# import torch # import numpy as np from garlic_validator import validators, checkers, errors try: email_address = validators.email('test@domain.dev') # The value of email_address will now be "test@domain.dev" email_address = validators.email('this-is-an-invalid-email') # Will raise a ValueError email_address = validators.email(None) # Will raise an EmptyValueError except errors.EmptyValueError: # Handling logic goes here print('Email address is empty') except errors.InvalidEmailError: # More handlign logic goes here print('Invalid email address') email_address = validators.email(None, allow_empty=True) print(email_address) # The value of email_address will now be None email_address = validators.email('', allow_empty=True) print(email_address) # The value of email_address will now be None is_email_address = checkers.is_email('test@domain.dev') print(is_email_address) # The value of is_email_address will now be True is_email_address = checkers.is_email('this-is-an-invalid-email') print(is_email_address) # The value of is_email_address will now be False is_email_address = checkers.is_email(None) print(is_email_address) # The value of is_email_address will now be False ## Custom validators: ## is_empty(val, trim_str=False) is_empty = checkers.is_empty(None) # True is_empty = checkers.is_empty('') # True is_empty = checkers.is_empty(' ') # False is_empty = checkers.is_empty(' ', trim_str=True) # True is_empty = checkers.is_empty([]) # True is_empty = checkers.is_empty({}) # True is_empty = checkers.is_empty(()) # True is_empty = checkers.is_empty(set()) # True is_empty = checkers.is_empty(range(0)) # True is_empty = checkers.is_empty(np.array([])) # True # ## is_numpy(val) # is_numpy = checkers.is_numpy(np.array([])) # # True # is_numpy = checkers.is_numpy(np.array([1, 2, 3])) # # True # is_numpy = checkers.is_numpy(None) # # False # ## is_numpy(val) # is_numpy = checkers.is_tensor(torch.empty(2, 3)) # # True # is_numpy = checkers.is_tensor(torch.Tensor([1, 2, 3])) # # True # is_numpy = checkers.is_tensor(None) # # False ## is_float(val) is_float = checkers.is_float(1) # True is_float = checkers.is_float(-1.1123) # True is_float = checkers.is_float(1e+123) # True is_float = checkers.is_float('0123.000') # True is_float = checkers.is_float('1e+12') # True is_float = checkers.is_float('2002_12') # False ## is_truthy(val) is_truthy = checkers.is_truthy(True) # True is_truthy = checkers.is_truthy(1) # True is_truthy = checkers.is_truthy('1') # True is_truthy = checkers.is_truthy('1.0') # True is_truthy = checkers.is_truthy('TRUE') # True is_truthy = checkers.is_truthy('True') # True is_truthy = checkers.is_truthy('true') # True is_truthy = checkers.is_truthy('YES') # True is_truthy = checkers.is_truthy('Yes') # True is_truthy = checkers.is_truthy('yes') # True is_truthy = checkers.is_truthy('Y') # True is_truthy = checkers.is_truthy('y') # True is_truthy = checkers.is_truthy(1.1) # False is_truthy = checkers.is_truthy([1]) # False is_truthy = checkers.is_truthy(False) # False ## is_falsy(val) is_falsy = checkers.is_falsy(False) # True is_falsy = checkers.is_falsy(0) # True is_falsy = checkers.is_falsy('0') # True is_falsy = checkers.is_falsy('0.0') # True is_falsy = checkers.is_falsy('FALSE') # True is_falsy = checkers.is_falsy('False') # True is_falsy = checkers.is_falsy('false') # True is_falsy = checkers.is_falsy('NO') # True is_falsy = checkers.is_falsy('No') # True is_falsy = checkers.is_falsy('no') # True is_falsy = checkers.is_falsy('N') # True is_falsy = checkers.is_falsy('n') # True is_falsy = checkers.is_falsy(2) # False is_falsy = checkers.is_falsy('a') # False is_falsy = checkers.is_falsy(True) # False ## is_bool(val, coerce_value=False) is_bool = checkers.is_bool(True) # True is_bool = checkers.is_bool(False) # True is_bool = checkers.is_bool(1) # False is_bool = checkers.is_bool('1', coerce_value=True) # True is_bool = checkers.is_bool('NO', coerce_value=True) # True

from cerberus import Validator v = Validator({ 'name': { 'type': 'string' } }) print(v.validate({ 'name': 'john doe' })) # True v.schema = {'amount': {'type': 'integer'}} print(v.validate({'amount': '1'})) # False print(v.errors) # {'amount': ['must be of integer type']} v.schema = {'amount': {'type': 'integer', 'coerce': int}} print(v.validate({'amount': '1'})) # True print(v.document) # {'amount': 1} to_bool = lambda v: v.lower() in ('true', '1') v.schema = {'flag': {'type': 'boolean', 'coerce': (str, to_bool)}} print(v.validate({'flag': 'true'})) # True print(v.document) # {'flag': True}

from pydantic import validate_arguments, ValidationError @validate_arguments def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes: b = s.encode() return separator.join(b for _ in range(count)) a = repeat('hello', 3) print(a) #> b'hellohellohello' b = repeat('x', '4', separator=' ') print(b) #> b'x x x x' try: c = repeat('hello', 'wrong') except ValidationError as exc: print(exc) """  1 validation error for Repeat  count  value is not a valid integer (type=type_error.integer)  """