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356 lines
12 KiB
356 lines
12 KiB
import math
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import json
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from jmespath import exceptions
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from jmespath.compat import string_type as STRING_TYPE
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from jmespath.compat import get_methods, with_metaclass
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# python types -> jmespath types
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TYPES_MAP = {
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'bool': 'boolean',
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'list': 'array',
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'dict': 'object',
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'NoneType': 'null',
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'unicode': 'string',
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'str': 'string',
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'float': 'number',
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'int': 'number',
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'long': 'number',
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'OrderedDict': 'object',
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'_Projection': 'array',
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'_Expression': 'expref',
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}
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# jmespath types -> python types
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REVERSE_TYPES_MAP = {
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'boolean': ('bool',),
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'array': ('list', '_Projection'),
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'object': ('dict', 'OrderedDict',),
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'null': ('None',),
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'string': ('unicode', 'str'),
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'number': ('float', 'int', 'long'),
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'expref': ('_Expression',),
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}
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def signature(*arguments):
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def _record_signature(func):
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func.signature = arguments
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return func
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return _record_signature
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class FunctionRegistry(type):
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def __init__(cls, name, bases, attrs):
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cls._populate_function_table()
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super(FunctionRegistry, cls).__init__(name, bases, attrs)
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def _populate_function_table(cls):
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function_table = {}
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# Any method with a @signature decorator that also
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# starts with "_func_" is registered as a function.
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# _func_max_by -> max_by function.
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for name, method in get_methods(cls):
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if not name.startswith('_func_'):
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continue
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signature = getattr(method, 'signature', None)
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if signature is not None:
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function_table[name[6:]] = {
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'function': method,
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'signature': signature,
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}
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cls.FUNCTION_TABLE = function_table
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class Functions(with_metaclass(FunctionRegistry, object)):
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FUNCTION_TABLE = {
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}
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def call_function(self, function_name, resolved_args):
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try:
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spec = self.FUNCTION_TABLE[function_name]
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except KeyError:
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raise exceptions.UnknownFunctionError(
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"Unknown function: %s()" % function_name)
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function = spec['function']
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signature = spec['signature']
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self._validate_arguments(resolved_args, signature, function_name)
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return function(self, *resolved_args)
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def _validate_arguments(self, args, signature, function_name):
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if signature and signature[-1].get('variadic'):
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if len(args) < len(signature):
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raise exceptions.VariadictArityError(
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len(signature), len(args), function_name)
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elif len(args) != len(signature):
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raise exceptions.ArityError(
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len(signature), len(args), function_name)
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return self._type_check(args, signature, function_name)
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def _type_check(self, actual, signature, function_name):
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for i in range(len(signature)):
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allowed_types = signature[i]['types']
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if allowed_types:
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self._type_check_single(actual[i], allowed_types,
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function_name)
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def _type_check_single(self, current, types, function_name):
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# Type checking involves checking the top level type,
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# and in the case of arrays, potentially checking the types
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# of each element.
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allowed_types, allowed_subtypes = self._get_allowed_pytypes(types)
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# We're not using isinstance() on purpose.
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# The type model for jmespath does not map
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# 1-1 with python types (booleans are considered
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# integers in python for example).
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actual_typename = type(current).__name__
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if actual_typename not in allowed_types:
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raise exceptions.JMESPathTypeError(
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function_name, current,
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self._convert_to_jmespath_type(actual_typename), types)
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# If we're dealing with a list type, we can have
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# additional restrictions on the type of the list
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# elements (for example a function can require a
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# list of numbers or a list of strings).
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# Arrays are the only types that can have subtypes.
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if allowed_subtypes:
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self._subtype_check(current, allowed_subtypes,
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types, function_name)
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def _get_allowed_pytypes(self, types):
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allowed_types = []
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allowed_subtypes = []
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for t in types:
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type_ = t.split('-', 1)
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if len(type_) == 2:
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type_, subtype = type_
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allowed_subtypes.append(REVERSE_TYPES_MAP[subtype])
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else:
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type_ = type_[0]
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allowed_types.extend(REVERSE_TYPES_MAP[type_])
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return allowed_types, allowed_subtypes
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def _subtype_check(self, current, allowed_subtypes, types, function_name):
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if len(allowed_subtypes) == 1:
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# The easy case, we know up front what type
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# we need to validate.
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allowed_subtypes = allowed_subtypes[0]
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for element in current:
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actual_typename = type(element).__name__
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if actual_typename not in allowed_subtypes:
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raise exceptions.JMESPathTypeError(
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function_name, element, actual_typename, types)
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elif len(allowed_subtypes) > 1 and current:
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# Dynamic type validation. Based on the first
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# type we see, we validate that the remaining types
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# match.
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first = type(current[0]).__name__
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for subtypes in allowed_subtypes:
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if first in subtypes:
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allowed = subtypes
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break
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else:
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raise exceptions.JMESPathTypeError(
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function_name, current[0], first, types)
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for element in current:
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actual_typename = type(element).__name__
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if actual_typename not in allowed:
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raise exceptions.JMESPathTypeError(
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function_name, element, actual_typename, types)
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@signature({'types': ['number']})
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def _func_abs(self, arg):
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return abs(arg)
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@signature({'types': ['array-number']})
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def _func_avg(self, arg):
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if arg:
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return sum(arg) / float(len(arg))
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else:
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return None
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@signature({'types': [], 'variadic': True})
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def _func_not_null(self, *arguments):
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for argument in arguments:
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if argument is not None:
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return argument
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@signature({'types': []})
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def _func_to_array(self, arg):
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if isinstance(arg, list):
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return arg
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else:
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return [arg]
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@signature({'types': []})
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def _func_to_string(self, arg):
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if isinstance(arg, STRING_TYPE):
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return arg
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else:
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return json.dumps(arg, separators=(',', ':'),
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default=str)
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@signature({'types': []})
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def _func_to_number(self, arg):
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if isinstance(arg, (list, dict, bool)):
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return None
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elif arg is None:
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return None
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elif isinstance(arg, (int, float)):
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return arg
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else:
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try:
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return int(arg)
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except ValueError:
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try:
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return float(arg)
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except ValueError:
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return None
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@signature({'types': ['array', 'string']}, {'types': []})
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def _func_contains(self, subject, search):
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return search in subject
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@signature({'types': ['string', 'array', 'object']})
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def _func_length(self, arg):
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return len(arg)
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@signature({'types': ['string']}, {'types': ['string']})
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def _func_ends_with(self, search, suffix):
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return search.endswith(suffix)
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@signature({'types': ['string']}, {'types': ['string']})
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def _func_starts_with(self, search, suffix):
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return search.startswith(suffix)
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@signature({'types': ['array', 'string']})
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def _func_reverse(self, arg):
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if isinstance(arg, STRING_TYPE):
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return arg[::-1]
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else:
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return list(reversed(arg))
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@signature({"types": ['number']})
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def _func_ceil(self, arg):
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return math.ceil(arg)
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@signature({"types": ['number']})
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def _func_floor(self, arg):
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return math.floor(arg)
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@signature({"types": ['string']}, {"types": ['array-string']})
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def _func_join(self, separator, array):
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return separator.join(array)
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@signature({'types': ['expref']}, {'types': ['array']})
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def _func_map(self, expref, arg):
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result = []
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for element in arg:
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result.append(expref.visit(expref.expression, element))
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return result
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@signature({"types": ['array-number', 'array-string']})
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def _func_max(self, arg):
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if arg:
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return max(arg)
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else:
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return None
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@signature({"types": ["object"], "variadic": True})
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def _func_merge(self, *arguments):
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merged = {}
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for arg in arguments:
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merged.update(arg)
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return merged
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@signature({"types": ['array-number', 'array-string']})
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def _func_min(self, arg):
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if arg:
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return min(arg)
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else:
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return None
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@signature({"types": ['array-string', 'array-number']})
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def _func_sort(self, arg):
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return list(sorted(arg))
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@signature({"types": ['array-number']})
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def _func_sum(self, arg):
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return sum(arg)
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@signature({"types": ['object']})
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def _func_keys(self, arg):
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# To be consistent with .values()
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# should we also return the indices of a list?
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return list(arg.keys())
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@signature({"types": ['object']})
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def _func_values(self, arg):
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return list(arg.values())
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@signature({'types': []})
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def _func_type(self, arg):
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if isinstance(arg, STRING_TYPE):
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return "string"
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elif isinstance(arg, bool):
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return "boolean"
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elif isinstance(arg, list):
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return "array"
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elif isinstance(arg, dict):
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return "object"
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elif isinstance(arg, (float, int)):
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return "number"
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elif arg is None:
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return "null"
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@signature({'types': ['array']}, {'types': ['expref']})
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def _func_sort_by(self, array, expref):
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if not array:
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return array
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# sort_by allows for the expref to be either a number of
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# a string, so we have some special logic to handle this.
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# We evaluate the first array element and verify that it's
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# either a string of a number. We then create a key function
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# that validates that type, which requires that remaining array
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# elements resolve to the same type as the first element.
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required_type = self._convert_to_jmespath_type(
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type(expref.visit(expref.expression, array[0])).__name__)
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if required_type not in ['number', 'string']:
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raise exceptions.JMESPathTypeError(
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'sort_by', array[0], required_type, ['string', 'number'])
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keyfunc = self._create_key_func(expref,
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[required_type],
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'sort_by')
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return list(sorted(array, key=keyfunc))
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@signature({'types': ['array']}, {'types': ['expref']})
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def _func_min_by(self, array, expref):
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keyfunc = self._create_key_func(expref,
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['number', 'string'],
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'min_by')
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return min(array, key=keyfunc)
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@signature({'types': ['array']}, {'types': ['expref']})
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def _func_max_by(self, array, expref):
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keyfunc = self._create_key_func(expref,
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['number', 'string'],
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'min_by')
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return max(array, key=keyfunc)
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def _create_key_func(self, expref, allowed_types, function_name):
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def keyfunc(x):
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result = expref.visit(expref.expression, x)
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actual_typename = type(result).__name__
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jmespath_type = self._convert_to_jmespath_type(actual_typename)
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# allowed_types is in term of jmespath types, not python types.
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if jmespath_type not in allowed_types:
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raise exceptions.JMESPathTypeError(
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function_name, result, jmespath_type, allowed_types)
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return result
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return keyfunc
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def _convert_to_jmespath_type(self, pyobject):
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return TYPES_MAP.get(pyobject, 'unknown')
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