def generate_response_header_validator(headers, context, **kwargs):
validators = ValidationDict()
for key, header_definition in headers.items():
# generate a function that will attempt to cast the header to the
# appropriate type.
header_processor = generate_value_processor(
context=context,
**header_definition
)
# generate a function that will validate the header.
header_validator = functools.partial(
validate_object,
field_validators=construct_header_validators(header_definition, context=context),
)
# Chain the type casting function, the individual header validation
# function with a methodcaller that will fetch the header with
# `response.headers.get(header_name, EMPTY)` and then feed that into
# the type casting function and then into the validation function.
validators.add_validator(key, chain_reduce_partial(
methodcaller('get', key, EMPTY),
header_processor,
header_validator,
))
return chain_reduce_partial(
attrgetter('headers'),
functools.partial(validate_object, field_validators=validators),
)
def generate_response_header_validator(headers, context, **kwargs):
validators = ValidationDict()
for key, header_definition in headers.items():
# generate a function that will attempt to cast the header to the
# appropriate type.
header_processor = generate_value_processor(
context=context,
**header_definition
)
# generate a function that will validate the header.
header_validator = functools.partial(
validate_object,
field_validators=construct_header_validators(header_definition, context=context),
)
# Chain the type casting function, the individual header validation
# function with a methodcaller that will fetch the header with
# `response.headers.get(header_name, EMPTY)` and then feed that into
# the type casting function and then into the validation function.
validators.add_validator(key, chain_reduce_partial(
methodcaller('get', key, EMPTY),
header_processor,
header_validator,
))
return chain_reduce_partial(
attrgetter('headers'),
functools.partial(validate_object, field_validators=validators),
)
def generate_path_validator(api_path, path_definition, parameters,
context, **kwargs):
"""
Generates a callable for validating the parameters in a response object.
"""
path_level_parameters = dereference_parameter_list(
path_definition.get('parameters', []),
context,
)
operation_level_parameters = dereference_parameter_list(
parameters,
context,
)
all_parameters = merge_parameter_lists(
path_level_parameters,
operation_level_parameters,
)
# PATH
in_path_parameters = filter_parameters(all_parameters, in_=PATH)
return chain_reduce_partial(
attrgetter('path'),
generate_path_parameters_validator(api_path, in_path_parameters, context),
)
def generate_path_validator(api_path, path_definition, parameters,
context, **kwargs):
"""
Generates a callable for validating the parameters in a response object.
"""
path_level_parameters = dereference_parameter_list(
path_definition.get('parameters', []),
context,
)
operation_level_parameters = dereference_parameter_list(
parameters,
context,
)
all_parameters = merge_parameter_lists(
path_level_parameters,
operation_level_parameters,
)
# PATH
in_path_parameters = filter_parameters(all_parameters, in_=PATH)
return chain_reduce_partial(
attrgetter('path'),
generate_path_parameters_validator(api_path, in_path_parameters, context),
)
def generate_response_body_validator(schema, context, **kwargs):
return chain_reduce_partial(
attrgetter('data'),
functools.partial(
validate_object,
schema=schema,
context=context,
),
)
def generate_response_body_validator(schema, context, **kwargs):
return chain_reduce_partial(
attrgetter('data'),
functools.partial(
validate_object,
schema=schema,
context=context,
),
)
def test_chain_reduce_partial():
def fn_a(v):
return v * 3
def fn_b(v):
return v * 5
fn_c = chain_reduce_partial(fn_a, fn_b)
for _ in range(100):
v = random.randint(-1000000, 1000000)
assert fn_c(v) == fn_b(fn_a(v))
def test_chain_reduce_partial():
def fn_a(v):
return v * 3
def fn_b(v):
return v * 5
fn_c = chain_reduce_partial(fn_a, fn_b)
for _ in range(100):
v = random.randint(-1000000, 1000000)
assert fn_c(v) == fn_b(fn_a(v))
def generate_header_validator(headers, context, **kwargs):
"""
Generates a validation function that will validate a dictionary of headers.
"""
validators = ValidationDict()
for header_definition in headers:
header_processor = generate_value_processor(
context=context,
**header_definition
)
header_validator = generate_object_validator(
field_validators=construct_header_validators(header_definition, context=context),
)
validators.add_property_validator(
header_definition['name'],
chain_reduce_partial(
header_processor,
header_validator,
),
)
return generate_object_validator(field_validators=validators)
def generate_parameters_validator(api_path, path_definition, parameters,
context, **kwargs):
"""
Generates a validator function to validate.
- request.path against the path parameters.
- request.query against the query parameters.
- request.headers against the header parameters.
- TODO: request.body against the body parameters.
- TODO: request.formData against any form data.
"""
# TODO: figure out how to merge this with the same code in response
# validation.
validators = ValidationDict()
path_level_parameters = dereference_parameter_list(
path_definition.get('parameters', []),
context,
)
operation_level_parameters = dereference_parameter_list(
parameters,
context,
)
all_parameters = merge_parameter_lists(
path_level_parameters,
operation_level_parameters,
)
# PATH
in_path_parameters = filter_parameters(all_parameters, in_=PATH)
validators.add_validator(
'path',
chain_reduce_partial(
attrgetter('path'),
generate_path_parameters_validator(api_path, in_path_parameters, context),
),
)
# QUERY
in_query_parameters = filter_parameters(all_parameters, in_=QUERY)
validators.add_validator(
'query',
chain_reduce_partial(
attrgetter('query_data'),
functools.partial(
validate_query_parameters,
query_parameters=in_query_parameters,
context=context,
),
),
)
# HEADERS
in_header_parameters = filter_parameters(all_parameters, in_=HEADER)
validators.add_validator(
'headers',
chain_reduce_partial(
attrgetter('headers'),
generate_header_validator(in_header_parameters, context),
),
)
# FORM_DATA
# in_form_data_parameters = filter_parameters(all_parameters, in_=FORM_DATA)
# validators.add_validator(
# 'form_data',
# chain_reduce_partial(
# attrgetter('data'),
# generate_form_data_validator(in_form_data_parameters, context),
# )
# )
# REQUEST_BODY
in_request_body_parameters = filter_parameters(all_parameters, in_=BODY)
validators.add_validator(
'request_body',
chain_reduce_partial(
attrgetter('data'),
generate_request_body_validator(in_request_body_parameters, context),
)
)
return generate_object_validator(field_validators=validators)
def processor(value, **kwargs):
try:
return chain_reduce_partial(*processors)(value)
except (ValueError, TypeError):
return value
def generate_value_processor(type_,
collectionFormat=None,
items=None,
**kwargs):
"""
Create a callable that will take the string value of a header and cast it
to the appropriate type. This can involve:
- splitting a header of type 'array' by its delimeters.
- type casting the internal elements of the array.
"""
processors = []
if is_non_string_iterable(type_):
assert False, "This should not be possible"
else:
if type_ == ARRAY and collectionFormat:
if collectionFormat in DELIMETERS:
delimeter = DELIMETERS[collectionFormat]
# split the string based on the delimeter specified by the
# `collectionFormat`
processors.append(operator.methodcaller('split', delimeter))
else:
if collectionFormat != MULTI:
raise TypeError("collectionFormat not implemented")
processors.append(add_string_into_list)
# remove any Falsy values like empty strings.
processors.append(functools.partial(filter, bool))
# strip off any whitespace
processors.append(
functools.partial(map, operator.methodcaller('strip')))
if items is not None:
if isinstance(items, collections.Mapping):
items_processors = itertools.repeat(
generate_value_processor(**items))
elif isinstance(items, collections.Sequence):
items_processors = itertools.chain(
(generate_value_processor(**item) for item in items),
itertools.repeat(lambda v: v),
)
elif isinstance(items, six.string_types):
raise NotImplementedError("Not implemented")
else:
assert False, "Should not be possible"
# 1. zip the processor and the array items together
# 2. apply the processor to each array item.
# 3. cast the starmap generator to a list.
processors.append(
chain_reduce_partial(
functools.partial(zip, items_processors),
functools.partial(itertools.starmap,
lambda fn, v: fn(v)),
list,
))
else:
processors.append(
functools.partial(cast_value_to_type, type_=type_))
def processor(value, **kwargs):
try:
return chain_reduce_partial(*processors)(value)
except (ValueError, TypeError):
return value
return processor
