2x1TwVz8geOOV2EPrVZ/6G9o0HSmhf/5xpGctYODK7+G740bUqRryob/Qf+nlZDIZBr72Y5TtW9E+Mgu7pp6eMy7GqqhGtG6Dx9YAoJ9zNH0nLCFz5CK8S+bCt79E+rhTGWg8Iqd9oTtvcT+cNJ3k0SeS/NDp+L76KThpOrY3QN85S8nMWuAmS5eSzHW3ELzpGpLfv5Lem/+EDATxNHnhpOlYxeX0XnQZmcnT3Syj1/5f1O9+Gbumnt45C4n+27WIeNzVQv565yipo7ZsBqDTX0jirE+42ktpJbGm+e4g9JtbsEorMNtbUX/1o+F2N80bsw9Tx59ObOkVaA/92e3Dt9bQf+nlJGce5ea5XvE86XTaHfy6Oxm486dor6+AR++D8xczMDAwPLgcuQhefAJN0ygocDUusWsHxVdfiveNFWQmT8eePhvZ7ND5yh46X9lD6tIvjZbwRx3vvu4/v4eB1/vpfGUP1rRZyHX/wO/3M/C1H6OvfCHbR+njTiUajWI1zkbGYijHVqPc+9vs+fcOgQG7oAi1vwd92wbU3i6csgoys47EnNw4GGzhdxfPZ1ce7Z1OdqxtJBz3cMY+RDKO2r7DjQJLxBCWjV1agdnQhNQ97j0c18Z2giGkz48wTZR4dDhqbB8L+q2Kati+BU3TRiVs8/l8qNW1qKtXwI1fxefzuT8pOkD8vM+4eyF986fYWyy48HOIQJDoJ7+MU1KGc/1/oO/cRtkx5Tg/uApz4hSc6bOhcQ72A39wB4lEFOkLkLp/OZ7TPkbw6qWkX3wSe8QOj+aM+Sg9nbB9M8opZ6M+9yiZh/5McaQbz8Y1yIpqErMXuF184edQdjQT/9tdhGfMwzr+NKxps0hvSMGzm6C0HCdUgF1agfLsI7DyOYJx1zdgv/gkMhrBmj4L+5nNeCbUEVh6Wo56qa571f1w3KlkjlzkbocCpD97JcZLT5NubUHW1iPXrkLOPirbbm/tpLH78OxPoL2+gswNX8Hv9xNc/xqUVZCYdZSrrax4Fk3TyEyejvLmalRVxamb4ppzfn+WqPbso5G+ANary0bl2xJCEAqFkGUVqPf9Nkd9jtz67VHmjDl9jpsyaajcSdOhuxPrlI+h6zrh/7qJdFkVzsOr3Wfe00myotYdQH95I4qi4FNVlLaWQ6pyHxICS58fKQRq9x6MTetQu3ZjF5VhNs4lM+sozIYm7IoJ7tRSNvG7GCbwyGOIpEOfpQRnxHln8H/LRCTjboz1YBSYNWWG67Ry7MFrHaSqufUqqls+nRpepriftaQyEBqVpDxz9qXYs4/GrJ+GaN6Ix+PB29DopmFduwqnfhrafb/LeSHkggrkhIlu7uPmDQSDQYQQlJeX49TWozx2v/syLPwwPp8P46G7CH3uDFj1Eum6BmI/+QNFs4/MWbRvV05A7GhGbWjCKSnDXr8an8+HpmlYk6agbtuEXV3r2nd/vRO7cQ7qlCbkMSe6N9jL5nN+cBVObT28+QY+nw85scG9dvtmwjd/De8NX0Fu20x6/iISv/qfUTtPiGQcTj4L0d0Jy59FBEPYRy1G/OYWPPOOdtu89h+I6lq33aqKNU4fyklTYat73ufzYVdOcNXcalfdFUK4pC2rQEYH0KfMcAfHNa+Omd1TFpePGoTj1/zYbVNNPbz8DMFgkPLy8uwRe/j1rEROXPZNV1MaHNDLy8spT/Sj9XSixKPEPvllBq78HpqioLz8DMrq5cjtW1FOcHdilI/8hWAwiBMMIbp2H3AGz8NHYFUbjABxULt3Y2x4A711K0gHu6qOzJxjSM9fjDltFnbFBKQ/iNT1wTlgcsmbJeze0ngcySwlIh5zY6Z9AVeaj7yPrruBHo6DiMfATGcJvK9ILL15I878YzG/eqM7Ap+4hJ77XyF++vnYr72EXVsP7TvQdX1YvXrkLyiJGPbCDxFomkvR+Z9GvBkjfNX38Kkq0hdAHHsSia/dTNdz2zBPHEw89/gDaKuWIY79MJlMhu5H1+MsuYCSqy9BW/kCJOL0RaM5zhmrvBqxZxfazm1uEvYFJ6BpmuvhLSmHh+6BhiaUDWsR/30z+AOYp56D6B90Zn3jFsrLy/H88SnUF7dRdP6nsxJX0zTsQdtQHHsSXSs78aoqhVdehLJxLTIepa+vL/dF6umEEz+C8srzCCGQRy5C2bGNzLKnUOcscKXX9s04Q+3WtPH7sHs39sIT0XWdzNmXkjrhI2hPPIioqnXzjus68Wt+7JLvzTegyc0u6qx7LWfAVde96rbrmh/gVE/KErfzlT0Yrz5PdPFHsgPL3ml4S85b6KrDkwWx67+MOXEKYnd79v6Jy76JXVeP+shfsecfi7JhLdZS937O/EWonbsQM49wfzegaVp20B8vhfChIfAB7E445o5pclg6KgO9GBvW4H31BYy3VqP2dCILCjFnzie1+FRSi07BbJyLXVLhbsUykrwjSTrq/FgHKLEBl6SqBvaI6xx7WAKbJkoiPriuGHdNcjI+7n39v7wB7YkHsD77FTpf2UPfj36H/soLyAuPJ1FRk3VoqIoyrF4BoXv+G/wB4o++Qd+Pfofn0fuI/vQ7qL+/DaWtBXn3MyQ/dDqem6+jv6LW7bs3V+N/9iFk3WSSJ52N/5F7iH98KT2runFOOhPvT65D7NiS0z4lHsU6/eM419xE4N474Yzz6V/dR+qYE/DcfB328r9DWSXsacfYtR399VeQJ34U56kH0V96Gj5/lWvHTZqCdt2/MzBtTlbi6oPqqdi2Gf21lzBeepr+b9xC/+o+RHklxg+vcVO6jGxPl5scwbrjJtSh0NU//xe6riOrarKSZ6jd9vfu2E8fBomtjTJw5fcwHr0P8+fX433hcZzaejIb06QHfRD2i0+i9XW7/XjvMtS92lXwo68jgL7//Ydr3x5zAr6rPknffb9HjFDn1TF2sywuKqK8rIzysjJkaTnW+Z8h+WbClcpnXoTnJ9dhvfw0vjdewZ5/LLLZgcWnuPnFIgNQOfy7kXJ4wBp65w/B7oSjcmL5fL6D1tGt8mqc4jLXYbVXAjrp8+MUlrgriarrsCtrcQqKkbhEV7v3oO7a4TqjOjsQeyVn36fkVxSE46BvXIP0+gf3PCJLbGGbmA1NJE6/AKW/F8/ql9G3bwYzA6qG0teNEunf91RSPJ5j7wUCAfx+P1JKuru7KSoqQtM0EokE6XSaoqIiLMsiEolkbdaR1/T392NZFqqqEgwGkVISiUQoLS3Ftm36+vqy9xhydgyV3VtKRKNRUqlUNp/UUHlN0wgEAhiGQV9fH4ZhEAgEiERcuzYcDmevHSofDocxTZNYLEbpYGqdSCSCqqoEAoExy+8tSSKRCOl0mtLSUpLJJPF4POsAjMfj2LadU3cgECAej++3D4eS3vn9ftcZOOikMgyDTCZDaWkpjuPQ29uLqqqj1PuhvhxKRq9pGqFQCE3TSKVSJBKJUdeMhe7u7hwtaORzSafT2f41DAPbtvF6vW5ifimziei7u7sJhUJ49uFA3ee0YzqdkxPrkBD4QOGECrCmzsRsmIE1eTpWdR1OUSkiGUdr2YK+ZT1ay2bUznaXzO90sYGikFpwArHzP4fnjVfwPfsI2u62d20D8DzyeKfYm8Da4axciQ5gvL4CY81KnMJSzPppmLOOJNM0F6t+GubUmSiRPrSWTRhvrnaXFUb6EYm4G3xxkAnMnFAhdlmVu9C/ew9qf0+evHm8r6Ad9hqlBNtG6dmDZ6AXfdtGPMv/jlU7GXPabKyGRszZCzAb56H0daNt34yxcQ36pnXu4n3bGnZg7W96q6gUu7QSkUyg9HW7qnMeeeQJfIhgW64dOtCH1roNfctb2NW1WLUNWJOmYlVPJH30iZjTZ6PtakVr2Yy2bSNa+3bU3q59k1hRcYrdhf1qXzdKZMBNv5NHHu8jjMrIoev6qJ3u/vlS2UFYJkpsALVzF9r2zWitW938zukUeLxY1bVYDU1YE93tV5yCYjAMhG25tvJe6rXUDdKzF2BOn4Pesglj0xqUfAqdPP7FYdv2+Bk55LudJNt2VyWJdGowA8dOjLWvYpdWYk6ejjltNnbFBDJzF5KZOR+1pxO9eSP65nVoHW2IaD9KKgW2hVVVizlpGlLT3PS3fT35p5/H+w45Xmhwt8442FUxh0VI+wJYVbXY1ROxausx6xpcB5V0UPp7Xcm9swVtTztScdPKmg1NaLt2EPzbn1C7OvJPO49/aTiOg2ma408jgTtH5jjOuy+N90Vmw4NVPRGzoQlr8nR3U7NQASKVRBnoRfoCOB4feusW/C8+gb55fT7zRh7/8shkMkgp901gKSW6rucEzr+nR6WCYsxJUzGnznSlcnk1Uijo2zbif+lJ9E3r8uTN430hfdPpNKqqoqpqDoHTXq/XGElgKeUhjdfMI4883hl5h3ZpHCKwcLcnMhVg9Uh1eci7lclksCzrPa9O55HH+xFSyixxh0JYFUXJ8UDrutGvSSnvllIulFJmvxharmVZFpZlZaVyHnnkcXghhMgSd0jyDiEUCrUIwzAMr9f7lqqqDSPV5iHSOo6Ts0l1HnnkcXgJPETioc+D0rdvypSpj2mO42SEECcrivKq4zjlWeN4sPDQxXnkkce7S+IhGIbRW1c36VlFUVrE0CqkWbNmV0QiA3+Ix+PzLcsq4t0Os8wjjzxGktgyDKMvGAy1lJdXbFYUpQW49f8PAOe6+AI7wAjWAAAAAElFTkSuQmCC'
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116ww630A8eRrJhKnYOzajpTTKfahWOEkITziTePoc2L4F0dSCmDoTMekwrFKRcPY8/AvfSu4bn4N8AzqTpXD1t4njBH/yDJgxl2DGHIITToPchchqleycI8n+8L8gCvGFMAa1rxtbK4R0n9Ohoh0H0dhCVpqCGhH4LyxDa1noOCT9h+tRXXsYeM1bSXJ5dNtSyqeeD7kCojpA+pnHyF71JdxH7yUeLOC55dfw0F1YhQINKY/4zt/hX/R21OELqKXzNN78K7jlOkqWgxg/iWxTEzIK8cMq1ZoPUYBfq+EAWA6seZT4qeVkbIt8GFDevI6wCjy5nOiZx8k2NpKKakTFHkJvOn4YkUkSSKVgxaPoZx5DCkGudRTVzesIasDG1YRrV5Jvbia1fQtx53aSCVOIW8fAuEkGJlmzwuADCxaRTqcQxV5qO7YQn7QEP9+MFScIx4V1z6I3r8dxLPKjxw7xV73YRJD2oVoXq7sT+dj92O1rDRInpbGce3YZ11fF9YDeQtuOscbC7JP60w3GQmzbhAx8ZHkA1dhkYlYpoVrGGiia4ocgRDuuQfoQ2Fs3kv7d1Wg3haiVIE6w+nqQfT2IJEI7HiBw163E6ukCnSCKfQYZFeZjFH17yPz+54gkwtqxFVkrQxSh8w0Iv0bqvj8ilEbu2YWoVk0FmesdEqAl/Rrlsy5EZdKwYA589zdUohCRzqAKFsSgJs3Ar9Tw8g3IygDaEuQfuA3rgWXEUhIrRWS7cMp5qLd/iMpr3kjkeDR97TOIJEYkoGfPR65+AlEt7fuRCAFxgigXzSvLN7xwGEUIhO+DBen21YiffAXfD1CeS6glnH0R+oI3UTvmROLeXpq2bETEEYQhWJIUkM1kSJIY7bi4e3biz1uA3zIGtXE14pzX4B1zEtr1KBcaCecsIJk6FYS7F/AcjP4sCy+Xw+33IPT3oty2gw24nouSEhEGoNhLwigkRCGOFHgp14Bv9mCBi4WdziAtiXakwXGikKShGRpa0GnQl30Y3vQeyrZNVUhTxNSagzYIWseS8lLGgMQxlkpwPW+IBPIlzwMfTAmlCGqIziqi2EfSNgbd2IJOZ4inzkBPn4vO5QCJ3NWBvWMrstgLKkGEPnb7WpLJ0wgXn4xOZbC6d2O1r8Hq3oN2XZKxE4mmzkAdewqyaxfu048galWiWUeg0lnszeux+rpRmQzJxGkEi09Gp1LYWzfirF2JrAwgu3eDZROPn4RqaCaZfjh+Uyt2925E1y60l0aLNMkxJ5lwvVarv4AI4hBteyRt40lObMXesh537dOmIOIgP/j4yOMIjz4B0deH/ukvYGAAZdtmjGwezn0dyYJj8BefTGrbWkof+Cy6uZWGr3+ObOceYgGJNqlWUeoldiTVT1+Jf9xpVAeKuCsfJzjpNFh4PPzuZ1DqRzS1mo8vSZDlIqqxhdI/fBEyOXLXXoVoX/OCQDlZ6qdyyTuID59H5oafk3nkAVNIpyAC0Al6zROUv3Ud4dIz8b//BVTgg20hosjgUo6LqleM6UwOEo3u3EH1lFeRXP5xVFMLulwicT3Us0+gH7gd3v5R9P6TphBIyzYsMcMzCHW6JyklCeLAYY9SSMFehpnhx0pp0p+DY9afk/EcgUfugyceRKUzKJWAX4M9u8AOUSsfI1l65lDNv9QaKa2XTHlfWCXW4E1KAa6HamolGTMelcmjU2lU62i0l8ZZtxIRR6Z8EiBRYGmS0eMJFi1FNY/CXf0kclu7ebmWRI0aQzR3IdGs+di7t2NvXodVrZC0jUcVGrG2bgS/is43kEw4jOD401Eto3CffgS7YzOUiyaebWgimjkPnc6aKqIJU+HRe3F6u0jGTUKn0sSTDkM1NCNrVewt67G2bIB0lmT8FJJRY4inzUU99RDOlg1mVv+LiI9x0/1XvR4amhDX/RjxxU8iw2CvBfRSaC9FfMX7CN/8HpIv/wPh8WcQnr0Ye80zpB69B7fYZ3AEraFaotqxiWrFB79GZaBM0323U3nNW0mOOIrw2FNxHr4Dx6+ZD8v1SEZPJDjhdMof/jjsLGJf9yPSL9CNE9Uq4eKTqV1xEUp6FHZuIxWFEAakpYT+LvwooFoto5wUQeAbwNKyYNpsdGsbFHtN+DTzCKKpM5E7d6A62ql++J9JzliCvGUZ1qMPIC0L66E7UV6K6B0f3VvttY8D+PwuqR6eHjzAN6uHGm2eB94dlokQvV2InVtJasC6lchv/9uQoiggaWlDOymcnl1InaCGpSs1L+0qJi+sFrruqtg7tw2lGmQQmhmtXEJnc8gwMAizGpa7tWxEpYy9fQu62Ie1swOrPIB2XYSQyHIJa+d2tGUZF0pYxm3fswNR6jOK7njGHS4XsbdtRA0UEUENTZ34SwgIfaweY9XVQBGnUsJe9QTOpnWoTA5daMBGE0/UiDjG3rQWZ9WTqJbRxm0PfBAWolapI7sHUUFWxw9qC49D1KqoX30PLwrJtTRj27ZpwK5VCR+5i763vQ81bxG+lyV3w4/pmzGX0hUfJ5k+l+y138fauAZUQjTvGGqXfgjyKeS6Z9GA1b4K99F7qL3mzdQ+8BmcTBb79t8g+rpRhSaCcy5h4G3vhwGwfvldSutW4iTRC1pZQ6VSpB69j/CE0/HPvhjZ1Unh1z+C4la0myKZMIXKx7+AGjcW7r6LpKcHCQilUWe+FrX6KcTPv4s6bBq1i9+JGj0a6/abkEqZ3G8M6s+3Yv/827jpDPHp55N84DNgUW+UeRmq9hwHejqxenajyj7JJVfg7txC8y+/g8rlCV53OaU3vBvV1Ezh/a9DaUXpZawutJ+3WOIvXVSSQK2K3NWBWy6Z2mjHRTU2g+shB0pQLqIyOWSlBNqgiVbHZlO94nrQ34PwK8YFSRLknp24tRpO+yrQphEAIbA6NmFpDYFP0joGnS9gde7AK/WBtCEO61VeErTA6tqNiGITgwsgiU1cW6vgrH/WVPmkM9gbViODKtbODmS5ZJoC4hjSadSap01qrFr5y89DJehMjtol7yQ6bj7yrocRO7aScmxcx92rPFLiblpH+uF7qL36FPw3vIum319N9odfoXzyuVQvvJT4hJMRPb0gFDrbSNI6GnnXMtRVXzJE+1FI9prvEQtJdNQSyu/5FMElb0OUK+hCAdU2yUx2P/sWyXU/JlV3H1VTK4wBvLR51baDammD5gZTf30AhdHpDN79t+G1jqH6qtdTfe3bSE49C1EcMO87myNpHo18/FHUj7+KHCgiUmkITA128LrL6Hn1BehMC2rMROzHHib+7yvxtMJ94A4qs+agL/sQ8clnmkKfXAHbtgn7B9Cz5sG4ieClUKNS0DzK5OmVgsSU6TIayDfs/XYF5vsbUwfgvDSqzYGmFuMVJAoQxpUfDRQa9x6bJObY0S660IR37x9xfvp1Bt74HqIPfo7i+a9H2x5q8nQsaaN/8lVq2zYj5x8LbZ65Pts+4HP8q5ZSPqd0wbb/xbbtvasy1K2FUuovz9paIwIfq78X2dNlcq6WDY5pZlBjJxLOXwyWZcAklaBGjSWZOM2kj5LIHBdFaMtCtY5GjR6HamgGaWH195hyu0oFMlnChScQHb7QxJOWRTJ+iskJC7B6u4z7JqSJfcdOIBk1BqTE2rEVq7fbgBdJgho70bjSDc1YWzcid203L1ZKExJMmIIqGGDL6uuGJP4LpkqDlyI66gSSko+6/ic47WvIpjykZQ+5flpKRBRjFXuJUk1EW9rx7v0j7ub1UC4Rp9JE6RSxkyb2UsTFfsT9y7Buvg75yD1k8nkcz8Pesh56utD9PSTpNHE2Z47RimTzBuTvf4l18zVYW9vJNzRgC0EyeTpxRzf69t+T6t6N09xKks4Qb1yPvu92Ul27sF1vv6/DQXbuQOzegSr1o9BEjY3EVorYsolDH+69Deu2GxHLbiSjFJx4JtHiJegVj6I3riGeNZe4GqAevQ/7D9fA/beTSadI93ahw5ikdRSqtQ2VyaG2tmPfeRNyczu6Zw/iwbtIeS4q00Ly2ANYT9yPF/hmfaC2ccQqjbrndpy1T+NlsmgBatQ44s4S+qbrSWUyiFQr8SP3Yq1YTioKEak0KlcgjlOoe5fhrH0a17YQSpFMnka8swd1/zIy7WvxOrcRaUhsh7ChiQhJ3LEJsex3iG/9K5TLOGMnIJwC8WMPYD1+P6koQNjOX784t75KQ1LXy6G/VCqlPc8bWtjMq6NoUXQIbtdgqiebR008jGj2fJLxU1Cto4mmz8G79zay110FgU9w5mvxz7qIpG0s3oPLyPzq+1hdO1HZPOEJZxAcs5Rk0nRkbxfZq7+Js/YZsBzCo0+gesk70LZN+sar0a1jqF5yhUGTu3aR/dV/Y+3cinZdwqOWEpzyKpLR45Hdu/Huu53UQ3eiLYtkzET8My8gWnAcSds4Mtf/gNSfbkBWBkhGjyNYejbBSWeTjB6P98g9ZK+5ClHsOegHXa1WGRgYIJvNks1m91kobrgEQUB/fz+ZTIZcLgeA7/tUKhWSYQisEALXdfE8j3Q6vc/2MAwJggDf959zTCaTwfM8HMepG39Jb28vYRhSKBTIZrOEYcjAwABhGNLY2Ijrus/78cRxjO/7BEHwnHV6PM/DdV0yfoXqh/+V8sc/i/72f+Fc+Umk5xEEQd07dUilUqTTaQM6JQmVSoVarTb0jBzHwfM84jgmDEOam5sB6O3txXEc8vn80Bpe1WqVUqlEJpMhn88PjTEwMECtVqOpqQkhBP39/di2TUNDw9D7GHz+g+9JCIGUkv7+foIgIJ/Pk8lkiKKISqVCEAT7LDTmeR65XA7HcQiCgGKxiJSSxsbGF4WOefAdRFE0ZGyFEC8gBo5jkyqw6mmiYYig8GtYO7aAX8XetBYtJG7raGT3bpNaSmLsVU+SKfaZQvLd2xHVsqnOimPs9SsRvXtQjS2IKED27AGlEJU+rO2bcR+5G1Gr4qx7Br2tnWy1bNzxWgUxUDTVVBrsbe1w5x8gnTFu/u7t6HqjhSj14T1yL86G1eh0Bqt9jSkkcRxEtYrzzKPInk6T2969HVErH/QkJqKQVBJhZdLYqdRzmPSHEJMoxo0CGjNprHQaoTUiifGSGMuSaEvsbYqQEum6SM/bd7w4xk5ipFa49n7H2DaW6yAH0yVCoGtVckKjcjls10XFEVYYkJOgcllsx3nevD9JghVHpLTClQLlDvtspIXlOEjPQ/pldCaHHoMJUYCsFKRdG4REOg5WKoWQEp0kyCggoxWeYw2lfKRtI9FoCel8Hqk1IvRp8FxEKo206vuGIW4UDj3DoWU4A5+MTvAKBdO0EfgUPBfhDXsfUTT0/AffE0Kg/RoZFKlsFtt10UmMFYVk0KRsq05KIRCWhUxnsCwLHUXYoU/Bdcw5XuJ+90OvxCo0kuQKiNA3DeFxvFeB48gUGAwUUa1jSFrbkH09yEq53mCgEZUBZPcutOOZGFcng4XNCL9mOkt833SC1KokY8aTjB4PSuE9fBeivxdRq6BaRiP37DRprTBEqGSfiUT2dUPVgyBE+pUhbFDEEaLUj1QJeClktTxU/il0gqhWsLp3o0v9iGKfKUI5SHReux6Wl8KpLwF5YMRUgOMgXZfMsP20dLFcD3vYMjfD0dX9EVhtWUg7g0yDc4Bjhh+H1mgvhZvO7Pt7OoObzf0FhNe4/ngp7FR6n2V49j+fTmVwVz2Be9W1hI/cC1LiNbfg7ncPepBiyUtjpzL7XP/g78PWwwU7R6quGKoeY2rbxiqY8GCf+61fpzO43XVJDR+rDlTt//zRGu16uKn0vs8jlcbNZIes4PB3opQy7yFXeO45XpEKLITJ1c6ej7VnJ86zTyKiYS2CQpgCjlSaeNocguNPReUb8J54CGvHFkSiSCZOI1x4HKqxBXfNCrzeLqjVQLok46cQT5uNamzG6u1C9naTTJtD7eyLsDevw/7Dr5DlEipXIDlsDv5JZxnAqtRPqthnYnDbRrWOIT58IUlzK7LUj7P6SWRfn2GQyORMc/qUmaimVlIP3lGPwxOU65FMmUE8ez4qm8duX4vV12Xi7IPGGfQ+7uyh7HeolTv7KOnBoMoH+LgO5YP7i9eXzePddxvq5utIohiRyfzFoobnG3P/bQca4386dvh29Tz7HOg9vZBn9HIuvWsfMMd7INRVm2oY1TyKeM6R4LjYq1YY19geFjvpBO2miCcdRjJxmknL6DpZvBAmV9zUgm4cZfolB4nktUEIVTqLGjW2PhGkSFrHokaPRe/Zafa36m2Jtm0aKcLQlGICUmm0UmjXRuULqEKTGdv2jKXXtrmGTB7V2IpqaTPN9XovOZ92HFRjM8mYiaZJ/f8ck8j/tmNG46bTNKYFwnXRL1c66O9V9vN+notCW9bzo9B1sjoRhcj+XlPyGIXGAbasvf6/ZaNdF9nXjbP+WZw1K5C9XSbWq8ehdscm7E1rTZzp10xaSkpEpYws9mDt6sDa1WH4okr92JvXI/fsNFxQAhM3JwlWxyZTiNGxGVRsFDaOEUEN2dOF1dGO1bHJpJmkhNAHlSD7us2xG1Yh+3qGSPpEFCEqZaydW3A2rkbu3IrQjDBqHqwCC4lwXGzXNevbav2KXxhPC4EIaqZjTVqHdr1aG0aaekebeNH01jxL9T+h0LZt4zjO/4hCq3wjpDNoxzH5NGFADlPPZoHtmJuqVRDlEvbu7UMxC7aDKjSiM3l0KmWaFgLfxJpeCtXcapqmgxpW506TQop8VDpHMnYiuqHJ1FrXaqYDKa7zRDmOiZv7e8w5GprBderXJUxXTn8vIvJJGprA9UzCXlgQhchSv6lkymRR+SYDNKnEvIwwQPb21GP1Q6mCUAfoORZDzA0vKAeo1HN5pwYJDIZT+v6tEgcO3qPY7x60Mqm64SVTUg4jhtj/d7G3f3zwt8HxDjS+StC5gqm4i6JDn6y9NLqO4bxYE70QgiRJiON4CDE/NBS67kJHC48jmToTZ8VyU6Y450iSQhOyXiWVjJuAthycTWuw1q9CPHqvUZAkRntpwsWnEB69lGTiFMOZ1bEJa8tGkjETSKbOBGnhrH2a1I1XY7evRiNI2sYRLDmL+Kjj0Y6DvWUj9toVWLs6UPlGwhPPRhR7SP/hGtSoMdTOewM4DlbHZpK2cehUmvQt12JvWkftordDJm/KMUeNQe7ZSequW7Da1xAvWExw7KnI6gCqsRWtNc6mtaSv/6Gx4AdL2iZEncUxfK4HM8gYeShupdb13uYqqqFp31rewDcWoE6igLTQ2dzfntsqBETREB2PzhWG7kGEAQTBMEYXTJlsOgNJYp5N4NcLNTD0PekseCmEHxgyiTqBgqiWwXHRmeze8StlotMvwF6zAtmx2VQQHux7kdKElKGP/dTDz+E7e8Wh0AZF7sTq2o0u9qGaWtFhYDqRLAfQKMetu8DbTYw8mGpSCbJnD/am1Sa32tCM8GvGrfZrWDu3gVZYm9fVEWpd73aqYu3Zge7YZLiOesz5rd07EOUBZM8e00guzGwsKmVT9dW1C6RlKmzqM6+IYxjoNy8yjqAysJdQII6Q/T3I/h6IY+M1RKGZ2Q9hYhXVCrULLyU+eilks/W63hixezfeQ3fiLr8bHQZ7FVyD9lyTWvOrxgoMbfdQzaNI5hxJ7fTzyf/wvxDdnYbqVUF43EnERyxCVEoGIyj1kfntL1C5DDqTQ9SqJlWXb9jbWRVHdRrbwFybLVHZvFGEKKzjEoBro/KNJkwql8w26vvnCibkGCgZlpVEGa8Ghq5fZ7PmfQ30I4I6N5bATELDix2EQJT6SabOpHLeG7F3biV987WoVBrimHjeYoKlZ5FMnoawTe9t6o+/xn3oblRTM/ERi/BPfTVq0lRziu1bSd/6a+wNz+KfcAbJ7Pl4y++CgRLBGRdgd2zCu+dWlO1AUEPNXoB/7uvx2tfi9vUhUw7Ur1d7hv9K+DWT/WhsQYS+8RzDhNrrLydecALOshtIohArlX5JAS37kGZIrbE3roYtG7F6Ok2OdqAfncmbFBKGuAxLYvX1IOokZ4M8VyIKsTevQ/Z2Gvd5UCtsx8QPUQhxiOzcWVdgQNQrsh6/H3v9KnBdU+vc3Yks9SNtB5EkqEwWWW8ESC270UwIxT5ULo/KFbD2GJfcffjP5kMNQ8PeYdvIUh+yXMJe+wxWx5a6MqTNNSpl2uMOxe3VitpbP0A0YRp07TAuXCEHzWNR+Qas+25DLViMaqw3fCchsq8Pq7eLqHkUOt9gLKkQplClXCI88Wyqb/sQ3rf+FTHzCPTo8YiBPvwLLyWeOQ/ZtRvVOgbRtQtn/bOmNLV7N2rMBOIJU7C3b0EUe837SGWIJx6Gah0Njo3wA+yOzSSZHCqXr3NKS2RfL9b2zSQtbSQTp9Y5rRViYAB75zaUtIgXHm/aLh3XYAm2bXiikwRr93as3i7iuUejWlpA2ogkxm5faybOQYkidDpDuPQsKh/6J+wnH8O58efIVBpcj9pFlxMceRyJEOBZMHEyGoG94nHU5BnULnkn/vGnoeLQfDOnX4CQDtlvfo5g6Vn4516C3LAaXWii8q5PkvrtT7Fv+w0i56BbRpvxl5yMXnYEcvd25PjJJE3NJvTq2onVuZNk7ERUcyv2upWoljaStnHI3j1UX/tW1PSZiOV3mCq7lxWFPgiXQZaKZmaqxyGyrwf6e4fCDznEW7UXeR4et6h8A8mkGZBKI7duwNnwbF1RZd3k1OMUxF6rpxJkqYgsl/bGesp8NIABquqxuKgMGAta572y+rvNb3U01Fm/at9YafBatcaqVkDsfA51J0l88LGN1tDYim4bh/zG5+Hqb6G0gtkL4N++RzB2Ipblot75KWqnn2vID3SN1J3LyNx6HZUL3kJ45GKQNjqdJfOL75D76ddR46ZgP7WcYk8P9qe/SnzeG9AqQcQJ1gN3kJT6jeIJ6PviT8jefQupX3yL6MSzKX3wn2n8wsdwHrjdWJyJh1F724eonXMxFFysTVtp+OpnCRafjH/kYtS4SejGLN7991P4j48QHrWE8v/7MrqxACrBWreOhq/+EyrfSP/nvoFV7CMZMx7vgTvQqTThkceB1uS+/0UyN11D9b2fxj/9LNAuYqBI8yfeilz1xF6vqFYhPuZkwpNfhYg1cb6BciZPAaCxFf/083Gv+yHW968ktF34xrX4845BHrMUe/4x1M6+EO83PyP56meIlIaf3UbljNfgXPNddDqD7u0i3LQeNW0WGojWPEMtCMkUJMkRi6idcxGqKUVwxgVYDa0kS04jOOlU8GNSN/2KwlVfJjzrIsqXXE7rFecSLj2b6mvfgqhUiCdNQ80dTTh/Me7Nv36Z00gHI0lsFGCQrCuOhtgwhj7gehyyD7ujVmjbJpk8nWDJmejmUbjL78LetAZRKu1LcCet5wIxSQzxXrZLs3/9gDCox+j1VQ9Ushcw2odAT9RdYrGXvG44IKLiffm5BkEh2z6kSS4+4miU46Lf+k6YvwiKPdA8GjudRdx1E36lTMO1/0287LeEpRL8438QtI6GWfNNDrpUgjtugsUnEZx4NuKRe4lb2xC7OtCXXE5y3GnIH32VePwU9MVvQWzfAn4V5i5APvsk8awF+KPHY/V2Uz3vDchalYFN68lVq3iBT3DMUoITTkcs+wPqjl8T10KKu7Yh3/xekplHIH7+PfTUwwgWn0rtsFm4999OOgyo+lX04UcRv/fTlA+bg93ahm5rJr71tzBlGsExJ2Mvv9uMe9qr8afORk2bS3DSmchlt5L8/ho0UFz9NIUkwbasoT7p4JRXEQuJe9WXCc69mGjeMYhVj9U5zlKER5+Adck78Hq7cH/zI4LO3VTzBbwlZ2LfdzvhL75Hqr+PNMAPvkQlnaW2eyfR2InQsRm9tR2x9Ex0GKAfewDpuMYgbV6L2L0Dip3oNc/gL1qCXatgfeBNxEcsJjjtVVQnTcd59jHUOz5G6e0fQy1agnX/MuLtW1Dv+Dj84RbCP/4G37bJH7D67sWTQ4crpdyrvIPKZtkG4LEs87Hb9r4KNmjvwtCwZHTuwNq5zYBbSLO/45i4yHYOjKJKac5jO3X2SDE8GWa2D6KLtrP3GgZ/G0QlB38b3G47e6/VqqPoQ/fhHJry1gPXeOY8w7wYheiFx8GH3g/nXmQaE266Fsu2ET17sFc9Bds3Q08nqms3wYSpEPiIP/wK5xv/gnvvbcSjxlLNNZIUGqG3C3n5R5A7t2F97bO4P/8Ock8XqjKAkhYqCtH3L8Pa3UHc2II/eyHRzHnIq79NvOZplFaIyOTpdaEB6fcjN29GPvs4ons3cds45CP3Ib71LzjX/wykpNo2jqQ8YFJ+mzfCrq1Q7CMo9hNNmYm1fiP89BvYd91i0Ny7bsG59irklg3E1Qq1KEJn08hiP3RsQq54BLo7hxrvRRgSLTwB/5wLUWMnmIk1nUEfcRQqjhC9nWR++V3U2mdJTjsP3vQeZC6P9/j9WEIQTZmOfcNPkeufxW1qItvSTO6235H93S8hk4O2sbB6BToMsGYeDju3QfsanIwBEq0Nq0w69MEHEV27SGbMRTc2Ye3aBQO9qNHj8VvGIB/+M86qp/Bf+xaigX7E1d/CWn6P6bS76SbEU8sRmczfgAV+wVOFBXGMs+pJ7E3rDKtkUDNAimXzdyNaowpNyFKR5ANvMjHnt6+H3TuIrvoSsqeT9Nz5lD/xnyT5BmOhMxLre9+EiYdBTxfysftx/QqioYGwu9PweuUKyJ4u08729c+jXI9MPk+wqwM/3wijxiA6NqFXPoYT1AjHTyZ62wexdm8n/v0vSCcxTq4BrWLsrZuwdm0nuvw9cMm78e6/jfz3r6SnbSzW9/4TVS7i5XOwZyeRm6Jy0eWI11+BGj0OxjbBkysRj95H8oZ3IjauwdqxCattDGLTOuIVjyBHjcZxXaKdW5FbN8L2nURvfgdcfDn24w/Q8Nl3IXq70XEM2ZyxvhMnQQLB+z4FA2XEqLFEUYQbBmS/fyXxnj3El3+E6KwLCd7zafJArlykXymSYh/ZdArXcUgA2tpI1ypYx5+CdhySLevRbWNg4lTY1l6HXZx6OWmWZMZcrLtuhdnzSRpbCKdMgV/fBx6weZtx8d007s4OomOWIH/2HaJN63Eu/wgOEAQ1UkCuXnn2Uop8yT7qdIbgpHPxz38z/jkXU3vDO6le+kGCk88x+V2t/m4UOG4bC6V+rDCgsGkdmUfvgVFjcI5YSGb0GMIrPkFi2dj/9C7kw3ebMH/tSnQuj2pqxdm9HTFhMvGxJyO3tYNSqJZR8NTD6KYsse2SthzipWeihMTK5pDNLbCrAxEGWNUKesp0knMvxrnpGkR/L17Kwwp8whPOpPaW9yPv+RPeq46Fe28nOnoJlYZWdCZDYtlkbTM2pSLi5HNg6RlYP/4a9pf/H9Q0FPvQu7ahJk2FPbtwggA1YQpUyjgDRURLG9r3EdNmo9//aez7luFctAQef5Bo9nxqSqO1Qvb04J90LvGxJ2P/8gdw+XnwqXfD6mfQCxYT+zX6v/Yr/IuuICeg+epvkrrzD9DQiN/YQlDsh6YW1OFH4dZ8rN49hK96I/1X3URyynnoSdNhoIjs3oNoaUO5KeSOrSZSq2dGkrkLIZsnue23KCEQvXvg0++D1x0PFxwP776A+JF7iM6+mGDREoQfoKbPJZNJI8ZOQPf1YXfuwIKXvJHh+S3wX9uHr3NIJ5MOMyWKcYQaNdoQ1JWL6DVPm1X5/g5WXRFaE81fBKuewrYkHhrnkbvxT3k1nP9G6OsiPvYU4oyHPHwhVncnKgO6WjY8TJOnoy77EFFjM1pIrPtuRx2z1CD8zzyG1b6N5JzXkTQ0Elx8uYntHBcaW2GghB1FONs2oZuz0LGH5OZr8Sxpum5qZvXDZNxEdBJgrZiCjALkjg5qM+aC0qjXXYaaPRd19BL4xfcQx5+GOuF01LpnoXs3or+MThQi34ie3Ip++jHsfIHwqBMQ9y0jVeonnrOAxPOQe3ajjj+VxIrRYyebDrOVT1ArlUj5Psm8o/Df8j5UVyfWj76ObF+LVe9dDt//ScJpc4nnHEl17lFkxozFWr8S5izAbl+LWvU08c4dOLu2E7/xXfiuhcgWCE99DXHbGCqlEmrqLFSisXs60VNmELeOxtrZYT76OgVSfMQiVGMGveQMnHIJFQaoiROh2I3sK2L19KDnHU104duwnniIJFtAv/qNyD9djxo7kWjODOy5C7FWPo6OwhfMJX5Q4OhBKfDBMnIc6ocdRdhb25F7dptlN9IZdK4B2bNrL2nc30PNbJ0vTK9+ClnqR3kO1uYNeBtW4ucK+FGCveZpwlNOJfjCD/Du+ROiox/dYJhM8GuEr7sMpIX7w68gH/oz9rv/EZ5aTtzfS/aPv6b2hndQGT8Z4biI1SsQvd3IxhZ0b7exBP094CvEDT8h3NpOrqGAQKC8FM5TD+He9hsqV3yAeMkp2CtX4tx8LfHsBcjuTtThR1JetAjv0eVw3Q+QxX7U/EUEl34QuasDa9Ma1K4OxOhxqB1F1GP3I/MNSK1JVjyCLJeQ6TSqpxN72e+x/Br+pz4Fs3+GtWEV7veuJKoYUv3o+NMNh9p3/h3RvpZ0IU/aSxGsXUFfbz/hlFnkf/Edym95P6U3v9eQ5XX34v382+jld2Pl8th/vomB113GwKe+BLHCWvcs3lc+Q3DvbYjz32Twlr5u9OELiQb6sTs2Y1vDyOuEQOzYg37Te/B+8xOiWfOI3/8PgIXcuJnUnX9ANbcSWJLMVz5NsnAJyWe/RtDQgiqX0I0uesbh0NCErhTBcV/cb2t/vdq/lNK27X1Ktv6aMbDO5vfyLQ+CYUm8d/WEvxMXOpl7JH3r15AuF8m6phc1GTuRsu+TbNtEYeZcyq5HGCXQ24WTbyCeOgv9D19AXP9D9EN3mZe1YyvpWpnUtFn4SYK/8kma586j1thKtVo15+vajWtZ2I3NqPa1WJYF/3U1lcUnwrkL8Ho7yecLQ+6dUAo1ZgLlphb8IICBAVOW9/F/N0wq//0FVBxBfx/2lvVkJ04hHjuJShCaNYWTGBkGyN49ZOYvov+he2hqbETMPZK+VU+TKRfJzJ3PQBQTb95IfsJEgrFjqQ74UC5hd+4glUSkbRvGTqTW0ERtwxrcYh/5pkaTScwVCCZMpfTMkzSOGkXUNo7K4KofYYDcvYPUQB/pXA5GjaXS0IyfJKaYpFzC7t6NVxnAmzOfSq1mesfHTSRqbMFvX0c2DvDSGZNpbG7Db26l3F/E7t5FetxEapkscRSD72OX+kjl8iSuS/zsU2THTSSZOJXKM0+QnTCZqLkRf9t2Uj2d5NPei2eBMV1RSZLsU0r50ilwvRxwiNFSa1PgMEgM8Pey8LcQyJ49lBNwCgWcdNpUf5WLhEFMnEqRURFBmBDVE1UuUP3w50ne8l70R96Eu/weBpfzdvM5vDgirAVEuRwZFRNXfQYXTFGAIwWO0gTTZ+N/7PPII5ei77qF6HPvo6Gh4TlMG7JSJqzWGOTaVNNmEV69DOuuWxH//P6hSMbOZkkLSMoVfIYW9jPVTpk0qWqNAdchm8tjFXspa7AbmkhHAUG5SuS5ZGyLpFKjVr8faUtShUak4yBqFZJSmcCS2IUGPNc1IFDgQ2mAshRkXAf8EJ991wbyGhtxHBtdHiCuBYTDfrcsidfYhFMtU6sFqGyWtBQkA2UCx8ZrqLNm1IkGVblCrZ76TKGJlKH1HTqXLSFWhKkUWddGl8pUHJu0ABXG+ICdy5HKZF5UL/JACmy/lB/2S10n+rKh0M2jyNWpW7RSpiG+0IQrBC6mf9QTYqgJnFoF5bkEj99PUqviWZJs2+ihZnOlNU5e4NSPtXMF8vs18Mv+PuKjjiM57VzU8odxf/0jsKwD0uSobA4nl8cVAuHXiFta6O/tQqxZgZPPkx1kzaj3uVqZLLn9mtlNqWSOQr0pfvg9J7aNk8kNXa+VzQ9d71APs1JoL43VliEz7AMFzDrFo1JDY4uCGDr/8HvWWkOugJMXuAcgQUhyBbz83vNaaXMuPSxE1OkMIp0ZunYGJ6kDjDd4P4xKk69fmyXMuV+unuCXzgKPyP8c2zS1UI0Tou49pIUmlT7ElIRKIN+I39DEQOdurGIvOdd9Xp6rfc7tesRNrZR2dJCOQ1KDNDMj8oqSl9eFHpH/0TsR1QqxX0N5Kax0Zm9J6kGPIRGhjyoW8TWIlIeXL/zlHgwhTP/0QD81LXDyBSzbHnknIwo8Ioeux891217IGOIF8jPJYW7ziPxtKLB9wLhiRF4mT1r/VcZ4oeO81FVEI3Lok7vWeh/aWjmiwCMyIn87E/z++mnvPwOPzMIjMiKvTPf5QFTFcn8TnSTJwS2rMiIjMiIvmSRJMhT/7m+BA8AbJNMeZKQEhpgqR2RERuTlc5vjOCaO4yHC+0ElFkIkNrBCa33sgXxtpdSIAo/IiLwCFHhQeQf1UQiB4zh9ttb698BsrXXDcL7ZQeb6g1llYERGZEReHOUdHuIO94gty4qz2dx2y7KsbbZtn6W1Hru/Aj/fYCMyIiPy0sigPkopzWJqdaqndDrdOXr02LU2sG3MmLFf9n3/I0EQHKuUkqCGEsX7LEg1IiMyIi+5AgNDa3dblk06ne5ubGzYnE6nN9gAo0a1XV8qFSf29/fnwjCcqpTKAHJ/azyixCMyIi+98tYVWEkpB1Kp1K7GxsbNhULDdqVU+/8fANRQTK2s9sySAAAAAElFTkSuQmCC'
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'
button_role_mass_mentioner = 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mUZ6nqDKvzFf30wmKQJNTNtZaUzTwTIq3+0nt6hg6a1vt6G4YttbPTXTVrkTpaMN3e3WxflwVmRz96ypub7vlkCfj8iFc/puzQCVSUl1NRXo7vkRegxsT3yAsU9+vXMz3y09Me+Ct6+UAwTaJX3pxrne1NnbvkSx17CpETT7XSVy2z3rVskfX/S64hfOHVYJoYFZU0PvmmZUj7fAOSJKF0XcB22Ec9zObDjyZ2ywPd3sGqZciyjGfxu1a+K64ndPYldj+GfvpbxKow8ns1VJSXI3X9XPkV88Mu/fqCCq3UbyMZLCB+0Fj8C95Ezrj2SVLniatemaHNNCPuQmwruzNyj6wkx4zHCBYiNTf0uVU2cO+NRMdPwjhoDPUvfJArlZrqif7qJkQ43Ovrb7LpNbz4Yedqvrm295boadOJTJtOvlPzYu1KzKcfxlVUhPqXh1COnox22ESi195G9Nrbcvac5h3X4dpTauqmGrTpl1E3/bKc/jFvvxa3quJ671XU+W+SPGYK7ff+ia5fvY0/zqKpuZkQ+w+6SeDMtP4qf74l7yMl4hgFRUQPOwZDVdPPhH3iqre0MHRb+vYuv2EZunTdXswTw0aRGDEGDAPPgrd2u332WrEbebuml5470fpEko0lCzBnXoC8eQM+n2+X6BV//3ykpQtyjG7Ggnm9qnuPiEasTzJTx6CqKq70uBZffSbKnCes51l157unIy1+h1Ao1I1uvneZO0gHYOUS5L89nmNFzvSPPxDABIpu+I5Vl8b6nHzMug3zqYcIpPOxi+O4t37dFsuu3kiBQIBo9CuOViHJtJ5/JYlRhyPFIvT73S+Qm7bTftH3iB37LZQvNlH0wK1IbS073s6WVtB66XXoJRUE5z6Ld+FbO/XpDZ9+EZEpZyK3NlH48M9R6rbQfsm1xCadgohFKPrNrbg2rNk9I108TltbG4FAYKdGrEgkQjgcpqCgIMfamaHRNT0cDud86vB4PAQCAdvA0xO9fOmGYdDR0WF/Sw0EAvY7upbPRiqVorm5uce9vNfrtWl1bWs0GrUtx13rnqHr9/tzyre1tRGPxyktLbVtBYZh0NDQgMfjoaCgIKesEML2aVdVlVAo1M0AFolEiMVi9jdoVVXxer12m3uqy75GLBbrA95Iho5v8Tukho7ECBURHzsB/7yX7U8/vVehsVRoQ6fXJ2B13TJ8aRoilUTrP4jksEPANPH+503kui273TyPx9PrTw9+vz8vk/dEIxAI7HBC9UQvX7okSRQUFFBQUNAt784sveXl5V/Kar0j2j3RzVdHSZJy8nYtu7M2fNm6OFbozCeFz1ajbN4IQhD7xhRMrw9h6FkMvAv74F2JY20atuFLaCniR56AXtofkUriXr7Q8QV2sF9hnwZ2933wNqnqEeglFSRGT+iUpqKX9cg8FyK9SehFmbQVWupoJTWwmsTIsSAEnkXzULZs3GdB7R042DMMvBfhXrUE19ZakqFxhE89H2EYILt6L32/lPpuIJIJjFAh4annWV5Juobng3f2iSODAwd7lIH3tj9l4JVnaCsuRx8wGFTVVqG71kMvLEZKxBHxaF4p2c3aqrgwggVIHa05wd5Nw7SidQQLoZ8VMM//8jMotetxZK8DRwLvagU211L4+18SPf5UEkcej9GvNM3EnXlS1QfRfuH3EIkYBU/MQm7sPAvbk8ErctqFxI48Ht9bL+F773VE9jUt6TKuDWvwzf8X6qqlTkRKB/sl+kRgd7mhjuCcx6zjjGnnhGxoBwzGKChE6z8IPdTlxoYeDF6pwUMxVTda/0GYXc7gmrKC3NpE8PnZuJcvcpjXgcPAewQ9HJUTumar1iInlA6doXa6QKSSVn5D7x5+x46vtWea3/znt6hftJ3oVZ3B7MwBg2l4u4b6RdsxBwzebyZEw9s1xC++pld5k6dfTP2i7d3aaA4YTP2i7aSOm7rb9Wl55J90/OyRPtlX+ugJND2/aJ+Obx9jYKPzl8uOPTBcmnHzMXFGMgspV0JnzltL0i75Hu9wINPHD8MeH7GYpapHL7racgGMRmhY/uG+uzB9Fyek6fXTsfAdEomd35/ccck1uJ9/EqpFThvFF59TemQZLXOeRNN2L0SRXjmE1NZN+/yygXxIjZ2AUVxGyyfL9tn49j0JnDnb3LU/RA9MJ8mdDhBdGTizl84uIonOM9d7osoDBluMuqkGKgZimibmgMFEp55jRX5Y8zFCCGLfu9WWyNnSOnrVLXZaZjU3Bwym6flF3fJmI185gMgN9+Sk66Mn2JK15ZF/5n1/pl6tP/2t5ei/cB6p79+Rl34GTc8vQh9YReKcS3F//3aoMWl6dRX1i7YTueEemv/+AWVlZRhnXUrj3E+oX7SdhrdrSJ5+sS1Zm55fZD9ru+9piymOm2q3ven5RRjFpRgfL845SRW54R67XPOf37IlYaa+kRvuyUur4e0aUsdNJXLDPTQ9vyinLZky+fov33gkT7+Y9mtuxfT60Z55F+34aTnt3BPax/7HwPY55XzOCaI7owphRbDMK00z+aXuC4HcA9N/CSROPM36Y+HbiPIB6LpO9KKrkTfVYtRvQ9r+Ba5jphCeMRP3ff+HGCLBkgWEjzyOaMkAwjNm4vn5D+DEEej9ymiadgEdV96EiERQhrvgudmEp51nS/bMopGvXPSq/yN+5CR8l50C1QJ9+zZazpxB4thTrIm2fhXukR5Yu4LwkccR/sbJnfWafBC6xw+N2xFVI4heem03+tlSpvBn11p/nDiCxGdrreH790vIwxQiHh/UfkrzsLG0XXcH3kfuQh6mYL70LK0XX00ymUQ7aDRmOIw0cQDS3x4nccwU2vtV0Pbje3B98C7KcBf6diuwgrFySY6fb7J6BCYm8pSD0b41mua7Z6OmyzDzAiJnzyBS3L8bLdPrp/WjhSTLB1j1Sx8H1QdWEd9UQ+uVN+ftv/Y846E/90eUZQutwAbTv0HrNbehvjPXaufnG2iZdv5uax/7IQNnXAP1/BI1r6osWVK4WzqdqnJOi7Py7wENWu9faUnajjYoLUcbNITo1HNQ/vYYHDwWqW4LypL5eP/+JPFJpyDe2QCHH43UUEdC0xCxCPFbf4PrjocIzJmNedcP0Tva0EaMQn9rPe4DKvHPOKnb+fR85aInnoY+sIroE69BjQmHHw3BELGBVZZk/ckVuFwuXLEoUu164t88HWXZQuJPPEBBy3bcaz+GlUuRFSUv/ez4U6mxExCxCGxcT3DEwdbfP7kCr9eLPPYIRN1WzAuvxPT6idzya/RPNTjvuwh/gHDpAZheP+bv7rLOTssy0uZa4t88A9PnI3XTFdY56U+WIDbXwsb1ORLYKC1HmveK9e7r78AsLiN29qVo61Pw4DOWJL1kZl5aZu06jAOHIuq24nK5bEmpv/ws2jdPz9t/env+8TAqh6Js32pFU4lFiJ99KeYry/CvWob6P6fnLLpfEwZO+/fqPZ1t7nJOWoh03Gi5ZwksddGhpT0rgbXyAYiGOuQtGzGLy9Avvw55Uy2Jd14Hrx99+Qck7/szySOPQ5YErl/fBtEIxhebUTZvoOT4Knh1DqmScsIzrkX9/k8puu8mPD//AWbNehKHHU300Rfpl+XvK774PG85Y1AV8u3X5jqozzgZY0Al0qYahBD4fD6M0nJEuB2qR8Bna3G73aiqinbgUORIB+rW2rz0vV5v7sKVpqkNOwTRWN9Jv7gMc8tGzOrhludPVn3M8eUYE0+wNIlXniUQCGAEgoiGOqSBByJtqsEwDHw+H3rFAUgNdbajTUb70AdWwZbP8Xq9GP0rkdet7O6cD4geaOkDqzDTPsL6oGp7Ieqp/1y3X91tPIpGjctR7/tdMhl5zuMYdV8QOXsGxm/+ulcCzPc9I5au57m4rIuzP3n2tPRkxBI9SHKJPSGCtQOHIjasRa61oj0Y516OtOAt5Ekn25PUGH8sYuE7iO9OQzvzO5Zz/tbPST76D1qemkfZHdfg/sO9lkruC1K/aDseWabwuvOR1q7AjHTQ0tLpmdV239PdyzU2IGIR9BOmUVhYSCDtpF48+VTE0JGI2vW2GqoPrIJVyxGRMPpRx+NyuYhedYs9sROPvJiXfrZhK1k9wqaplQ1A+mSpxRBpQ5jxynM2ff/IsRRNvxSxKkzo+jvwjj7CDmGrKAqpquGIDWsRAozKapRvTCF13FTik062+jZL+mrDDrElZibdqKxGOeMiSg8dj/xeDd5H/o5fUTDz0JImnmiVDxUixk8i8u2L7YWop/6LrYp2H4906B1j5RL06+6k4e0a+j3zKP57bkQ01aO1te4Vw9s+icixw2gZmfPQRp4IFdk3MNj7sSyDVNfIBUKAmb5HyU7P+oy0J6KLDKzC3LIRdel/rHc21pP8+Q8QhxxqT1L3R/PRpl+Gtj6FkpEkrc14/vE02qAq6hdtJzHrKTzPPwl3/RDl/Tdovfl+Wpe2IMoqUH95g+U+ln6n76W/di/39MO4n30MjjuZ1qUtRE89H/d9/0fzvLnoldWYHW0Ws0xKq4zv/QvvXx8Fn5/wig5ik0/tTP/nX/LSF1l9bJSWwxebLYlWOQQz3IaqqrZqbdauI/TsH8DnJzJ3OS2/egL33Dl0/PqnJMr6I9LSENO0rPhbP8fz5G8RjfWk/vwv2q+5DdPrR99cazn8Z6J+HDzOlpiqy4Xv6YcQjfVov36ahhcWIzVsJzXzPIzZs/LSUpf+x5LY199B+J4nrKmxYR0ulwv/32bn7T91/pvdx6O50Rrvv72P7x9PIRrraXhhMZG5y1E21SDfciXSHppjO4rI0c0f2Ov17hXdPe9niTO+Q3ziiShN9RT8/m6kdkvqxI+eTPi8KzG9PgrvvRHXp6usyVYxkLaZt6OXlBOY8xied1+zD2W0XXcnyYPG4v7ofQLP/QGpvdXaG505g+jJZyO3NBB66OcoWzfu/rfThgZCoRBut5uGhgZLperXj2g0SiKRoKioyPaFBctVMB6PU1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button_reaction_adder = b'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'
|
"""
Some functions about numbers
"""
###############################################
def is_number(v):
try:
v = float(v)
return True
except ValueError:
return False
except TypeError:
return False
###############################################
def get_number(v):
defaultVal = None
try:
v = float(v)
return v
except ValueError:
return defaultVal
except TypeError:
return defaultVal
###############################################
def represents_int(s):
try:
int(s)
return True
except ValueError:
return False
###############################################
def represents_float(s):
try:
float(s)
return True
except ValueError:
return False
|
"""
Some functions about numbers
"""
def is_number(v):
try:
v = float(v)
return True
except ValueError:
return False
except TypeError:
return False
def get_number(v):
default_val = None
try:
v = float(v)
return v
except ValueError:
return defaultVal
except TypeError:
return defaultVal
def represents_int(s):
try:
int(s)
return True
except ValueError:
return False
def represents_float(s):
try:
float(s)
return True
except ValueError:
return False
|
"""
There is a fence with n posts, each post can be painted with one of the k
colors.
You have to paint all the posts such that no more than two adjacent fence posts
have the same color.
Return the total number of ways you can paint the fence.
Note:
n and k are non-negative integers.
"""
class Solution(object):
def numWays(self, n, k):
"""
:type n: int
:type k: int
:rtype: int
"""
if n==0 or k<=0:
return 0
elif n<=2:
return k**n
soln=[0 for i in range(n+1)]
soln[1],soln[2]=k,k*k
for i in range(3,n+1):
soln[i]=(k-1)*(soln[i-1]+soln[i-2])
return soln[-1]
"""
Note:
Choose the right boundary condition
"""
|
"""
There is a fence with n posts, each post can be painted with one of the k
colors.
You have to paint all the posts such that no more than two adjacent fence posts
have the same color.
Return the total number of ways you can paint the fence.
Note:
n and k are non-negative integers.
"""
class Solution(object):
def num_ways(self, n, k):
"""
:type n: int
:type k: int
:rtype: int
"""
if n == 0 or k <= 0:
return 0
elif n <= 2:
return k ** n
soln = [0 for i in range(n + 1)]
(soln[1], soln[2]) = (k, k * k)
for i in range(3, n + 1):
soln[i] = (k - 1) * (soln[i - 1] + soln[i - 2])
return soln[-1]
'\nNote:\n Choose the right boundary condition\n'
|
ENTRY_POINT = 'f'
#[PROMPT]
def f(n):
""" Implement the function f that takes n as a parameter,
and returns a list of size n, such that the value of the element at index i is the factorial of i if i is even
or the sum of numbers from 1 to i otherwise.
i starts from 1.
the factorial of i is the multiplication of the numbers from 1 to i (1 * 2 * ... * i).
Example:
f(5) == [1, 2, 6, 24, 15]
"""
#[SOLUTION]
ret = []
for i in range(1,n+1):
if i%2 == 0:
x = 1
for j in range(1,i+1): x *= j
ret += [x]
else:
x = 0
for j in range(1,i+1): x += j
ret += [x]
return ret
#[CHECK]
def check(candidate):
assert candidate(5) == [1, 2, 6, 24, 15]
assert candidate(7) == [1, 2, 6, 24, 15, 720, 28]
assert candidate(1) == [1]
assert candidate(3) == [1, 2, 6]
|
entry_point = 'f'
def f(n):
""" Implement the function f that takes n as a parameter,
and returns a list of size n, such that the value of the element at index i is the factorial of i if i is even
or the sum of numbers from 1 to i otherwise.
i starts from 1.
the factorial of i is the multiplication of the numbers from 1 to i (1 * 2 * ... * i).
Example:
f(5) == [1, 2, 6, 24, 15]
"""
ret = []
for i in range(1, n + 1):
if i % 2 == 0:
x = 1
for j in range(1, i + 1):
x *= j
ret += [x]
else:
x = 0
for j in range(1, i + 1):
x += j
ret += [x]
return ret
def check(candidate):
assert candidate(5) == [1, 2, 6, 24, 15]
assert candidate(7) == [1, 2, 6, 24, 15, 720, 28]
assert candidate(1) == [1]
assert candidate(3) == [1, 2, 6]
|
def fibonacci():
"""generartor of fibonacci numbers"""
a, b, n = 0, 1, 1
yield n, b
while True:
n += 1
a, b = b, b + a
yield n, b
def triangle():
n = 1
while True:
yield n, n * (n + 1) // 2
n += 1
|
def fibonacci():
"""generartor of fibonacci numbers"""
(a, b, n) = (0, 1, 1)
yield (n, b)
while True:
n += 1
(a, b) = (b, b + a)
yield (n, b)
def triangle():
n = 1
while True:
yield (n, n * (n + 1) // 2)
n += 1
|
DATA = {
"website": None,
"myspace_name": None,
"last_name": "Bizness",
"reposts_count": 0,
"public_favorites_count": 0,
"followings_count": 2,
"full_name": "Nonya Bizness",
"id": 12345,
"city": "Los Angeles",
"first_name": "Nonya",
"track_count": 123,
"playlist_count": 0,
"discogs_name": None,
"followers_count": 54321,
"online": False,
"username": "some podcast",
"description": None,
"kind": "user",
"last_modified": "2016/09/29 05:31:52 +0000",
"website_title": None,
"permalink_url": "http://soundcloud.com/some-podcast",
"permalink": "some-podcast",
"country": "United States",
"uri": "https://api.soundcloud.com/users/12345",
"avatar_url": "https://i1.sndcdn.com/avatars-000067787890-onwv2r-large.jpg",
"plan": "Pro Plus"
}
|
data = {'website': None, 'myspace_name': None, 'last_name': 'Bizness', 'reposts_count': 0, 'public_favorites_count': 0, 'followings_count': 2, 'full_name': 'Nonya Bizness', 'id': 12345, 'city': 'Los Angeles', 'first_name': 'Nonya', 'track_count': 123, 'playlist_count': 0, 'discogs_name': None, 'followers_count': 54321, 'online': False, 'username': 'some podcast', 'description': None, 'kind': 'user', 'last_modified': '2016/09/29 05:31:52 +0000', 'website_title': None, 'permalink_url': 'http://soundcloud.com/some-podcast', 'permalink': 'some-podcast', 'country': 'United States', 'uri': 'https://api.soundcloud.com/users/12345', 'avatar_url': 'https://i1.sndcdn.com/avatars-000067787890-onwv2r-large.jpg', 'plan': 'Pro Plus'}
|
# parsetab.py
# This file is automatically generated. Do not edit.
# pylint: disable=W,C,R
_tabversion = '3.10'
_lr_method = 'LALR'
_lr_signature = 'leftpuntobipuntoleftcomarightigualleftcor1cor2leftmasmenosleftasteriscodivporcentajeleftpotrightumenosumasleftpar1par2leftt_orleftt_andleftdiferenteleftmayormenormayorimenorirightt_notasterisco bipunto char coma cor1 cor2 decimal diferente diferentede div entero id igual mas mayor mayori menor menori menos par1 par2 porcentaje pot punto pyc string t_abs t_acos t_acosd t_acosh t_add t_all t_alter t_and t_as t_asc t_asin t_asind t_asinh t_atan t_atan2 t_atan2d t_atand t_atanh t_avg t_bigint t_bool t_boolean t_by t_cbrt t_ceil t_ceiling t_character t_charn t_check t_column t_constraint t_convert t_cos t_cosd t_cosh t_cot t_cotd t_count t_create t_current t_current_user t_database t_databases t_date t_decimal t_decode t_default t_degrees t_delete t_desc t_distinct t_div t_double t_drop t_encode t_enum t_exists t_exp t_factorial t_false t_first t_floor t_foreign t_from t_full t_gcd t_get_byte t_group t_having t_if t_inherits t_inner t_insert t_integer t_into t_join t_key t_last t_left t_length t_like t_limit t_ln t_log t_max t_md5 t_min t_min_scale t_mod t_mode t_money t_natural t_not t_null t_nulls t_numeric t_of t_offset t_on t_only t_or t_order t_outer t_owner t_pi t_power t_precision t_primary t_radians t_random t_real t_references t_rename t_replace t_returning t_right t_round t_scale t_select t_session_user t_set t_set_byte t_setseed t_sha256 t_show t_sign t_sin t_sind t_sinh t_smallint t_sqrt t_substr t_substring t_sum t_table t_tan t_tand t_tanh t_text t_to t_trim t_trim_scale t_true t_trunc t_type t_unique t_update t_use t_using t_values t_varchar t_varying t_where t_width_bucketSQL : Sentencias_SQLSQL : emptySentencias_SQL : Sentencias_SQL Sentencia_SQLSentencias_SQL : Sentencia_SQLSentencia_SQL : Sentencias_DMLSentencia_SQL : Sentencias_DDLSentencias_DML : t_select Lista_EXP Select_SQL Condiciones GRP ORD pyc\n | t_select asterisco Select_SQL Condiciones GRP ORD pyc\n | t_insert t_into id Insert_SQL pyc\n | t_update id t_set Lista_EXP Condiciones1 pyc\n | t_delete t_from id Condiciones1 pyc\n | t_use id pycSelect_SQL : t_from Table_ExpressionSelect_SQL : emptyTable_Expression : Alias_Tabla\n | SubqueriesAlias_Tabla : Lista_ID\n | Lista_AliasSubqueries : par1 t_select par2Insert_SQL : par1 Lista_ID par2 t_values par1 Lista_EXP par2Insert_SQL : t_values par1 Lista_EXP par2Condiciones : t_where EXP\n | emptyCondiciones1 : t_where EXP\n | emptyGRP : t_group t_by Lista_ID\n | t_group t_by Lista_ID HV\n | emptyHV : t_having EXPORD : t_order t_by LSORT\n | t_order t_by LSORT LMT\n | emptyLSORT : LSORT coma SORT\n | SORTSORT : EXP AD NFL\n | EXP AD\n | EXPAD : t_asc\n | t_descNFL : t_nulls t_first\n | t_nulls t_lastLMT : t_limit NAL t_offset entero\n | t_limit NAL\n | t_offset entero NAL : entero\n | t_all Sentencias_DDL : t_show t_databases Show_DB_Like_Char pyc\n | Enum_Type\n | t_drop Drop pyc\n | t_alter Alter pyc\n | t_create Create pycShow_DB_Like_Char : t_like char \n | empty Enum_Type : t_create t_type id t_as t_enum par1 Lista_Enum par2 pycDrop : t_database DropDB id\n | t_table id DropDB : t_if t_exists\n | emptyAlter : t_database id AlterDB\n | t_table id AlterTB AlterDB : t_rename t_to id\n | t_owner t_to SesionDB SesionDB : id\n | t_current_user\n | t_session_user AlterTB : t_add Add_Opc\n | t_drop Drop_Opc\n | t_alter t_column Alter_Column\n | t_rename t_column id t_to id Add_Opc : t_column id Tipo\n | Constraint_AlterTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2\n | Constraint_AlterTB t_unique par1 id par2\n | Constraint_AlterTB t_check EXP Constraint_AlterTB : t_constraint id\n | empty Drop_Opc : t_column id\n | t_constraint id Alter_Column : id t_set t_not t_null\n | Alter_Columns Alter_Columns : Alter_Columns coma Alter_Column1\n | Alter_Column1Alter_Column1 : id t_type t_varchar par1 entero par2\n | t_alter t_column id t_type t_varchar par1 entero par2Create : CreateDBCreate : CreateTB CreateDB : OrReplace_CreateDB t_database IfNotExist_CreateDB id Sesion OrReplace_CreateDB : t_or t_replace\n | empty IfNotExist_CreateDB : t_if t_not t_exists\n | empty Sesion : t_owner Op_Sesion Sesion_mode\n | t_mode Op_Mode\n | empty Op_Sesion : igual char\n | char Sesion_mode : t_mode Op_Mode\n | empty Op_Mode : igual entero\n | entero CreateTB : t_table id par1 Columnas par2 Inherits Inherits : t_inherits par1 id par2\n | empty Columnas : Columnas coma Columna\n | Columna Columna : id Tipo Cond_CreateTB\n | Constraint Cond_CreateTB : Constraint_CreateTB t_default id Cond_CreateTB\n | Constraint_CreateTB t_not t_null Cond_CreateTB\n | Constraint_CreateTB t_null Cond_CreateTB\n | Constraint_CreateTB t_unique Cond_CreateTB\n | Constraint_CreateTB t_check par1 EXP par2 Cond_CreateTB\n | Constraint_CreateTB t_primary t_key Cond_CreateTB\n | Constraint_CreateTB t_references id Cond_CreateTB\n | emptyConstraint_CreateTB : t_constraint id\n | empty Constraint : Constraint_CreateTB t_unique par1 Lista_ID par2\n | Constraint_CreateTB t_check par1 EXP par2\n | Constraint_CreateTB t_primary t_key par1 Lista_ID par2\n | Constraint_CreateTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2\n | empty Tipo : t_smallint\n | t_integer\n | t_bigint\n | t_decimal\n | t_numeric par1 entero par2\n | t_real\n | t_double t_precision\n | t_money\n | t_character t_varying par1 entero par2\n | t_varchar par1 entero par2\n | t_character par1 entero par2\n | t_charn par1 entero par2\n | t_text\n | t_boolean\n | t_date\n | id Valor : decimal\n | entero\n | string\n | char\n | t_true\n | t_falseValor : idempty :EXP : EXP mas EXP\n | EXP menos EXP\n | EXP asterisco EXP\n | EXP div EXP\n | EXP pot EXP\n | EXP porcentaje EXPEXP : par1 EXP par2EXP : id par1 Lista_EXP par2EXP : EXP mayor EXP\n | EXP mayori EXP\n | EXP menor EXP\n | EXP menori EXP\n | EXP igual EXP\n | EXP diferente EXP\n | EXP diferentede EXPEXP : EXP t_and EXP\n | EXP t_or EXP\n EXP : mas EXP %prec umas\n | menos EXP %prec umenos\n | t_not EXPEXP : ValorEXP : id punto idEXP : EXP t_as EXPEXP : t_avg par1 EXP par2\n | t_sum par1 EXP par2\n | t_count par1 EXP par2\n | t_count par1 asterisco par2\n | t_max par1 EXP par2\n | t_min par1 EXP par2EXP : t_abs par1 EXP par2\n | t_cbrt par1 EXP par2\n | t_ceil par1 EXP par2\n | t_ceiling par1 EXP par2\n | t_degrees par1 EXP par2\n | t_exp par1 EXP par2\n | t_factorial par1 EXP par2\n | t_floor par1 EXP par2\n | t_gcd par1 Lista_EXP par2\n | t_ln par1 EXP par2\n | t_log par1 EXP par2\n | t_pi par1 par2\n | t_radians par1 EXP par2\n | t_round par1 EXP par2\n | t_min_scale par1 EXP par2\n | t_scale par1 EXP par2\n | t_sign par1 EXP par2\n | t_sqrt par1 EXP par2\n | t_trim_scale par1 EXP par2\n | t_trunc par1 EXP par2\n | t_width_bucket par1 Lista_EXP par2\n | t_random par1 par2\n | t_setseed par1 EXP par2 EXP : t_div par1 EXP coma EXP par2\n | t_mod par1 EXP coma EXP par2\n | t_power par1 EXP coma EXP par2 EXP : t_acos par1 EXP par2\n | t_acosd par1 EXP par2\n | t_asin par1 EXP par2\n | t_asind par1 EXP par2\n | t_atan par1 EXP par2\n | t_atand par1 EXP par2\n | t_cos par1 EXP par2\n | t_cosd par1 EXP par2\n | t_cot par1 EXP par2\n | t_cotd par1 EXP par2\n | t_sin par1 EXP par2\n | t_sind par1 EXP par2\n | t_tan par1 EXP par2\n | t_tand par1 EXP par2 EXP : t_atan2 par1 EXP coma EXP par2\n | t_atan2d par1 EXP coma EXP par2 EXP : t_length par1 id par2\n | t_substring par1 char coma entero coma entero par2\n | t_trim par1 char par2\n | t_md5 par1 char par2\n | t_sha256 par1 par2\n | t_substr par1 par2\n | t_get_byte par1 par2\n | t_set_byte par1 par2\n | t_convert par1 EXP t_as Tipo par2\n | t_encode par1 par2\n | t_decode par1 par2 Lista_ID : Lista_ID coma id\n | id Lista_Enum : Lista_Enum coma char\n | char Lista_EXP : Lista_EXP coma EXP\n | EXP Lista_Alias : Lista_Alias coma Nombre_Alias\n | Nombre_Alias Nombre_Alias : id id'
_lr_action_items = {'$end':([0,1,2,3,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[-145,0,-1,-2,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_select':([0,2,4,5,6,13,17,198,202,207,210,224,313,391,396,453,487,490,593,],[7,7,-4,-5,-6,-48,-3,-12,-49,-50,-51,336,-47,-9,-11,-10,-7,-8,-54,]),'t_insert':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[8,8,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_update':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[9,9,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_delete':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[10,10,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_use':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[11,11,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_show':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[12,12,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_drop':([0,2,4,5,6,13,17,198,202,207,209,210,313,391,396,453,487,490,593,],[14,14,-4,-5,-6,-48,-3,-12,-49,-50,322,-51,-47,-9,-11,-10,-7,-8,-54,]),'t_alter':([0,2,4,5,6,13,17,198,202,207,209,210,313,391,396,408,453,487,490,513,593,],[15,15,-4,-5,-6,-48,-3,-12,-49,-50,323,-51,-47,-9,-11,466,-10,-7,-8,466,-54,]),'t_create':([0,2,4,5,6,13,17,198,202,207,210,313,391,396,453,487,490,593,],[16,16,-4,-5,-6,-48,-3,-12,-49,-50,-51,-47,-9,-11,-10,-7,-8,-54,]),'asterisco':([7,20,24,26,76,77,87,88,89,90,131,132,133,136,139,218,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,246,247,248,249,251,252,253,254,255,256,257,258,259,260,262,263,264,265,266,267,268,269,270,271,272,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,299,300,301,302,303,304,305,333,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,370,371,372,373,374,375,376,377,378,379,380,381,382,383,386,388,389,397,429,430,431,432,433,450,491,492,493,494,495,497,509,536,570,580,581,622,],[19,117,-144,-166,-141,-139,-138,-140,-142,-143,-163,-164,117,-165,250,117,117,117,-148,-149,-150,-151,-154,-155,-156,-157,117,-159,117,-161,-162,117,-152,-167,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,-186,117,117,117,117,117,117,117,117,-196,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,-221,-222,-223,-224,117,-226,-227,117,-153,-169,-170,-171,-172,-173,-174,-175,-176,-177,-178,-179,-180,-181,-182,-183,-184,-185,-187,-188,-189,-190,-191,-192,-193,-194,-195,-197,-201,-202,-203,-204,-205,-206,-207,-208,-209,-210,-211,-212,-213,-214,-217,-219,-220,117,117,117,117,117,117,-144,-198,-199,-200,-215,-216,-225,117,117,117,117,-218,117,]),'par1':([7,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,78,79,80,81,82,83,84,85,86,111,112,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,134,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,192,195,196,213,216,308,311,367,368,369,384,385,390,394,410,440,444,445,446,450,461,462,482,483,488,500,504,507,527,531,532,533,538,545,550,565,574,600,618,637,640,],[23,23,23,23,134,23,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,23,224,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,307,23,329,23,394,23,23,23,23,23,23,23,23,472,498,501,502,503,134,508,23,530,531,23,541,545,546,568,23,571,572,23,23,592,600,23,23,632,641,643,]),'id':([7,9,11,21,22,23,25,91,93,97,98,100,101,103,107,111,112,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,134,135,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,192,196,203,205,212,216,225,307,311,316,326,328,329,334,335,367,368,369,384,385,390,394,398,399,401,403,406,407,408,409,413,419,423,426,459,462,477,481,488,508,510,513,514,530,531,538,545,546,561,567,568,571,572,574,600,631,635,641,643,],[24,92,94,24,24,24,24,195,197,-145,206,208,209,211,213,24,225,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,246,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,295,24,24,315,-58,-145,24,337,393,24,-57,411,-90,413,424,426,24,24,24,24,24,450,24,454,456,459,463,464,465,468,471,478,486,393,337,478,24,-89,413,24,547,548,552,553,393,24,24,24,393,596,602,603,393,393,24,24,637,640,393,3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_lr_action = {}
for _k, _v in _lr_action_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_action: _lr_action[_x] = {}
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'SQL':([0,],[1,]),'Sentencias_SQL':([0,],[2,]),'empty':([0,16,18,19,95,97,110,114,197,212,215,227,309,321,329,330,338,411,479,480,481,517,563,564,596,597,601,602,634,],[3,109,113,113,201,205,217,217,312,328,332,332,312,404,418,422,422,476,525,528,418,558,525,525,525,525,525,525,525,]),'Sentencia_SQL':([0,2,],[4,17,]),'Sentencias_DML':([0,2,],[5,5,]),'Sentencias_DDL':([0,2,],[6,6,]),'Enum_Type':([0,2,],[13,13,]),'Lista_EXP':([7,134,150,162,196,394,545,],[18,245,261,273,309,452,587,]),'EXP':([7,21,22,23,25,111,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,134,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,192,196,216,311,367,368,369,384,385,390,394,462,488,531,538,545,574,600,],[20,131,132,133,136,218,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,20,247,248,249,251,252,253,254,255,256,257,258,259,260,20,262,263,265,266,267,268,269,270,271,272,20,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,303,20,333,397,429,430,431,432,433,243,20,509,536,570,580,20,536,622,]),'Valor':([7,21,22,23,25,111,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,134,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,192,196,216,311,367,368,369,384,385,390,394,462,488,531,538,545,574,600,],[26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,]),'Drop':([14,],[96,]),'Alter':([15,],[99,]),'Create':([16,],[102,]),'CreateDB':([16,],[104,]),'CreateTB':([16,],[105,]),'OrReplace_CreateDB':([16,],[106,]),'Select_SQL':([18,19,],[110,114,]),'Show_DB_Like_Char':([95,],[199,]),'DropDB':([97,],[203,]),'Condiciones':([110,114,],[215,227,]),'Table_Expression':([112,],[219,]),'Alias_Tabla':([112,],[220,]),'Subqueries':([112,],[221,]),'Lista_ID':([112,307,423,530,546,571,572,641,643,],[222,392,489,569,588,606,607,644,645,]),'Lista_Alias':([112,],[223,]),'Nombre_Alias':([112,335,],[226,425,]),'Insert_SQL':([195,],[306,]),'Condiciones1':([197,309,],[310,395,]),'AlterDB':([208,],[317,]),'AlterTB':([209,],[320,]),'IfNotExist_CreateDB':([212,],[326,]),'GRP':([215,227,],[330,338,]),'Add_Opc':([321,],[400,]),'Constraint_AlterTB':([321,],[402,]),'Drop_Opc':([322,],[405,]),'Columnas':([329,],[414,]),'Columna':([329,481,],[415,529,]),'Constraint':([329,481,],[416,416,]),'Constraint_CreateTB':([329,479,481,563,564,596,597,601,602,634,],[417,524,417,524,524,524,524,524,524,524,]),'ORD':([330,338,],[420,428,]),'Tipo':([390,413,459,],[435,479,506,]),'SesionDB':([399,],[455,]),'Alter_Column':([408,],[467,]),'Alter_Columns':([408,],[469,]),'Alter_Column1':([408,513,],[470,551,]),'Sesion':([411,],[473,]),'Lista_Enum':([472,],[515,]),'Op_Sesion':([474,],[517,]),'Op_Mode':([475,557,],[520,595,]),'Cond_CreateTB':([479,563,564,596,597,601,602,634,],[523,598,599,620,621,623,624,639,]),'Inherits':([480,],[526,]),'LSORT':([488,],[534,]),'SORT':([488,574,],[535,608,]),'HV':([489,],[537,]),'Sesion_mode':([517,],[556,]),'LMT':([534,],[573,]),'AD':([536,],[577,]),'NAL':([575,],[609,]),'NFL':([577,],[613,]),}
_lr_goto = {}
for _k, _v in _lr_goto_items.items():
for _x, _y in zip(_v[0], _v[1]):
if not _x in _lr_goto: _lr_goto[_x] = {}
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [
("S' -> SQL","S'",1,None,None,None),
('SQL -> Sentencias_SQL','SQL',1,'p_sql','Gramatica.py',318),
('SQL -> empty','SQL',1,'p_sql2','Gramatica.py',322),
('Sentencias_SQL -> Sentencias_SQL Sentencia_SQL','Sentencias_SQL',2,'p_Sentencias_SQL_Sentencia_SQL','Gramatica.py',326),
('Sentencias_SQL -> Sentencia_SQL','Sentencias_SQL',1,'p_Sentencias_SQL','Gramatica.py',332),
('Sentencia_SQL -> Sentencias_DML','Sentencia_SQL',1,'p_Sentencia_SQL_DML','Gramatica.py',337),
('Sentencia_SQL -> Sentencias_DDL','Sentencia_SQL',1,'p_Sentencia_SQL_DDL','Gramatica.py',346),
('Sentencias_DML -> t_select Lista_EXP Select_SQL Condiciones GRP ORD pyc','Sentencias_DML',7,'p_Sentencias_DML','Gramatica.py',352),
('Sentencias_DML -> t_select asterisco Select_SQL Condiciones GRP ORD pyc','Sentencias_DML',7,'p_Sentencias_DML','Gramatica.py',353),
('Sentencias_DML -> t_insert t_into id Insert_SQL pyc','Sentencias_DML',5,'p_Sentencias_DML','Gramatica.py',354),
('Sentencias_DML -> t_update id t_set Lista_EXP Condiciones1 pyc','Sentencias_DML',6,'p_Sentencias_DML','Gramatica.py',355),
('Sentencias_DML -> t_delete t_from id Condiciones1 pyc','Sentencias_DML',5,'p_Sentencias_DML','Gramatica.py',356),
('Sentencias_DML -> t_use id pyc','Sentencias_DML',3,'p_Sentencias_DML','Gramatica.py',357),
('Select_SQL -> t_from Table_Expression','Select_SQL',2,'p_Select_SQL','Gramatica.py',376),
('Select_SQL -> empty','Select_SQL',1,'p_Select2_SQL','Gramatica.py',382),
('Table_Expression -> Alias_Tabla','Table_Expression',1,'p_Table_Expression','Gramatica.py',388),
('Table_Expression -> Subqueries','Table_Expression',1,'p_Table_Expression','Gramatica.py',389),
('Alias_Tabla -> Lista_ID','Alias_Tabla',1,'p_Alias_Tabla','Gramatica.py',395),
('Alias_Tabla -> Lista_Alias','Alias_Tabla',1,'p_Alias_Tabla','Gramatica.py',396),
('Subqueries -> par1 t_select par2','Subqueries',3,'p_Subqueries','Gramatica.py',401),
('Insert_SQL -> par1 Lista_ID par2 t_values par1 Lista_EXP par2','Insert_SQL',7,'p_Insert_SQL','Gramatica.py',406),
('Insert_SQL -> t_values par1 Lista_EXP par2','Insert_SQL',4,'p_Insert_SQL2','Gramatica.py',411),
('Condiciones -> t_where EXP','Condiciones',2,'p_Condiciones','Gramatica.py',416),
('Condiciones -> empty','Condiciones',1,'p_Condiciones','Gramatica.py',417),
('Condiciones1 -> t_where EXP','Condiciones1',2,'p_Condiciones1','Gramatica.py',426),
('Condiciones1 -> empty','Condiciones1',1,'p_Condiciones1','Gramatica.py',427),
('GRP -> t_group t_by Lista_ID','GRP',3,'p_GRP','Gramatica.py',438),
('GRP -> t_group t_by Lista_ID HV','GRP',4,'p_GRP','Gramatica.py',439),
('GRP -> empty','GRP',1,'p_GRP','Gramatica.py',440),
('HV -> t_having EXP','HV',2,'p_HV','Gramatica.py',447),
('ORD -> t_order t_by LSORT','ORD',3,'p_ORD','Gramatica.py',451),
('ORD -> t_order t_by LSORT LMT','ORD',4,'p_ORD','Gramatica.py',452),
('ORD -> empty','ORD',1,'p_ORD','Gramatica.py',453),
('LSORT -> LSORT coma SORT','LSORT',3,'p_L_SORT','Gramatica.py',461),
('LSORT -> SORT','LSORT',1,'p_L_SORT','Gramatica.py',462),
('SORT -> EXP AD NFL','SORT',3,'p_SORT','Gramatica.py',469),
('SORT -> EXP AD','SORT',2,'p_SORT','Gramatica.py',470),
('SORT -> EXP','SORT',1,'p_SORT','Gramatica.py',471),
('AD -> t_asc','AD',1,'p_AD','Gramatica.py',480),
('AD -> t_desc','AD',1,'p_AD','Gramatica.py',481),
('NFL -> t_nulls t_first','NFL',2,'p_NFL','Gramatica.py',486),
('NFL -> t_nulls t_last','NFL',2,'p_NFL','Gramatica.py',487),
('LMT -> t_limit NAL t_offset entero','LMT',4,'p_LMT','Gramatica.py',491),
('LMT -> t_limit NAL','LMT',2,'p_LMT','Gramatica.py',492),
('LMT -> t_offset entero','LMT',2,'p_LMT','Gramatica.py',493),
('NAL -> entero','NAL',1,'p_NAL','Gramatica.py',500),
('NAL -> t_all','NAL',1,'p_NAL','Gramatica.py',501),
('Sentencias_DDL -> t_show t_databases Show_DB_Like_Char pyc','Sentencias_DDL',4,'p_Sentencias_DDL','Gramatica.py',506),
('Sentencias_DDL -> Enum_Type','Sentencias_DDL',1,'p_Sentencias_DDL','Gramatica.py',507),
('Sentencias_DDL -> t_drop Drop pyc','Sentencias_DDL',3,'p_Sentencias_DDL','Gramatica.py',508),
('Sentencias_DDL -> t_alter Alter pyc','Sentencias_DDL',3,'p_Sentencias_DDL','Gramatica.py',509),
('Sentencias_DDL -> t_create Create pyc','Sentencias_DDL',3,'p_Sentencias_DDL','Gramatica.py',510),
('Show_DB_Like_Char -> t_like char','Show_DB_Like_Char',2,'p_show_db_like_regex','Gramatica.py',530),
('Show_DB_Like_Char -> empty','Show_DB_Like_Char',1,'p_show_db_like_regex','Gramatica.py',531),
('Enum_Type -> t_create t_type id t_as t_enum par1 Lista_Enum par2 pyc','Enum_Type',9,'p_Enum_Type','Gramatica.py',540),
('Drop -> t_database DropDB id','Drop',3,'p_Drop','Gramatica.py',545),
('Drop -> t_table id','Drop',2,'p_Drop','Gramatica.py',546),
('DropDB -> t_if t_exists','DropDB',2,'p_DropDB','Gramatica.py',555),
('DropDB -> empty','DropDB',1,'p_DropDB','Gramatica.py',556),
('Alter -> t_database id AlterDB','Alter',3,'p_Alter','Gramatica.py',565),
('Alter -> t_table id AlterTB','Alter',3,'p_Alter','Gramatica.py',566),
('AlterDB -> t_rename t_to id','AlterDB',3,'p_AlterDB','Gramatica.py',575),
('AlterDB -> t_owner t_to SesionDB','AlterDB',3,'p_AlterDB','Gramatica.py',576),
('SesionDB -> id','SesionDB',1,'p_SesionDB','Gramatica.py',585),
('SesionDB -> t_current_user','SesionDB',1,'p_SesionDB','Gramatica.py',586),
('SesionDB -> t_session_user','SesionDB',1,'p_SesionDB','Gramatica.py',587),
('AlterTB -> t_add Add_Opc','AlterTB',2,'p_AlterTB','Gramatica.py',597),
('AlterTB -> t_drop Drop_Opc','AlterTB',2,'p_AlterTB','Gramatica.py',598),
('AlterTB -> t_alter t_column Alter_Column','AlterTB',3,'p_AlterTB','Gramatica.py',599),
('AlterTB -> t_rename t_column id t_to id','AlterTB',5,'p_AlterTB','Gramatica.py',600),
('Add_Opc -> t_column id Tipo','Add_Opc',3,'p_Add_Opc','Gramatica.py',615),
('Add_Opc -> Constraint_AlterTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2','Add_Opc',11,'p_Add_Opc','Gramatica.py',616),
('Add_Opc -> Constraint_AlterTB t_unique par1 id par2','Add_Opc',5,'p_Add_Opc','Gramatica.py',617),
('Add_Opc -> Constraint_AlterTB t_check EXP','Add_Opc',3,'p_Add_Opc','Gramatica.py',618),
('Constraint_AlterTB -> t_constraint id','Constraint_AlterTB',2,'p_Constraint_AlterTB','Gramatica.py',633),
('Constraint_AlterTB -> empty','Constraint_AlterTB',1,'p_Constraint_AlterTB','Gramatica.py',634),
('Drop_Opc -> t_column id','Drop_Opc',2,'p_Drop_Opc','Gramatica.py',643),
('Drop_Opc -> t_constraint id','Drop_Opc',2,'p_Drop_Opc','Gramatica.py',644),
('Alter_Column -> id t_set t_not t_null','Alter_Column',4,'p_Alter_Column','Gramatica.py',653),
('Alter_Column -> Alter_Columns','Alter_Column',1,'p_Alter_Column','Gramatica.py',654),
('Alter_Columns -> Alter_Columns coma Alter_Column1','Alter_Columns',3,'p_Alter_Columns','Gramatica.py',663),
('Alter_Columns -> Alter_Column1','Alter_Columns',1,'p_Alter_Columns','Gramatica.py',664),
('Alter_Column1 -> id t_type t_varchar par1 entero par2','Alter_Column1',6,'p_Alter_Colum1','Gramatica.py',674),
('Alter_Column1 -> t_alter t_column id t_type t_varchar par1 entero par2','Alter_Column1',8,'p_Alter_Colum1','Gramatica.py',675),
('Create -> CreateDB','Create',1,'p_Create','Gramatica.py',690),
('Create -> CreateTB','Create',1,'p_Create1','Gramatica.py',695),
('CreateDB -> OrReplace_CreateDB t_database IfNotExist_CreateDB id Sesion','CreateDB',5,'p_CreateDB','Gramatica.py',700),
('OrReplace_CreateDB -> t_or t_replace','OrReplace_CreateDB',2,'p_CreateDB_or_replace','Gramatica.py',705),
('OrReplace_CreateDB -> empty','OrReplace_CreateDB',1,'p_CreateDB_or_replace','Gramatica.py',706),
('IfNotExist_CreateDB -> t_if t_not t_exists','IfNotExist_CreateDB',3,'p_IfNotExist_CreateDB','Gramatica.py',715),
('IfNotExist_CreateDB -> empty','IfNotExist_CreateDB',1,'p_IfNotExist_CreateDB','Gramatica.py',716),
('Sesion -> t_owner Op_Sesion Sesion_mode','Sesion',3,'p_Sesion','Gramatica.py',725),
('Sesion -> t_mode Op_Mode','Sesion',2,'p_Sesion','Gramatica.py',726),
('Sesion -> empty','Sesion',1,'p_Sesion','Gramatica.py',727),
('Op_Sesion -> igual char','Op_Sesion',2,'p_Op_Sesion','Gramatica.py',739),
('Op_Sesion -> char','Op_Sesion',1,'p_Op_Sesion','Gramatica.py',740),
('Sesion_mode -> t_mode Op_Mode','Sesion_mode',2,'p_Sesion_mode','Gramatica.py',749),
('Sesion_mode -> empty','Sesion_mode',1,'p_Sesion_mode','Gramatica.py',750),
('Op_Mode -> igual entero','Op_Mode',2,'p_Op_Mode','Gramatica.py',759),
('Op_Mode -> entero','Op_Mode',1,'p_Op_Mode','Gramatica.py',760),
('CreateTB -> t_table id par1 Columnas par2 Inherits','CreateTB',6,'p_CreateTB','Gramatica.py',769),
('Inherits -> t_inherits par1 id par2','Inherits',4,'p_Inherits','Gramatica.py',774),
('Inherits -> empty','Inherits',1,'p_Inherits','Gramatica.py',775),
('Columnas -> Columnas coma Columna','Columnas',3,'p_Columnas','Gramatica.py',784),
('Columnas -> Columna','Columnas',1,'p_Columnas','Gramatica.py',785),
('Columna -> id Tipo Cond_CreateTB','Columna',3,'p_Columna','Gramatica.py',795),
('Columna -> Constraint','Columna',1,'p_Columna','Gramatica.py',796),
('Cond_CreateTB -> Constraint_CreateTB t_default id Cond_CreateTB','Cond_CreateTB',4,'p_Cond_CreateTB','Gramatica.py',805),
('Cond_CreateTB -> Constraint_CreateTB t_not t_null Cond_CreateTB','Cond_CreateTB',4,'p_Cond_CreateTB','Gramatica.py',806),
('Cond_CreateTB -> Constraint_CreateTB t_null Cond_CreateTB','Cond_CreateTB',3,'p_Cond_CreateTB','Gramatica.py',807),
('Cond_CreateTB -> Constraint_CreateTB t_unique Cond_CreateTB','Cond_CreateTB',3,'p_Cond_CreateTB','Gramatica.py',808),
('Cond_CreateTB -> Constraint_CreateTB t_check par1 EXP par2 Cond_CreateTB','Cond_CreateTB',6,'p_Cond_CreateTB','Gramatica.py',809),
('Cond_CreateTB -> Constraint_CreateTB t_primary t_key Cond_CreateTB','Cond_CreateTB',4,'p_Cond_CreateTB','Gramatica.py',810),
('Cond_CreateTB -> Constraint_CreateTB t_references id Cond_CreateTB','Cond_CreateTB',4,'p_Cond_CreateTB','Gramatica.py',811),
('Cond_CreateTB -> empty','Cond_CreateTB',1,'p_Cond_CreateTB','Gramatica.py',812),
('Constraint_CreateTB -> t_constraint id','Constraint_CreateTB',2,'p_Constraint_CreateTB','Gramatica.py',846),
('Constraint_CreateTB -> empty','Constraint_CreateTB',1,'p_Constraint_CreateTB','Gramatica.py',847),
('Constraint -> Constraint_CreateTB t_unique par1 Lista_ID par2','Constraint',5,'p_Constraint','Gramatica.py',856),
('Constraint -> Constraint_CreateTB t_check par1 EXP par2','Constraint',5,'p_Constraint','Gramatica.py',857),
('Constraint -> Constraint_CreateTB t_primary t_key par1 Lista_ID par2','Constraint',6,'p_Constraint','Gramatica.py',858),
('Constraint -> Constraint_CreateTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2','Constraint',11,'p_Constraint','Gramatica.py',859),
('Constraint -> empty','Constraint',1,'p_Constraint','Gramatica.py',860),
('Tipo -> t_smallint','Tipo',1,'p_Tipo','Gramatica.py',878),
('Tipo -> t_integer','Tipo',1,'p_Tipo','Gramatica.py',879),
('Tipo -> t_bigint','Tipo',1,'p_Tipo','Gramatica.py',880),
('Tipo -> t_decimal','Tipo',1,'p_Tipo','Gramatica.py',881),
('Tipo -> t_numeric par1 entero par2','Tipo',4,'p_Tipo','Gramatica.py',882),
('Tipo -> t_real','Tipo',1,'p_Tipo','Gramatica.py',883),
('Tipo -> t_double t_precision','Tipo',2,'p_Tipo','Gramatica.py',884),
('Tipo -> t_money','Tipo',1,'p_Tipo','Gramatica.py',885),
('Tipo -> t_character t_varying par1 entero par2','Tipo',5,'p_Tipo','Gramatica.py',886),
('Tipo -> t_varchar par1 entero par2','Tipo',4,'p_Tipo','Gramatica.py',887),
('Tipo -> t_character par1 entero par2','Tipo',4,'p_Tipo','Gramatica.py',888),
('Tipo -> t_charn par1 entero par2','Tipo',4,'p_Tipo','Gramatica.py',889),
('Tipo -> t_text','Tipo',1,'p_Tipo','Gramatica.py',890),
('Tipo -> t_boolean','Tipo',1,'p_Tipo','Gramatica.py',891),
('Tipo -> t_date','Tipo',1,'p_Tipo','Gramatica.py',892),
('Tipo -> id','Tipo',1,'p_Tipo','Gramatica.py',893),
('Valor -> decimal','Valor',1,'p_Valor','Gramatica.py',978),
('Valor -> entero','Valor',1,'p_Valor','Gramatica.py',979),
('Valor -> string','Valor',1,'p_Valor','Gramatica.py',980),
('Valor -> char','Valor',1,'p_Valor','Gramatica.py',981),
('Valor -> t_true','Valor',1,'p_Valor','Gramatica.py',982),
('Valor -> t_false','Valor',1,'p_Valor','Gramatica.py',983),
('Valor -> id','Valor',1,'p_Valor2','Gramatica.py',989),
('empty -> <empty>','empty',0,'p_empty','Gramatica.py',994),
('EXP -> EXP mas EXP','EXP',3,'p_aritmeticas','Gramatica.py',1001),
('EXP -> EXP menos EXP','EXP',3,'p_aritmeticas','Gramatica.py',1002),
('EXP -> EXP asterisco EXP','EXP',3,'p_aritmeticas','Gramatica.py',1003),
('EXP -> EXP div EXP','EXP',3,'p_aritmeticas','Gramatica.py',1004),
('EXP -> EXP pot EXP','EXP',3,'p_aritmeticas','Gramatica.py',1005),
('EXP -> EXP porcentaje EXP','EXP',3,'p_aritmeticas','Gramatica.py',1006),
('EXP -> par1 EXP par2','EXP',3,'p_parentesis','Gramatica.py',1011),
('EXP -> id par1 Lista_EXP par2','EXP',4,'p_funciones','Gramatica.py',1017),
('EXP -> EXP mayor EXP','EXP',3,'p_relacionales','Gramatica.py',1024),
('EXP -> EXP mayori EXP','EXP',3,'p_relacionales','Gramatica.py',1025),
('EXP -> EXP menor EXP','EXP',3,'p_relacionales','Gramatica.py',1026),
('EXP -> EXP menori EXP','EXP',3,'p_relacionales','Gramatica.py',1027),
('EXP -> EXP igual EXP','EXP',3,'p_relacionales','Gramatica.py',1028),
('EXP -> EXP diferente EXP','EXP',3,'p_relacionales','Gramatica.py',1029),
('EXP -> EXP diferentede EXP','EXP',3,'p_relacionales','Gramatica.py',1030),
('EXP -> EXP t_and EXP','EXP',3,'p_logicos','Gramatica.py',1035),
('EXP -> EXP t_or EXP','EXP',3,'p_logicos','Gramatica.py',1036),
('EXP -> mas EXP','EXP',2,'p_unario','Gramatica.py',1042),
('EXP -> menos EXP','EXP',2,'p_unario','Gramatica.py',1043),
('EXP -> t_not EXP','EXP',2,'p_unario','Gramatica.py',1044),
('EXP -> Valor','EXP',1,'p_EXP_Valor','Gramatica.py',1053),
('EXP -> id punto id','EXP',3,'p_EXP_Indices','Gramatica.py',1058),
('EXP -> EXP t_as EXP','EXP',3,'p_EXP_IndicesAS','Gramatica.py',1064),
('EXP -> t_avg par1 EXP par2','EXP',4,'p_exp_agregacion','Gramatica.py',1071),
('EXP -> t_sum par1 EXP par2','EXP',4,'p_exp_agregacion','Gramatica.py',1072),
('EXP -> t_count par1 EXP par2','EXP',4,'p_exp_agregacion','Gramatica.py',1073),
('EXP -> t_count par1 asterisco par2','EXP',4,'p_exp_agregacion','Gramatica.py',1074),
('EXP -> t_max par1 EXP par2','EXP',4,'p_exp_agregacion','Gramatica.py',1075),
('EXP -> t_min par1 EXP par2','EXP',4,'p_exp_agregacion','Gramatica.py',1076),
('EXP -> t_abs par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1081),
('EXP -> t_cbrt par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1082),
('EXP -> t_ceil par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1083),
('EXP -> t_ceiling par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1084),
('EXP -> t_degrees par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1085),
('EXP -> t_exp par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1086),
('EXP -> t_factorial par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1087),
('EXP -> t_floor par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1088),
('EXP -> t_gcd par1 Lista_EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1089),
('EXP -> t_ln par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1090),
('EXP -> t_log par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1091),
('EXP -> t_pi par1 par2','EXP',3,'p_funciones_matematicas','Gramatica.py',1092),
('EXP -> t_radians par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1093),
('EXP -> t_round par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1094),
('EXP -> t_min_scale par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1095),
('EXP -> t_scale par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1096),
('EXP -> t_sign par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1097),
('EXP -> t_sqrt par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1098),
('EXP -> t_trim_scale par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1099),
('EXP -> t_trunc par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1100),
('EXP -> t_width_bucket par1 Lista_EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1101),
('EXP -> t_random par1 par2','EXP',3,'p_funciones_matematicas','Gramatica.py',1102),
('EXP -> t_setseed par1 EXP par2','EXP',4,'p_funciones_matematicas','Gramatica.py',1103),
('EXP -> t_div par1 EXP coma EXP par2','EXP',6,'p_funciones_matematicas2','Gramatica.py',1108),
('EXP -> t_mod par1 EXP coma EXP par2','EXP',6,'p_funciones_matematicas2','Gramatica.py',1109),
('EXP -> t_power par1 EXP coma EXP par2','EXP',6,'p_funciones_matematicas2','Gramatica.py',1110),
('EXP -> t_acos par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1115),
('EXP -> t_acosd par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1116),
('EXP -> t_asin par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1117),
('EXP -> t_asind par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1118),
('EXP -> t_atan par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1119),
('EXP -> t_atand par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1120),
('EXP -> t_cos par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1121),
('EXP -> t_cosd par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1122),
('EXP -> t_cot par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1123),
('EXP -> t_cotd par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1124),
('EXP -> t_sin par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1125),
('EXP -> t_sind par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1126),
('EXP -> t_tan par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1127),
('EXP -> t_tand par1 EXP par2','EXP',4,'p_funciones_Trigonometricas','Gramatica.py',1128),
('EXP -> t_atan2 par1 EXP coma EXP par2','EXP',6,'p_funciones_Trigonometricas1','Gramatica.py',1133),
('EXP -> t_atan2d par1 EXP coma EXP par2','EXP',6,'p_funciones_Trigonometricas1','Gramatica.py',1134),
('EXP -> t_length par1 id par2','EXP',4,'p_funciones_String_Binarias','Gramatica.py',1138),
('EXP -> t_substring par1 char coma entero coma entero par2','EXP',8,'p_funciones_String_Binarias','Gramatica.py',1139),
('EXP -> t_trim par1 char par2','EXP',4,'p_funciones_String_Binarias','Gramatica.py',1140),
('EXP -> t_md5 par1 char par2','EXP',4,'p_funciones_String_Binarias','Gramatica.py',1141),
('EXP -> t_sha256 par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1142),
('EXP -> t_substr par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1143),
('EXP -> t_get_byte par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1144),
('EXP -> t_set_byte par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1145),
('EXP -> t_convert par1 EXP t_as Tipo par2','EXP',6,'p_funciones_String_Binarias','Gramatica.py',1146),
('EXP -> t_encode par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1147),
('EXP -> t_decode par1 par2','EXP',3,'p_funciones_String_Binarias','Gramatica.py',1148),
('Lista_ID -> Lista_ID coma id','Lista_ID',3,'p_Lista_ID','Gramatica.py',1158),
('Lista_ID -> id','Lista_ID',1,'p_Lista_ID','Gramatica.py',1159),
('Lista_Enum -> Lista_Enum coma char','Lista_Enum',3,'p_Lista_Enum','Gramatica.py',1168),
('Lista_Enum -> char','Lista_Enum',1,'p_Lista_Enum','Gramatica.py',1169),
('Lista_EXP -> Lista_EXP coma EXP','Lista_EXP',3,'p_Lista_EXP','Gramatica.py',1178),
('Lista_EXP -> EXP','Lista_EXP',1,'p_Lista_EXP','Gramatica.py',1179),
('Lista_Alias -> Lista_Alias coma Nombre_Alias','Lista_Alias',3,'p_Lista_Alias','Gramatica.py',1194),
('Lista_Alias -> Nombre_Alias','Lista_Alias',1,'p_Lista_Alias','Gramatica.py',1195),
('Nombre_Alias -> id id','Nombre_Alias',2,'p_Nombre_Alias','Gramatica.py',1204),
]
|
_tabversion = '3.10'
_lr_method = 'LALR'
_lr_signature = 'leftpuntobipuntoleftcomarightigualleftcor1cor2leftmasmenosleftasteriscodivporcentajeleftpotrightumenosumasleftpar1par2leftt_orleftt_andleftdiferenteleftmayormenormayorimenorirightt_notasterisco bipunto char coma cor1 cor2 decimal diferente diferentede div entero id igual mas mayor mayori menor menori menos par1 par2 porcentaje pot punto pyc string t_abs t_acos t_acosd t_acosh t_add t_all t_alter t_and t_as t_asc t_asin t_asind t_asinh t_atan t_atan2 t_atan2d t_atand t_atanh t_avg t_bigint t_bool t_boolean t_by t_cbrt t_ceil t_ceiling t_character t_charn t_check t_column t_constraint t_convert t_cos t_cosd t_cosh t_cot t_cotd t_count t_create t_current t_current_user t_database t_databases t_date t_decimal t_decode t_default t_degrees t_delete t_desc t_distinct t_div t_double t_drop t_encode t_enum t_exists t_exp t_factorial t_false t_first t_floor t_foreign t_from t_full t_gcd t_get_byte t_group t_having t_if t_inherits t_inner t_insert t_integer t_into t_join t_key t_last t_left t_length t_like t_limit t_ln t_log t_max t_md5 t_min t_min_scale t_mod t_mode t_money t_natural t_not t_null t_nulls t_numeric t_of t_offset t_on t_only t_or t_order t_outer t_owner t_pi t_power t_precision t_primary t_radians t_random t_real t_references t_rename t_replace t_returning t_right t_round t_scale t_select t_session_user t_set t_set_byte t_setseed t_sha256 t_show t_sign t_sin t_sind t_sinh t_smallint t_sqrt t_substr t_substring t_sum t_table t_tan t_tand t_tanh t_text t_to t_trim t_trim_scale t_true t_trunc t_type t_unique t_update t_use t_using t_values t_varchar t_varying t_where t_width_bucketSQL : Sentencias_SQLSQL : emptySentencias_SQL : Sentencias_SQL Sentencia_SQLSentencias_SQL : Sentencia_SQLSentencia_SQL : Sentencias_DMLSentencia_SQL : Sentencias_DDLSentencias_DML : t_select Lista_EXP Select_SQL Condiciones GRP ORD pyc\n | t_select asterisco Select_SQL Condiciones GRP ORD pyc\n | t_insert t_into id Insert_SQL pyc\n | t_update id t_set Lista_EXP Condiciones1 pyc\n | t_delete t_from id Condiciones1 pyc\n | t_use id pycSelect_SQL : t_from Table_ExpressionSelect_SQL : emptyTable_Expression : Alias_Tabla\n | SubqueriesAlias_Tabla : Lista_ID\n | Lista_AliasSubqueries : par1 t_select par2Insert_SQL : par1 Lista_ID par2 t_values par1 Lista_EXP par2Insert_SQL : t_values par1 Lista_EXP par2Condiciones : t_where EXP\n | emptyCondiciones1 : t_where EXP\n | emptyGRP : t_group t_by Lista_ID\n | t_group t_by Lista_ID HV\n | emptyHV : t_having EXPORD : t_order t_by LSORT\n | t_order t_by LSORT LMT\n | emptyLSORT : LSORT coma SORT\n | SORTSORT : EXP AD NFL\n | EXP AD\n | EXPAD : t_asc\n | t_descNFL : t_nulls t_first\n | t_nulls t_lastLMT : t_limit NAL t_offset entero\n | t_limit NAL\n | t_offset entero NAL : entero\n | t_all Sentencias_DDL : t_show t_databases Show_DB_Like_Char pyc\n | Enum_Type\n | t_drop Drop pyc\n | t_alter Alter pyc\n | t_create Create pycShow_DB_Like_Char : t_like char \n | empty Enum_Type : t_create t_type id t_as t_enum par1 Lista_Enum par2 pycDrop : t_database DropDB id\n | t_table id DropDB : t_if t_exists\n | emptyAlter : t_database id AlterDB\n | t_table id AlterTB AlterDB : t_rename t_to id\n | t_owner t_to SesionDB SesionDB : id\n | t_current_user\n | t_session_user AlterTB : t_add Add_Opc\n | t_drop Drop_Opc\n | t_alter t_column Alter_Column\n | t_rename t_column id t_to id Add_Opc : t_column id Tipo\n | Constraint_AlterTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2\n | Constraint_AlterTB t_unique par1 id par2\n | Constraint_AlterTB t_check EXP Constraint_AlterTB : t_constraint id\n | empty Drop_Opc : t_column id\n | t_constraint id Alter_Column : id t_set t_not t_null\n | Alter_Columns Alter_Columns : Alter_Columns coma Alter_Column1\n | Alter_Column1Alter_Column1 : id t_type t_varchar par1 entero par2\n | t_alter t_column id t_type t_varchar par1 entero par2Create : CreateDBCreate : CreateTB CreateDB : OrReplace_CreateDB t_database IfNotExist_CreateDB id Sesion OrReplace_CreateDB : t_or t_replace\n | empty IfNotExist_CreateDB : t_if t_not t_exists\n | empty Sesion : t_owner Op_Sesion Sesion_mode\n | t_mode Op_Mode\n | empty Op_Sesion : igual char\n | char Sesion_mode : t_mode Op_Mode\n | empty Op_Mode : igual entero\n | entero CreateTB : t_table id par1 Columnas par2 Inherits Inherits : t_inherits par1 id par2\n | empty Columnas : Columnas coma Columna\n | Columna Columna : id Tipo Cond_CreateTB\n | Constraint Cond_CreateTB : Constraint_CreateTB t_default id Cond_CreateTB\n | Constraint_CreateTB t_not t_null Cond_CreateTB\n | Constraint_CreateTB t_null Cond_CreateTB\n | Constraint_CreateTB t_unique Cond_CreateTB\n | Constraint_CreateTB t_check par1 EXP par2 Cond_CreateTB\n | Constraint_CreateTB t_primary t_key Cond_CreateTB\n | Constraint_CreateTB t_references id Cond_CreateTB\n | emptyConstraint_CreateTB : t_constraint id\n | empty Constraint : Constraint_CreateTB t_unique par1 Lista_ID par2\n | Constraint_CreateTB t_check par1 EXP par2\n | Constraint_CreateTB t_primary t_key par1 Lista_ID par2\n | Constraint_CreateTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2\n | empty Tipo : t_smallint\n | t_integer\n | t_bigint\n | t_decimal\n | t_numeric par1 entero par2\n | t_real\n | t_double t_precision\n | t_money\n | t_character t_varying par1 entero par2\n | t_varchar par1 entero par2\n | t_character par1 entero par2\n | t_charn par1 entero par2\n | t_text\n | t_boolean\n | t_date\n | id Valor : decimal\n | entero\n | string\n | char\n | t_true\n | t_falseValor : idempty :EXP : EXP mas EXP\n | EXP menos EXP\n | EXP asterisco EXP\n | EXP div EXP\n | EXP pot EXP\n | EXP porcentaje EXPEXP : par1 EXP par2EXP : id par1 Lista_EXP par2EXP : EXP mayor EXP\n | EXP mayori EXP\n | EXP menor EXP\n | EXP menori EXP\n | EXP igual EXP\n | EXP diferente EXP\n | EXP diferentede EXPEXP : EXP t_and EXP\n | EXP t_or EXP\n EXP : mas EXP %prec umas\n | menos EXP %prec umenos\n | t_not EXPEXP : ValorEXP : id punto idEXP : EXP t_as EXPEXP : t_avg par1 EXP par2\n | t_sum par1 EXP par2\n | t_count par1 EXP par2\n | t_count par1 asterisco par2\n | t_max par1 EXP par2\n | t_min par1 EXP par2EXP : t_abs par1 EXP par2\n | t_cbrt par1 EXP par2\n | t_ceil par1 EXP par2\n | t_ceiling par1 EXP par2\n | t_degrees par1 EXP par2\n | t_exp par1 EXP par2\n | t_factorial par1 EXP par2\n | t_floor par1 EXP par2\n | t_gcd par1 Lista_EXP par2\n | t_ln par1 EXP par2\n | t_log par1 EXP par2\n | t_pi par1 par2\n | t_radians par1 EXP par2\n | t_round par1 EXP par2\n | t_min_scale par1 EXP par2\n | t_scale par1 EXP par2\n | t_sign par1 EXP par2\n | t_sqrt par1 EXP par2\n | t_trim_scale par1 EXP par2\n | t_trunc par1 EXP par2\n | t_width_bucket par1 Lista_EXP par2\n | t_random par1 par2\n | t_setseed par1 EXP par2 EXP : t_div par1 EXP coma EXP par2\n | t_mod par1 EXP coma EXP par2\n | t_power par1 EXP coma EXP par2 EXP : t_acos par1 EXP par2\n | t_acosd par1 EXP par2\n | t_asin par1 EXP par2\n | t_asind par1 EXP par2\n | t_atan par1 EXP par2\n | t_atand par1 EXP par2\n | t_cos par1 EXP par2\n | t_cosd par1 EXP par2\n | t_cot par1 EXP par2\n | t_cotd par1 EXP par2\n | t_sin par1 EXP par2\n | t_sind par1 EXP par2\n | t_tan par1 EXP par2\n | t_tand par1 EXP par2 EXP : t_atan2 par1 EXP coma EXP par2\n | t_atan2d par1 EXP coma EXP par2 EXP : t_length par1 id par2\n | t_substring par1 char coma entero coma entero par2\n | t_trim par1 char par2\n | t_md5 par1 char par2\n | t_sha256 par1 par2\n | t_substr par1 par2\n | t_get_byte par1 par2\n | t_set_byte par1 par2\n | t_convert par1 EXP t_as Tipo par2\n | t_encode par1 par2\n | t_decode par1 par2 Lista_ID : Lista_ID coma id\n | id Lista_Enum : Lista_Enum coma char\n | char Lista_EXP : Lista_EXP coma EXP\n | EXP Lista_Alias : Lista_Alias coma Nombre_Alias\n | Nombre_Alias Nombre_Alias : id id'
_lr_action_items = {'$end': ([0, 1, 2, 3, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [-145, 0, -1, -2, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_select': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 224, 313, 391, 396, 453, 487, 490, 593], [7, 7, -4, -5, -6, -48, -3, -12, -49, -50, -51, 336, -47, -9, -11, -10, -7, -8, -54]), 't_insert': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [8, 8, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_update': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [9, 9, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_delete': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [10, 10, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_use': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [11, 11, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_show': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [12, 12, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 't_drop': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 209, 210, 313, 391, 396, 453, 487, 490, 593], [14, 14, -4, -5, -6, -48, -3, -12, -49, -50, 322, -51, -47, -9, -11, -10, -7, -8, -54]), 't_alter': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 209, 210, 313, 391, 396, 408, 453, 487, 490, 513, 593], [15, 15, -4, -5, -6, -48, -3, -12, -49, -50, 323, -51, -47, -9, -11, 466, -10, -7, -8, 466, -54]), 't_create': ([0, 2, 4, 5, 6, 13, 17, 198, 202, 207, 210, 313, 391, 396, 453, 487, 490, 593], [16, 16, -4, -5, -6, -48, -3, -12, -49, -50, -51, -47, -9, -11, -10, -7, -8, -54]), 'asterisco': ([7, 20, 24, 26, 76, 77, 87, 88, 89, 90, 131, 132, 133, 136, 139, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 300, 301, 302, 303, 304, 305, 333, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 386, 388, 389, 397, 429, 430, 431, 432, 433, 450, 491, 492, 493, 494, 495, 497, 509, 536, 570, 580, 581, 622], [19, 117, -144, -166, -141, -139, -138, -140, -142, -143, -163, -164, 117, -165, 250, 117, 117, 117, -148, -149, -150, -151, -154, -155, -156, -157, 117, -159, 117, -161, -162, 117, -152, -167, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, -186, 117, 117, 117, 117, 117, 117, 117, 117, -196, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, -221, -222, -223, -224, 117, -226, -227, 117, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, -217, -219, -220, 117, 117, 117, 117, 117, 117, -144, -198, -199, -200, -215, -216, -225, 117, 117, 117, 117, -218, 117]), 'par1': ([7, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 86, 111, 112, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 195, 196, 213, 216, 308, 311, 367, 368, 369, 384, 385, 390, 394, 410, 440, 444, 445, 446, 450, 461, 462, 482, 483, 488, 500, 504, 507, 527, 531, 532, 533, 538, 545, 550, 565, 574, 600, 618, 637, 640], [23, 23, 23, 23, 134, 23, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 23, 224, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 307, 23, 329, 23, 394, 23, 23, 23, 23, 23, 23, 23, 23, 472, 498, 501, 502, 503, 134, 508, 23, 530, 531, 23, 541, 545, 546, 568, 23, 571, 572, 23, 23, 592, 600, 23, 23, 632, 641, 643]), 'id': ([7, 9, 11, 21, 22, 23, 25, 91, 93, 97, 98, 100, 101, 103, 107, 111, 112, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 192, 196, 203, 205, 212, 216, 225, 307, 311, 316, 326, 328, 329, 334, 335, 367, 368, 369, 384, 385, 390, 394, 398, 399, 401, 403, 406, 407, 408, 409, 413, 419, 423, 426, 459, 462, 477, 481, 488, 508, 510, 513, 514, 530, 531, 538, 545, 546, 561, 567, 568, 571, 572, 574, 600, 631, 635, 641, 643], [24, 92, 94, 24, 24, 24, 24, 195, 197, -145, 206, 208, 209, 211, 213, 24, 225, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 246, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 295, 24, 24, 315, -58, -145, 24, 337, 393, 24, -57, 411, -90, 413, 424, 426, 24, 24, 24, 24, 24, 450, 24, 454, 456, 459, 463, 464, 465, 468, 471, 478, 486, 393, 337, 478, 24, -89, 413, 24, 547, 548, 552, 553, 393, 24, 24, 24, 393, 596, 602, 603, 393, 393, 24, 24, 637, 640, 393, 393]), 'mas': ([7, 20, 21, 22, 23, 24, 25, 26, 76, 77, 87, 88, 89, 90, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 300, 301, 302, 303, 304, 305, 311, 333, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 388, 389, 390, 394, 397, 429, 430, 431, 432, 433, 450, 462, 488, 491, 492, 493, 494, 495, 497, 509, 531, 536, 538, 545, 570, 574, 580, 581, 600, 622], [21, 115, 21, 21, 21, -144, 21, -166, -141, -139, -138, -140, -142, -143, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, -163, -164, 115, 21, -165, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 115, -146, -147, -148, -149, -150, -151, -154, -155, -156, -157, 115, -159, 115, -161, -162, 115, -152, -167, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, -186, 115, 115, 115, 115, 115, 115, 115, 115, -196, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, -221, -222, -223, -224, 115, -226, -227, 21, 115, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, 21, 21, 21, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, 21, 21, -217, -219, -220, 21, 21, 115, 115, 115, 115, 115, 115, -144, 21, 21, -198, -199, -200, -215, -216, -225, 115, 21, 115, 21, 21, 115, 21, 115, -218, 21, 115]), 'menos': ([7, 20, 21, 22, 23, 24, 25, 26, 76, 77, 87, 88, 89, 90, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 300, 301, 302, 303, 304, 305, 311, 333, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 388, 389, 390, 394, 397, 429, 430, 431, 432, 433, 450, 462, 488, 491, 492, 493, 494, 495, 497, 509, 531, 536, 538, 545, 570, 574, 580, 581, 600, 622], [22, 116, 22, 22, 22, -144, 22, -166, -141, -139, -138, -140, -142, -143, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, -163, -164, 116, 22, -165, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 116, -146, -147, -148, -149, -150, -151, -154, -155, -156, -157, 116, -159, 116, -161, -162, 116, -152, -167, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, -186, 116, 116, 116, 116, 116, 116, 116, 116, -196, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, -221, -222, -223, -224, 116, -226, -227, 22, 116, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, 22, 22, 22, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, 22, 22, -217, -219, -220, 22, 22, 116, 116, 116, 116, 116, 116, -144, 22, 22, -198, -199, -200, -215, -216, -225, 116, 22, 116, 22, 22, 116, 22, 116, -218, 22, 116]), 't_not': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 327, 367, 368, 369, 384, 385, 390, 394, 436, 437, 438, 439, 441, 443, 447, 448, 449, 462, 478, 479, 486, 488, 499, 511, 524, 525, 531, 538, 545, 563, 564, 574, 582, 584, 585, 586, 596, 597, 600, 601, 602, 615, 634], [25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 412, 25, 25, 25, 25, 25, 25, 25, -122, -123, -124, -125, -127, -129, -134, -135, -136, 25, -137, -145, -115, 25, -128, 549, 562, -116, 25, 25, 25, -145, -145, 25, -126, -132, -131, -133, -145, -145, 25, -145, -145, -130, -145]), 't_avg': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27]), 't_sum': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28]), 't_count': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29]), 't_max': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]), 't_min': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]), 't_abs': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32]), 't_cbrt': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33]), 't_ceil': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34]), 't_ceiling': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), 't_degrees': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36]), 't_exp': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37]), 't_factorial': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38]), 't_floor': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39]), 't_gcd': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40]), 't_ln': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41]), 't_log': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42]), 't_pi': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43]), 't_radians': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44]), 't_round': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45]), 't_min_scale': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46]), 't_scale': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47]), 't_sign': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48]), 't_sqrt': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49]), 't_trim_scale': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]), 't_trunc': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51]), 't_width_bucket': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52]), 't_random': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53]), 't_setseed': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54]), 't_div': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55]), 't_mod': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56]), 't_power': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57]), 't_acos': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58]), 't_acosd': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59]), 't_asin': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60]), 't_asind': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61]), 't_atan': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62]), 't_atand': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63]), 't_cos': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64]), 't_cosd': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65]), 't_cot': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66]), 't_cotd': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67]), 't_sin': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68]), 't_sind': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69]), 't_tan': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70]), 't_tand': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71]), 't_atan2': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72]), 't_atan2d': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73]), 't_length': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74]), 't_substring': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75]), 't_trim': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78]), 't_md5': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79]), 't_sha256': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80]), 't_substr': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81]), 't_get_byte': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82]), 't_set_byte': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83]), 't_convert': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84]), 't_encode': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85]), 't_decode': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86]), 'decimal': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87]), 'entero': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 387, 390, 394, 462, 475, 488, 496, 498, 501, 502, 503, 521, 531, 538, 541, 545, 557, 574, 575, 576, 592, 600, 628, 632], [77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 434, 77, 77, 77, 522, 77, 539, 540, 542, 543, 544, 560, 77, 77, 583, 77, 522, 77, 610, 612, 619, 77, 636, 638]), 'string': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88]), 'char': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 185, 186, 187, 192, 196, 200, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 472, 474, 488, 518, 531, 538, 545, 555, 574, 600], [76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 296, 297, 298, 76, 76, 314, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 516, 519, 76, 559, 76, 76, 76, 594, 76, 76]), 't_true': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89]), 't_false': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90]), 't_into': ([8], [91]), 't_from': ([10, 18, 19, 20, 24, 26, 76, 77, 87, 88, 89, 90, 131, 132, 136, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 264, 274, 299, 300, 301, 302, 304, 305, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 386, 388, 389, 491, 492, 493, 494, 495, 497, 581], [93, 112, 112, -233, -144, -166, -141, -139, -138, -140, -142, -143, -163, -164, -165, -232, -146, -147, -148, -149, -150, -151, -154, -155, -156, -157, -158, -159, -160, -161, -162, -168, -152, -167, -186, -196, -221, -222, -223, -224, -226, -227, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, -217, -219, -220, -198, -199, -200, -215, -216, -225, -218]), 't_databases': ([12], [95]), 't_database': ([14, 15, 16, 106, 109, 214], [97, 100, -145, 212, -88, -87]), 't_table': ([14, 15, 16], [98, 101, 107]), 't_type': ([16, 468, 548, 552], [103, 512, 590, 512]), 't_or': ([16, 20, 24, 26, 76, 77, 87, 88, 89, 90, 131, 132, 133, 136, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 300, 301, 302, 303, 304, 305, 333, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 386, 388, 389, 397, 429, 430, 431, 432, 433, 450, 491, 492, 493, 494, 495, 497, 509, 536, 570, 580, 581, 622], [108, 129, -144, -166, -141, -139, -138, -140, -142, -143, 129, 129, 129, -165, 129, 129, 129, 129, 129, 129, 129, -154, -155, -156, -157, 129, -159, 129, -161, -162, 129, -152, -167, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, -186, 129, 129, 129, 129, 129, 129, 129, 129, -196, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, -221, -222, -223, -224, 129, -226, -227, 129, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, -217, -219, -220, 129, 129, 129, 129, 129, 129, -144, -198, -199, -200, -215, -216, -225, 129, 129, 129, 129, -218, 129]), 'coma': ([18, 20, 24, 26, 76, 77, 87, 88, 89, 90, 131, 132, 136, 218, 222, 223, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 261, 264, 273, 274, 276, 277, 278, 293, 294, 296, 299, 300, 301, 302, 304, 305, 309, 329, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 386, 388, 389, 392, 393, 414, 415, 416, 418, 424, 425, 434, 436, 437, 438, 439, 441, 443, 447, 448, 449, 452, 469, 470, 478, 479, 481, 489, 491, 492, 493, 494, 495, 497, 499, 515, 516, 523, 525, 529, 534, 535, 536, 551, 563, 564, 569, 577, 578, 579, 581, 582, 584, 585, 586, 587, 588, 594, 596, 597, 598, 599, 601, 602, 604, 605, 606, 607, 608, 613, 615, 620, 621, 623, 624, 626, 629, 630, 633, 634, 639, 642, 644, 645, 647], [111, -233, -144, -166, -141, -139, -138, -140, -142, -143, -163, -164, -165, -232, 334, 335, -229, -235, -146, -147, -148, -149, -150, -151, -154, -155, -156, -157, -158, -159, -160, -161, -162, -168, -152, 111, -167, 111, -186, 111, -196, 367, 368, 369, 384, 385, 387, -221, -222, -223, -224, -226, -227, 111, -145, -236, -153, -169, -170, -171, -172, -173, -174, -175, -176, -177, -178, -179, -180, -181, -182, -183, -184, -185, -187, -188, -189, -190, -191, -192, -193, -194, -195, -197, -201, -202, -203, -204, -205, -206, -207, -208, -209, -210, -211, -212, -213, -214, -217, -219, -220, 334, -229, 481, -104, 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628, -45, -46, -35, -40, -41]), 't_set': ([92, 468], [196, 511]), 't_like': ([95], [200]), 't_if': ([97, 212], [204, 327]), 't_replace': ([108], [214]), 't_values': ([195, 451], [308, 504]), 't_exists': ([204, 412], [316, 477]), 't_rename': ([208, 209], [318, 324]), 't_owner': ([208, 411], [319, 474]), 't_add': ([209], [321]), 't_to': ([318, 319, 471], [398, 399, 514]), 't_column': ([321, 322, 323, 324, 466], [401, 406, 408, 409, 510]), 't_constraint': ([321, 322, 329, 436, 437, 438, 439, 441, 443, 447, 448, 449, 478, 479, 481, 499, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [403, 407, 419, -122, -123, -124, -125, -127, -129, -134, -135, -136, -137, 419, 419, -128, 419, 419, -126, -132, -131, -133, 419, 419, 419, 419, -130, 419]), 't_foreign': ([321, 329, 402, 404, 417, 418, 463, 481, 486], [-145, -145, 460, -75, 485, -116, -74, -145, -115]), 't_unique': ([321, 329, 402, 404, 417, 418, 436, 437, 438, 439, 441, 443, 447, 448, 449, 463, 478, 479, 481, 486, 499, 524, 525, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [-145, -145, 461, -75, 482, -116, -122, -123, -124, -125, -127, -129, -134, -135, -136, -74, -137, -145, -145, -115, -128, 564, -116, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, -145]), 't_check': ([321, 329, 402, 404, 417, 418, 436, 437, 438, 439, 441, 443, 447, 448, 449, 463, 478, 479, 481, 486, 499, 524, 525, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [-145, -145, 462, -75, 483, -116, -122, -123, -124, -125, -127, -129, -134, -135, -136, -74, -137, -145, -145, -115, -128, 565, -116, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, -145]), 't_enum': ([325], [410]), 't_primary': ([329, 417, 418, 436, 437, 438, 439, 441, 443, 447, 448, 449, 478, 479, 481, 486, 499, 524, 525, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [-145, 484, -116, -122, -123, -124, -125, -127, -129, -134, -135, -136, -137, -145, -145, -115, -128, 566, -116, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, -145]), 't_by': ([331, 421], [423, 488]), 't_smallint': ([390, 413, 459], [436, 436, 436]), 't_integer': ([390, 413, 459], [437, 437, 437]), 't_bigint': ([390, 413, 459], [438, 438, 438]), 't_decimal': ([390, 413, 459], [439, 439, 439]), 't_numeric': ([390, 413, 459], [440, 440, 440]), 't_real': ([390, 413, 459], [441, 441, 441]), 't_double': ([390, 413, 459], [442, 442, 442]), 't_money': ([390, 413, 459], [443, 443, 443]), 't_character': ([390, 413, 459], [444, 444, 444]), 't_varchar': ([390, 413, 459, 512, 590], [445, 445, 445, 550, 618]), 't_charn': ([390, 413, 459], [446, 446, 446]), 't_text': ([390, 413, 459], [447, 447, 447]), 't_boolean': ([390, 413, 459], [448, 448, 448]), 't_date': ([390, 413, 459], [449, 449, 449]), 't_having': ([393, 424, 489], [-229, -228, 538]), 't_current_user': ([399], [457]), 't_session_user': ([399], [458]), 't_mode': ([411, 517, 519, 559], [475, 557, -95, -94]), 't_default': ([436, 437, 438, 439, 441, 443, 447, 448, 449, 478, 479, 486, 499, 524, 525, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [-122, -123, -124, -125, -127, -129, -134, -135, -136, -137, -145, -115, -128, 561, -116, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, -145]), 't_null': ([436, 437, 438, 439, 441, 443, 447, 448, 449, 478, 479, 486, 499, 524, 525, 549, 562, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 634], [-122, -123, -124, -125, -127, -129, -134, -135, -136, -137, -145, -115, -128, 563, -116, 591, 597, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, -145]), 't_references': ([436, 437, 438, 439, 441, 443, 447, 448, 449, 478, 479, 486, 499, 524, 525, 563, 564, 582, 584, 585, 586, 596, 597, 601, 602, 615, 617, 627, 634], [-122, -123, -124, -125, -127, -129, -134, -135, -136, -137, -145, -115, -128, 567, -116, -145, -145, -126, -132, -131, -133, -145, -145, -145, -145, -130, 631, 635, -145]), 't_precision': ([442], [499]), 't_varying': ([444], [500]), 't_key': ([460, 484, 485, 566], [507, 532, 533, 601]), 't_inherits': ([480], [527]), 't_all': ([575], [611]), 't_nulls': ([577, 578, 579], [614, -38, -39]), 't_first': ([614], [629]), 't_last': ([614], [630])}
_lr_action = {}
for (_k, _v) in _lr_action_items.items():
for (_x, _y) in zip(_v[0], _v[1]):
if not _x in _lr_action:
_lr_action[_x] = {}
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'SQL': ([0], [1]), 'Sentencias_SQL': ([0], [2]), 'empty': ([0, 16, 18, 19, 95, 97, 110, 114, 197, 212, 215, 227, 309, 321, 329, 330, 338, 411, 479, 480, 481, 517, 563, 564, 596, 597, 601, 602, 634], [3, 109, 113, 113, 201, 205, 217, 217, 312, 328, 332, 332, 312, 404, 418, 422, 422, 476, 525, 528, 418, 558, 525, 525, 525, 525, 525, 525, 525]), 'Sentencia_SQL': ([0, 2], [4, 17]), 'Sentencias_DML': ([0, 2], [5, 5]), 'Sentencias_DDL': ([0, 2], [6, 6]), 'Enum_Type': ([0, 2], [13, 13]), 'Lista_EXP': ([7, 134, 150, 162, 196, 394, 545], [18, 245, 261, 273, 309, 452, 587]), 'EXP': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [20, 131, 132, 133, 136, 218, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 20, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 20, 262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 20, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 303, 20, 333, 397, 429, 430, 431, 432, 433, 243, 20, 509, 536, 570, 580, 20, 536, 622]), 'Valor': ([7, 21, 22, 23, 25, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 192, 196, 216, 311, 367, 368, 369, 384, 385, 390, 394, 462, 488, 531, 538, 545, 574, 600], [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26]), 'Drop': ([14], [96]), 'Alter': ([15], [99]), 'Create': ([16], [102]), 'CreateDB': ([16], [104]), 'CreateTB': ([16], [105]), 'OrReplace_CreateDB': ([16], [106]), 'Select_SQL': ([18, 19], [110, 114]), 'Show_DB_Like_Char': ([95], [199]), 'DropDB': ([97], [203]), 'Condiciones': ([110, 114], [215, 227]), 'Table_Expression': ([112], [219]), 'Alias_Tabla': ([112], [220]), 'Subqueries': ([112], [221]), 'Lista_ID': ([112, 307, 423, 530, 546, 571, 572, 641, 643], [222, 392, 489, 569, 588, 606, 607, 644, 645]), 'Lista_Alias': ([112], [223]), 'Nombre_Alias': ([112, 335], [226, 425]), 'Insert_SQL': ([195], [306]), 'Condiciones1': ([197, 309], [310, 395]), 'AlterDB': ([208], [317]), 'AlterTB': ([209], [320]), 'IfNotExist_CreateDB': ([212], [326]), 'GRP': ([215, 227], [330, 338]), 'Add_Opc': ([321], [400]), 'Constraint_AlterTB': ([321], [402]), 'Drop_Opc': ([322], [405]), 'Columnas': ([329], [414]), 'Columna': ([329, 481], [415, 529]), 'Constraint': ([329, 481], [416, 416]), 'Constraint_CreateTB': ([329, 479, 481, 563, 564, 596, 597, 601, 602, 634], [417, 524, 417, 524, 524, 524, 524, 524, 524, 524]), 'ORD': ([330, 338], [420, 428]), 'Tipo': ([390, 413, 459], [435, 479, 506]), 'SesionDB': ([399], [455]), 'Alter_Column': ([408], [467]), 'Alter_Columns': ([408], [469]), 'Alter_Column1': ([408, 513], [470, 551]), 'Sesion': ([411], [473]), 'Lista_Enum': ([472], [515]), 'Op_Sesion': ([474], [517]), 'Op_Mode': ([475, 557], [520, 595]), 'Cond_CreateTB': ([479, 563, 564, 596, 597, 601, 602, 634], [523, 598, 599, 620, 621, 623, 624, 639]), 'Inherits': ([480], [526]), 'LSORT': ([488], [534]), 'SORT': ([488, 574], [535, 608]), 'HV': ([489], [537]), 'Sesion_mode': ([517], [556]), 'LMT': ([534], [573]), 'AD': ([536], [577]), 'NAL': ([575], [609]), 'NFL': ([577], [613])}
_lr_goto = {}
for (_k, _v) in _lr_goto_items.items():
for (_x, _y) in zip(_v[0], _v[1]):
if not _x in _lr_goto:
_lr_goto[_x] = {}
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [("S' -> SQL", "S'", 1, None, None, None), ('SQL -> Sentencias_SQL', 'SQL', 1, 'p_sql', 'Gramatica.py', 318), ('SQL -> empty', 'SQL', 1, 'p_sql2', 'Gramatica.py', 322), ('Sentencias_SQL -> Sentencias_SQL Sentencia_SQL', 'Sentencias_SQL', 2, 'p_Sentencias_SQL_Sentencia_SQL', 'Gramatica.py', 326), ('Sentencias_SQL -> Sentencia_SQL', 'Sentencias_SQL', 1, 'p_Sentencias_SQL', 'Gramatica.py', 332), ('Sentencia_SQL -> Sentencias_DML', 'Sentencia_SQL', 1, 'p_Sentencia_SQL_DML', 'Gramatica.py', 337), ('Sentencia_SQL -> Sentencias_DDL', 'Sentencia_SQL', 1, 'p_Sentencia_SQL_DDL', 'Gramatica.py', 346), ('Sentencias_DML -> t_select Lista_EXP Select_SQL Condiciones GRP ORD pyc', 'Sentencias_DML', 7, 'p_Sentencias_DML', 'Gramatica.py', 352), ('Sentencias_DML -> t_select asterisco Select_SQL Condiciones GRP ORD pyc', 'Sentencias_DML', 7, 'p_Sentencias_DML', 'Gramatica.py', 353), ('Sentencias_DML -> t_insert t_into id Insert_SQL pyc', 'Sentencias_DML', 5, 'p_Sentencias_DML', 'Gramatica.py', 354), ('Sentencias_DML -> t_update id t_set Lista_EXP Condiciones1 pyc', 'Sentencias_DML', 6, 'p_Sentencias_DML', 'Gramatica.py', 355), ('Sentencias_DML -> t_delete t_from id Condiciones1 pyc', 'Sentencias_DML', 5, 'p_Sentencias_DML', 'Gramatica.py', 356), ('Sentencias_DML -> t_use id pyc', 'Sentencias_DML', 3, 'p_Sentencias_DML', 'Gramatica.py', 357), ('Select_SQL -> t_from Table_Expression', 'Select_SQL', 2, 'p_Select_SQL', 'Gramatica.py', 376), ('Select_SQL -> empty', 'Select_SQL', 1, 'p_Select2_SQL', 'Gramatica.py', 382), ('Table_Expression -> Alias_Tabla', 'Table_Expression', 1, 'p_Table_Expression', 'Gramatica.py', 388), ('Table_Expression -> Subqueries', 'Table_Expression', 1, 'p_Table_Expression', 'Gramatica.py', 389), ('Alias_Tabla -> Lista_ID', 'Alias_Tabla', 1, 'p_Alias_Tabla', 'Gramatica.py', 395), ('Alias_Tabla -> Lista_Alias', 'Alias_Tabla', 1, 'p_Alias_Tabla', 'Gramatica.py', 396), ('Subqueries -> par1 t_select par2', 'Subqueries', 3, 'p_Subqueries', 'Gramatica.py', 401), ('Insert_SQL -> par1 Lista_ID par2 t_values par1 Lista_EXP par2', 'Insert_SQL', 7, 'p_Insert_SQL', 'Gramatica.py', 406), ('Insert_SQL -> t_values par1 Lista_EXP par2', 'Insert_SQL', 4, 'p_Insert_SQL2', 'Gramatica.py', 411), ('Condiciones -> t_where EXP', 'Condiciones', 2, 'p_Condiciones', 'Gramatica.py', 416), ('Condiciones -> empty', 'Condiciones', 1, 'p_Condiciones', 'Gramatica.py', 417), ('Condiciones1 -> t_where EXP', 'Condiciones1', 2, 'p_Condiciones1', 'Gramatica.py', 426), ('Condiciones1 -> empty', 'Condiciones1', 1, 'p_Condiciones1', 'Gramatica.py', 427), ('GRP -> t_group t_by Lista_ID', 'GRP', 3, 'p_GRP', 'Gramatica.py', 438), ('GRP -> t_group t_by Lista_ID HV', 'GRP', 4, 'p_GRP', 'Gramatica.py', 439), ('GRP -> empty', 'GRP', 1, 'p_GRP', 'Gramatica.py', 440), ('HV -> t_having EXP', 'HV', 2, 'p_HV', 'Gramatica.py', 447), ('ORD -> t_order t_by LSORT', 'ORD', 3, 'p_ORD', 'Gramatica.py', 451), ('ORD -> t_order t_by LSORT LMT', 'ORD', 4, 'p_ORD', 'Gramatica.py', 452), ('ORD -> empty', 'ORD', 1, 'p_ORD', 'Gramatica.py', 453), ('LSORT -> LSORT coma SORT', 'LSORT', 3, 'p_L_SORT', 'Gramatica.py', 461), ('LSORT -> SORT', 'LSORT', 1, 'p_L_SORT', 'Gramatica.py', 462), ('SORT -> EXP AD NFL', 'SORT', 3, 'p_SORT', 'Gramatica.py', 469), ('SORT -> EXP AD', 'SORT', 2, 'p_SORT', 'Gramatica.py', 470), ('SORT -> EXP', 'SORT', 1, 'p_SORT', 'Gramatica.py', 471), ('AD -> t_asc', 'AD', 1, 'p_AD', 'Gramatica.py', 480), ('AD -> t_desc', 'AD', 1, 'p_AD', 'Gramatica.py', 481), ('NFL -> t_nulls t_first', 'NFL', 2, 'p_NFL', 'Gramatica.py', 486), ('NFL -> t_nulls t_last', 'NFL', 2, 'p_NFL', 'Gramatica.py', 487), ('LMT -> t_limit NAL t_offset entero', 'LMT', 4, 'p_LMT', 'Gramatica.py', 491), ('LMT -> t_limit NAL', 'LMT', 2, 'p_LMT', 'Gramatica.py', 492), ('LMT -> t_offset entero', 'LMT', 2, 'p_LMT', 'Gramatica.py', 493), ('NAL -> entero', 'NAL', 1, 'p_NAL', 'Gramatica.py', 500), ('NAL -> t_all', 'NAL', 1, 'p_NAL', 'Gramatica.py', 501), ('Sentencias_DDL -> t_show t_databases Show_DB_Like_Char pyc', 'Sentencias_DDL', 4, 'p_Sentencias_DDL', 'Gramatica.py', 506), ('Sentencias_DDL -> Enum_Type', 'Sentencias_DDL', 1, 'p_Sentencias_DDL', 'Gramatica.py', 507), ('Sentencias_DDL -> t_drop Drop pyc', 'Sentencias_DDL', 3, 'p_Sentencias_DDL', 'Gramatica.py', 508), ('Sentencias_DDL -> t_alter Alter pyc', 'Sentencias_DDL', 3, 'p_Sentencias_DDL', 'Gramatica.py', 509), ('Sentencias_DDL -> t_create Create pyc', 'Sentencias_DDL', 3, 'p_Sentencias_DDL', 'Gramatica.py', 510), ('Show_DB_Like_Char -> t_like char', 'Show_DB_Like_Char', 2, 'p_show_db_like_regex', 'Gramatica.py', 530), ('Show_DB_Like_Char -> empty', 'Show_DB_Like_Char', 1, 'p_show_db_like_regex', 'Gramatica.py', 531), ('Enum_Type -> t_create t_type id t_as t_enum par1 Lista_Enum par2 pyc', 'Enum_Type', 9, 'p_Enum_Type', 'Gramatica.py', 540), ('Drop -> t_database DropDB id', 'Drop', 3, 'p_Drop', 'Gramatica.py', 545), ('Drop -> t_table id', 'Drop', 2, 'p_Drop', 'Gramatica.py', 546), ('DropDB -> t_if t_exists', 'DropDB', 2, 'p_DropDB', 'Gramatica.py', 555), ('DropDB -> empty', 'DropDB', 1, 'p_DropDB', 'Gramatica.py', 556), ('Alter -> t_database id AlterDB', 'Alter', 3, 'p_Alter', 'Gramatica.py', 565), ('Alter -> t_table id AlterTB', 'Alter', 3, 'p_Alter', 'Gramatica.py', 566), ('AlterDB -> t_rename t_to id', 'AlterDB', 3, 'p_AlterDB', 'Gramatica.py', 575), ('AlterDB -> t_owner t_to SesionDB', 'AlterDB', 3, 'p_AlterDB', 'Gramatica.py', 576), ('SesionDB -> id', 'SesionDB', 1, 'p_SesionDB', 'Gramatica.py', 585), ('SesionDB -> t_current_user', 'SesionDB', 1, 'p_SesionDB', 'Gramatica.py', 586), ('SesionDB -> t_session_user', 'SesionDB', 1, 'p_SesionDB', 'Gramatica.py', 587), ('AlterTB -> t_add Add_Opc', 'AlterTB', 2, 'p_AlterTB', 'Gramatica.py', 597), ('AlterTB -> t_drop Drop_Opc', 'AlterTB', 2, 'p_AlterTB', 'Gramatica.py', 598), ('AlterTB -> t_alter t_column Alter_Column', 'AlterTB', 3, 'p_AlterTB', 'Gramatica.py', 599), ('AlterTB -> t_rename t_column id t_to id', 'AlterTB', 5, 'p_AlterTB', 'Gramatica.py', 600), ('Add_Opc -> t_column id Tipo', 'Add_Opc', 3, 'p_Add_Opc', 'Gramatica.py', 615), ('Add_Opc -> Constraint_AlterTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2', 'Add_Opc', 11, 'p_Add_Opc', 'Gramatica.py', 616), ('Add_Opc -> Constraint_AlterTB t_unique par1 id par2', 'Add_Opc', 5, 'p_Add_Opc', 'Gramatica.py', 617), ('Add_Opc -> Constraint_AlterTB t_check EXP', 'Add_Opc', 3, 'p_Add_Opc', 'Gramatica.py', 618), ('Constraint_AlterTB -> t_constraint id', 'Constraint_AlterTB', 2, 'p_Constraint_AlterTB', 'Gramatica.py', 633), ('Constraint_AlterTB -> empty', 'Constraint_AlterTB', 1, 'p_Constraint_AlterTB', 'Gramatica.py', 634), ('Drop_Opc -> t_column id', 'Drop_Opc', 2, 'p_Drop_Opc', 'Gramatica.py', 643), ('Drop_Opc -> t_constraint id', 'Drop_Opc', 2, 'p_Drop_Opc', 'Gramatica.py', 644), ('Alter_Column -> id t_set t_not t_null', 'Alter_Column', 4, 'p_Alter_Column', 'Gramatica.py', 653), ('Alter_Column -> Alter_Columns', 'Alter_Column', 1, 'p_Alter_Column', 'Gramatica.py', 654), ('Alter_Columns -> Alter_Columns coma Alter_Column1', 'Alter_Columns', 3, 'p_Alter_Columns', 'Gramatica.py', 663), ('Alter_Columns -> Alter_Column1', 'Alter_Columns', 1, 'p_Alter_Columns', 'Gramatica.py', 664), ('Alter_Column1 -> id t_type t_varchar par1 entero par2', 'Alter_Column1', 6, 'p_Alter_Colum1', 'Gramatica.py', 674), ('Alter_Column1 -> t_alter t_column id t_type t_varchar par1 entero par2', 'Alter_Column1', 8, 'p_Alter_Colum1', 'Gramatica.py', 675), ('Create -> CreateDB', 'Create', 1, 'p_Create', 'Gramatica.py', 690), ('Create -> CreateTB', 'Create', 1, 'p_Create1', 'Gramatica.py', 695), ('CreateDB -> OrReplace_CreateDB t_database IfNotExist_CreateDB id Sesion', 'CreateDB', 5, 'p_CreateDB', 'Gramatica.py', 700), ('OrReplace_CreateDB -> t_or t_replace', 'OrReplace_CreateDB', 2, 'p_CreateDB_or_replace', 'Gramatica.py', 705), ('OrReplace_CreateDB -> empty', 'OrReplace_CreateDB', 1, 'p_CreateDB_or_replace', 'Gramatica.py', 706), ('IfNotExist_CreateDB -> t_if t_not t_exists', 'IfNotExist_CreateDB', 3, 'p_IfNotExist_CreateDB', 'Gramatica.py', 715), ('IfNotExist_CreateDB -> empty', 'IfNotExist_CreateDB', 1, 'p_IfNotExist_CreateDB', 'Gramatica.py', 716), ('Sesion -> t_owner Op_Sesion Sesion_mode', 'Sesion', 3, 'p_Sesion', 'Gramatica.py', 725), ('Sesion -> t_mode Op_Mode', 'Sesion', 2, 'p_Sesion', 'Gramatica.py', 726), ('Sesion -> empty', 'Sesion', 1, 'p_Sesion', 'Gramatica.py', 727), ('Op_Sesion -> igual char', 'Op_Sesion', 2, 'p_Op_Sesion', 'Gramatica.py', 739), ('Op_Sesion -> char', 'Op_Sesion', 1, 'p_Op_Sesion', 'Gramatica.py', 740), ('Sesion_mode -> t_mode Op_Mode', 'Sesion_mode', 2, 'p_Sesion_mode', 'Gramatica.py', 749), ('Sesion_mode -> empty', 'Sesion_mode', 1, 'p_Sesion_mode', 'Gramatica.py', 750), ('Op_Mode -> igual entero', 'Op_Mode', 2, 'p_Op_Mode', 'Gramatica.py', 759), ('Op_Mode -> entero', 'Op_Mode', 1, 'p_Op_Mode', 'Gramatica.py', 760), ('CreateTB -> t_table id par1 Columnas par2 Inherits', 'CreateTB', 6, 'p_CreateTB', 'Gramatica.py', 769), ('Inherits -> t_inherits par1 id par2', 'Inherits', 4, 'p_Inherits', 'Gramatica.py', 774), ('Inherits -> empty', 'Inherits', 1, 'p_Inherits', 'Gramatica.py', 775), ('Columnas -> Columnas coma Columna', 'Columnas', 3, 'p_Columnas', 'Gramatica.py', 784), ('Columnas -> Columna', 'Columnas', 1, 'p_Columnas', 'Gramatica.py', 785), ('Columna -> id Tipo Cond_CreateTB', 'Columna', 3, 'p_Columna', 'Gramatica.py', 795), ('Columna -> Constraint', 'Columna', 1, 'p_Columna', 'Gramatica.py', 796), ('Cond_CreateTB -> Constraint_CreateTB t_default id Cond_CreateTB', 'Cond_CreateTB', 4, 'p_Cond_CreateTB', 'Gramatica.py', 805), ('Cond_CreateTB -> Constraint_CreateTB t_not t_null Cond_CreateTB', 'Cond_CreateTB', 4, 'p_Cond_CreateTB', 'Gramatica.py', 806), ('Cond_CreateTB -> Constraint_CreateTB t_null Cond_CreateTB', 'Cond_CreateTB', 3, 'p_Cond_CreateTB', 'Gramatica.py', 807), ('Cond_CreateTB -> Constraint_CreateTB t_unique Cond_CreateTB', 'Cond_CreateTB', 3, 'p_Cond_CreateTB', 'Gramatica.py', 808), ('Cond_CreateTB -> Constraint_CreateTB t_check par1 EXP par2 Cond_CreateTB', 'Cond_CreateTB', 6, 'p_Cond_CreateTB', 'Gramatica.py', 809), ('Cond_CreateTB -> Constraint_CreateTB t_primary t_key Cond_CreateTB', 'Cond_CreateTB', 4, 'p_Cond_CreateTB', 'Gramatica.py', 810), ('Cond_CreateTB -> Constraint_CreateTB t_references id Cond_CreateTB', 'Cond_CreateTB', 4, 'p_Cond_CreateTB', 'Gramatica.py', 811), ('Cond_CreateTB -> empty', 'Cond_CreateTB', 1, 'p_Cond_CreateTB', 'Gramatica.py', 812), ('Constraint_CreateTB -> t_constraint id', 'Constraint_CreateTB', 2, 'p_Constraint_CreateTB', 'Gramatica.py', 846), ('Constraint_CreateTB -> empty', 'Constraint_CreateTB', 1, 'p_Constraint_CreateTB', 'Gramatica.py', 847), ('Constraint -> Constraint_CreateTB t_unique par1 Lista_ID par2', 'Constraint', 5, 'p_Constraint', 'Gramatica.py', 856), ('Constraint -> Constraint_CreateTB t_check par1 EXP par2', 'Constraint', 5, 'p_Constraint', 'Gramatica.py', 857), ('Constraint -> Constraint_CreateTB t_primary t_key par1 Lista_ID par2', 'Constraint', 6, 'p_Constraint', 'Gramatica.py', 858), ('Constraint -> Constraint_CreateTB t_foreign t_key par1 Lista_ID par2 t_references id par1 Lista_ID par2', 'Constraint', 11, 'p_Constraint', 'Gramatica.py', 859), ('Constraint -> empty', 'Constraint', 1, 'p_Constraint', 'Gramatica.py', 860), ('Tipo -> t_smallint', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 878), ('Tipo -> t_integer', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 879), ('Tipo -> t_bigint', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 880), ('Tipo -> t_decimal', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 881), ('Tipo -> t_numeric par1 entero par2', 'Tipo', 4, 'p_Tipo', 'Gramatica.py', 882), ('Tipo -> t_real', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 883), ('Tipo -> t_double t_precision', 'Tipo', 2, 'p_Tipo', 'Gramatica.py', 884), ('Tipo -> t_money', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 885), ('Tipo -> t_character t_varying par1 entero par2', 'Tipo', 5, 'p_Tipo', 'Gramatica.py', 886), ('Tipo -> t_varchar par1 entero par2', 'Tipo', 4, 'p_Tipo', 'Gramatica.py', 887), ('Tipo -> t_character par1 entero par2', 'Tipo', 4, 'p_Tipo', 'Gramatica.py', 888), ('Tipo -> t_charn par1 entero par2', 'Tipo', 4, 'p_Tipo', 'Gramatica.py', 889), ('Tipo -> t_text', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 890), ('Tipo -> t_boolean', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 891), ('Tipo -> t_date', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 892), ('Tipo -> id', 'Tipo', 1, 'p_Tipo', 'Gramatica.py', 893), ('Valor -> decimal', 'Valor', 1, 'p_Valor', 'Gramatica.py', 978), ('Valor -> entero', 'Valor', 1, 'p_Valor', 'Gramatica.py', 979), ('Valor -> string', 'Valor', 1, 'p_Valor', 'Gramatica.py', 980), ('Valor -> char', 'Valor', 1, 'p_Valor', 'Gramatica.py', 981), ('Valor -> t_true', 'Valor', 1, 'p_Valor', 'Gramatica.py', 982), ('Valor -> t_false', 'Valor', 1, 'p_Valor', 'Gramatica.py', 983), ('Valor -> id', 'Valor', 1, 'p_Valor2', 'Gramatica.py', 989), ('empty -> <empty>', 'empty', 0, 'p_empty', 'Gramatica.py', 994), ('EXP -> EXP mas EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1001), ('EXP -> EXP menos EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1002), ('EXP -> EXP asterisco EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1003), ('EXP -> EXP div EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1004), ('EXP -> EXP pot EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1005), ('EXP -> EXP porcentaje EXP', 'EXP', 3, 'p_aritmeticas', 'Gramatica.py', 1006), ('EXP -> par1 EXP par2', 'EXP', 3, 'p_parentesis', 'Gramatica.py', 1011), ('EXP -> id par1 Lista_EXP par2', 'EXP', 4, 'p_funciones', 'Gramatica.py', 1017), ('EXP -> EXP mayor EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1024), ('EXP -> EXP mayori EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1025), ('EXP -> EXP menor EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1026), ('EXP -> EXP menori EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1027), ('EXP -> EXP igual EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1028), ('EXP -> EXP diferente EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1029), ('EXP -> EXP diferentede EXP', 'EXP', 3, 'p_relacionales', 'Gramatica.py', 1030), ('EXP -> EXP t_and EXP', 'EXP', 3, 'p_logicos', 'Gramatica.py', 1035), ('EXP -> EXP t_or EXP', 'EXP', 3, 'p_logicos', 'Gramatica.py', 1036), ('EXP -> mas EXP', 'EXP', 2, 'p_unario', 'Gramatica.py', 1042), ('EXP -> menos EXP', 'EXP', 2, 'p_unario', 'Gramatica.py', 1043), ('EXP -> t_not EXP', 'EXP', 2, 'p_unario', 'Gramatica.py', 1044), ('EXP -> Valor', 'EXP', 1, 'p_EXP_Valor', 'Gramatica.py', 1053), ('EXP -> id punto id', 'EXP', 3, 'p_EXP_Indices', 'Gramatica.py', 1058), ('EXP -> EXP t_as EXP', 'EXP', 3, 'p_EXP_IndicesAS', 'Gramatica.py', 1064), ('EXP -> t_avg par1 EXP par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1071), ('EXP -> t_sum par1 EXP par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1072), ('EXP -> t_count par1 EXP par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1073), ('EXP -> t_count par1 asterisco par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1074), ('EXP -> t_max par1 EXP par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1075), ('EXP -> t_min par1 EXP par2', 'EXP', 4, 'p_exp_agregacion', 'Gramatica.py', 1076), ('EXP -> t_abs par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1081), ('EXP -> t_cbrt par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1082), ('EXP -> t_ceil par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1083), ('EXP -> t_ceiling par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1084), ('EXP -> t_degrees par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1085), ('EXP -> t_exp par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1086), ('EXP -> t_factorial par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1087), ('EXP -> t_floor par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1088), ('EXP -> t_gcd par1 Lista_EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1089), ('EXP -> t_ln par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1090), ('EXP -> t_log par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1091), ('EXP -> t_pi par1 par2', 'EXP', 3, 'p_funciones_matematicas', 'Gramatica.py', 1092), ('EXP -> t_radians par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1093), ('EXP -> t_round par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1094), ('EXP -> t_min_scale par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1095), ('EXP -> t_scale par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1096), ('EXP -> t_sign par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1097), ('EXP -> t_sqrt par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1098), ('EXP -> t_trim_scale par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1099), ('EXP -> t_trunc par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1100), ('EXP -> t_width_bucket par1 Lista_EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1101), ('EXP -> t_random par1 par2', 'EXP', 3, 'p_funciones_matematicas', 'Gramatica.py', 1102), ('EXP -> t_setseed par1 EXP par2', 'EXP', 4, 'p_funciones_matematicas', 'Gramatica.py', 1103), ('EXP -> t_div par1 EXP coma EXP par2', 'EXP', 6, 'p_funciones_matematicas2', 'Gramatica.py', 1108), ('EXP -> t_mod par1 EXP coma EXP par2', 'EXP', 6, 'p_funciones_matematicas2', 'Gramatica.py', 1109), ('EXP -> t_power par1 EXP coma EXP par2', 'EXP', 6, 'p_funciones_matematicas2', 'Gramatica.py', 1110), ('EXP -> t_acos par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1115), ('EXP -> t_acosd par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1116), ('EXP -> t_asin par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1117), ('EXP -> t_asind par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1118), ('EXP -> t_atan par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1119), ('EXP -> t_atand par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1120), ('EXP -> t_cos par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1121), ('EXP -> t_cosd par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1122), ('EXP -> t_cot par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1123), ('EXP -> t_cotd par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1124), ('EXP -> t_sin par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1125), ('EXP -> t_sind par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1126), ('EXP -> t_tan par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1127), ('EXP -> t_tand par1 EXP par2', 'EXP', 4, 'p_funciones_Trigonometricas', 'Gramatica.py', 1128), ('EXP -> t_atan2 par1 EXP coma EXP par2', 'EXP', 6, 'p_funciones_Trigonometricas1', 'Gramatica.py', 1133), ('EXP -> t_atan2d par1 EXP coma EXP par2', 'EXP', 6, 'p_funciones_Trigonometricas1', 'Gramatica.py', 1134), ('EXP -> t_length par1 id par2', 'EXP', 4, 'p_funciones_String_Binarias', 'Gramatica.py', 1138), ('EXP -> t_substring par1 char coma entero coma entero par2', 'EXP', 8, 'p_funciones_String_Binarias', 'Gramatica.py', 1139), ('EXP -> t_trim par1 char par2', 'EXP', 4, 'p_funciones_String_Binarias', 'Gramatica.py', 1140), ('EXP -> t_md5 par1 char par2', 'EXP', 4, 'p_funciones_String_Binarias', 'Gramatica.py', 1141), ('EXP -> t_sha256 par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1142), ('EXP -> t_substr par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1143), ('EXP -> t_get_byte par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1144), ('EXP -> t_set_byte par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1145), ('EXP -> t_convert par1 EXP t_as Tipo par2', 'EXP', 6, 'p_funciones_String_Binarias', 'Gramatica.py', 1146), ('EXP -> t_encode par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1147), ('EXP -> t_decode par1 par2', 'EXP', 3, 'p_funciones_String_Binarias', 'Gramatica.py', 1148), ('Lista_ID -> Lista_ID coma id', 'Lista_ID', 3, 'p_Lista_ID', 'Gramatica.py', 1158), ('Lista_ID -> id', 'Lista_ID', 1, 'p_Lista_ID', 'Gramatica.py', 1159), ('Lista_Enum -> Lista_Enum coma char', 'Lista_Enum', 3, 'p_Lista_Enum', 'Gramatica.py', 1168), ('Lista_Enum -> char', 'Lista_Enum', 1, 'p_Lista_Enum', 'Gramatica.py', 1169), ('Lista_EXP -> Lista_EXP coma EXP', 'Lista_EXP', 3, 'p_Lista_EXP', 'Gramatica.py', 1178), ('Lista_EXP -> EXP', 'Lista_EXP', 1, 'p_Lista_EXP', 'Gramatica.py', 1179), ('Lista_Alias -> Lista_Alias coma Nombre_Alias', 'Lista_Alias', 3, 'p_Lista_Alias', 'Gramatica.py', 1194), ('Lista_Alias -> Nombre_Alias', 'Lista_Alias', 1, 'p_Lista_Alias', 'Gramatica.py', 1195), ('Nombre_Alias -> id id', 'Nombre_Alias', 2, 'p_Nombre_Alias', 'Gramatica.py', 1204)]
|
"""
This hard-coded list would need to be maintained differently if/when the
available units, available upgrades or their in-game IDs change.
"""
codeMap = {
"protoss" : {
"ground" : {
4 : "Colossus",
73 : "Zealot",
74 : "Stalker",
75 : "HighTemplar",
76 : "DarkTemplar",
77 : "Sentry",
83 : "Immortal",
84 : "Probe",
141 : "Archon",
694 : "Disruptor",
311 : "Adept",
},
"air" : {
78 : "Phoenix",
79 : "Carrier",
80 : "VoidRay",
81 : "WarpPrism",
495 : "Oracle",
496 : "Tempest",
},
"defense" : {
66 : "PhotonCannon",
1910: "ShieldBattery",
},
"detection" : {
82 : "Observer",
},
"upgrades" : {
4 : "CarrierLaunchSpeedUpgrade",
42 : "ProtossGroundWeaponsLevel1",
43 : "ProtossGroundWeaponsLevel2",
44 : "ProtossGroundWeaponsLevel3",
45 : "ProtossGroundArmorsLevel1",
46 : "ProtossGroundArmorsLevel2",
47 : "ProtossGroundArmorsLevel3",
48 : "ProtossShieldsLevel1",
49 : "ProtossShieldsLevel2",
50 : "ProtossShieldsLevel3",
51 : "ObserverGraviticBooster",
52 : "GraviticDrive",
53 : "ExtendedThermalLance",
55 : "PsiStormTech",
87 : "WarpGateResearch",
90 : "BlinkTech",
89 : "Charge",
81 : "ProtossAirWeaponsLevel1",
82 : "ProtossAirWeaponsLevel2",
83 : "ProtossAirWeaponsLevel3",
84 : "ProtossAirArmorsLevel1",
85 : "ProtossAirArmorsLevel2",
86 : "ProtossAirArmorsLevel3",
148 : "PhoneixRangeUpgrade",
181 : "AdeptPiercingAttack",
198 : "DarkTemplarBlinkUpgrade",
},
},
"terran" : {
"ground" : {
33 : "SiegeTank",
45 : "SCV",
48 : "Marine",
49 : "Reaper",
50 : "Ghost",
51 : "Marauder",
52 : "Thor",
53 : "Hellion",
498 : "WidowMine",
692 : "Cyclone",
},
"air" : {
35 : "VikingFighter",
54 : "Medivac",
55 : "Banshee",
57 : "Battlecruiser",
689 : "Liberator",
},
"defense" : {
23 : "MissileTurret",
24 : "Bunker",
25 : "SensorTower",
},
"detection": {
56 : "Raven",
},
"upgrades" : {
8 : "HiSecAutoTracking",
9 : "TerranBuildingArmor",
10 : "TerranInfantryWeaponsLevel1",
11 : "TerranInfantryWeaponsLevel2",
12 : "TerranInfantryWeaponsLevel3",
13 : "NeosteelFrame",
14 : "TerranInfantryArmorsLevel1",
15 : "TerranInfantryArmorsLevel2",
16 : "TerranInfantryArmorsLevel3",
18 : "Stimpack",
19 : "CombatShields",
20 : "PunisherGrenades",
22 : "HighCapacityBarrels",
23 : "BansheeCloak",
25 : "RavenCorvidReactor",
28 : "PersonalCloaking",
33 : "TerranVehicleWeaponsLevel1",
34 : "TerranVehicleWeaponsLevel2",
35 : "TerranVehicleWeaponsLevel3",
39 : "TerranShipWeaponsLevel1",
40 : "TerranShipWeaponsLevel2",
41 : "TerranShipWeaponsLevel3",
79 : "BattlecruiserEnableSpecializations",
162 : "TerranVehicleAndShipArmorsLevel1",
163 : "TerranVehicleAndShipArmorsLevel2",
164 : "TerranVehicleAndShipArmorsLevel3",
168 : "DrillClaws",
187 : "SmartServos",
189 : "CycloneRapidFireLaunchers",
192 : "BansheeSpeed",
195 : "MedivacIncreaseSpeedBoost",
196 : "LiberatorAGRangeUpgrade",
},
},
"zerg" : {
"ground" : {
9 : "Baneling",
104 : "Drone",
105 : "Zergling",
107 : "Hydralisk",
109 : "Ultralisk",
110 : "Roach",
111 : "Infestor",
126 : "Queen",
494 : "SwarmHostMP",
502 : "LurkerMP",
#898 : "InfestedTerran",
},
"air" : {
106 : "Overlord",
108 : "Mutalisk",
112 : "Corruptor",
114 : "BroodLord",
499 : "Viper",
893 : "OverlordTransport",
},
"defense" : {
98 : "SpineCrawler",
99 : "SporeCrawler",
},
"detection": {
129 : "Overseer",
},
"upgrades" : {
5 : "GlialReconstitution",
6 : "TunnelingClaws",
7 : "ChitinousPlating",
56 : "ZergMeleeWeaponsLevel1",
57 : "ZergMeleeWeaponsLevel2",
58 : "ZergMeleeWeaponsLevel3",
59 : "ZergGroundArmorsLevel1",
60 : "ZergGroundArmorsLevel2",
61 : "ZergGroundArmorsLevel3",
62 : "ZergMissleWeaponsLevel",
63 : "ZergMissleWeaponsLeve2",
64 : "ZergMissleWeaponsLeve3",
65 : "overlordsepeed",
67 : "Burrow",
68 : "zerglingattackspeed",
69 : "zerglingmovementspeed",
71 : "ZergFlyerWeaponsLevel1",
72 : "ZergFlyerWeaponsLevel2",
73 : "ZergFlyerWeaponsLevel3",
74 : "ZergFlyerArmorsLevel1",
75 : "ZergFlyerArmorsLevel2",
76 : "ZergFlyerArmorsLevel3",
77 : "InfestorEnergyUpgrade",
78 : "CentrificalHooks",
150 : "NeuralParasite",
199 : "DiggingClaws",
190 : "EvolveGroovedSpines",
191 : "EvolveMuscularAguments",
},
}
}
|
"""
This hard-coded list would need to be maintained differently if/when the
available units, available upgrades or their in-game IDs change.
"""
code_map = {'protoss': {'ground': {4: 'Colossus', 73: 'Zealot', 74: 'Stalker', 75: 'HighTemplar', 76: 'DarkTemplar', 77: 'Sentry', 83: 'Immortal', 84: 'Probe', 141: 'Archon', 694: 'Disruptor', 311: 'Adept'}, 'air': {78: 'Phoenix', 79: 'Carrier', 80: 'VoidRay', 81: 'WarpPrism', 495: 'Oracle', 496: 'Tempest'}, 'defense': {66: 'PhotonCannon', 1910: 'ShieldBattery'}, 'detection': {82: 'Observer'}, 'upgrades': {4: 'CarrierLaunchSpeedUpgrade', 42: 'ProtossGroundWeaponsLevel1', 43: 'ProtossGroundWeaponsLevel2', 44: 'ProtossGroundWeaponsLevel3', 45: 'ProtossGroundArmorsLevel1', 46: 'ProtossGroundArmorsLevel2', 47: 'ProtossGroundArmorsLevel3', 48: 'ProtossShieldsLevel1', 49: 'ProtossShieldsLevel2', 50: 'ProtossShieldsLevel3', 51: 'ObserverGraviticBooster', 52: 'GraviticDrive', 53: 'ExtendedThermalLance', 55: 'PsiStormTech', 87: 'WarpGateResearch', 90: 'BlinkTech', 89: 'Charge', 81: 'ProtossAirWeaponsLevel1', 82: 'ProtossAirWeaponsLevel2', 83: 'ProtossAirWeaponsLevel3', 84: 'ProtossAirArmorsLevel1', 85: 'ProtossAirArmorsLevel2', 86: 'ProtossAirArmorsLevel3', 148: 'PhoneixRangeUpgrade', 181: 'AdeptPiercingAttack', 198: 'DarkTemplarBlinkUpgrade'}}, 'terran': {'ground': {33: 'SiegeTank', 45: 'SCV', 48: 'Marine', 49: 'Reaper', 50: 'Ghost', 51: 'Marauder', 52: 'Thor', 53: 'Hellion', 498: 'WidowMine', 692: 'Cyclone'}, 'air': {35: 'VikingFighter', 54: 'Medivac', 55: 'Banshee', 57: 'Battlecruiser', 689: 'Liberator'}, 'defense': {23: 'MissileTurret', 24: 'Bunker', 25: 'SensorTower'}, 'detection': {56: 'Raven'}, 'upgrades': {8: 'HiSecAutoTracking', 9: 'TerranBuildingArmor', 10: 'TerranInfantryWeaponsLevel1', 11: 'TerranInfantryWeaponsLevel2', 12: 'TerranInfantryWeaponsLevel3', 13: 'NeosteelFrame', 14: 'TerranInfantryArmorsLevel1', 15: 'TerranInfantryArmorsLevel2', 16: 'TerranInfantryArmorsLevel3', 18: 'Stimpack', 19: 'CombatShields', 20: 'PunisherGrenades', 22: 'HighCapacityBarrels', 23: 'BansheeCloak', 25: 'RavenCorvidReactor', 28: 'PersonalCloaking', 33: 'TerranVehicleWeaponsLevel1', 34: 'TerranVehicleWeaponsLevel2', 35: 'TerranVehicleWeaponsLevel3', 39: 'TerranShipWeaponsLevel1', 40: 'TerranShipWeaponsLevel2', 41: 'TerranShipWeaponsLevel3', 79: 'BattlecruiserEnableSpecializations', 162: 'TerranVehicleAndShipArmorsLevel1', 163: 'TerranVehicleAndShipArmorsLevel2', 164: 'TerranVehicleAndShipArmorsLevel3', 168: 'DrillClaws', 187: 'SmartServos', 189: 'CycloneRapidFireLaunchers', 192: 'BansheeSpeed', 195: 'MedivacIncreaseSpeedBoost', 196: 'LiberatorAGRangeUpgrade'}}, 'zerg': {'ground': {9: 'Baneling', 104: 'Drone', 105: 'Zergling', 107: 'Hydralisk', 109: 'Ultralisk', 110: 'Roach', 111: 'Infestor', 126: 'Queen', 494: 'SwarmHostMP', 502: 'LurkerMP'}, 'air': {106: 'Overlord', 108: 'Mutalisk', 112: 'Corruptor', 114: 'BroodLord', 499: 'Viper', 893: 'OverlordTransport'}, 'defense': {98: 'SpineCrawler', 99: 'SporeCrawler'}, 'detection': {129: 'Overseer'}, 'upgrades': {5: 'GlialReconstitution', 6: 'TunnelingClaws', 7: 'ChitinousPlating', 56: 'ZergMeleeWeaponsLevel1', 57: 'ZergMeleeWeaponsLevel2', 58: 'ZergMeleeWeaponsLevel3', 59: 'ZergGroundArmorsLevel1', 60: 'ZergGroundArmorsLevel2', 61: 'ZergGroundArmorsLevel3', 62: 'ZergMissleWeaponsLevel', 63: 'ZergMissleWeaponsLeve2', 64: 'ZergMissleWeaponsLeve3', 65: 'overlordsepeed', 67: 'Burrow', 68: 'zerglingattackspeed', 69: 'zerglingmovementspeed', 71: 'ZergFlyerWeaponsLevel1', 72: 'ZergFlyerWeaponsLevel2', 73: 'ZergFlyerWeaponsLevel3', 74: 'ZergFlyerArmorsLevel1', 75: 'ZergFlyerArmorsLevel2', 76: 'ZergFlyerArmorsLevel3', 77: 'InfestorEnergyUpgrade', 78: 'CentrificalHooks', 150: 'NeuralParasite', 199: 'DiggingClaws', 190: 'EvolveGroovedSpines', 191: 'EvolveMuscularAguments'}}}
|
items = "ABCDE"
pairs = []
for a in range(len(items)):
for b in range(len(items)):
pairs.append((items[a], items[b]))
print(pairs)
ret = [(items[a], items[b]) for a in range(len(items)) for b in range( len(items))]
print(ret)
ret2 = [(x, y) for x in range(2) for y in range(2)]
ret3 = [(x, y) for x in range(2) for y in range(x, 2)]
print(ret2)
print(ret3)
|
items = 'ABCDE'
pairs = []
for a in range(len(items)):
for b in range(len(items)):
pairs.append((items[a], items[b]))
print(pairs)
ret = [(items[a], items[b]) for a in range(len(items)) for b in range(len(items))]
print(ret)
ret2 = [(x, y) for x in range(2) for y in range(2)]
ret3 = [(x, y) for x in range(2) for y in range(x, 2)]
print(ret2)
print(ret3)
|
# -*- coding: utf-8 -*-
def skip(model, layer, inputs):
inputs[layer.name] = inputs[layer.input.name]
return model, layer, inputs
|
def skip(model, layer, inputs):
inputs[layer.name] = inputs[layer.input.name]
return (model, layer, inputs)
|
def fixing_float(size, n_float):
fmt = ".{n}f"
fix = [None]
for i in range(size):
fix.append(fmt.format(n=n_float))
return fix
|
def fixing_float(size, n_float):
fmt = '.{n}f'
fix = [None]
for i in range(size):
fix.append(fmt.format(n=n_float))
return fix
|
#
# PySNMP MIB module UPS-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/UPS-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 18:50:47 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueRangeConstraint, ConstraintsUnion, ValueSizeConstraint, ConstraintsIntersection, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsUnion", "ValueSizeConstraint", "ConstraintsIntersection", "SingleValueConstraint")
ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance")
MibScalar, MibTable, MibTableRow, MibTableColumn, iso, Unsigned32, Integer32, MibIdentifier, Counter32, mib_2, ModuleIdentity, ObjectIdentity, Gauge32, TimeTicks, Counter64, Bits, IpAddress, NotificationType = mibBuilder.importSymbols("SNMPv2-SMI", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "iso", "Unsigned32", "Integer32", "MibIdentifier", "Counter32", "mib-2", "ModuleIdentity", "ObjectIdentity", "Gauge32", "TimeTicks", "Counter64", "Bits", "IpAddress", "NotificationType")
TimeStamp, AutonomousType, TimeInterval, TestAndIncr, TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TimeStamp", "AutonomousType", "TimeInterval", "TestAndIncr", "TextualConvention", "DisplayString")
upsMIB = ModuleIdentity((1, 3, 6, 1, 2, 1, 33))
if mibBuilder.loadTexts: upsMIB.setLastUpdated('9402230000Z')
if mibBuilder.loadTexts: upsMIB.setOrganization('IETF UPS MIB Working Group')
class PositiveInteger(TextualConvention, Integer32):
status = 'current'
displayHint = 'd'
subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(1, 2147483647)
class NonNegativeInteger(TextualConvention, Integer32):
status = 'current'
displayHint = 'd'
subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647)
upsObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1))
upsIdent = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 1))
upsIdentManufacturer = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 1), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 31))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsIdentManufacturer.setStatus('current')
upsIdentModel = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 2), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 63))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsIdentModel.setStatus('current')
upsIdentUPSSoftwareVersion = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 3), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 63))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsIdentUPSSoftwareVersion.setStatus('current')
upsIdentAgentSoftwareVersion = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 4), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 63))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsIdentAgentSoftwareVersion.setStatus('current')
upsIdentName = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 5), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 63))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsIdentName.setStatus('current')
upsIdentAttachedDevices = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 6), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 63))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsIdentAttachedDevices.setStatus('current')
upsBattery = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 2))
upsBatteryStatus = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("unknown", 1), ("batteryNormal", 2), ("batteryLow", 3), ("batteryDepleted", 4)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBatteryStatus.setStatus('current')
upsSecondsOnBattery = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 2), NonNegativeInteger()).setUnits('seconds').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsSecondsOnBattery.setStatus('current')
upsEstimatedMinutesRemaining = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 3), PositiveInteger()).setUnits('minutes').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsEstimatedMinutesRemaining.setStatus('current')
upsEstimatedChargeRemaining = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setUnits('percent').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsEstimatedChargeRemaining.setStatus('current')
upsBatteryVoltage = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 5), NonNegativeInteger()).setUnits('0.1 Volt DC').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBatteryVoltage.setStatus('current')
upsBatteryCurrent = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 6), Integer32()).setUnits('0.1 Amp DC').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBatteryCurrent.setStatus('current')
upsBatteryTemperature = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 7), Integer32()).setUnits('degrees Centigrade').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBatteryTemperature.setStatus('current')
upsInput = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 3))
upsInputLineBads = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 3, 1), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputLineBads.setStatus('current')
upsInputNumLines = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 3, 2), NonNegativeInteger()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputNumLines.setStatus('current')
upsInputTable = MibTable((1, 3, 6, 1, 2, 1, 33, 1, 3, 3), )
if mibBuilder.loadTexts: upsInputTable.setStatus('current')
upsInputEntry = MibTableRow((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1), ).setIndexNames((0, "UPS-MIB", "upsInputLineIndex"))
if mibBuilder.loadTexts: upsInputEntry.setStatus('current')
upsInputLineIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 1), PositiveInteger())
if mibBuilder.loadTexts: upsInputLineIndex.setStatus('current')
upsInputFrequency = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 2), NonNegativeInteger()).setUnits('0.1 Hertz').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputFrequency.setStatus('current')
upsInputVoltage = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 3), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputVoltage.setStatus('current')
upsInputCurrent = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 4), NonNegativeInteger()).setUnits('0.1 RMS Amp').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputCurrent.setStatus('current')
upsInputTruePower = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 5), NonNegativeInteger()).setUnits('Watts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsInputTruePower.setStatus('current')
upsOutput = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 4))
upsOutputSource = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7))).clone(namedValues=NamedValues(("other", 1), ("none", 2), ("normal", 3), ("bypass", 4), ("battery", 5), ("booster", 6), ("reducer", 7)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputSource.setStatus('current')
upsOutputFrequency = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 2), NonNegativeInteger()).setUnits('0.1 Hertz').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputFrequency.setStatus('current')
upsOutputNumLines = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 3), NonNegativeInteger()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputNumLines.setStatus('current')
upsOutputTable = MibTable((1, 3, 6, 1, 2, 1, 33, 1, 4, 4), )
if mibBuilder.loadTexts: upsOutputTable.setStatus('current')
upsOutputEntry = MibTableRow((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1), ).setIndexNames((0, "UPS-MIB", "upsOutputLineIndex"))
if mibBuilder.loadTexts: upsOutputEntry.setStatus('current')
upsOutputLineIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 1), PositiveInteger())
if mibBuilder.loadTexts: upsOutputLineIndex.setStatus('current')
upsOutputVoltage = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 2), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputVoltage.setStatus('current')
upsOutputCurrent = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 3), NonNegativeInteger()).setUnits('0.1 RMS Amp').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputCurrent.setStatus('current')
upsOutputPower = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 4), NonNegativeInteger()).setUnits('Watts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputPower.setStatus('current')
upsOutputPercentLoad = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 200))).setUnits('percent').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsOutputPercentLoad.setStatus('current')
upsBypass = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 5))
upsBypassFrequency = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 5, 1), NonNegativeInteger()).setUnits('0.1 Hertz').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBypassFrequency.setStatus('current')
upsBypassNumLines = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 5, 2), NonNegativeInteger()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBypassNumLines.setStatus('current')
upsBypassTable = MibTable((1, 3, 6, 1, 2, 1, 33, 1, 5, 3), )
if mibBuilder.loadTexts: upsBypassTable.setStatus('current')
upsBypassEntry = MibTableRow((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1), ).setIndexNames((0, "UPS-MIB", "upsBypassLineIndex"))
if mibBuilder.loadTexts: upsBypassEntry.setStatus('current')
upsBypassLineIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 1), PositiveInteger())
if mibBuilder.loadTexts: upsBypassLineIndex.setStatus('current')
upsBypassVoltage = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 2), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBypassVoltage.setStatus('current')
upsBypassCurrent = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 3), NonNegativeInteger()).setUnits('0.1 RMS Amp').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBypassCurrent.setStatus('current')
upsBypassPower = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 4), NonNegativeInteger()).setUnits('Watts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsBypassPower.setStatus('current')
upsAlarm = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 6))
upsAlarmsPresent = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 6, 1), Gauge32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsAlarmsPresent.setStatus('current')
upsAlarmTable = MibTable((1, 3, 6, 1, 2, 1, 33, 1, 6, 2), )
if mibBuilder.loadTexts: upsAlarmTable.setStatus('current')
upsAlarmEntry = MibTableRow((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1), ).setIndexNames((0, "UPS-MIB", "upsAlarmId"))
if mibBuilder.loadTexts: upsAlarmEntry.setStatus('current')
upsAlarmId = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 1), PositiveInteger())
if mibBuilder.loadTexts: upsAlarmId.setStatus('current')
upsAlarmDescr = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 2), AutonomousType()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsAlarmDescr.setStatus('current')
upsAlarmTime = MibTableColumn((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 3), TimeStamp()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsAlarmTime.setStatus('current')
upsWellKnownAlarms = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 6, 3))
upsAlarmBatteryBad = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 1))
if mibBuilder.loadTexts: upsAlarmBatteryBad.setStatus('current')
upsAlarmOnBattery = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 2))
if mibBuilder.loadTexts: upsAlarmOnBattery.setStatus('current')
upsAlarmLowBattery = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 3))
if mibBuilder.loadTexts: upsAlarmLowBattery.setStatus('current')
upsAlarmDepletedBattery = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 4))
if mibBuilder.loadTexts: upsAlarmDepletedBattery.setStatus('current')
upsAlarmTempBad = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 5))
if mibBuilder.loadTexts: upsAlarmTempBad.setStatus('current')
upsAlarmInputBad = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 6))
if mibBuilder.loadTexts: upsAlarmInputBad.setStatus('current')
upsAlarmOutputBad = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 7))
if mibBuilder.loadTexts: upsAlarmOutputBad.setStatus('current')
upsAlarmOutputOverload = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 8))
if mibBuilder.loadTexts: upsAlarmOutputOverload.setStatus('current')
upsAlarmOnBypass = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 9))
if mibBuilder.loadTexts: upsAlarmOnBypass.setStatus('current')
upsAlarmBypassBad = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 10))
if mibBuilder.loadTexts: upsAlarmBypassBad.setStatus('current')
upsAlarmOutputOffAsRequested = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 11))
if mibBuilder.loadTexts: upsAlarmOutputOffAsRequested.setStatus('current')
upsAlarmUpsOffAsRequested = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 12))
if mibBuilder.loadTexts: upsAlarmUpsOffAsRequested.setStatus('current')
upsAlarmChargerFailed = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 13))
if mibBuilder.loadTexts: upsAlarmChargerFailed.setStatus('current')
upsAlarmUpsOutputOff = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 14))
if mibBuilder.loadTexts: upsAlarmUpsOutputOff.setStatus('current')
upsAlarmUpsSystemOff = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 15))
if mibBuilder.loadTexts: upsAlarmUpsSystemOff.setStatus('current')
upsAlarmFanFailure = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 16))
if mibBuilder.loadTexts: upsAlarmFanFailure.setStatus('current')
upsAlarmFuseFailure = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 17))
if mibBuilder.loadTexts: upsAlarmFuseFailure.setStatus('current')
upsAlarmGeneralFault = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 18))
if mibBuilder.loadTexts: upsAlarmGeneralFault.setStatus('current')
upsAlarmDiagnosticTestFailed = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 19))
if mibBuilder.loadTexts: upsAlarmDiagnosticTestFailed.setStatus('current')
upsAlarmCommunicationsLost = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 20))
if mibBuilder.loadTexts: upsAlarmCommunicationsLost.setStatus('current')
upsAlarmAwaitingPower = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 21))
if mibBuilder.loadTexts: upsAlarmAwaitingPower.setStatus('current')
upsAlarmShutdownPending = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 22))
if mibBuilder.loadTexts: upsAlarmShutdownPending.setStatus('current')
upsAlarmShutdownImminent = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 23))
if mibBuilder.loadTexts: upsAlarmShutdownImminent.setStatus('current')
upsAlarmTestInProgress = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 24))
if mibBuilder.loadTexts: upsAlarmTestInProgress.setStatus('current')
upsTest = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 7))
upsTestId = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 1), ObjectIdentifier()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsTestId.setStatus('current')
upsTestSpinLock = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 2), TestAndIncr()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsTestSpinLock.setStatus('current')
upsTestResultsSummary = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("donePass", 1), ("doneWarning", 2), ("doneError", 3), ("aborted", 4), ("inProgress", 5), ("noTestsInitiated", 6)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsTestResultsSummary.setStatus('current')
upsTestResultsDetail = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 4), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsTestResultsDetail.setStatus('current')
upsTestStartTime = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 5), TimeStamp()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsTestStartTime.setStatus('current')
upsTestElapsedTime = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 6), TimeInterval()).setMaxAccess("readonly")
if mibBuilder.loadTexts: upsTestElapsedTime.setStatus('current')
upsWellKnownTests = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 7, 7))
upsTestNoTestsInitiated = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 1))
if mibBuilder.loadTexts: upsTestNoTestsInitiated.setStatus('current')
upsTestAbortTestInProgress = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 2))
if mibBuilder.loadTexts: upsTestAbortTestInProgress.setStatus('current')
upsTestGeneralSystemsTest = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 3))
if mibBuilder.loadTexts: upsTestGeneralSystemsTest.setStatus('current')
upsTestQuickBatteryTest = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 4))
if mibBuilder.loadTexts: upsTestQuickBatteryTest.setStatus('current')
upsTestDeepBatteryCalibration = ObjectIdentity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 5))
if mibBuilder.loadTexts: upsTestDeepBatteryCalibration.setStatus('current')
upsControl = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 8))
upsShutdownType = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("output", 1), ("system", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsShutdownType.setStatus('current')
upsShutdownAfterDelay = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(-1, 2147483648))).setUnits('seconds').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsShutdownAfterDelay.setStatus('current')
upsStartupAfterDelay = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(-1, 2147483648))).setUnits('seconds').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsStartupAfterDelay.setStatus('current')
upsRebootWithDuration = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(-1, 300))).setUnits('seconds').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsRebootWithDuration.setStatus('current')
upsAutoRestart = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("on", 1), ("off", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsAutoRestart.setStatus('current')
upsConfig = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 1, 9))
upsConfigInputVoltage = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 1), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigInputVoltage.setStatus('current')
upsConfigInputFreq = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 2), NonNegativeInteger()).setUnits('0.1 Hertz').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigInputFreq.setStatus('current')
upsConfigOutputVoltage = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 3), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigOutputVoltage.setStatus('current')
upsConfigOutputFreq = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 4), NonNegativeInteger()).setUnits('0.1 Hertz').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigOutputFreq.setStatus('current')
upsConfigOutputVA = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 5), NonNegativeInteger()).setUnits('Volt-Amps').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsConfigOutputVA.setStatus('current')
upsConfigOutputPower = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 6), NonNegativeInteger()).setUnits('Watts').setMaxAccess("readonly")
if mibBuilder.loadTexts: upsConfigOutputPower.setStatus('current')
upsConfigLowBattTime = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 7), NonNegativeInteger()).setUnits('minutes').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigLowBattTime.setStatus('current')
upsConfigAudibleStatus = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2), ("muted", 3)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigAudibleStatus.setStatus('current')
upsConfigLowVoltageTransferPoint = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 9), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigLowVoltageTransferPoint.setStatus('current')
upsConfigHighVoltageTransferPoint = MibScalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 10), NonNegativeInteger()).setUnits('RMS Volts').setMaxAccess("readwrite")
if mibBuilder.loadTexts: upsConfigHighVoltageTransferPoint.setStatus('current')
upsTraps = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 2))
upsTrapOnBattery = NotificationType((1, 3, 6, 1, 2, 1, 33, 2, 1)).setObjects(("UPS-MIB", "upsEstimatedMinutesRemaining"), ("UPS-MIB", "upsSecondsOnBattery"), ("UPS-MIB", "upsConfigLowBattTime"))
if mibBuilder.loadTexts: upsTrapOnBattery.setStatus('current')
upsTrapTestCompleted = NotificationType((1, 3, 6, 1, 2, 1, 33, 2, 2)).setObjects(("UPS-MIB", "upsTestId"), ("UPS-MIB", "upsTestSpinLock"), ("UPS-MIB", "upsTestResultsSummary"), ("UPS-MIB", "upsTestResultsDetail"), ("UPS-MIB", "upsTestStartTime"), ("UPS-MIB", "upsTestElapsedTime"))
if mibBuilder.loadTexts: upsTrapTestCompleted.setStatus('current')
upsTrapAlarmEntryAdded = NotificationType((1, 3, 6, 1, 2, 1, 33, 2, 3)).setObjects(("UPS-MIB", "upsAlarmId"), ("UPS-MIB", "upsAlarmDescr"))
if mibBuilder.loadTexts: upsTrapAlarmEntryAdded.setStatus('current')
upsTrapAlarmEntryRemoved = NotificationType((1, 3, 6, 1, 2, 1, 33, 2, 4)).setObjects(("UPS-MIB", "upsAlarmId"), ("UPS-MIB", "upsAlarmDescr"))
if mibBuilder.loadTexts: upsTrapAlarmEntryRemoved.setStatus('current')
upsConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3))
upsCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3, 1))
upsSubsetCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 1)).setObjects(("UPS-MIB", "upsSubsetIdentGroup"), ("UPS-MIB", "upsSubsetBatteryGroup"), ("UPS-MIB", "upsSubsetInputGroup"), ("UPS-MIB", "upsSubsetOutputGroup"), ("UPS-MIB", "upsSubsetAlarmGroup"), ("UPS-MIB", "upsSubsetControlGroup"), ("UPS-MIB", "upsSubsetConfigGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetCompliance = upsSubsetCompliance.setStatus('current')
upsBasicCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 2)).setObjects(("UPS-MIB", "upsBasicIdentGroup"), ("UPS-MIB", "upsBasicBatteryGroup"), ("UPS-MIB", "upsBasicInputGroup"), ("UPS-MIB", "upsBasicOutputGroup"), ("UPS-MIB", "upsBasicAlarmGroup"), ("UPS-MIB", "upsBasicTestGroup"), ("UPS-MIB", "upsBasicControlGroup"), ("UPS-MIB", "upsBasicConfigGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicCompliance = upsBasicCompliance.setStatus('current')
upsFullCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 3)).setObjects(("UPS-MIB", "upsFullIdentGroup"), ("UPS-MIB", "upsFullBatteryGroup"), ("UPS-MIB", "upsFullInputGroup"), ("UPS-MIB", "upsFullOutputGroup"), ("UPS-MIB", "upsFullAlarmGroup"), ("UPS-MIB", "upsFullTestGroup"), ("UPS-MIB", "upsFullControlGroup"), ("UPS-MIB", "upsFullConfigGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullCompliance = upsFullCompliance.setStatus('current')
upsGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3, 2))
upsSubsetGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 1))
upsSubsetIdentGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 1)).setObjects(("UPS-MIB", "upsIdentManufacturer"), ("UPS-MIB", "upsIdentModel"), ("UPS-MIB", "upsIdentAgentSoftwareVersion"), ("UPS-MIB", "upsIdentName"), ("UPS-MIB", "upsIdentAttachedDevices"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetIdentGroup = upsSubsetIdentGroup.setStatus('current')
upsSubsetBatteryGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 2)).setObjects(("UPS-MIB", "upsBatteryStatus"), ("UPS-MIB", "upsSecondsOnBattery"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetBatteryGroup = upsSubsetBatteryGroup.setStatus('current')
upsSubsetInputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 3)).setObjects(("UPS-MIB", "upsInputLineBads"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetInputGroup = upsSubsetInputGroup.setStatus('current')
upsSubsetOutputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 4)).setObjects(("UPS-MIB", "upsOutputSource"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetOutputGroup = upsSubsetOutputGroup.setStatus('current')
upsSubsetAlarmGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 6)).setObjects(("UPS-MIB", "upsAlarmsPresent"), ("UPS-MIB", "upsAlarmDescr"), ("UPS-MIB", "upsAlarmTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetAlarmGroup = upsSubsetAlarmGroup.setStatus('current')
upsSubsetControlGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 8)).setObjects(("UPS-MIB", "upsShutdownType"), ("UPS-MIB", "upsShutdownAfterDelay"), ("UPS-MIB", "upsAutoRestart"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetControlGroup = upsSubsetControlGroup.setStatus('current')
upsSubsetConfigGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 9)).setObjects(("UPS-MIB", "upsConfigInputVoltage"), ("UPS-MIB", "upsConfigInputFreq"), ("UPS-MIB", "upsConfigOutputVoltage"), ("UPS-MIB", "upsConfigOutputFreq"), ("UPS-MIB", "upsConfigOutputVA"), ("UPS-MIB", "upsConfigOutputPower"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsSubsetConfigGroup = upsSubsetConfigGroup.setStatus('current')
upsBasicGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 2))
upsBasicIdentGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 1)).setObjects(("UPS-MIB", "upsIdentManufacturer"), ("UPS-MIB", "upsIdentModel"), ("UPS-MIB", "upsIdentUPSSoftwareVersion"), ("UPS-MIB", "upsIdentAgentSoftwareVersion"), ("UPS-MIB", "upsIdentName"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicIdentGroup = upsBasicIdentGroup.setStatus('current')
upsBasicBatteryGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 2)).setObjects(("UPS-MIB", "upsBatteryStatus"), ("UPS-MIB", "upsSecondsOnBattery"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicBatteryGroup = upsBasicBatteryGroup.setStatus('current')
upsBasicInputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 3)).setObjects(("UPS-MIB", "upsInputLineBads"), ("UPS-MIB", "upsInputNumLines"), ("UPS-MIB", "upsInputFrequency"), ("UPS-MIB", "upsInputVoltage"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicInputGroup = upsBasicInputGroup.setStatus('current')
upsBasicOutputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 4)).setObjects(("UPS-MIB", "upsOutputSource"), ("UPS-MIB", "upsOutputFrequency"), ("UPS-MIB", "upsOutputNumLines"), ("UPS-MIB", "upsOutputVoltage"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicOutputGroup = upsBasicOutputGroup.setStatus('current')
upsBasicBypassGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 5)).setObjects(("UPS-MIB", "upsBypassFrequency"), ("UPS-MIB", "upsBypassNumLines"), ("UPS-MIB", "upsBypassVoltage"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicBypassGroup = upsBasicBypassGroup.setStatus('current')
upsBasicAlarmGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 6)).setObjects(("UPS-MIB", "upsAlarmsPresent"), ("UPS-MIB", "upsAlarmDescr"), ("UPS-MIB", "upsAlarmTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicAlarmGroup = upsBasicAlarmGroup.setStatus('current')
upsBasicTestGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 7)).setObjects(("UPS-MIB", "upsTestId"), ("UPS-MIB", "upsTestSpinLock"), ("UPS-MIB", "upsTestResultsSummary"), ("UPS-MIB", "upsTestResultsDetail"), ("UPS-MIB", "upsTestStartTime"), ("UPS-MIB", "upsTestElapsedTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicTestGroup = upsBasicTestGroup.setStatus('current')
upsBasicControlGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 8)).setObjects(("UPS-MIB", "upsShutdownType"), ("UPS-MIB", "upsShutdownAfterDelay"), ("UPS-MIB", "upsStartupAfterDelay"), ("UPS-MIB", "upsRebootWithDuration"), ("UPS-MIB", "upsAutoRestart"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicControlGroup = upsBasicControlGroup.setStatus('current')
upsBasicConfigGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 9)).setObjects(("UPS-MIB", "upsConfigInputVoltage"), ("UPS-MIB", "upsConfigInputFreq"), ("UPS-MIB", "upsConfigOutputVoltage"), ("UPS-MIB", "upsConfigOutputFreq"), ("UPS-MIB", "upsConfigOutputVA"), ("UPS-MIB", "upsConfigOutputPower"), ("UPS-MIB", "upsConfigLowBattTime"), ("UPS-MIB", "upsConfigAudibleStatus"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsBasicConfigGroup = upsBasicConfigGroup.setStatus('current')
upsFullGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 3))
upsFullIdentGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 1)).setObjects(("UPS-MIB", "upsIdentManufacturer"), ("UPS-MIB", "upsIdentModel"), ("UPS-MIB", "upsIdentUPSSoftwareVersion"), ("UPS-MIB", "upsIdentAgentSoftwareVersion"), ("UPS-MIB", "upsIdentName"), ("UPS-MIB", "upsIdentAttachedDevices"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullIdentGroup = upsFullIdentGroup.setStatus('current')
upsFullBatteryGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 2)).setObjects(("UPS-MIB", "upsBatteryStatus"), ("UPS-MIB", "upsSecondsOnBattery"), ("UPS-MIB", "upsEstimatedMinutesRemaining"), ("UPS-MIB", "upsEstimatedChargeRemaining"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullBatteryGroup = upsFullBatteryGroup.setStatus('current')
upsFullInputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 3)).setObjects(("UPS-MIB", "upsInputLineBads"), ("UPS-MIB", "upsInputNumLines"), ("UPS-MIB", "upsInputFrequency"), ("UPS-MIB", "upsInputVoltage"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullInputGroup = upsFullInputGroup.setStatus('current')
upsFullOutputGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 4)).setObjects(("UPS-MIB", "upsOutputSource"), ("UPS-MIB", "upsOutputFrequency"), ("UPS-MIB", "upsOutputNumLines"), ("UPS-MIB", "upsOutputVoltage"), ("UPS-MIB", "upsOutputCurrent"), ("UPS-MIB", "upsOutputPower"), ("UPS-MIB", "upsOutputPercentLoad"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullOutputGroup = upsFullOutputGroup.setStatus('current')
upsFullBypassGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 5)).setObjects(("UPS-MIB", "upsBypassFrequency"), ("UPS-MIB", "upsBypassNumLines"), ("UPS-MIB", "upsBypassVoltage"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullBypassGroup = upsFullBypassGroup.setStatus('current')
upsFullAlarmGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 6)).setObjects(("UPS-MIB", "upsAlarmsPresent"), ("UPS-MIB", "upsAlarmDescr"), ("UPS-MIB", "upsAlarmTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullAlarmGroup = upsFullAlarmGroup.setStatus('current')
upsFullTestGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 7)).setObjects(("UPS-MIB", "upsTestId"), ("UPS-MIB", "upsTestSpinLock"), ("UPS-MIB", "upsTestResultsSummary"), ("UPS-MIB", "upsTestResultsDetail"), ("UPS-MIB", "upsTestStartTime"), ("UPS-MIB", "upsTestElapsedTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullTestGroup = upsFullTestGroup.setStatus('current')
upsFullControlGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 8)).setObjects(("UPS-MIB", "upsShutdownType"), ("UPS-MIB", "upsShutdownAfterDelay"), ("UPS-MIB", "upsStartupAfterDelay"), ("UPS-MIB", "upsRebootWithDuration"), ("UPS-MIB", "upsAutoRestart"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullControlGroup = upsFullControlGroup.setStatus('current')
upsFullConfigGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 9)).setObjects(("UPS-MIB", "upsConfigInputVoltage"), ("UPS-MIB", "upsConfigInputFreq"), ("UPS-MIB", "upsConfigOutputVoltage"), ("UPS-MIB", "upsConfigOutputFreq"), ("UPS-MIB", "upsConfigOutputVA"), ("UPS-MIB", "upsConfigOutputPower"), ("UPS-MIB", "upsConfigLowBattTime"), ("UPS-MIB", "upsConfigAudibleStatus"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
upsFullConfigGroup = upsFullConfigGroup.setStatus('current')
mibBuilder.exportSymbols("UPS-MIB", upsEstimatedChargeRemaining=upsEstimatedChargeRemaining, upsInputTable=upsInputTable, upsInputCurrent=upsInputCurrent, upsAlarmOutputBad=upsAlarmOutputBad, upsIdentUPSSoftwareVersion=upsIdentUPSSoftwareVersion, upsInputVoltage=upsInputVoltage, upsOutputEntry=upsOutputEntry, upsAlarmShutdownPending=upsAlarmShutdownPending, upsOutputFrequency=upsOutputFrequency, upsAlarmOutputOverload=upsAlarmOutputOverload, upsSubsetControlGroup=upsSubsetControlGroup, upsAlarmShutdownImminent=upsAlarmShutdownImminent, upsAlarmLowBattery=upsAlarmLowBattery, upsBatteryCurrent=upsBatteryCurrent, upsConfigOutputFreq=upsConfigOutputFreq, upsWellKnownTests=upsWellKnownTests, upsIdentManufacturer=upsIdentManufacturer, upsTestAbortTestInProgress=upsTestAbortTestInProgress, upsConfig=upsConfig, upsFullInputGroup=upsFullInputGroup, upsAlarmsPresent=upsAlarmsPresent, upsAlarmTempBad=upsAlarmTempBad, upsBypassFrequency=upsBypassFrequency, upsShutdownType=upsShutdownType, upsBatteryStatus=upsBatteryStatus, upsTrapTestCompleted=upsTrapTestCompleted, upsBasicIdentGroup=upsBasicIdentGroup, upsFullBatteryGroup=upsFullBatteryGroup, upsAlarmFuseFailure=upsAlarmFuseFailure, upsOutputCurrent=upsOutputCurrent, upsWellKnownAlarms=upsWellKnownAlarms, upsAlarmOnBattery=upsAlarmOnBattery, upsFullTestGroup=upsFullTestGroup, upsOutputNumLines=upsOutputNumLines, upsAlarmGeneralFault=upsAlarmGeneralFault, upsInputLineIndex=upsInputLineIndex, upsOutputPower=upsOutputPower, upsSubsetOutputGroup=upsSubsetOutputGroup, upsAlarmChargerFailed=upsAlarmChargerFailed, upsBasicBatteryGroup=upsBasicBatteryGroup, upsAlarmOnBypass=upsAlarmOnBypass, upsBasicOutputGroup=upsBasicOutputGroup, upsAlarmDiagnosticTestFailed=upsAlarmDiagnosticTestFailed, upsTestGeneralSystemsTest=upsTestGeneralSystemsTest, upsTestId=upsTestId, upsTrapAlarmEntryRemoved=upsTrapAlarmEntryRemoved, upsEstimatedMinutesRemaining=upsEstimatedMinutesRemaining, upsIdentAttachedDevices=upsIdentAttachedDevices, upsAlarmCommunicationsLost=upsAlarmCommunicationsLost, upsTestStartTime=upsTestStartTime, upsBasicInputGroup=upsBasicInputGroup, upsAlarmId=upsAlarmId, upsAlarmTime=upsAlarmTime, upsSubsetAlarmGroup=upsSubsetAlarmGroup, upsAlarmUpsOutputOff=upsAlarmUpsOutputOff, upsIdentName=upsIdentName, upsGroups=upsGroups, upsConfigOutputPower=upsConfigOutputPower, upsAlarmTestInProgress=upsAlarmTestInProgress, upsTestNoTestsInitiated=upsTestNoTestsInitiated, upsBasicConfigGroup=upsBasicConfigGroup, upsBatteryTemperature=upsBatteryTemperature, upsInputLineBads=upsInputLineBads, upsInputTruePower=upsInputTruePower, upsTest=upsTest, upsIdent=upsIdent, upsBypassVoltage=upsBypassVoltage, upsFullControlGroup=upsFullControlGroup, upsTraps=upsTraps, upsOutputTable=upsOutputTable, upsIdentModel=upsIdentModel, upsSubsetCompliance=upsSubsetCompliance, upsInputFrequency=upsInputFrequency, upsOutputVoltage=upsOutputVoltage, upsTrapOnBattery=upsTrapOnBattery, upsOutput=upsOutput, upsFullConfigGroup=upsFullConfigGroup, upsSubsetConfigGroup=upsSubsetConfigGroup, upsTestQuickBatteryTest=upsTestQuickBatteryTest, upsConfigOutputVoltage=upsConfigOutputVoltage, upsAlarmBypassBad=upsAlarmBypassBad, upsSecondsOnBattery=upsSecondsOnBattery, upsFullAlarmGroup=upsFullAlarmGroup, upsBypass=upsBypass, upsBypassLineIndex=upsBypassLineIndex, upsBypassNumLines=upsBypassNumLines, upsBypassCurrent=upsBypassCurrent, upsInput=upsInput, upsOutputSource=upsOutputSource, upsConfigAudibleStatus=upsConfigAudibleStatus, upsAlarmTable=upsAlarmTable, upsAlarmFanFailure=upsAlarmFanFailure, upsSubsetGroups=upsSubsetGroups, upsBasicControlGroup=upsBasicControlGroup, upsConfigHighVoltageTransferPoint=upsConfigHighVoltageTransferPoint, upsAlarmDepletedBattery=upsAlarmDepletedBattery, upsAutoRestart=upsAutoRestart, upsBasicGroups=upsBasicGroups, upsConfigOutputVA=upsConfigOutputVA, upsAlarmUpsSystemOff=upsAlarmUpsSystemOff, upsAlarmUpsOffAsRequested=upsAlarmUpsOffAsRequested, upsConformance=upsConformance, PYSNMP_MODULE_ID=upsMIB, upsIdentAgentSoftwareVersion=upsIdentAgentSoftwareVersion, upsRebootWithDuration=upsRebootWithDuration, upsObjects=upsObjects, upsTestResultsDetail=upsTestResultsDetail, upsOutputPercentLoad=upsOutputPercentLoad, upsBypassTable=upsBypassTable, upsFullBypassGroup=upsFullBypassGroup, upsSubsetBatteryGroup=upsSubsetBatteryGroup, upsAlarmEntry=upsAlarmEntry, upsControl=upsControl, upsTestDeepBatteryCalibration=upsTestDeepBatteryCalibration, upsStartupAfterDelay=upsStartupAfterDelay, upsCompliances=upsCompliances, upsFullOutputGroup=upsFullOutputGroup, NonNegativeInteger=NonNegativeInteger, upsFullIdentGroup=upsFullIdentGroup, upsInputNumLines=upsInputNumLines, upsBatteryVoltage=upsBatteryVoltage, upsBasicCompliance=upsBasicCompliance, upsSubsetInputGroup=upsSubsetInputGroup, upsOutputLineIndex=upsOutputLineIndex, upsAlarmBatteryBad=upsAlarmBatteryBad, upsBypassEntry=upsBypassEntry, upsConfigLowVoltageTransferPoint=upsConfigLowVoltageTransferPoint, upsMIB=upsMIB, upsBypassPower=upsBypassPower, upsConfigLowBattTime=upsConfigLowBattTime, upsBasicTestGroup=upsBasicTestGroup, upsConfigInputVoltage=upsConfigInputVoltage, upsTrapAlarmEntryAdded=upsTrapAlarmEntryAdded, upsTestSpinLock=upsTestSpinLock, upsBasicBypassGroup=upsBasicBypassGroup, upsTestElapsedTime=upsTestElapsedTime, upsInputEntry=upsInputEntry, PositiveInteger=PositiveInteger, upsFullCompliance=upsFullCompliance, upsAlarmAwaitingPower=upsAlarmAwaitingPower, upsShutdownAfterDelay=upsShutdownAfterDelay, upsConfigInputFreq=upsConfigInputFreq, upsAlarmDescr=upsAlarmDescr, upsAlarmOutputOffAsRequested=upsAlarmOutputOffAsRequested, upsBasicAlarmGroup=upsBasicAlarmGroup, upsBattery=upsBattery, upsSubsetIdentGroup=upsSubsetIdentGroup, upsAlarmInputBad=upsAlarmInputBad, upsFullGroups=upsFullGroups, upsTestResultsSummary=upsTestResultsSummary, upsAlarm=upsAlarm)
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(octet_string, object_identifier, integer) = mibBuilder.importSymbols('ASN1', 'OctetString', 'ObjectIdentifier', 'Integer')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(value_range_constraint, constraints_union, value_size_constraint, constraints_intersection, single_value_constraint) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ValueRangeConstraint', 'ConstraintsUnion', 'ValueSizeConstraint', 'ConstraintsIntersection', 'SingleValueConstraint')
(object_group, notification_group, module_compliance) = mibBuilder.importSymbols('SNMPv2-CONF', 'ObjectGroup', 'NotificationGroup', 'ModuleCompliance')
(mib_scalar, mib_table, mib_table_row, mib_table_column, iso, unsigned32, integer32, mib_identifier, counter32, mib_2, module_identity, object_identity, gauge32, time_ticks, counter64, bits, ip_address, notification_type) = mibBuilder.importSymbols('SNMPv2-SMI', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'iso', 'Unsigned32', 'Integer32', 'MibIdentifier', 'Counter32', 'mib-2', 'ModuleIdentity', 'ObjectIdentity', 'Gauge32', 'TimeTicks', 'Counter64', 'Bits', 'IpAddress', 'NotificationType')
(time_stamp, autonomous_type, time_interval, test_and_incr, textual_convention, display_string) = mibBuilder.importSymbols('SNMPv2-TC', 'TimeStamp', 'AutonomousType', 'TimeInterval', 'TestAndIncr', 'TextualConvention', 'DisplayString')
ups_mib = module_identity((1, 3, 6, 1, 2, 1, 33))
if mibBuilder.loadTexts:
upsMIB.setLastUpdated('9402230000Z')
if mibBuilder.loadTexts:
upsMIB.setOrganization('IETF UPS MIB Working Group')
class Positiveinteger(TextualConvention, Integer32):
status = 'current'
display_hint = 'd'
subtype_spec = Integer32.subtypeSpec + value_range_constraint(1, 2147483647)
class Nonnegativeinteger(TextualConvention, Integer32):
status = 'current'
display_hint = 'd'
subtype_spec = Integer32.subtypeSpec + value_range_constraint(0, 2147483647)
ups_objects = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1))
ups_ident = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 1))
ups_ident_manufacturer = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 1), display_string().subtype(subtypeSpec=value_size_constraint(0, 31))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsIdentManufacturer.setStatus('current')
ups_ident_model = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 2), display_string().subtype(subtypeSpec=value_size_constraint(0, 63))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsIdentModel.setStatus('current')
ups_ident_ups_software_version = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 3), display_string().subtype(subtypeSpec=value_size_constraint(0, 63))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsIdentUPSSoftwareVersion.setStatus('current')
ups_ident_agent_software_version = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 4), display_string().subtype(subtypeSpec=value_size_constraint(0, 63))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsIdentAgentSoftwareVersion.setStatus('current')
ups_ident_name = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 5), display_string().subtype(subtypeSpec=value_size_constraint(0, 63))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsIdentName.setStatus('current')
ups_ident_attached_devices = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 1, 6), display_string().subtype(subtypeSpec=value_size_constraint(0, 63))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsIdentAttachedDevices.setStatus('current')
ups_battery = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 2))
ups_battery_status = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 1), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2, 3, 4))).clone(namedValues=named_values(('unknown', 1), ('batteryNormal', 2), ('batteryLow', 3), ('batteryDepleted', 4)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBatteryStatus.setStatus('current')
ups_seconds_on_battery = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 2), non_negative_integer()).setUnits('seconds').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsSecondsOnBattery.setStatus('current')
ups_estimated_minutes_remaining = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 3), positive_integer()).setUnits('minutes').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsEstimatedMinutesRemaining.setStatus('current')
ups_estimated_charge_remaining = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 4), integer32().subtype(subtypeSpec=value_range_constraint(0, 100))).setUnits('percent').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsEstimatedChargeRemaining.setStatus('current')
ups_battery_voltage = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 5), non_negative_integer()).setUnits('0.1 Volt DC').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBatteryVoltage.setStatus('current')
ups_battery_current = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 6), integer32()).setUnits('0.1 Amp DC').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBatteryCurrent.setStatus('current')
ups_battery_temperature = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 2, 7), integer32()).setUnits('degrees Centigrade').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBatteryTemperature.setStatus('current')
ups_input = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 3))
ups_input_line_bads = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 3, 1), counter32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputLineBads.setStatus('current')
ups_input_num_lines = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 3, 2), non_negative_integer()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputNumLines.setStatus('current')
ups_input_table = mib_table((1, 3, 6, 1, 2, 1, 33, 1, 3, 3))
if mibBuilder.loadTexts:
upsInputTable.setStatus('current')
ups_input_entry = mib_table_row((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1)).setIndexNames((0, 'UPS-MIB', 'upsInputLineIndex'))
if mibBuilder.loadTexts:
upsInputEntry.setStatus('current')
ups_input_line_index = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 1), positive_integer())
if mibBuilder.loadTexts:
upsInputLineIndex.setStatus('current')
ups_input_frequency = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 2), non_negative_integer()).setUnits('0.1 Hertz').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputFrequency.setStatus('current')
ups_input_voltage = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 3), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputVoltage.setStatus('current')
ups_input_current = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 4), non_negative_integer()).setUnits('0.1 RMS Amp').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputCurrent.setStatus('current')
ups_input_true_power = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 3, 3, 1, 5), non_negative_integer()).setUnits('Watts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsInputTruePower.setStatus('current')
ups_output = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 4))
ups_output_source = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 1), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2, 3, 4, 5, 6, 7))).clone(namedValues=named_values(('other', 1), ('none', 2), ('normal', 3), ('bypass', 4), ('battery', 5), ('booster', 6), ('reducer', 7)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputSource.setStatus('current')
ups_output_frequency = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 2), non_negative_integer()).setUnits('0.1 Hertz').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputFrequency.setStatus('current')
ups_output_num_lines = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 4, 3), non_negative_integer()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputNumLines.setStatus('current')
ups_output_table = mib_table((1, 3, 6, 1, 2, 1, 33, 1, 4, 4))
if mibBuilder.loadTexts:
upsOutputTable.setStatus('current')
ups_output_entry = mib_table_row((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1)).setIndexNames((0, 'UPS-MIB', 'upsOutputLineIndex'))
if mibBuilder.loadTexts:
upsOutputEntry.setStatus('current')
ups_output_line_index = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 1), positive_integer())
if mibBuilder.loadTexts:
upsOutputLineIndex.setStatus('current')
ups_output_voltage = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 2), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputVoltage.setStatus('current')
ups_output_current = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 3), non_negative_integer()).setUnits('0.1 RMS Amp').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputCurrent.setStatus('current')
ups_output_power = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 4), non_negative_integer()).setUnits('Watts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputPower.setStatus('current')
ups_output_percent_load = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 4, 4, 1, 5), integer32().subtype(subtypeSpec=value_range_constraint(0, 200))).setUnits('percent').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsOutputPercentLoad.setStatus('current')
ups_bypass = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 5))
ups_bypass_frequency = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 5, 1), non_negative_integer()).setUnits('0.1 Hertz').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBypassFrequency.setStatus('current')
ups_bypass_num_lines = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 5, 2), non_negative_integer()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBypassNumLines.setStatus('current')
ups_bypass_table = mib_table((1, 3, 6, 1, 2, 1, 33, 1, 5, 3))
if mibBuilder.loadTexts:
upsBypassTable.setStatus('current')
ups_bypass_entry = mib_table_row((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1)).setIndexNames((0, 'UPS-MIB', 'upsBypassLineIndex'))
if mibBuilder.loadTexts:
upsBypassEntry.setStatus('current')
ups_bypass_line_index = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 1), positive_integer())
if mibBuilder.loadTexts:
upsBypassLineIndex.setStatus('current')
ups_bypass_voltage = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 2), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBypassVoltage.setStatus('current')
ups_bypass_current = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 3), non_negative_integer()).setUnits('0.1 RMS Amp').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBypassCurrent.setStatus('current')
ups_bypass_power = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 5, 3, 1, 4), non_negative_integer()).setUnits('Watts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsBypassPower.setStatus('current')
ups_alarm = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 6))
ups_alarms_present = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 6, 1), gauge32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsAlarmsPresent.setStatus('current')
ups_alarm_table = mib_table((1, 3, 6, 1, 2, 1, 33, 1, 6, 2))
if mibBuilder.loadTexts:
upsAlarmTable.setStatus('current')
ups_alarm_entry = mib_table_row((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1)).setIndexNames((0, 'UPS-MIB', 'upsAlarmId'))
if mibBuilder.loadTexts:
upsAlarmEntry.setStatus('current')
ups_alarm_id = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 1), positive_integer())
if mibBuilder.loadTexts:
upsAlarmId.setStatus('current')
ups_alarm_descr = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 2), autonomous_type()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsAlarmDescr.setStatus('current')
ups_alarm_time = mib_table_column((1, 3, 6, 1, 2, 1, 33, 1, 6, 2, 1, 3), time_stamp()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsAlarmTime.setStatus('current')
ups_well_known_alarms = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 6, 3))
ups_alarm_battery_bad = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 1))
if mibBuilder.loadTexts:
upsAlarmBatteryBad.setStatus('current')
ups_alarm_on_battery = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 2))
if mibBuilder.loadTexts:
upsAlarmOnBattery.setStatus('current')
ups_alarm_low_battery = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 3))
if mibBuilder.loadTexts:
upsAlarmLowBattery.setStatus('current')
ups_alarm_depleted_battery = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 4))
if mibBuilder.loadTexts:
upsAlarmDepletedBattery.setStatus('current')
ups_alarm_temp_bad = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 5))
if mibBuilder.loadTexts:
upsAlarmTempBad.setStatus('current')
ups_alarm_input_bad = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 6))
if mibBuilder.loadTexts:
upsAlarmInputBad.setStatus('current')
ups_alarm_output_bad = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 7))
if mibBuilder.loadTexts:
upsAlarmOutputBad.setStatus('current')
ups_alarm_output_overload = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 8))
if mibBuilder.loadTexts:
upsAlarmOutputOverload.setStatus('current')
ups_alarm_on_bypass = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 9))
if mibBuilder.loadTexts:
upsAlarmOnBypass.setStatus('current')
ups_alarm_bypass_bad = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 10))
if mibBuilder.loadTexts:
upsAlarmBypassBad.setStatus('current')
ups_alarm_output_off_as_requested = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 11))
if mibBuilder.loadTexts:
upsAlarmOutputOffAsRequested.setStatus('current')
ups_alarm_ups_off_as_requested = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 12))
if mibBuilder.loadTexts:
upsAlarmUpsOffAsRequested.setStatus('current')
ups_alarm_charger_failed = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 13))
if mibBuilder.loadTexts:
upsAlarmChargerFailed.setStatus('current')
ups_alarm_ups_output_off = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 14))
if mibBuilder.loadTexts:
upsAlarmUpsOutputOff.setStatus('current')
ups_alarm_ups_system_off = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 15))
if mibBuilder.loadTexts:
upsAlarmUpsSystemOff.setStatus('current')
ups_alarm_fan_failure = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 16))
if mibBuilder.loadTexts:
upsAlarmFanFailure.setStatus('current')
ups_alarm_fuse_failure = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 17))
if mibBuilder.loadTexts:
upsAlarmFuseFailure.setStatus('current')
ups_alarm_general_fault = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 18))
if mibBuilder.loadTexts:
upsAlarmGeneralFault.setStatus('current')
ups_alarm_diagnostic_test_failed = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 19))
if mibBuilder.loadTexts:
upsAlarmDiagnosticTestFailed.setStatus('current')
ups_alarm_communications_lost = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 20))
if mibBuilder.loadTexts:
upsAlarmCommunicationsLost.setStatus('current')
ups_alarm_awaiting_power = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 21))
if mibBuilder.loadTexts:
upsAlarmAwaitingPower.setStatus('current')
ups_alarm_shutdown_pending = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 22))
if mibBuilder.loadTexts:
upsAlarmShutdownPending.setStatus('current')
ups_alarm_shutdown_imminent = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 23))
if mibBuilder.loadTexts:
upsAlarmShutdownImminent.setStatus('current')
ups_alarm_test_in_progress = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 6, 3, 24))
if mibBuilder.loadTexts:
upsAlarmTestInProgress.setStatus('current')
ups_test = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 7))
ups_test_id = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 1), object_identifier()).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsTestId.setStatus('current')
ups_test_spin_lock = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 2), test_and_incr()).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsTestSpinLock.setStatus('current')
ups_test_results_summary = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 3), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2, 3, 4, 5, 6))).clone(namedValues=named_values(('donePass', 1), ('doneWarning', 2), ('doneError', 3), ('aborted', 4), ('inProgress', 5), ('noTestsInitiated', 6)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsTestResultsSummary.setStatus('current')
ups_test_results_detail = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 4), display_string().subtype(subtypeSpec=value_size_constraint(0, 255))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsTestResultsDetail.setStatus('current')
ups_test_start_time = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 5), time_stamp()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsTestStartTime.setStatus('current')
ups_test_elapsed_time = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 7, 6), time_interval()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsTestElapsedTime.setStatus('current')
ups_well_known_tests = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 7, 7))
ups_test_no_tests_initiated = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 1))
if mibBuilder.loadTexts:
upsTestNoTestsInitiated.setStatus('current')
ups_test_abort_test_in_progress = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 2))
if mibBuilder.loadTexts:
upsTestAbortTestInProgress.setStatus('current')
ups_test_general_systems_test = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 3))
if mibBuilder.loadTexts:
upsTestGeneralSystemsTest.setStatus('current')
ups_test_quick_battery_test = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 4))
if mibBuilder.loadTexts:
upsTestQuickBatteryTest.setStatus('current')
ups_test_deep_battery_calibration = object_identity((1, 3, 6, 1, 2, 1, 33, 1, 7, 7, 5))
if mibBuilder.loadTexts:
upsTestDeepBatteryCalibration.setStatus('current')
ups_control = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 8))
ups_shutdown_type = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 1), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2))).clone(namedValues=named_values(('output', 1), ('system', 2)))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsShutdownType.setStatus('current')
ups_shutdown_after_delay = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 2), integer32().subtype(subtypeSpec=value_range_constraint(-1, 2147483648))).setUnits('seconds').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsShutdownAfterDelay.setStatus('current')
ups_startup_after_delay = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 3), integer32().subtype(subtypeSpec=value_range_constraint(-1, 2147483648))).setUnits('seconds').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsStartupAfterDelay.setStatus('current')
ups_reboot_with_duration = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 4), integer32().subtype(subtypeSpec=value_range_constraint(-1, 300))).setUnits('seconds').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsRebootWithDuration.setStatus('current')
ups_auto_restart = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 8, 5), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2))).clone(namedValues=named_values(('on', 1), ('off', 2)))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsAutoRestart.setStatus('current')
ups_config = mib_identifier((1, 3, 6, 1, 2, 1, 33, 1, 9))
ups_config_input_voltage = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 1), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigInputVoltage.setStatus('current')
ups_config_input_freq = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 2), non_negative_integer()).setUnits('0.1 Hertz').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigInputFreq.setStatus('current')
ups_config_output_voltage = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 3), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigOutputVoltage.setStatus('current')
ups_config_output_freq = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 4), non_negative_integer()).setUnits('0.1 Hertz').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigOutputFreq.setStatus('current')
ups_config_output_va = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 5), non_negative_integer()).setUnits('Volt-Amps').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsConfigOutputVA.setStatus('current')
ups_config_output_power = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 6), non_negative_integer()).setUnits('Watts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
upsConfigOutputPower.setStatus('current')
ups_config_low_batt_time = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 7), non_negative_integer()).setUnits('minutes').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigLowBattTime.setStatus('current')
ups_config_audible_status = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 8), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2, 3))).clone(namedValues=named_values(('disabled', 1), ('enabled', 2), ('muted', 3)))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigAudibleStatus.setStatus('current')
ups_config_low_voltage_transfer_point = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 9), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigLowVoltageTransferPoint.setStatus('current')
ups_config_high_voltage_transfer_point = mib_scalar((1, 3, 6, 1, 2, 1, 33, 1, 9, 10), non_negative_integer()).setUnits('RMS Volts').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
upsConfigHighVoltageTransferPoint.setStatus('current')
ups_traps = mib_identifier((1, 3, 6, 1, 2, 1, 33, 2))
ups_trap_on_battery = notification_type((1, 3, 6, 1, 2, 1, 33, 2, 1)).setObjects(('UPS-MIB', 'upsEstimatedMinutesRemaining'), ('UPS-MIB', 'upsSecondsOnBattery'), ('UPS-MIB', 'upsConfigLowBattTime'))
if mibBuilder.loadTexts:
upsTrapOnBattery.setStatus('current')
ups_trap_test_completed = notification_type((1, 3, 6, 1, 2, 1, 33, 2, 2)).setObjects(('UPS-MIB', 'upsTestId'), ('UPS-MIB', 'upsTestSpinLock'), ('UPS-MIB', 'upsTestResultsSummary'), ('UPS-MIB', 'upsTestResultsDetail'), ('UPS-MIB', 'upsTestStartTime'), ('UPS-MIB', 'upsTestElapsedTime'))
if mibBuilder.loadTexts:
upsTrapTestCompleted.setStatus('current')
ups_trap_alarm_entry_added = notification_type((1, 3, 6, 1, 2, 1, 33, 2, 3)).setObjects(('UPS-MIB', 'upsAlarmId'), ('UPS-MIB', 'upsAlarmDescr'))
if mibBuilder.loadTexts:
upsTrapAlarmEntryAdded.setStatus('current')
ups_trap_alarm_entry_removed = notification_type((1, 3, 6, 1, 2, 1, 33, 2, 4)).setObjects(('UPS-MIB', 'upsAlarmId'), ('UPS-MIB', 'upsAlarmDescr'))
if mibBuilder.loadTexts:
upsTrapAlarmEntryRemoved.setStatus('current')
ups_conformance = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3))
ups_compliances = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3, 1))
ups_subset_compliance = module_compliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 1)).setObjects(('UPS-MIB', 'upsSubsetIdentGroup'), ('UPS-MIB', 'upsSubsetBatteryGroup'), ('UPS-MIB', 'upsSubsetInputGroup'), ('UPS-MIB', 'upsSubsetOutputGroup'), ('UPS-MIB', 'upsSubsetAlarmGroup'), ('UPS-MIB', 'upsSubsetControlGroup'), ('UPS-MIB', 'upsSubsetConfigGroup'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_compliance = upsSubsetCompliance.setStatus('current')
ups_basic_compliance = module_compliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 2)).setObjects(('UPS-MIB', 'upsBasicIdentGroup'), ('UPS-MIB', 'upsBasicBatteryGroup'), ('UPS-MIB', 'upsBasicInputGroup'), ('UPS-MIB', 'upsBasicOutputGroup'), ('UPS-MIB', 'upsBasicAlarmGroup'), ('UPS-MIB', 'upsBasicTestGroup'), ('UPS-MIB', 'upsBasicControlGroup'), ('UPS-MIB', 'upsBasicConfigGroup'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_compliance = upsBasicCompliance.setStatus('current')
ups_full_compliance = module_compliance((1, 3, 6, 1, 2, 1, 33, 3, 1, 3)).setObjects(('UPS-MIB', 'upsFullIdentGroup'), ('UPS-MIB', 'upsFullBatteryGroup'), ('UPS-MIB', 'upsFullInputGroup'), ('UPS-MIB', 'upsFullOutputGroup'), ('UPS-MIB', 'upsFullAlarmGroup'), ('UPS-MIB', 'upsFullTestGroup'), ('UPS-MIB', 'upsFullControlGroup'), ('UPS-MIB', 'upsFullConfigGroup'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_compliance = upsFullCompliance.setStatus('current')
ups_groups = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3, 2))
ups_subset_groups = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 1))
ups_subset_ident_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 1)).setObjects(('UPS-MIB', 'upsIdentManufacturer'), ('UPS-MIB', 'upsIdentModel'), ('UPS-MIB', 'upsIdentAgentSoftwareVersion'), ('UPS-MIB', 'upsIdentName'), ('UPS-MIB', 'upsIdentAttachedDevices'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_ident_group = upsSubsetIdentGroup.setStatus('current')
ups_subset_battery_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 2)).setObjects(('UPS-MIB', 'upsBatteryStatus'), ('UPS-MIB', 'upsSecondsOnBattery'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_battery_group = upsSubsetBatteryGroup.setStatus('current')
ups_subset_input_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 3)).setObjects(('UPS-MIB', 'upsInputLineBads'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_input_group = upsSubsetInputGroup.setStatus('current')
ups_subset_output_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 4)).setObjects(('UPS-MIB', 'upsOutputSource'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_output_group = upsSubsetOutputGroup.setStatus('current')
ups_subset_alarm_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 6)).setObjects(('UPS-MIB', 'upsAlarmsPresent'), ('UPS-MIB', 'upsAlarmDescr'), ('UPS-MIB', 'upsAlarmTime'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_alarm_group = upsSubsetAlarmGroup.setStatus('current')
ups_subset_control_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 8)).setObjects(('UPS-MIB', 'upsShutdownType'), ('UPS-MIB', 'upsShutdownAfterDelay'), ('UPS-MIB', 'upsAutoRestart'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_control_group = upsSubsetControlGroup.setStatus('current')
ups_subset_config_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 1, 9)).setObjects(('UPS-MIB', 'upsConfigInputVoltage'), ('UPS-MIB', 'upsConfigInputFreq'), ('UPS-MIB', 'upsConfigOutputVoltage'), ('UPS-MIB', 'upsConfigOutputFreq'), ('UPS-MIB', 'upsConfigOutputVA'), ('UPS-MIB', 'upsConfigOutputPower'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_subset_config_group = upsSubsetConfigGroup.setStatus('current')
ups_basic_groups = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 2))
ups_basic_ident_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 1)).setObjects(('UPS-MIB', 'upsIdentManufacturer'), ('UPS-MIB', 'upsIdentModel'), ('UPS-MIB', 'upsIdentUPSSoftwareVersion'), ('UPS-MIB', 'upsIdentAgentSoftwareVersion'), ('UPS-MIB', 'upsIdentName'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_ident_group = upsBasicIdentGroup.setStatus('current')
ups_basic_battery_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 2)).setObjects(('UPS-MIB', 'upsBatteryStatus'), ('UPS-MIB', 'upsSecondsOnBattery'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_battery_group = upsBasicBatteryGroup.setStatus('current')
ups_basic_input_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 3)).setObjects(('UPS-MIB', 'upsInputLineBads'), ('UPS-MIB', 'upsInputNumLines'), ('UPS-MIB', 'upsInputFrequency'), ('UPS-MIB', 'upsInputVoltage'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_input_group = upsBasicInputGroup.setStatus('current')
ups_basic_output_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 4)).setObjects(('UPS-MIB', 'upsOutputSource'), ('UPS-MIB', 'upsOutputFrequency'), ('UPS-MIB', 'upsOutputNumLines'), ('UPS-MIB', 'upsOutputVoltage'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_output_group = upsBasicOutputGroup.setStatus('current')
ups_basic_bypass_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 5)).setObjects(('UPS-MIB', 'upsBypassFrequency'), ('UPS-MIB', 'upsBypassNumLines'), ('UPS-MIB', 'upsBypassVoltage'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_bypass_group = upsBasicBypassGroup.setStatus('current')
ups_basic_alarm_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 6)).setObjects(('UPS-MIB', 'upsAlarmsPresent'), ('UPS-MIB', 'upsAlarmDescr'), ('UPS-MIB', 'upsAlarmTime'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_alarm_group = upsBasicAlarmGroup.setStatus('current')
ups_basic_test_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 7)).setObjects(('UPS-MIB', 'upsTestId'), ('UPS-MIB', 'upsTestSpinLock'), ('UPS-MIB', 'upsTestResultsSummary'), ('UPS-MIB', 'upsTestResultsDetail'), ('UPS-MIB', 'upsTestStartTime'), ('UPS-MIB', 'upsTestElapsedTime'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_test_group = upsBasicTestGroup.setStatus('current')
ups_basic_control_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 8)).setObjects(('UPS-MIB', 'upsShutdownType'), ('UPS-MIB', 'upsShutdownAfterDelay'), ('UPS-MIB', 'upsStartupAfterDelay'), ('UPS-MIB', 'upsRebootWithDuration'), ('UPS-MIB', 'upsAutoRestart'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_control_group = upsBasicControlGroup.setStatus('current')
ups_basic_config_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 2, 9)).setObjects(('UPS-MIB', 'upsConfigInputVoltage'), ('UPS-MIB', 'upsConfigInputFreq'), ('UPS-MIB', 'upsConfigOutputVoltage'), ('UPS-MIB', 'upsConfigOutputFreq'), ('UPS-MIB', 'upsConfigOutputVA'), ('UPS-MIB', 'upsConfigOutputPower'), ('UPS-MIB', 'upsConfigLowBattTime'), ('UPS-MIB', 'upsConfigAudibleStatus'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_basic_config_group = upsBasicConfigGroup.setStatus('current')
ups_full_groups = mib_identifier((1, 3, 6, 1, 2, 1, 33, 3, 2, 3))
ups_full_ident_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 1)).setObjects(('UPS-MIB', 'upsIdentManufacturer'), ('UPS-MIB', 'upsIdentModel'), ('UPS-MIB', 'upsIdentUPSSoftwareVersion'), ('UPS-MIB', 'upsIdentAgentSoftwareVersion'), ('UPS-MIB', 'upsIdentName'), ('UPS-MIB', 'upsIdentAttachedDevices'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_ident_group = upsFullIdentGroup.setStatus('current')
ups_full_battery_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 2)).setObjects(('UPS-MIB', 'upsBatteryStatus'), ('UPS-MIB', 'upsSecondsOnBattery'), ('UPS-MIB', 'upsEstimatedMinutesRemaining'), ('UPS-MIB', 'upsEstimatedChargeRemaining'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_battery_group = upsFullBatteryGroup.setStatus('current')
ups_full_input_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 3)).setObjects(('UPS-MIB', 'upsInputLineBads'), ('UPS-MIB', 'upsInputNumLines'), ('UPS-MIB', 'upsInputFrequency'), ('UPS-MIB', 'upsInputVoltage'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_input_group = upsFullInputGroup.setStatus('current')
ups_full_output_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 4)).setObjects(('UPS-MIB', 'upsOutputSource'), ('UPS-MIB', 'upsOutputFrequency'), ('UPS-MIB', 'upsOutputNumLines'), ('UPS-MIB', 'upsOutputVoltage'), ('UPS-MIB', 'upsOutputCurrent'), ('UPS-MIB', 'upsOutputPower'), ('UPS-MIB', 'upsOutputPercentLoad'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_output_group = upsFullOutputGroup.setStatus('current')
ups_full_bypass_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 5)).setObjects(('UPS-MIB', 'upsBypassFrequency'), ('UPS-MIB', 'upsBypassNumLines'), ('UPS-MIB', 'upsBypassVoltage'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_bypass_group = upsFullBypassGroup.setStatus('current')
ups_full_alarm_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 6)).setObjects(('UPS-MIB', 'upsAlarmsPresent'), ('UPS-MIB', 'upsAlarmDescr'), ('UPS-MIB', 'upsAlarmTime'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_alarm_group = upsFullAlarmGroup.setStatus('current')
ups_full_test_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 7)).setObjects(('UPS-MIB', 'upsTestId'), ('UPS-MIB', 'upsTestSpinLock'), ('UPS-MIB', 'upsTestResultsSummary'), ('UPS-MIB', 'upsTestResultsDetail'), ('UPS-MIB', 'upsTestStartTime'), ('UPS-MIB', 'upsTestElapsedTime'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_test_group = upsFullTestGroup.setStatus('current')
ups_full_control_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 8)).setObjects(('UPS-MIB', 'upsShutdownType'), ('UPS-MIB', 'upsShutdownAfterDelay'), ('UPS-MIB', 'upsStartupAfterDelay'), ('UPS-MIB', 'upsRebootWithDuration'), ('UPS-MIB', 'upsAutoRestart'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_control_group = upsFullControlGroup.setStatus('current')
ups_full_config_group = object_group((1, 3, 6, 1, 2, 1, 33, 3, 2, 3, 9)).setObjects(('UPS-MIB', 'upsConfigInputVoltage'), ('UPS-MIB', 'upsConfigInputFreq'), ('UPS-MIB', 'upsConfigOutputVoltage'), ('UPS-MIB', 'upsConfigOutputFreq'), ('UPS-MIB', 'upsConfigOutputVA'), ('UPS-MIB', 'upsConfigOutputPower'), ('UPS-MIB', 'upsConfigLowBattTime'), ('UPS-MIB', 'upsConfigAudibleStatus'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ups_full_config_group = upsFullConfigGroup.setStatus('current')
mibBuilder.exportSymbols('UPS-MIB', upsEstimatedChargeRemaining=upsEstimatedChargeRemaining, upsInputTable=upsInputTable, upsInputCurrent=upsInputCurrent, upsAlarmOutputBad=upsAlarmOutputBad, upsIdentUPSSoftwareVersion=upsIdentUPSSoftwareVersion, upsInputVoltage=upsInputVoltage, upsOutputEntry=upsOutputEntry, upsAlarmShutdownPending=upsAlarmShutdownPending, upsOutputFrequency=upsOutputFrequency, upsAlarmOutputOverload=upsAlarmOutputOverload, upsSubsetControlGroup=upsSubsetControlGroup, upsAlarmShutdownImminent=upsAlarmShutdownImminent, upsAlarmLowBattery=upsAlarmLowBattery, upsBatteryCurrent=upsBatteryCurrent, upsConfigOutputFreq=upsConfigOutputFreq, upsWellKnownTests=upsWellKnownTests, upsIdentManufacturer=upsIdentManufacturer, upsTestAbortTestInProgress=upsTestAbortTestInProgress, upsConfig=upsConfig, upsFullInputGroup=upsFullInputGroup, upsAlarmsPresent=upsAlarmsPresent, upsAlarmTempBad=upsAlarmTempBad, upsBypassFrequency=upsBypassFrequency, upsShutdownType=upsShutdownType, upsBatteryStatus=upsBatteryStatus, upsTrapTestCompleted=upsTrapTestCompleted, upsBasicIdentGroup=upsBasicIdentGroup, upsFullBatteryGroup=upsFullBatteryGroup, upsAlarmFuseFailure=upsAlarmFuseFailure, upsOutputCurrent=upsOutputCurrent, upsWellKnownAlarms=upsWellKnownAlarms, upsAlarmOnBattery=upsAlarmOnBattery, upsFullTestGroup=upsFullTestGroup, upsOutputNumLines=upsOutputNumLines, upsAlarmGeneralFault=upsAlarmGeneralFault, upsInputLineIndex=upsInputLineIndex, upsOutputPower=upsOutputPower, upsSubsetOutputGroup=upsSubsetOutputGroup, upsAlarmChargerFailed=upsAlarmChargerFailed, upsBasicBatteryGroup=upsBasicBatteryGroup, upsAlarmOnBypass=upsAlarmOnBypass, upsBasicOutputGroup=upsBasicOutputGroup, upsAlarmDiagnosticTestFailed=upsAlarmDiagnosticTestFailed, upsTestGeneralSystemsTest=upsTestGeneralSystemsTest, upsTestId=upsTestId, upsTrapAlarmEntryRemoved=upsTrapAlarmEntryRemoved, upsEstimatedMinutesRemaining=upsEstimatedMinutesRemaining, upsIdentAttachedDevices=upsIdentAttachedDevices, upsAlarmCommunicationsLost=upsAlarmCommunicationsLost, upsTestStartTime=upsTestStartTime, upsBasicInputGroup=upsBasicInputGroup, upsAlarmId=upsAlarmId, upsAlarmTime=upsAlarmTime, upsSubsetAlarmGroup=upsSubsetAlarmGroup, upsAlarmUpsOutputOff=upsAlarmUpsOutputOff, upsIdentName=upsIdentName, upsGroups=upsGroups, upsConfigOutputPower=upsConfigOutputPower, upsAlarmTestInProgress=upsAlarmTestInProgress, upsTestNoTestsInitiated=upsTestNoTestsInitiated, upsBasicConfigGroup=upsBasicConfigGroup, upsBatteryTemperature=upsBatteryTemperature, upsInputLineBads=upsInputLineBads, upsInputTruePower=upsInputTruePower, upsTest=upsTest, upsIdent=upsIdent, upsBypassVoltage=upsBypassVoltage, upsFullControlGroup=upsFullControlGroup, upsTraps=upsTraps, upsOutputTable=upsOutputTable, upsIdentModel=upsIdentModel, upsSubsetCompliance=upsSubsetCompliance, upsInputFrequency=upsInputFrequency, upsOutputVoltage=upsOutputVoltage, upsTrapOnBattery=upsTrapOnBattery, upsOutput=upsOutput, upsFullConfigGroup=upsFullConfigGroup, upsSubsetConfigGroup=upsSubsetConfigGroup, upsTestQuickBatteryTest=upsTestQuickBatteryTest, upsConfigOutputVoltage=upsConfigOutputVoltage, upsAlarmBypassBad=upsAlarmBypassBad, upsSecondsOnBattery=upsSecondsOnBattery, upsFullAlarmGroup=upsFullAlarmGroup, upsBypass=upsBypass, upsBypassLineIndex=upsBypassLineIndex, upsBypassNumLines=upsBypassNumLines, upsBypassCurrent=upsBypassCurrent, upsInput=upsInput, upsOutputSource=upsOutputSource, upsConfigAudibleStatus=upsConfigAudibleStatus, upsAlarmTable=upsAlarmTable, upsAlarmFanFailure=upsAlarmFanFailure, upsSubsetGroups=upsSubsetGroups, upsBasicControlGroup=upsBasicControlGroup, upsConfigHighVoltageTransferPoint=upsConfigHighVoltageTransferPoint, upsAlarmDepletedBattery=upsAlarmDepletedBattery, upsAutoRestart=upsAutoRestart, upsBasicGroups=upsBasicGroups, upsConfigOutputVA=upsConfigOutputVA, upsAlarmUpsSystemOff=upsAlarmUpsSystemOff, upsAlarmUpsOffAsRequested=upsAlarmUpsOffAsRequested, upsConformance=upsConformance, PYSNMP_MODULE_ID=upsMIB, upsIdentAgentSoftwareVersion=upsIdentAgentSoftwareVersion, upsRebootWithDuration=upsRebootWithDuration, upsObjects=upsObjects, upsTestResultsDetail=upsTestResultsDetail, upsOutputPercentLoad=upsOutputPercentLoad, upsBypassTable=upsBypassTable, upsFullBypassGroup=upsFullBypassGroup, upsSubsetBatteryGroup=upsSubsetBatteryGroup, upsAlarmEntry=upsAlarmEntry, upsControl=upsControl, upsTestDeepBatteryCalibration=upsTestDeepBatteryCalibration, upsStartupAfterDelay=upsStartupAfterDelay, upsCompliances=upsCompliances, upsFullOutputGroup=upsFullOutputGroup, NonNegativeInteger=NonNegativeInteger, upsFullIdentGroup=upsFullIdentGroup, upsInputNumLines=upsInputNumLines, upsBatteryVoltage=upsBatteryVoltage, upsBasicCompliance=upsBasicCompliance, upsSubsetInputGroup=upsSubsetInputGroup, upsOutputLineIndex=upsOutputLineIndex, upsAlarmBatteryBad=upsAlarmBatteryBad, upsBypassEntry=upsBypassEntry, upsConfigLowVoltageTransferPoint=upsConfigLowVoltageTransferPoint, upsMIB=upsMIB, upsBypassPower=upsBypassPower, upsConfigLowBattTime=upsConfigLowBattTime, upsBasicTestGroup=upsBasicTestGroup, upsConfigInputVoltage=upsConfigInputVoltage, upsTrapAlarmEntryAdded=upsTrapAlarmEntryAdded, upsTestSpinLock=upsTestSpinLock, upsBasicBypassGroup=upsBasicBypassGroup, upsTestElapsedTime=upsTestElapsedTime, upsInputEntry=upsInputEntry, PositiveInteger=PositiveInteger, upsFullCompliance=upsFullCompliance, upsAlarmAwaitingPower=upsAlarmAwaitingPower, upsShutdownAfterDelay=upsShutdownAfterDelay, upsConfigInputFreq=upsConfigInputFreq, upsAlarmDescr=upsAlarmDescr, upsAlarmOutputOffAsRequested=upsAlarmOutputOffAsRequested, upsBasicAlarmGroup=upsBasicAlarmGroup, upsBattery=upsBattery, upsSubsetIdentGroup=upsSubsetIdentGroup, upsAlarmInputBad=upsAlarmInputBad, upsFullGroups=upsFullGroups, upsTestResultsSummary=upsTestResultsSummary, upsAlarm=upsAlarm)
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#
# PySNMP MIB module ELTEX-MES-SNMP-COMMUNITY-EXT-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ELTEX-MES-SNMP-COMMUNITY-EXT-MIB
# Produced by pysmi-0.3.4 at Wed May 1 13:01:57 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ConstraintsIntersection, ValueRangeConstraint, SingleValueConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ConstraintsIntersection", "ValueRangeConstraint", "SingleValueConstraint", "ValueSizeConstraint")
eltMesSnmpCommExtMIB, = mibBuilder.importSymbols("ELTEX-MES-MNG-MIB", "eltMesSnmpCommExtMIB")
snmpCommunityEntry, = mibBuilder.importSymbols("SNMP-COMMUNITY-MIB", "snmpCommunityEntry")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
TimeTicks, ObjectIdentity, Counter64, Bits, MibScalar, MibTable, MibTableRow, MibTableColumn, NotificationType, MibIdentifier, Unsigned32, iso, Gauge32, IpAddress, Integer32, ModuleIdentity, Counter32 = mibBuilder.importSymbols("SNMPv2-SMI", "TimeTicks", "ObjectIdentity", "Counter64", "Bits", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "NotificationType", "MibIdentifier", "Unsigned32", "iso", "Gauge32", "IpAddress", "Integer32", "ModuleIdentity", "Counter32")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
eltSnmpCommunityTable = MibTable((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1), )
if mibBuilder.loadTexts: eltSnmpCommunityTable.setStatus('current')
if mibBuilder.loadTexts: eltSnmpCommunityTable.setDescription("The table of community strings configured in the SNMP engine's Local Configuration Datastore (LCD).")
eltSnmpCommunityEntry = MibTableRow((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1, 1), )
snmpCommunityEntry.registerAugmentions(("ELTEX-MES-SNMP-COMMUNITY-EXT-MIB", "eltSnmpCommunityEntry"))
eltSnmpCommunityEntry.setIndexNames(*snmpCommunityEntry.getIndexNames())
if mibBuilder.loadTexts: eltSnmpCommunityEntry.setStatus('current')
if mibBuilder.loadTexts: eltSnmpCommunityEntry.setDescription('Information about a particular community string.')
eltSnmpCommunityAccessList = MibTableColumn((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1, 1, 1), Integer32()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: eltSnmpCommunityAccessList.setStatus('current')
if mibBuilder.loadTexts: eltSnmpCommunityAccessList.setDescription('Index assigned to the ACL for SNMP community to filter SNMP requests.')
mibBuilder.exportSymbols("ELTEX-MES-SNMP-COMMUNITY-EXT-MIB", eltSnmpCommunityTable=eltSnmpCommunityTable, eltSnmpCommunityEntry=eltSnmpCommunityEntry, eltSnmpCommunityAccessList=eltSnmpCommunityAccessList)
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(integer, octet_string, object_identifier) = mibBuilder.importSymbols('ASN1', 'Integer', 'OctetString', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, constraints_intersection, value_range_constraint, single_value_constraint, value_size_constraint) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'ConstraintsIntersection', 'ValueRangeConstraint', 'SingleValueConstraint', 'ValueSizeConstraint')
(elt_mes_snmp_comm_ext_mib,) = mibBuilder.importSymbols('ELTEX-MES-MNG-MIB', 'eltMesSnmpCommExtMIB')
(snmp_community_entry,) = mibBuilder.importSymbols('SNMP-COMMUNITY-MIB', 'snmpCommunityEntry')
(module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup')
(time_ticks, object_identity, counter64, bits, mib_scalar, mib_table, mib_table_row, mib_table_column, notification_type, mib_identifier, unsigned32, iso, gauge32, ip_address, integer32, module_identity, counter32) = mibBuilder.importSymbols('SNMPv2-SMI', 'TimeTicks', 'ObjectIdentity', 'Counter64', 'Bits', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'NotificationType', 'MibIdentifier', 'Unsigned32', 'iso', 'Gauge32', 'IpAddress', 'Integer32', 'ModuleIdentity', 'Counter32')
(textual_convention, display_string) = mibBuilder.importSymbols('SNMPv2-TC', 'TextualConvention', 'DisplayString')
elt_snmp_community_table = mib_table((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1))
if mibBuilder.loadTexts:
eltSnmpCommunityTable.setStatus('current')
if mibBuilder.loadTexts:
eltSnmpCommunityTable.setDescription("The table of community strings configured in the SNMP engine's Local Configuration Datastore (LCD).")
elt_snmp_community_entry = mib_table_row((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1, 1))
snmpCommunityEntry.registerAugmentions(('ELTEX-MES-SNMP-COMMUNITY-EXT-MIB', 'eltSnmpCommunityEntry'))
eltSnmpCommunityEntry.setIndexNames(*snmpCommunityEntry.getIndexNames())
if mibBuilder.loadTexts:
eltSnmpCommunityEntry.setStatus('current')
if mibBuilder.loadTexts:
eltSnmpCommunityEntry.setDescription('Information about a particular community string.')
elt_snmp_community_access_list = mib_table_column((1, 3, 6, 1, 4, 1, 35265, 1, 23, 1, 4, 1, 1, 1), integer32()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
eltSnmpCommunityAccessList.setStatus('current')
if mibBuilder.loadTexts:
eltSnmpCommunityAccessList.setDescription('Index assigned to the ACL for SNMP community to filter SNMP requests.')
mibBuilder.exportSymbols('ELTEX-MES-SNMP-COMMUNITY-EXT-MIB', eltSnmpCommunityTable=eltSnmpCommunityTable, eltSnmpCommunityEntry=eltSnmpCommunityEntry, eltSnmpCommunityAccessList=eltSnmpCommunityAccessList)
|
def fun(x):
return 2*x
fun(4)
|
def fun(x):
return 2 * x
fun(4)
|
# Define a class for the maze board
class Maze:
# Initialize number of rows, cols and start position
def __init__(self, rows, cols, start):
self.rows = rows
self.cols = cols
self.i = start[0]
self.j = start[1]
self.start = start
def set(self, rewards, actions):
self.rewards = rewards
self.actions = actions
def set_state(self, state):
self.i = state[0]
self.j = state[1]
def current_state(self):
return (self.i, self.j)
def is_terminal(self, state):
return state not in self.actions
def move(self, action):
if action in self.actions[(self.i, self.j)]:
if action == 'U':
self.i -= 1
elif action == 'D':
self.i += 1
elif action == 'L':
self.j -= 1
elif action == 'R':
self.j += 1
return self.rewards.get((self.i, self.j), 0)
def undo_move(self, action):
if action == 'U':
self.i += 1
elif action == 'D':
self.i -= 1
elif action == 'L':
self.j += 1
elif action == 'R':
self.j -= 1
def game_over(self):
return (self.i, self.j) not in self.actions
def all_states(self):
return set(self.actions.keys()) | set(self.rewards.keys())
def standard_maze(rows=8, cols=10, start=(7, 0)):
g = Maze(rows, cols, start)
stoppers = []
stoppers = [[(7, 7)], [(7, 3)], [
(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (4, 1), (6, 1), (7, 1), (1, 2),
(6, 2), (1, 3), (3, 3), (4, 3), (5, 3), (6, 3), (0, 5), (1, 5), (7, 5),
(7, 6), (5, 6), (5, 7), (0, 8), (2, 8), (4, 8), (5, 8), (7, 8), (0, 9), (7, 9),
(7, 2)
]]
'''
temp = []
ar, b = input("Enter a win: ").split(',')
stoppers.append(([[int(ar), int(b)]]))
ar, b = input("Enter a loss: ").split(',')
stoppers.append(([[int(ar), int(b)]]))
for t in range(int(input("Enter number of rocks"))):
ar, b = input("Enter a rock: ").split(',')
temp.append(([int(ar), int(b)]))
stoppers.append(temp)
actions = {}'''
actions = {}
for i in range(rows):
for j in range(cols):
list1 = []
if [(i, j)] not in stoppers and (i, j) not in stoppers[-1]:
if (i + 1, j) not in stoppers[-1] and i + 1 < rows:
list1.append('D')
if (i - 1, j) not in stoppers[-1] and i - 1 >= 0:
list1.append('U')
if (i, j + 1) not in stoppers[-1] and j + 1 < cols:
list1.append('R')
if (i, j - 1) not in stoppers[-1] and j - 1 >= 0:
list1.append('L')
actions[i, j] = tuple(list1)
'''
for i in range(rows):
for j in range(cols):
list1 = []
if [[i, j]] not in stoppers and [i, j] not in stoppers[-1]:
if [i + 1, j] not in stoppers[-1] and i + 1 < rows:
list1.append('D')
if [i - 1, j] not in stoppers[-1] and i - 1 >= 0:
list1.append('U')
if [i, j + 1] not in stoppers[-1] and j + 1 < cols:
list1.append('R')
if [i, j - 1] not in stoppers[-1] and j - 1 >= 0:
list1.append('L')
actions[i, j] = tuple(list1)
'''
rewards = {}
for win in stoppers[0]:
rewards[tuple(win)] = 1
for loss in stoppers[1]:
rewards[tuple(loss)] = -5
actions = {k: v for k, v in actions.items() if v is not ()}
g.set(rewards, actions)
return g
def negative_maze(step_cost=-0.1, rows=8, cols=10, start=(7, 0)):
g = standard_maze(rows, cols, start)
for i in list(g.actions.keys()):
g.rewards[i] = step_cost
return g
def print_values(Val, g):
for i in range(g.rows):
print("----------------------------------------------------------------------")
for j in range(g.cols):
v = Val.get((i, j), 0)
if v > 0:
print(" %.2f|" % v, end="")
elif v == 0:
print(" ### |", end="")
else:
print("%.2f|" % v, end="")
print("")
def print_policy(P, g):
for i in range(g.rows):
print("----------------------------------------------------------------------")
for j in range(g.cols):
p = P.get((i, j), " ")
if p != '':
print("%s |" % p, end="")
else:
print(" ### |", end="")
print("")
|
class Maze:
def __init__(self, rows, cols, start):
self.rows = rows
self.cols = cols
self.i = start[0]
self.j = start[1]
self.start = start
def set(self, rewards, actions):
self.rewards = rewards
self.actions = actions
def set_state(self, state):
self.i = state[0]
self.j = state[1]
def current_state(self):
return (self.i, self.j)
def is_terminal(self, state):
return state not in self.actions
def move(self, action):
if action in self.actions[self.i, self.j]:
if action == 'U':
self.i -= 1
elif action == 'D':
self.i += 1
elif action == 'L':
self.j -= 1
elif action == 'R':
self.j += 1
return self.rewards.get((self.i, self.j), 0)
def undo_move(self, action):
if action == 'U':
self.i += 1
elif action == 'D':
self.i -= 1
elif action == 'L':
self.j += 1
elif action == 'R':
self.j -= 1
def game_over(self):
return (self.i, self.j) not in self.actions
def all_states(self):
return set(self.actions.keys()) | set(self.rewards.keys())
def standard_maze(rows=8, cols=10, start=(7, 0)):
g = maze(rows, cols, start)
stoppers = []
stoppers = [[(7, 7)], [(7, 3)], [(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (4, 1), (6, 1), (7, 1), (1, 2), (6, 2), (1, 3), (3, 3), (4, 3), (5, 3), (6, 3), (0, 5), (1, 5), (7, 5), (7, 6), (5, 6), (5, 7), (0, 8), (2, 8), (4, 8), (5, 8), (7, 8), (0, 9), (7, 9), (7, 2)]]
'\n temp = []\n ar, b = input("Enter a win: ").split(\',\')\n stoppers.append(([[int(ar), int(b)]]))\n\n ar, b = input("Enter a loss: ").split(\',\')\n stoppers.append(([[int(ar), int(b)]]))\n\n for t in range(int(input("Enter number of rocks"))):\n ar, b = input("Enter a rock: ").split(\',\')\n temp.append(([int(ar), int(b)]))\n stoppers.append(temp)\n actions = {}'
actions = {}
for i in range(rows):
for j in range(cols):
list1 = []
if [(i, j)] not in stoppers and (i, j) not in stoppers[-1]:
if (i + 1, j) not in stoppers[-1] and i + 1 < rows:
list1.append('D')
if (i - 1, j) not in stoppers[-1] and i - 1 >= 0:
list1.append('U')
if (i, j + 1) not in stoppers[-1] and j + 1 < cols:
list1.append('R')
if (i, j - 1) not in stoppers[-1] and j - 1 >= 0:
list1.append('L')
actions[i, j] = tuple(list1)
"\n for i in range(rows):\n for j in range(cols):\n list1 = []\n if [[i, j]] not in stoppers and [i, j] not in stoppers[-1]:\n\n if [i + 1, j] not in stoppers[-1] and i + 1 < rows:\n list1.append('D')\n if [i - 1, j] not in stoppers[-1] and i - 1 >= 0:\n list1.append('U')\n if [i, j + 1] not in stoppers[-1] and j + 1 < cols:\n list1.append('R')\n if [i, j - 1] not in stoppers[-1] and j - 1 >= 0:\n list1.append('L')\n\n actions[i, j] = tuple(list1)\n "
rewards = {}
for win in stoppers[0]:
rewards[tuple(win)] = 1
for loss in stoppers[1]:
rewards[tuple(loss)] = -5
actions = {k: v for (k, v) in actions.items() if v is not ()}
g.set(rewards, actions)
return g
def negative_maze(step_cost=-0.1, rows=8, cols=10, start=(7, 0)):
g = standard_maze(rows, cols, start)
for i in list(g.actions.keys()):
g.rewards[i] = step_cost
return g
def print_values(Val, g):
for i in range(g.rows):
print('----------------------------------------------------------------------')
for j in range(g.cols):
v = Val.get((i, j), 0)
if v > 0:
print(' %.2f|' % v, end='')
elif v == 0:
print(' ### |', end='')
else:
print('%.2f|' % v, end='')
print('')
def print_policy(P, g):
for i in range(g.rows):
print('----------------------------------------------------------------------')
for j in range(g.cols):
p = P.get((i, j), ' ')
if p != '':
print('%s |' % p, end='')
else:
print(' ### |', end='')
print('')
|
SUPPORTED_TRANS = {
"height": "h",
"width": "w",
"aspect_ratio": "ar",
"quality": "q",
"crop": "c",
"crop_mode": "cm",
"x": "x",
"y": "y",
"focus": "fo",
"format": "f",
"radius": "r",
"background": "bg",
"border": "bo",
"rotation": "rt",
"blur": "bl",
"named": "n",
"overlay_image": "oi",
"overlay_x": "ox",
"overlay_y": "oy",
"overlay_focus": "ofo",
"overlay_height": "oh",
"overlay_width": "ow",
"overlay_text": "ot",
"overlay_text_font_size": "ots",
"overlay_text_font_family": "otf",
"overlay_text_color": "otc",
"overlay_alpha": "oa",
"overlay_text_typography": "ott",
"overlay_background": "obg",
"overlay_image_trim": "oit",
"progressive": "pr",
"lossless": "lo",
"trim": "t",
"metadata": "md",
"color_profile": "cp",
"default_image": "di",
"dpr": "dpr",
"effect_sharpen": "e-sharpen",
"effect_usm": "e-usm",
"effect_contrast": "e-contrast",
"effect_gray": "e-grayscale",
"original": "orig",
}
|
supported_trans = {'height': 'h', 'width': 'w', 'aspect_ratio': 'ar', 'quality': 'q', 'crop': 'c', 'crop_mode': 'cm', 'x': 'x', 'y': 'y', 'focus': 'fo', 'format': 'f', 'radius': 'r', 'background': 'bg', 'border': 'bo', 'rotation': 'rt', 'blur': 'bl', 'named': 'n', 'overlay_image': 'oi', 'overlay_x': 'ox', 'overlay_y': 'oy', 'overlay_focus': 'ofo', 'overlay_height': 'oh', 'overlay_width': 'ow', 'overlay_text': 'ot', 'overlay_text_font_size': 'ots', 'overlay_text_font_family': 'otf', 'overlay_text_color': 'otc', 'overlay_alpha': 'oa', 'overlay_text_typography': 'ott', 'overlay_background': 'obg', 'overlay_image_trim': 'oit', 'progressive': 'pr', 'lossless': 'lo', 'trim': 't', 'metadata': 'md', 'color_profile': 'cp', 'default_image': 'di', 'dpr': 'dpr', 'effect_sharpen': 'e-sharpen', 'effect_usm': 'e-usm', 'effect_contrast': 'e-contrast', 'effect_gray': 'e-grayscale', 'original': 'orig'}
|
"""
Datos de entrada
edad_uno-->e1-->int
edad_dos-->e2-->int
edad_tres-->e3-->int
Datos de salida
promedio-->p-->float
"""
#Entradas
e1=int(input("Ingrese la edad de la primera persona: "))
e2=int(input("Ingrese la edad de la segunda persona: "))
e3=int(input("Ingrese la edad de la tercera persona: "))
#Caja negra
p=(e1+e2+e3)/3
#Salidas
print("El promedio de edad de los tres es: ", p)
|
"""
Datos de entrada
edad_uno-->e1-->int
edad_dos-->e2-->int
edad_tres-->e3-->int
Datos de salida
promedio-->p-->float
"""
e1 = int(input('Ingrese la edad de la primera persona: '))
e2 = int(input('Ingrese la edad de la segunda persona: '))
e3 = int(input('Ingrese la edad de la tercera persona: '))
p = (e1 + e2 + e3) / 3
print('El promedio de edad de los tres es: ', p)
|
""" Asked by: Google [Medium].
On our special chessboard, two bishops attack each other if they share the same diagonal.
This includes bishops that have another bishop located between them, i.e. bishops can attack through pieces.
You are given N bishops, represented as (row, column) tuples on a M by M chessboard.
Write a function to count the number of pairs of bishops that attack each other.
The ordering of the pair doesn't matter: (1, 2) is considered the same as (2, 1).
For example, given M = 5 and the list of bishops:
(0, 0)
(1, 2)
(2, 2)
(4, 0)
The board would look like this:
[b 0 0 0 0]
[0 0 b 0 0]
[0 0 b 0 0]
[0 0 0 0 0]
[b 0 0 0 0]
You should return 2, since bishops 1 and 3 attack each other, as well as bishops 3 and 4.
"""
|
""" Asked by: Google [Medium].
On our special chessboard, two bishops attack each other if they share the same diagonal.
This includes bishops that have another bishop located between them, i.e. bishops can attack through pieces.
You are given N bishops, represented as (row, column) tuples on a M by M chessboard.
Write a function to count the number of pairs of bishops that attack each other.
The ordering of the pair doesn't matter: (1, 2) is considered the same as (2, 1).
For example, given M = 5 and the list of bishops:
(0, 0)
(1, 2)
(2, 2)
(4, 0)
The board would look like this:
[b 0 0 0 0]
[0 0 b 0 0]
[0 0 b 0 0]
[0 0 0 0 0]
[b 0 0 0 0]
You should return 2, since bishops 1 and 3 attack each other, as well as bishops 3 and 4.
"""
|
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