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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | HTTPRouter.add_response_headers | Adds the specified response headers while keeping existing ones in-tact | hug/routing.py | def add_response_headers(self, headers, **overrides):
"""Adds the specified response headers while keeping existing ones in-tact"""
response_headers = self.route.get('response_headers', {}).copy()
response_headers.update(headers)
return self.where(response_headers=response_headers, **ove... | def add_response_headers(self, headers, **overrides):
"""Adds the specified response headers while keeping existing ones in-tact"""
response_headers = self.route.get('response_headers', {}).copy()
response_headers.update(headers)
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train | HTTPRouter.cache | Convenience method for quickly adding cache header to route | hug/routing.py | def cache(self, private=False, max_age=31536000, s_maxage=None, no_cache=False, no_store=False,
must_revalidate=False, **overrides):
"""Convenience method for quickly adding cache header to route"""
parts = ('private' if private else 'public', 'max-age={0}'.format(max_age),
... | def cache(self, private=False, max_age=31536000, s_maxage=None, no_cache=False, no_store=False,
must_revalidate=False, **overrides):
"""Convenience method for quickly adding cache header to route"""
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train | HTTPRouter.allow_origins | Convenience method for quickly allowing other resources to access this one | hug/routing.py | def allow_origins(self, *origins, methods=None, max_age=None, credentials=None, headers=None, **overrides):
"""Convenience method for quickly allowing other resources to access this one"""
response_headers = {}
if origins:
@hug.response_middleware()
def process_data(reque... | def allow_origins(self, *origins, methods=None, max_age=None, credentials=None, headers=None, **overrides):
"""Convenience method for quickly allowing other resources to access this one"""
response_headers = {}
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train | URLRouter.get | Sets the acceptable HTTP method to a GET | hug/routing.py | def get(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to a GET"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='GET', **overrides) | def get(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to a GET"""
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train | URLRouter.delete | Sets the acceptable HTTP method to DELETE | hug/routing.py | def delete(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to DELETE"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='DELETE', **overrides) | def delete(self, urls=None, **overrides):
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overrides['urls'] = urls
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train | URLRouter.post | Sets the acceptable HTTP method to POST | hug/routing.py | def post(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to POST"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='POST', **overrides) | def post(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to POST"""
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overrides['urls'] = urls
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train | URLRouter.put | Sets the acceptable HTTP method to PUT | hug/routing.py | def put(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to PUT"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='PUT', **overrides) | def put(self, urls=None, **overrides):
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train | URLRouter.trace | Sets the acceptable HTTP method to TRACE | hug/routing.py | def trace(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to TRACE"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='TRACE', **overrides) | def trace(self, urls=None, **overrides):
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overrides['urls'] = urls
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train | URLRouter.patch | Sets the acceptable HTTP method to PATCH | hug/routing.py | def patch(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to PATCH"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='PATCH', **overrides) | def patch(self, urls=None, **overrides):
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train | URLRouter.options | Sets the acceptable HTTP method to OPTIONS | hug/routing.py | def options(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to OPTIONS"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='OPTIONS', **overrides) | def options(self, urls=None, **overrides):
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train | URLRouter.head | Sets the acceptable HTTP method to HEAD | hug/routing.py | def head(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to HEAD"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='HEAD', **overrides) | def head(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to HEAD"""
if urls is not None:
overrides['urls'] = urls
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train | URLRouter.connect | Sets the acceptable HTTP method to CONNECT | hug/routing.py | def connect(self, urls=None, **overrides):
"""Sets the acceptable HTTP method to CONNECT"""
if urls is not None:
overrides['urls'] = urls
return self.where(accept='CONNECT', **overrides) | def connect(self, urls=None, **overrides):
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if urls is not None:
overrides['urls'] = urls
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train | Object.http_methods | Creates routes from a class, where the class method names should line up to HTTP METHOD types | hug/route.py | def http_methods(self, urls=None, **route_data):
"""Creates routes from a class, where the class method names should line up to HTTP METHOD types"""
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instance = class_definition
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train | Object.cli | Registers a method on an Object as a CLI route | hug/route.py | def cli(self, method):
"""Registers a method on an Object as a CLI route"""
routes = getattr(method, '_hug_cli_routes', [])
routes.append(self.route)
method._hug_cli_routes = routes
return method | def cli(self, method):
"""Registers a method on an Object as a CLI route"""
routes = getattr(method, '_hug_cli_routes', [])
routes.append(self.route)
method._hug_cli_routes = routes
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train | API.http | Starts the process of building a new HTTP route linked to this API instance | hug/route.py | def http(self, *args, **kwargs):
"""Starts the process of building a new HTTP route linked to this API instance"""
kwargs['api'] = self.api
return http(*args, **kwargs) | def http(self, *args, **kwargs):
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train | API.not_found | Defines the handler that should handle not found requests against this API | hug/route.py | def not_found(self, *args, **kwargs):
"""Defines the handler that should handle not found requests against this API"""
kwargs['api'] = self.api
return not_found(*args, **kwargs) | def not_found(self, *args, **kwargs):
"""Defines the handler that should handle not found requests against this API"""
kwargs['api'] = self.api
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train | API.static | Define the routes to static files the API should expose | hug/route.py | def static(self, *args, **kwargs):
"""Define the routes to static files the API should expose"""
kwargs['api'] = self.api
return static(*args, **kwargs) | def static(self, *args, **kwargs):
"""Define the routes to static files the API should expose"""
kwargs['api'] = self.api
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train | API.sink | Define URL prefixes/handler matches where everything under the URL prefix should be handled | hug/route.py | def sink(self, *args, **kwargs):
"""Define URL prefixes/handler matches where everything under the URL prefix should be handled"""
kwargs['api'] = self.api
return sink(*args, **kwargs) | def sink(self, *args, **kwargs):
"""Define URL prefixes/handler matches where everything under the URL prefix should be handled"""
kwargs['api'] = self.api
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train | API.exception | Defines how this API should handle the provided exceptions | hug/route.py | def exception(self, *args, **kwargs):
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train | API.cli | Defines a CLI function that should be routed by this API | hug/route.py | def cli(self, *args, **kwargs):
"""Defines a CLI function that should be routed by this API"""
kwargs['api'] = self.api
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train | API.object | Registers a class based router to this API | hug/route.py | def object(self, *args, **kwargs):
"""Registers a class based router to this API"""
kwargs['api'] = self.api
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train | HTTPInterfaceAPI.urls | Returns a generator of all URLs attached to this API | hug/api.py | def urls(self):
"""Returns a generator of all URLs attached to this API"""
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for url, _ in mapping.items():
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train | HTTPInterfaceAPI.handlers | Returns all registered handlers attached to this API | hug/api.py | def handlers(self):
"""Returns all registered handlers attached to this API"""
used = []
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train | HTTPInterfaceAPI.input_format | Returns the set input_format handler for the given content_type | hug/api.py | def input_format(self, content_type):
"""Returns the set input_format handler for the given content_type"""
return getattr(self, '_input_format', {}).get(content_type, hug.defaults.input_format.get(content_type, None)) | def input_format(self, content_type):
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train | HTTPInterfaceAPI.set_input_format | Sets an input format handler for this Hug API, given the specified content_type | hug/api.py | def set_input_format(self, content_type, handler):
"""Sets an input format handler for this Hug API, given the specified content_type"""
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self._input_format = {}
self._input_format[content_type] = handler | def set_input_format(self, content_type, handler):
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self._input_format = {}
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train | HTTPInterfaceAPI.add_middleware | Adds a middleware object used to process all incoming requests against the API | hug/api.py | def add_middleware(self, middleware):
"""Adds a middleware object used to process all incoming requests against the API"""
if self.middleware is None:
self._middleware = []
self.middleware.append(middleware) | def add_middleware(self, middleware):
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self.middleware.append(middleware) | [
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train | HTTPInterfaceAPI.add_exception_handler | Adds a error handler to the hug api | hug/api.py | def add_exception_handler(self, exception_type, error_handler, versions=(None, )):
"""Adds a error handler to the hug api"""
versions = (versions, ) if not isinstance(versions, (tuple, list)) else versions
if not hasattr(self, '_exception_handlers'):
self._exception_handlers = {}
... | def add_exception_handler(self, exception_type, error_handler, versions=(None, )):
"""Adds a error handler to the hug api"""
versions = (versions, ) if not isinstance(versions, (tuple, list)) else versions
if not hasattr(self, '_exception_handlers'):
self._exception_handlers = {}
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train | HTTPInterfaceAPI.extend | Adds handlers from a different Hug API to this one - to create a single API | hug/api.py | def extend(self, http_api, route="", base_url="", **kwargs):
"""Adds handlers from a different Hug API to this one - to create a single API"""
self.versions.update(http_api.versions)
base_url = base_url or self.base_url
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"""Adds handlers from a different Hug API to this one - to create a single API"""
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train | HTTPInterfaceAPI.set_not_found_handler | Sets the not_found handler for the specified version of the api | hug/api.py | def set_not_found_handler(self, handler, version=None):
"""Sets the not_found handler for the specified version of the api"""
if not self.not_found_handlers:
self._not_found_handlers = {}
self.not_found_handlers[version] = handler | def set_not_found_handler(self, handler, version=None):
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self._not_found_handlers = {}
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train | HTTPInterfaceAPI.documentation | Generates and returns documentation for this API endpoint | hug/api.py | def documentation(self, base_url=None, api_version=None, prefix=""):
"""Generates and returns documentation for this API endpoint"""
documentation = OrderedDict()
base_url = self.base_url if base_url is None else base_url
overview = self.api.doc
if overview:
documenta... | def documentation(self, base_url=None, api_version=None, prefix=""):
"""Generates and returns documentation for this API endpoint"""
documentation = OrderedDict()
base_url = self.base_url if base_url is None else base_url
overview = self.api.doc
if overview:
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train | HTTPInterfaceAPI.serve | Runs the basic hug development server against this API | hug/api.py | def serve(self, host='', port=8000, no_documentation=False, display_intro=True):
"""Runs the basic hug development server against this API"""
if no_documentation:
api = self.server(None)
else:
api = self.server()
if display_intro:
print(INTRO)
... | def serve(self, host='', port=8000, no_documentation=False, display_intro=True):
"""Runs the basic hug development server against this API"""
if no_documentation:
api = self.server(None)
else:
api = self.server()
if display_intro:
print(INTRO)
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train | HTTPInterfaceAPI.determine_version | Determines the appropriate version given the set api_version, the request header, and URL query params | hug/api.py | def determine_version(self, request, api_version=None):
"""Determines the appropriate version given the set api_version, the request header, and URL query params"""
if api_version is False:
api_version = None
for version in self.versions:
if version and "v{0}".for... | def determine_version(self, request, api_version=None):
"""Determines the appropriate version given the set api_version, the request header, and URL query params"""
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api_version = None
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train | HTTPInterfaceAPI.documentation_404 | Returns a smart 404 page that contains documentation for the written API | hug/api.py | def documentation_404(self, base_url=None):
"""Returns a smart 404 page that contains documentation for the written API"""
base_url = self.base_url if base_url is None else base_url
def handle_404(request, response, *args, **kwargs):
url_prefix = request.forwarded_uri[:-1]
... | def documentation_404(self, base_url=None):
"""Returns a smart 404 page that contains documentation for the written API"""
base_url = self.base_url if base_url is None else base_url
def handle_404(request, response, *args, **kwargs):
url_prefix = request.forwarded_uri[:-1]
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train | HTTPInterfaceAPI.version_router | Intelligently routes a request to the correct handler based on the version being requested | hug/api.py | def version_router(self, request, response, api_version=None, versions={}, not_found=None, **kwargs):
"""Intelligently routes a request to the correct handler based on the version being requested"""
request_version = self.determine_version(request, api_version)
if request_version:
re... | def version_router(self, request, response, api_version=None, versions={}, not_found=None, **kwargs):
"""Intelligently routes a request to the correct handler based on the version being requested"""
request_version = self.determine_version(request, api_version)
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train | HTTPInterfaceAPI.server | Returns a WSGI compatible API server for the given Hug API module | hug/api.py | def server(self, default_not_found=True, base_url=None):
"""Returns a WSGI compatible API server for the given Hug API module"""
falcon_api = falcon.API(middleware=self.middleware)
default_not_found = self.documentation_404() if default_not_found is True else None
base_url = self.base_ur... | def server(self, default_not_found=True, base_url=None):
"""Returns a WSGI compatible API server for the given Hug API module"""
falcon_api = falcon.API(middleware=self.middleware)
default_not_found = self.documentation_404() if default_not_found is True else None
base_url = self.base_ur... | [
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train | CLIInterfaceAPI.extend | Extends this CLI api with the commands present in the provided cli_api object | hug/api.py | def extend(self, cli_api, command_prefix="", sub_command="", **kwargs):
"""Extends this CLI api with the commands present in the provided cli_api object"""
if sub_command and command_prefix:
raise ValueError('It is not currently supported to provide both a command_prefix and sub_command')
... | def extend(self, cli_api, command_prefix="", sub_command="", **kwargs):
"""Extends this CLI api with the commands present in the provided cli_api object"""
if sub_command and command_prefix:
raise ValueError('It is not currently supported to provide both a command_prefix and sub_command')
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train | API.directives | Returns all directives applicable to this Hug API | hug/api.py | def directives(self):
"""Returns all directives applicable to this Hug API"""
directive_sources = chain(hug.defaults.directives.items(), getattr(self, '_directives', {}).items())
return {'hug_' + directive_name: directive for directive_name, directive in directive_sources} | def directives(self):
"""Returns all directives applicable to this Hug API"""
directive_sources = chain(hug.defaults.directives.items(), getattr(self, '_directives', {}).items())
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train | API.directive | Returns the loaded directive with the specified name, or default if passed name is not present | hug/api.py | def directive(self, name, default=None):
"""Returns the loaded directive with the specified name, or default if passed name is not present"""
return getattr(self, '_directives', {}).get(name, hug.defaults.directives.get(name, default)) | def directive(self, name, default=None):
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train | API.handlers | Returns all registered handlers attached to this API | hug/api.py | def handlers(self):
"""Returns all registered handlers attached to this API"""
if getattr(self, '_http'):
yield from self.http.handlers()
if getattr(self, '_cli'):
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if getattr(self, '_http'):
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train | API.extend | Adds handlers from a different Hug API to this one - to create a single API | hug/api.py | def extend(self, api, route="", base_url="", http=True, cli=True, **kwargs):
"""Adds handlers from a different Hug API to this one - to create a single API"""
api = API(api)
if http and hasattr(api, '_http'):
self.http.extend(api.http, route, base_url, **kwargs)
if cli and ... | def extend(self, api, route="", base_url="", http=True, cli=True, **kwargs):
"""Adds handlers from a different Hug API to this one - to create a single API"""
api = API(api)
if http and hasattr(api, '_http'):
self.http.extend(api.http, route, base_url, **kwargs)
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train | API.add_startup_handler | Adds a startup handler to the hug api | hug/api.py | def add_startup_handler(self, handler):
"""Adds a startup handler to the hug api"""
if not self.startup_handlers:
self._startup_handlers = []
self.startup_handlers.append(handler) | def add_startup_handler(self, handler):
"""Adds a startup handler to the hug api"""
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self._startup_handlers = []
self.startup_handlers.append(handler) | [
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train | API._ensure_started | Marks the API as started and runs all startup handlers | hug/api.py | def _ensure_started(self):
"""Marks the API as started and runs all startup handlers"""
if not self.started:
async_handlers = [startup_handler for startup_handler in self.startup_handlers if
introspect.is_coroutine(startup_handler)]
if async_handlers... | def _ensure_started(self):
"""Marks the API as started and runs all startup handlers"""
if not self.started:
async_handlers = [startup_handler for startup_handler in self.startup_handlers if
introspect.is_coroutine(startup_handler)]
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train | _weight_drop | Helper for `WeightDrop`. | torchnlp/nn/weight_drop.py | def _weight_drop(module, weights, dropout):
"""
Helper for `WeightDrop`.
"""
for name_w in weights:
w = getattr(module, name_w)
del module._parameters[name_w]
module.register_parameter(name_w + '_raw', Parameter(w))
original_module_forward = module.forward
def forward(... | def _weight_drop(module, weights, dropout):
"""
Helper for `WeightDrop`.
"""
for name_w in weights:
w = getattr(module, name_w)
del module._parameters[name_w]
module.register_parameter(name_w + '_raw', Parameter(w))
original_module_forward = module.forward
def forward(... | [
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train | imdb_dataset | Load the IMDB dataset (Large Movie Review Dataset v1.0).
This is a dataset for binary sentiment classification containing substantially more data than
previous benchmark datasets. Provided a set of 25,000 highly polar movie reviews for
training, and 25,000 for testing. There is additional unlabeled data fo... | torchnlp/datasets/imdb.py | def imdb_dataset(directory='data/',
train=False,
test=False,
train_directory='train',
test_directory='test',
extracted_name='aclImdb',
check_files=['aclImdb/README'],
url='http://ai.stanford.edu/~amaas... | def imdb_dataset(directory='data/',
train=False,
test=False,
train_directory='train',
test_directory='test',
extracted_name='aclImdb',
check_files=['aclImdb/README'],
url='http://ai.stanford.edu/~amaas... | [
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train | trec_dataset | Load the Text REtrieval Conference (TREC) Question Classification dataset.
TREC dataset contains 5500 labeled questions in training set and another 500 for test set. The
dataset has 6 labels, 50 level-2 labels. Average length of each sentence is 10, vocabulary size
of 8700.
References:
* https... | torchnlp/datasets/trec.py | def trec_dataset(directory='data/trec/',
train=False,
test=False,
train_filename='train_5500.label',
test_filename='TREC_10.label',
check_files=['train_5500.label'],
urls=[
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train=False,
test=False,
train_filename='train_5500.label',
test_filename='TREC_10.label',
check_files=['train_5500.label'],
urls=[
'http://cogcomp.org/Data... | [
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train | snli_dataset | Load the Stanford Natural Language Inference (SNLI) dataset.
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs
manually labeled for balanced classification with the labels entailment, contradiction, and
neutral, supporting the task of natural language inference (NLI... | torchnlp/datasets/snli.py | def snli_dataset(directory='data/',
train=False,
dev=False,
test=False,
train_filename='snli_1.0_train.jsonl',
dev_filename='snli_1.0_dev.jsonl',
test_filename='snli_1.0_test.jsonl',
extracted_name='sn... | def snli_dataset(directory='data/',
train=False,
dev=False,
test=False,
train_filename='snli_1.0_train.jsonl',
dev_filename='snli_1.0_dev.jsonl',
test_filename='snli_1.0_test.jsonl',
extracted_name='sn... | [
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train | Attention.forward | Args:
query (:class:`torch.FloatTensor` [batch size, output length, dimensions]): Sequence of
queries to query the context.
context (:class:`torch.FloatTensor` [batch size, query length, dimensions]): Data
overwhich to apply the attention mechanism.
Retur... | torchnlp/nn/attention.py | def forward(self, query, context):
"""
Args:
query (:class:`torch.FloatTensor` [batch size, output length, dimensions]): Sequence of
queries to query the context.
context (:class:`torch.FloatTensor` [batch size, query length, dimensions]): Data
ove... | def forward(self, query, context):
"""
Args:
query (:class:`torch.FloatTensor` [batch size, output length, dimensions]): Sequence of
queries to query the context.
context (:class:`torch.FloatTensor` [batch size, query length, dimensions]): Data
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train | iwslt_dataset | Load the International Workshop on Spoken Language Translation (IWSLT) 2017 translation dataset.
In-domain training, development and evaluation sets were supplied through the website of the
WIT3 project, while out-of-domain training data were linked in the workshop’s website. With
respect to edition 2016 o... | torchnlp/datasets/iwslt.py | def iwslt_dataset(
directory='data/iwslt/',
train=False,
dev=False,
test=False,
language_extensions=['en', 'de'],
train_filename='{source}-{target}/train.{source}-{target}.{lang}',
dev_filename='{source}-{target}/IWSLT16.TED.tst2013.{source}-{target}.{lang}',
... | def iwslt_dataset(
directory='data/iwslt/',
train=False,
dev=False,
test=False,
language_extensions=['en', 'de'],
train_filename='{source}-{target}/train.{source}-{target}.{lang}',
dev_filename='{source}-{target}/IWSLT16.TED.tst2013.{source}-{target}.{lang}',
... | [
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train | Encoder.encode | Encodes an object.
Args:
object_ (object): Object to encode.
Returns:
object: Encoding of the object. | torchnlp/encoders/encoder.py | def encode(self, object_):
""" Encodes an object.
Args:
object_ (object): Object to encode.
Returns:
object: Encoding of the object.
"""
if self.enforce_reversible:
self.enforce_reversible = False
if self.decode(self.encode(object... | def encode(self, object_):
""" Encodes an object.
Args:
object_ (object): Object to encode.
Returns:
object: Encoding of the object.
"""
if self.enforce_reversible:
self.enforce_reversible = False
if self.decode(self.encode(object... | [
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train | Encoder.batch_encode | Args:
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*args: Arguments passed to ``encode``.
**kwargs: Keyword arguments passed to ``encode``.
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Args:
batch (list): Batch of objects to encode.
*args: Arguments passed to ``encode``.
**kwargs: Keyword arguments passed to ``encode``.
Returns:
list: Batch of encoded objects.
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**kwargs: Keyword arguments passed to ``encode``.
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train | CNNEncoder.forward | Args:
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train | SubwordTextTokenizer._tokens_to_subtoken | Converts a list of tokens to a list of subtoken.
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tokens: a list of strings.
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tokens: a list of strings.
Returns:
a list of integers in the range [0, vocab_size)
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ret = []
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tokens: a list of strings.
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a list of integers in the range [0, vocab_size)
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train | SubwordTextTokenizer._subtoken_to_tokens | Converts a list of subtoken to a list of tokens.
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subtokens: a list of integers in the range [0, vocab_size)
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subtokens: a list of integers in the range [0, vocab_size)
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train | SubwordTextTokenizer._escaped_token_to_subtoken_strings | Converts an escaped token string to a list of subtoken strings.
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escaped_token: An escaped token as a unicode string.
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Args:
escaped_token: An escaped token as a unicode string.
Returns:
A list of subtokens as unicode strings.
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# NOTE: This algor... | def _escaped_token_to_subtoken_strings(self, escaped_token):
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escaped_token: An escaped token as a unicode string.
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train | SubwordTextTokenizer.build_to_target_size_from_token_counts | Builds a SubwordTextTokenizer that has `vocab_size` near `target_size`.
Uses simple recursive binary search to find a minimum token count that most
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target_size: Desired vocab_size to approximate.
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train | SubwordTextTokenizer.build_from_token_counts | Train a SubwordTextTokenizer based on a dictionary of word counts.
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token_counts: a dictionary of Unicode strings to int.
min_count: an integer - discard subtokens with lower counts.
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token_counts: a dictionary of Unicode strings to int.
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token_counts: a dictionary of Unicode strings to int.
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train | SubwordTextTokenizer._init_subtokens_from_list | Initialize token information from a list of subtoken strings. | torchnlp/encoders/text/subword_text_tokenizer.py | def _init_subtokens_from_list(self, subtoken_strings):
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train | SubwordTextTokenizer._init_alphabet_from_tokens | Initialize alphabet from an iterable of token or subtoken strings. | torchnlp/encoders/text/subword_text_tokenizer.py | def _init_alphabet_from_tokens(self, tokens):
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# Include all characters from all tokens in the alphabet to guarantee that
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train | get_moses_multi_bleu | Get the BLEU score using the moses `multi-bleu.perl` script.
**Script:**
https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/generic/multi-bleu.perl
Args:
hypotheses (list of str): List of predicted values
references (list of str): List of target values
lowercase (boo... | torchnlp/metrics/bleu.py | def get_moses_multi_bleu(hypotheses, references, lowercase=False):
"""Get the BLEU score using the moses `multi-bleu.perl` script.
**Script:**
https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/generic/multi-bleu.perl
Args:
hypotheses (list of str): List of predicted values
... | def get_moses_multi_bleu(hypotheses, references, lowercase=False):
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https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/generic/multi-bleu.perl
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hypotheses (list of str): List of predicted values
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train | ud_pos_dataset | Load the Universal Dependencies - English Dependency Treebank dataset.
Corpus of sentences annotated using Universal Dependencies annotation. The corpus comprises
254,830 words and 16,622 sentences, taken from various web media including weblogs, newsgroups,
emails, reviews, and Yahoo! answers.
Refere... | torchnlp/datasets/ud_pos.py | def ud_pos_dataset(directory='data/',
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test=False,
train_filename='en-ud-tag.v2.train.txt',
dev_filename='en-ud-tag.v2.dev.txt',
test_filename='en-ud-tag.v2.test.txt',
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train=False,
dev=False,
test=False,
train_filename='en-ud-tag.v2.train.txt',
dev_filename='en-ud-tag.v2.dev.txt',
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train | makedirs | helper function for python 2 and 3 to call os.makedirs()
avoiding an error if the directory to be created already exists | examples/snli/util.py | def makedirs(name):
"""helper function for python 2 and 3 to call os.makedirs()
avoiding an error if the directory to be created already exists"""
import os, errno
try:
os.makedirs(name)
except OSError as ex:
if ex.errno == errno.EEXIST and os.path.isdir(name):
# ign... | def makedirs(name):
"""helper function for python 2 and 3 to call os.makedirs()
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import os, errno
try:
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except OSError as ex:
if ex.errno == errno.EEXIST and os.path.isdir(name):
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train | collate_fn | list of tensors to a batch tensors | examples/snli/util.py | def collate_fn(batch, train=True):
""" list of tensors to a batch tensors """
premise_batch, _ = pad_batch([row['premise'] for row in batch])
hypothesis_batch, _ = pad_batch([row['hypothesis'] for row in batch])
label_batch = torch.stack([row['label'] for row in batch])
# PyTorch RNN requires batch... | def collate_fn(batch, train=True):
""" list of tensors to a batch tensors """
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hypothesis_batch, _ = pad_batch([row['hypothesis'] for row in batch])
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train | smt_dataset | Load the Stanford Sentiment Treebank dataset.
Semantic word spaces have been very useful but cannot express the meaning of longer phrases in
a principled way. Further progress towards understanding compositionality in tasks such as
sentiment detection requires richer supervised training and evaluation reso... | torchnlp/datasets/smt.py | def smt_dataset(directory='data/',
train=False,
dev=False,
test=False,
train_filename='train.txt',
dev_filename='dev.txt',
test_filename='test.txt',
extracted_name='trees',
check_files=['trees... | def smt_dataset(directory='data/',
train=False,
dev=False,
test=False,
train_filename='train.txt',
dev_filename='dev.txt',
test_filename='test.txt',
extracted_name='trees',
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train | simple_qa_dataset | Load the SimpleQuestions dataset.
Single-relation factoid questions (simple questions) are common in many settings
(e.g. Microsoft’s search query logs and WikiAnswers questions). The SimpleQuestions dataset is
one of the most commonly used benchmarks for studying single-relation factoid questions.
**R... | torchnlp/datasets/simple_qa.py | def simple_qa_dataset(
directory='data/',
train=False,
dev=False,
test=False,
extracted_name='SimpleQuestions_v2',
train_filename='annotated_fb_data_train.txt',
dev_filename='annotated_fb_data_valid.txt',
test_filename='annotated_fb_data_test.txt',
... | def simple_qa_dataset(
directory='data/',
train=False,
dev=False,
test=False,
extracted_name='SimpleQuestions_v2',
train_filename='annotated_fb_data_train.txt',
dev_filename='annotated_fb_data_valid.txt',
test_filename='annotated_fb_data_test.txt',
... | [
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train | LockedDropout.forward | Args:
x (:class:`torch.FloatTensor` [batch size, sequence length, rnn hidden size]): Input to
apply dropout too. | torchnlp/nn/lock_dropout.py | def forward(self, x):
"""
Args:
x (:class:`torch.FloatTensor` [batch size, sequence length, rnn hidden size]): Input to
apply dropout too.
"""
if not self.training or not self.p:
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mask = x.new_empty(1, x.size(1... | def forward(self, x):
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x (:class:`torch.FloatTensor` [batch size, sequence length, rnn hidden size]): Input to
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"""
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train | pad_tensor | Pad a ``tensor`` to ``length`` with ``padding_index``.
Args:
tensor (torch.Tensor [n, ...]): Tensor to pad.
length (int): Pad the ``tensor`` up to ``length``.
padding_index (int, optional): Index to pad tensor with.
Returns
(torch.Tensor [length, ...]) Padded Tensor. | torchnlp/encoders/text/text_encoder.py | def pad_tensor(tensor, length, padding_index=DEFAULT_PADDING_INDEX):
""" Pad a ``tensor`` to ``length`` with ``padding_index``.
Args:
tensor (torch.Tensor [n, ...]): Tensor to pad.
length (int): Pad the ``tensor`` up to ``length``.
padding_index (int, optional): Index to pad tensor with... | def pad_tensor(tensor, length, padding_index=DEFAULT_PADDING_INDEX):
""" Pad a ``tensor`` to ``length`` with ``padding_index``.
Args:
tensor (torch.Tensor [n, ...]): Tensor to pad.
length (int): Pad the ``tensor`` up to ``length``.
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train | stack_and_pad_tensors | Pad a :class:`list` of ``tensors`` (``batch``) with ``padding_index``.
Args:
batch (:class:`list` of :class:`torch.Tensor`): Batch of tensors to pad.
padding_index (int, optional): Index to pad tensors with.
dim (int, optional): Dimension on to which to concatenate the batch of tensors.
... | torchnlp/encoders/text/text_encoder.py | def stack_and_pad_tensors(batch, padding_index=DEFAULT_PADDING_INDEX, dim=0):
""" Pad a :class:`list` of ``tensors`` (``batch``) with ``padding_index``.
Args:
batch (:class:`list` of :class:`torch.Tensor`): Batch of tensors to pad.
padding_index (int, optional): Index to pad tensors with.
... | def stack_and_pad_tensors(batch, padding_index=DEFAULT_PADDING_INDEX, dim=0):
""" Pad a :class:`list` of ``tensors`` (``batch``) with ``padding_index``.
Args:
batch (:class:`list` of :class:`torch.Tensor`): Batch of tensors to pad.
padding_index (int, optional): Index to pad tensors with.
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train | TextEncoder.batch_encode | Args:
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dim (int, optional): Dimension along which to concatenate tensors.
**kwargs: Keyword arguments passed onto ``Encoder.__init__``.
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iterator (iterator): Batch of text to encode.
*args: Arguments passed onto ``Encoder.__init__``.
dim (int, optional): Dimension along which to concatenate tensors.
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iterator (iterator): Batch of text to encode.
*args: Arguments passed onto ``Encoder.__init__``.
dim (int, optional): Dimension along which to concatenate tensors.
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train | TextEncoder.batch_decode | Args:
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lengths (list of int): Original lengths of sequences.
dim (int, optional): Dimension along which to split tensors.
*args: Arguments passed to ``decode``.
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Args:
batch (list of :class:`torch.Tensor`): Batch of encoded sequences.
lengths (list of int): Original lengths of sequences.
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batch (list of :class:`torch.Tensor`): Batch of encoded sequences.
lengths (list of int): Original lengths of sequences.
dim (int, optional): Dimension along which to split tensors.
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train | StaticTokenizerEncoder.encode | Encodes a ``sequence``.
Args:
sequence (str): String ``sequence`` to encode.
Returns:
torch.Tensor: Encoding of the ``sequence``. | torchnlp/encoders/text/static_tokenizer_encoder.py | def encode(self, sequence):
""" Encodes a ``sequence``.
Args:
sequence (str): String ``sequence`` to encode.
Returns:
torch.Tensor: Encoding of the ``sequence``.
"""
sequence = super().encode(sequence)
sequence = self.tokenize(sequence)
v... | def encode(self, sequence):
""" Encodes a ``sequence``.
Args:
sequence (str): String ``sequence`` to encode.
Returns:
torch.Tensor: Encoding of the ``sequence``.
"""
sequence = super().encode(sequence)
sequence = self.tokenize(sequence)
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train | StaticTokenizerEncoder.decode | Decodes a tensor into a sequence.
Args:
encoded (torch.Tensor): Encoded sequence.
Returns:
str: Sequence decoded from ``encoded``. | torchnlp/encoders/text/static_tokenizer_encoder.py | def decode(self, encoded):
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Args:
encoded (torch.Tensor): Encoded sequence.
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str: Sequence decoded from ``encoded``.
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tokens = [self.itos[index] for index in encode... | def decode(self, encoded):
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encoded (torch.Tensor): Encoded sequence.
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str: Sequence decoded from ``encoded``.
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encoded = super().decode(encoded)
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train | _reporthook | ``reporthook`` to use with ``urllib.request`` that prints the process of the download.
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**Reference:**
https://github.com/tqdm/tqdm
Args:
t (tqdm.tqdm) Progress bar.
Example:
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""" ``reporthook`` to use with ``urllib.request`` that prints the process of the download.
Uses ``tqdm`` for progress bar.
**Reference:**
https://github.com/tqdm/tqdm
Args:
t (tqdm.tqdm) Progress bar.
Example:
>>> with tqdm(unit='B', unit_scale=True, minit... | def _reporthook(t):
""" ``reporthook`` to use with ``urllib.request`` that prints the process of the download.
Uses ``tqdm`` for progress bar.
**Reference:**
https://github.com/tqdm/tqdm
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t (tqdm.tqdm) Progress bar.
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train | _download_file_from_drive | Download filename from google drive unless it's already in directory.
Args:
filename (str): Name of the file to download to (do nothing if it already exists).
url (str): URL to download from. | torchnlp/download.py | def _download_file_from_drive(filename, url): # pragma: no cover
""" Download filename from google drive unless it's already in directory.
Args:
filename (str): Name of the file to download to (do nothing if it already exists).
url (str): URL to download from.
"""
confirm_token = None
... | def _download_file_from_drive(filename, url): # pragma: no cover
""" Download filename from google drive unless it's already in directory.
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filename (str): Name of the file to download to (do nothing if it already exists).
url (str): URL to download from.
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confirm_token = None
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directory (str): Extract to directory.
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train | _get_filename_from_url | Return a filename from a URL
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url (str): URL to extract filename from
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""" Return a filename from a URL
Args:
url (str): URL to extract filename from
Returns:
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"""
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url (str): Url of file.
directory (str): Directory to download to.
filename (str, optional): Name of the file to download; Otherwise, a filename is extracted
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train | download_files_maybe_extract | Download the files at ``urls`` to ``directory``. Extract to ``directory`` if tar or zip.
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directory (str): Directory to download to.
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train | get_accuracy | Get the accuracy top-k accuracy between two tensors.
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outputs (1 - 3D :class:`torch.Tensor`): Prediction or output vector
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Args:
targets (1 - 2D :class:`torch.Tensor`): Target or true vector against which to measure
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outputs (1 - 3D :class:`torch.Tensor`): Prediction or output vector
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targets (1 - 2D :class:`torch.Tensor`): Target or true vector against which to measure
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train | get_token_accuracy | Get the accuracy token accuracy between two tensors.
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outputs (1 - 3D :class:`torch.Tensor`): Prediction or output vector
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targets (1 - 2D :class:`torch.Tensor`): Target or true vector against which to measure
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train | get_tensors | Get all tensors associated with ``object_``
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object_ (any): Any object to look for tensors.
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""" Get all tensors associated with ``object_``
Args:
object_ (any): Any object to look for tensors.
Returns:
(list of torch.tensor): List of tensors that are associated with ``object_``.
"""
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object_ (any): Any object to look for tensors.
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(list of torch.tensor): List of tensors that are associated with ``object_``.
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train | sampler_to_iterator | Given a batch sampler or sampler returns examples instead of indices
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dataset (torch.utils.data.Dataset): Dataset to sample from.
sampler (torch.utils.data.sampler.Sampler): Sampler over the dataset.
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""" Given a batch sampler or sampler returns examples instead of indices
Args:
dataset (torch.utils.data.Dataset): Dataset to sample from.
sampler (torch.utils.data.sampler.Sampler): Sampler over the dataset.
Returns:
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train | resplit_datasets | Deterministic shuffle and split algorithm.
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Args:
dataset (lib.datasets.Dataset): First dataset.
other_dataset (lib.datasets.Dataset): Another dataset.
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"""Deterministic shuffle and split algorithm.
Given the same two datasets and the same ``random_seed``, the split happens the same exact way
every call.
Args:
dataset (lib.datasets.Dataset): First dataset.
othe... | def resplit_datasets(dataset, other_dataset, random_seed=None, split=None):
"""Deterministic shuffle and split algorithm.
Given the same two datasets and the same ``random_seed``, the split happens the same exact way
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train | torch_equals_ignore_index | Compute ``torch.equal`` with the optional mask parameter.
Args:
ignore_index (int, optional): Specifies a ``tensor`` index that is ignored.
Returns:
(bool) Returns ``True`` if target and prediction are equal. | torchnlp/utils.py | def torch_equals_ignore_index(tensor, tensor_other, ignore_index=None):
"""
Compute ``torch.equal`` with the optional mask parameter.
Args:
ignore_index (int, optional): Specifies a ``tensor`` index that is ignored.
Returns:
(bool) Returns ``True`` if target and prediction are equal.
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Compute ``torch.equal`` with the optional mask parameter.
Args:
ignore_index (int, optional): Specifies a ``tensor`` index that is ignored.
Returns:
(bool) Returns ``True`` if target and prediction are equal.
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train | lengths_to_mask | Given a list of lengths, create a batch mask.
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tensor([[1, 0, 0],
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>>> lengths_to_mask([1, 2, 2], [1, 2, 2])
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""" Given a list of lengths, create a batch mask.
Example:
>>> lengths_to_mask([1, 2, 3])
tensor([[1, 0, 0],
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>>> lengths_to_mask([1, 2, 2], [1, 2, 2])
tensor([[[1, ... | def lengths_to_mask(*lengths, **kwargs):
""" Given a list of lengths, create a batch mask.
Example:
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tensor([[1, 0, 0],
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train | collate_tensors | Collate a list of type ``k`` (dict, namedtuple, list, etc.) with tensors.
Inspired by:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py#L31
Args:
batch (list of k): List of rows of type ``k``.
stack_tensors (callable): Function to stack tensors into a batch... | torchnlp/utils.py | def collate_tensors(batch, stack_tensors=torch.stack):
""" Collate a list of type ``k`` (dict, namedtuple, list, etc.) with tensors.
Inspired by:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py#L31
Args:
batch (list of k): List of rows of type ``k``.
s... | def collate_tensors(batch, stack_tensors=torch.stack):
""" Collate a list of type ``k`` (dict, namedtuple, list, etc.) with tensors.
Inspired by:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py#L31
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batch (list of k): List of rows of type ``k``.
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train | tensors_to | Apply ``torch.Tensor.to`` to tensors in a generic data structure.
Inspired by:
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Args:
tensors (tensor, dict, list, namedtuple or tuple): Data structure with tensor values to
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""" Apply ``torch.Tensor.to`` to tensors in a generic data structure.
Inspired by:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py#L31
Args:
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""" Apply ``torch.Tensor.to`` to tensors in a generic data structure.
Inspired by:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py#L31
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train | zero_dataset | Load the Zero dataset.
The Zero dataset is a simple task of predicting zero from zero. This dataset is useful for
integration testing. The extreme simplicity of the dataset allows for models to learn the task
quickly allowing for quick end-to-end testing.
Args:
train (bool, optional): If to lo... | torchnlp/datasets/zero.py | def zero_dataset(train=False, dev=False, test=False, train_rows=256, dev_rows=64, test_rows=64):
"""
Load the Zero dataset.
The Zero dataset is a simple task of predicting zero from zero. This dataset is useful for
integration testing. The extreme simplicity of the dataset allows for models to learn th... | def zero_dataset(train=False, dev=False, test=False, train_rows=256, dev_rows=64, test_rows=64):
"""
Load the Zero dataset.
The Zero dataset is a simple task of predicting zero from zero. This dataset is useful for
integration testing. The extreme simplicity of the dataset allows for models to learn th... | [
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train | SubwordEncoder.decode | Decodes a tensor into a sequence.
Args:
encoded (torch.Tensor): Encoded sequence.
Returns:
str: Sequence decoded from ``encoded``. | torchnlp/encoders/text/subword_encoder.py | def decode(self, encoded):
""" Decodes a tensor into a sequence.
Args:
encoded (torch.Tensor): Encoded sequence.
Returns:
str: Sequence decoded from ``encoded``.
"""
encoded = super().decode(encoded)
return self.tokenizer.decode([self.itos[index]... | def decode(self, encoded):
""" Decodes a tensor into a sequence.
Args:
encoded (torch.Tensor): Encoded sequence.
Returns:
str: Sequence decoded from ``encoded``.
"""
encoded = super().decode(encoded)
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train | LabelEncoder.encode | Encodes a ``label``.
Args:
label (object): Label to encode.
Returns:
torch.Tensor: Encoding of the label. | torchnlp/encoders/label_encoder.py | def encode(self, label):
""" Encodes a ``label``.
Args:
label (object): Label to encode.
Returns:
torch.Tensor: Encoding of the label.
"""
label = super().encode(label)
return torch.tensor(self.stoi.get(label, self.unknown_index)) | def encode(self, label):
""" Encodes a ``label``.
Args:
label (object): Label to encode.
Returns:
torch.Tensor: Encoding of the label.
"""
label = super().encode(label)
return torch.tensor(self.stoi.get(label, self.unknown_index)) | [
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train | LabelEncoder.batch_encode | Args:
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*args: Arguments passed to ``Encoder.batch_encode``.
dim (int, optional): Dimension along which to concatenate tensors.
**kwargs: Keyword arguments passed to ``Encoder.batch_encode``.
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"""
Args:
iterator (iterator): Batch of labels to encode.
*args: Arguments passed to ``Encoder.batch_encode``.
dim (int, optional): Dimension along which to concatenate tensors.
**kwargs: Keyword ar... | def batch_encode(self, iterator, *args, dim=0, **kwargs):
"""
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iterator (iterator): Batch of labels to encode.
*args: Arguments passed to ``Encoder.batch_encode``.
dim (int, optional): Dimension along which to concatenate tensors.
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train | LabelEncoder.decode | Decodes ``encoded`` label.
Args:
encoded (torch.Tensor): Encoded label.
Returns:
object: Label decoded from ``encoded``. | torchnlp/encoders/label_encoder.py | def decode(self, encoded):
""" Decodes ``encoded`` label.
Args:
encoded (torch.Tensor): Encoded label.
Returns:
object: Label decoded from ``encoded``.
"""
encoded = super().decode(encoded)
if encoded.numel() > 1:
raise ValueError(
... | def decode(self, encoded):
""" Decodes ``encoded`` label.
Args:
encoded (torch.Tensor): Encoded label.
Returns:
object: Label decoded from ``encoded``.
"""
encoded = super().decode(encoded)
if encoded.numel() > 1:
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] | PetrochukM/PyTorch-NLP | python | https://github.com/PetrochukM/PyTorch-NLP/blob/5f7320da5c8d781df072fab3f7e421c6347e5bfa/torchnlp/encoders/label_encoder.py#L102-L117 | [
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train | LabelEncoder.batch_decode | Args:
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*args: Arguments passed to ``Encoder.batch_decode``.
dim (int, optional): Dimension along which to split tensors.
**kwargs: Keyword arguments passed to ``Encoder.batch_decode``.
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list: Batch of de... | torchnlp/encoders/label_encoder.py | def batch_decode(self, tensor, *args, dim=0, **kwargs):
"""
Args:
tensor (torch.Tensor): Batch of tensors.
*args: Arguments passed to ``Encoder.batch_decode``.
dim (int, optional): Dimension along which to split tensors.
**kwargs: Keyword arguments passed ... | def batch_decode(self, tensor, *args, dim=0, **kwargs):
"""
Args:
tensor (torch.Tensor): Batch of tensors.
*args: Arguments passed to ``Encoder.batch_decode``.
dim (int, optional): Dimension along which to split tensors.
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train | count_dataset | Load the Count dataset.
The Count dataset is a simple task of counting the number of integers in a sequence. This
dataset is useful for testing implementations of sequence to label models.
Args:
train (bool, optional): If to load the training split of the dataset.
dev (bool, optional): If ... | torchnlp/datasets/count.py | def count_dataset(train=False,
dev=False,
test=False,
train_rows=10000,
dev_rows=1000,
test_rows=1000,
seq_max_length=10):
"""
Load the Count dataset.
The Count dataset is a simple task of counting t... | def count_dataset(train=False,
dev=False,
test=False,
train_rows=10000,
dev_rows=1000,
test_rows=1000,
seq_max_length=10):
"""
Load the Count dataset.
The Count dataset is a simple task of counting t... | [
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] | PetrochukM/PyTorch-NLP | python | https://github.com/PetrochukM/PyTorch-NLP/blob/5f7320da5c8d781df072fab3f7e421c6347e5bfa/torchnlp/datasets/count.py#L6-L64 | [
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train | multi30k_dataset | Load the WMT 2016 machine translation dataset.
As a translation task, this task consists in translating English sentences that describe an
image into German, given the English sentence itself. As training and development data, we
provide 29,000 and 1,014 triples respectively, each containing an English sou... | torchnlp/datasets/multi30k.py | def multi30k_dataset(directory='data/multi30k/',
train=False,
dev=False,
test=False,
train_filename='train',
dev_filename='val',
test_filename='test',
check_files=['train.de... | def multi30k_dataset(directory='data/multi30k/',
train=False,
dev=False,
test=False,
train_filename='train',
dev_filename='val',
test_filename='test',
check_files=['train.de... | [
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] | PetrochukM/PyTorch-NLP | python | https://github.com/PetrochukM/PyTorch-NLP/blob/5f7320da5c8d781df072fab3f7e421c6347e5bfa/torchnlp/datasets/multi30k.py#L7-L100 | [
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train | penn_treebank_dataset | Load the Penn Treebank dataset.
This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall
Street Journal material.
**Reference:** https://catalog.ldc.upenn.edu/ldc99t42
**Citation:**
Marcus, Mitchell P., Marcinkiewicz, Mary Ann & Santorini, Beatrice (1993).
Bu... | torchnlp/datasets/penn_treebank.py | def penn_treebank_dataset(
directory='data/penn-treebank',
train=False,
dev=False,
test=False,
train_filename='ptb.train.txt',
dev_filename='ptb.valid.txt',
test_filename='ptb.test.txt',
check_files=['ptb.train.txt'],
urls=[
'https://ra... | def penn_treebank_dataset(
directory='data/penn-treebank',
train=False,
dev=False,
test=False,
train_filename='ptb.train.txt',
dev_filename='ptb.valid.txt',
test_filename='ptb.test.txt',
check_files=['ptb.train.txt'],
urls=[
'https://ra... | [
"Load",
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"dataset",
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] | PetrochukM/PyTorch-NLP | python | https://github.com/PetrochukM/PyTorch-NLP/blob/5f7320da5c8d781df072fab3f7e421c6347e5bfa/torchnlp/datasets/penn_treebank.py#L9-L80 | [
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train | repackage_hidden | Wraps hidden states in new Tensors, to detach them from their history. | examples/awd-lstm-lm/utils.py | def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h) | def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h) | [
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train | parse_to_ast | Parses the given vyper source code and returns a list of python AST objects
for all statements in the source. Performs pre-processing of source code
before parsing as well as post-processing of the resulting AST.
:param source_code: The vyper source code to be parsed.
:return: The post-processed list ... | vyper/parser/parser.py | def parse_to_ast(source_code: str) -> List[ast.stmt]:
"""
Parses the given vyper source code and returns a list of python AST objects
for all statements in the source. Performs pre-processing of source code
before parsing as well as post-processing of the resulting AST.
:param source_code: The vyp... | def parse_to_ast(source_code: str) -> List[ast.stmt]:
"""
Parses the given vyper source code and returns a list of python AST objects
for all statements in the source. Performs pre-processing of source code
before parsing as well as post-processing of the resulting AST.
:param source_code: The vyp... | [
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