partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | CSVLogger._init | CSV outputted with Headers as first set of results. | python/ray/tune/logger.py | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
self._file = open(progress_file, "a")
self._csv_out = None | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
self._file = open(progress_file, "a")
self._csv_out = None | [
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train | UnifiedLogger.sync_results_to_new_location | Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
with the Ray autoscaler. | python/ray/tune/logger.py | def sync_results_to_new_location(self, worker_ip):
"""Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
with the Ray autoscaler.
"""
if worker_ip != self._log_syncer.worker_ip:
self._log_syncer.set_worker_ip(worker_ip)
self._log_syncer.sync_to_worker_if_possible() | def sync_results_to_new_location(self, worker_ip):
"""Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
with the Ray autoscaler.
"""
if worker_ip != self._log_syncer.worker_ip:
self._log_syncer.set_worker_ip(worker_ip)
self._log_syncer.sync_to_worker_if_possible() | [
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train | deep_insert | Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {}) | python/ray/tune/automl/search_policy.py | def deep_insert(path_list, value, config):
"""Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {})
"""
if len(path_list) > 1:
inside_config = config.setdefault(path_list[0], {})
deep_insert(path_list[1:], value, inside_config)
else:
config[path_list[0]] = value | def deep_insert(path_list, value, config):
"""Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {})
"""
if len(path_list) > 1:
inside_config = config.setdefault(path_list[0], {})
deep_insert(path_list[1:], value, inside_config)
else:
config[path_list[0]] = value | [
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"... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionDescriptor.from_bytes_list | Create a FunctionDescriptor instance from list of bytes.
This function is used to create the function descriptor from
backend data.
Args:
cls: Current class which is required argument for classmethod.
function_descriptor_list: list of bytes to represent the
function descriptor.
Returns:
The FunctionDescriptor instance created from the bytes list. | python/ray/function_manager.py | def from_bytes_list(cls, function_descriptor_list):
"""Create a FunctionDescriptor instance from list of bytes.
This function is used to create the function descriptor from
backend data.
Args:
cls: Current class which is required argument for classmethod.
function_descriptor_list: list of bytes to represent the
function descriptor.
Returns:
The FunctionDescriptor instance created from the bytes list.
"""
assert isinstance(function_descriptor_list, list)
if len(function_descriptor_list) == 0:
# This is a function descriptor of driver task.
return FunctionDescriptor.for_driver_task()
elif (len(function_descriptor_list) == 3
or len(function_descriptor_list) == 4):
module_name = ensure_str(function_descriptor_list[0])
class_name = ensure_str(function_descriptor_list[1])
function_name = ensure_str(function_descriptor_list[2])
if len(function_descriptor_list) == 4:
return cls(module_name, function_name, class_name,
function_descriptor_list[3])
else:
return cls(module_name, function_name, class_name)
else:
raise Exception(
"Invalid input for FunctionDescriptor.from_bytes_list") | def from_bytes_list(cls, function_descriptor_list):
"""Create a FunctionDescriptor instance from list of bytes.
This function is used to create the function descriptor from
backend data.
Args:
cls: Current class which is required argument for classmethod.
function_descriptor_list: list of bytes to represent the
function descriptor.
Returns:
The FunctionDescriptor instance created from the bytes list.
"""
assert isinstance(function_descriptor_list, list)
if len(function_descriptor_list) == 0:
# This is a function descriptor of driver task.
return FunctionDescriptor.for_driver_task()
elif (len(function_descriptor_list) == 3
or len(function_descriptor_list) == 4):
module_name = ensure_str(function_descriptor_list[0])
class_name = ensure_str(function_descriptor_list[1])
function_name = ensure_str(function_descriptor_list[2])
if len(function_descriptor_list) == 4:
return cls(module_name, function_name, class_name,
function_descriptor_list[3])
else:
return cls(module_name, function_name, class_name)
else:
raise Exception(
"Invalid input for FunctionDescriptor.from_bytes_list") | [
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train | FunctionDescriptor.from_function | Create a FunctionDescriptor from a function instance.
This function is used to create the function descriptor from
a python function. If a function is a class function, it should
not be used by this function.
Args:
cls: Current class which is required argument for classmethod.
function: the python function used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the function. | python/ray/function_manager.py | def from_function(cls, function):
"""Create a FunctionDescriptor from a function instance.
This function is used to create the function descriptor from
a python function. If a function is a class function, it should
not be used by this function.
Args:
cls: Current class which is required argument for classmethod.
function: the python function used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the function.
"""
module_name = function.__module__
function_name = function.__name__
class_name = ""
function_source_hasher = hashlib.sha1()
try:
# If we are running a script or are in IPython, include the source
# code in the hash.
source = inspect.getsource(function)
if sys.version_info[0] >= 3:
source = source.encode()
function_source_hasher.update(source)
function_source_hash = function_source_hasher.digest()
except (IOError, OSError, TypeError):
# Source code may not be available:
# e.g. Cython or Python interpreter.
function_source_hash = b""
return cls(module_name, function_name, class_name,
function_source_hash) | def from_function(cls, function):
"""Create a FunctionDescriptor from a function instance.
This function is used to create the function descriptor from
a python function. If a function is a class function, it should
not be used by this function.
Args:
cls: Current class which is required argument for classmethod.
function: the python function used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the function.
"""
module_name = function.__module__
function_name = function.__name__
class_name = ""
function_source_hasher = hashlib.sha1()
try:
# If we are running a script or are in IPython, include the source
# code in the hash.
source = inspect.getsource(function)
if sys.version_info[0] >= 3:
source = source.encode()
function_source_hasher.update(source)
function_source_hash = function_source_hasher.digest()
except (IOError, OSError, TypeError):
# Source code may not be available:
# e.g. Cython or Python interpreter.
function_source_hash = b""
return cls(module_name, function_name, class_name,
function_source_hash) | [
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train | FunctionDescriptor.from_class | Create a FunctionDescriptor from a class.
Args:
cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the class. | python/ray/function_manager.py | def from_class(cls, target_class):
"""Create a FunctionDescriptor from a class.
Args:
cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the class.
"""
module_name = target_class.__module__
class_name = target_class.__name__
return cls(module_name, "__init__", class_name) | def from_class(cls, target_class):
"""Create a FunctionDescriptor from a class.
Args:
cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
descriptor.
Returns:
The FunctionDescriptor instance created according to the class.
"""
module_name = target_class.__module__
class_name = target_class.__name__
return cls(module_name, "__init__", class_name) | [
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train | FunctionDescriptor.is_for_driver_task | See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks. | python/ray/function_manager.py | def is_for_driver_task(self):
"""See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks.
"""
return all(
len(x) == 0
for x in [self.module_name, self.class_name, self.function_name]) | def is_for_driver_task(self):
"""See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks.
"""
return all(
len(x) == 0
for x in [self.module_name, self.class_name, self.function_name]) | [
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train | FunctionDescriptor._get_function_id | Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor. | python/ray/function_manager.py | def _get_function_id(self):
"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor.
"""
if self.is_for_driver_task:
return ray.FunctionID.nil()
function_id_hash = hashlib.sha1()
# Include the function module and name in the hash.
function_id_hash.update(self.module_name.encode("ascii"))
function_id_hash.update(self.function_name.encode("ascii"))
function_id_hash.update(self.class_name.encode("ascii"))
function_id_hash.update(self._function_source_hash)
# Compute the function ID.
function_id = function_id_hash.digest()
return ray.FunctionID(function_id) | def _get_function_id(self):
"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor.
"""
if self.is_for_driver_task:
return ray.FunctionID.nil()
function_id_hash = hashlib.sha1()
# Include the function module and name in the hash.
function_id_hash.update(self.module_name.encode("ascii"))
function_id_hash.update(self.function_name.encode("ascii"))
function_id_hash.update(self.class_name.encode("ascii"))
function_id_hash.update(self._function_source_hash)
# Compute the function ID.
function_id = function_id_hash.digest()
return ray.FunctionID(function_id) | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L221-L240 | [
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train | FunctionDescriptor.get_function_descriptor_list | Return a list of bytes representing the function descriptor.
This function is used to pass this function descriptor to backend.
Returns:
A list of bytes. | python/ray/function_manager.py | def get_function_descriptor_list(self):
"""Return a list of bytes representing the function descriptor.
This function is used to pass this function descriptor to backend.
Returns:
A list of bytes.
"""
descriptor_list = []
if self.is_for_driver_task:
# Driver task returns an empty list.
return descriptor_list
else:
descriptor_list.append(self.module_name.encode("ascii"))
descriptor_list.append(self.class_name.encode("ascii"))
descriptor_list.append(self.function_name.encode("ascii"))
if len(self._function_source_hash) != 0:
descriptor_list.append(self._function_source_hash)
return descriptor_list | def get_function_descriptor_list(self):
"""Return a list of bytes representing the function descriptor.
This function is used to pass this function descriptor to backend.
Returns:
A list of bytes.
"""
descriptor_list = []
if self.is_for_driver_task:
# Driver task returns an empty list.
return descriptor_list
else:
descriptor_list.append(self.module_name.encode("ascii"))
descriptor_list.append(self.class_name.encode("ascii"))
descriptor_list.append(self.function_name.encode("ascii"))
if len(self._function_source_hash) != 0:
descriptor_list.append(self._function_source_hash)
return descriptor_list | [
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train | FunctionActorManager.export_cached | Export cached remote functions
Note: this should be called only once when worker is connected. | python/ray/function_manager.py | def export_cached(self):
"""Export cached remote functions
Note: this should be called only once when worker is connected.
"""
for remote_function in self._functions_to_export:
self._do_export(remote_function)
self._functions_to_export = None
for info in self._actors_to_export:
(key, actor_class_info) = info
self._publish_actor_class_to_key(key, actor_class_info) | def export_cached(self):
"""Export cached remote functions
Note: this should be called only once when worker is connected.
"""
for remote_function in self._functions_to_export:
self._do_export(remote_function)
self._functions_to_export = None
for info in self._actors_to_export:
(key, actor_class_info) = info
self._publish_actor_class_to_key(key, actor_class_info) | [
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train | FunctionActorManager.export | Export a remote function.
Args:
remote_function: the RemoteFunction object. | python/ray/function_manager.py | def export(self, remote_function):
"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.mode is None:
# If the worker isn't connected, cache the function
# and export it later.
self._functions_to_export.append(remote_function)
return
if self._worker.mode != ray.worker.SCRIPT_MODE:
# Don't need to export if the worker is not a driver.
return
self._do_export(remote_function) | def export(self, remote_function):
"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.mode is None:
# If the worker isn't connected, cache the function
# and export it later.
self._functions_to_export.append(remote_function)
return
if self._worker.mode != ray.worker.SCRIPT_MODE:
# Don't need to export if the worker is not a driver.
return
self._do_export(remote_function) | [
"Export",
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L334-L348 | [
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train | FunctionActorManager._do_export | Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object. | python/ray/function_manager.py | def _do_export(self, remote_function):
"""Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.load_code_from_local:
return
# Work around limitations of Python pickling.
function = remote_function._function
function_name_global_valid = function.__name__ in function.__globals__
function_name_global_value = function.__globals__.get(
function.__name__)
# Allow the function to reference itself as a global variable
if not is_cython(function):
function.__globals__[function.__name__] = remote_function
try:
pickled_function = pickle.dumps(function)
finally:
# Undo our changes
if function_name_global_valid:
function.__globals__[function.__name__] = (
function_name_global_value)
else:
del function.__globals__[function.__name__]
check_oversized_pickle(pickled_function,
remote_function._function_name,
"remote function", self._worker)
key = (b"RemoteFunction:" + self._worker.task_driver_id.binary() + b":"
+ remote_function._function_descriptor.function_id.binary())
self._worker.redis_client.hmset(
key, {
"driver_id": self._worker.task_driver_id.binary(),
"function_id": remote_function._function_descriptor.
function_id.binary(),
"name": remote_function._function_name,
"module": function.__module__,
"function": pickled_function,
"max_calls": remote_function._max_calls
})
self._worker.redis_client.rpush("Exports", key) | def _do_export(self, remote_function):
"""Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.load_code_from_local:
return
# Work around limitations of Python pickling.
function = remote_function._function
function_name_global_valid = function.__name__ in function.__globals__
function_name_global_value = function.__globals__.get(
function.__name__)
# Allow the function to reference itself as a global variable
if not is_cython(function):
function.__globals__[function.__name__] = remote_function
try:
pickled_function = pickle.dumps(function)
finally:
# Undo our changes
if function_name_global_valid:
function.__globals__[function.__name__] = (
function_name_global_value)
else:
del function.__globals__[function.__name__]
check_oversized_pickle(pickled_function,
remote_function._function_name,
"remote function", self._worker)
key = (b"RemoteFunction:" + self._worker.task_driver_id.binary() + b":"
+ remote_function._function_descriptor.function_id.binary())
self._worker.redis_client.hmset(
key, {
"driver_id": self._worker.task_driver_id.binary(),
"function_id": remote_function._function_descriptor.
function_id.binary(),
"name": remote_function._function_name,
"module": function.__module__,
"function": pickled_function,
"max_calls": remote_function._max_calls
})
self._worker.redis_client.rpush("Exports", key) | [
"Pickle",
"a",
"remote",
"function",
"and",
"export",
"it",
"to",
"redis",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L350-L391 | [
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"function",
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"_function",
"function_name_global_valid... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager.fetch_and_register_remote_function | Import a remote function. | python/ray/function_manager.py | def fetch_and_register_remote_function(self, key):
"""Import a remote function."""
(driver_id_str, function_id_str, function_name, serialized_function,
num_return_vals, module, resources,
max_calls) = self._worker.redis_client.hmget(key, [
"driver_id", "function_id", "name", "function", "num_return_vals",
"module", "resources", "max_calls"
])
function_id = ray.FunctionID(function_id_str)
driver_id = ray.DriverID(driver_id_str)
function_name = decode(function_name)
max_calls = int(max_calls)
module = decode(module)
# This is a placeholder in case the function can't be unpickled. This
# will be overwritten if the function is successfully registered.
def f():
raise Exception("This function was not imported properly.")
# This function is called by ImportThread. This operation needs to be
# atomic. Otherwise, there is race condition. Another thread may use
# the temporary function above before the real function is ready.
with self.lock:
self._function_execution_info[driver_id][function_id] = (
FunctionExecutionInfo(
function=f,
function_name=function_name,
max_calls=max_calls))
self._num_task_executions[driver_id][function_id] = 0
try:
function = pickle.loads(serialized_function)
except Exception:
# If an exception was thrown when the remote function was
# imported, we record the traceback and notify the scheduler
# of the failure.
traceback_str = format_error_message(traceback.format_exc())
# Log the error message.
push_error_to_driver(
self._worker,
ray_constants.REGISTER_REMOTE_FUNCTION_PUSH_ERROR,
"Failed to unpickle the remote function '{}' with "
"function ID {}. Traceback:\n{}".format(
function_name, function_id.hex(), traceback_str),
driver_id=driver_id)
else:
# The below line is necessary. Because in the driver process,
# if the function is defined in the file where the python
# script was started from, its module is `__main__`.
# However in the worker process, the `__main__` module is a
# different module, which is `default_worker.py`
function.__module__ = module
self._function_execution_info[driver_id][function_id] = (
FunctionExecutionInfo(
function=function,
function_name=function_name,
max_calls=max_calls))
# Add the function to the function table.
self._worker.redis_client.rpush(
b"FunctionTable:" + function_id.binary(),
self._worker.worker_id) | def fetch_and_register_remote_function(self, key):
"""Import a remote function."""
(driver_id_str, function_id_str, function_name, serialized_function,
num_return_vals, module, resources,
max_calls) = self._worker.redis_client.hmget(key, [
"driver_id", "function_id", "name", "function", "num_return_vals",
"module", "resources", "max_calls"
])
function_id = ray.FunctionID(function_id_str)
driver_id = ray.DriverID(driver_id_str)
function_name = decode(function_name)
max_calls = int(max_calls)
module = decode(module)
# This is a placeholder in case the function can't be unpickled. This
# will be overwritten if the function is successfully registered.
def f():
raise Exception("This function was not imported properly.")
# This function is called by ImportThread. This operation needs to be
# atomic. Otherwise, there is race condition. Another thread may use
# the temporary function above before the real function is ready.
with self.lock:
self._function_execution_info[driver_id][function_id] = (
FunctionExecutionInfo(
function=f,
function_name=function_name,
max_calls=max_calls))
self._num_task_executions[driver_id][function_id] = 0
try:
function = pickle.loads(serialized_function)
except Exception:
# If an exception was thrown when the remote function was
# imported, we record the traceback and notify the scheduler
# of the failure.
traceback_str = format_error_message(traceback.format_exc())
# Log the error message.
push_error_to_driver(
self._worker,
ray_constants.REGISTER_REMOTE_FUNCTION_PUSH_ERROR,
"Failed to unpickle the remote function '{}' with "
"function ID {}. Traceback:\n{}".format(
function_name, function_id.hex(), traceback_str),
driver_id=driver_id)
else:
# The below line is necessary. Because in the driver process,
# if the function is defined in the file where the python
# script was started from, its module is `__main__`.
# However in the worker process, the `__main__` module is a
# different module, which is `default_worker.py`
function.__module__ = module
self._function_execution_info[driver_id][function_id] = (
FunctionExecutionInfo(
function=function,
function_name=function_name,
max_calls=max_calls))
# Add the function to the function table.
self._worker.redis_client.rpush(
b"FunctionTable:" + function_id.binary(),
self._worker.worker_id) | [
"Import",
"a",
"remote",
"function",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L393-L453 | [
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",",
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")",
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"se... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager.get_execution_info | Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
A FunctionExecutionInfo object. | python/ray/function_manager.py | def get_execution_info(self, driver_id, function_descriptor):
"""Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
A FunctionExecutionInfo object.
"""
if self._worker.load_code_from_local:
# Load function from local code.
# Currently, we don't support isolating code by drivers,
# thus always set driver ID to NIL here.
driver_id = ray.DriverID.nil()
if not function_descriptor.is_actor_method():
self._load_function_from_local(driver_id, function_descriptor)
else:
# Load function from GCS.
# Wait until the function to be executed has actually been
# registered on this worker. We will push warnings to the user if
# we spend too long in this loop.
# The driver function may not be found in sys.path. Try to load
# the function from GCS.
with profiling.profile("wait_for_function"):
self._wait_for_function(function_descriptor, driver_id)
try:
function_id = function_descriptor.function_id
info = self._function_execution_info[driver_id][function_id]
except KeyError as e:
message = ("Error occurs in get_execution_info: "
"driver_id: %s, function_descriptor: %s. Message: %s" %
(driver_id, function_descriptor, e))
raise KeyError(message)
return info | def get_execution_info(self, driver_id, function_descriptor):
"""Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
A FunctionExecutionInfo object.
"""
if self._worker.load_code_from_local:
# Load function from local code.
# Currently, we don't support isolating code by drivers,
# thus always set driver ID to NIL here.
driver_id = ray.DriverID.nil()
if not function_descriptor.is_actor_method():
self._load_function_from_local(driver_id, function_descriptor)
else:
# Load function from GCS.
# Wait until the function to be executed has actually been
# registered on this worker. We will push warnings to the user if
# we spend too long in this loop.
# The driver function may not be found in sys.path. Try to load
# the function from GCS.
with profiling.profile("wait_for_function"):
self._wait_for_function(function_descriptor, driver_id)
try:
function_id = function_descriptor.function_id
info = self._function_execution_info[driver_id][function_id]
except KeyError as e:
message = ("Error occurs in get_execution_info: "
"driver_id: %s, function_descriptor: %s. Message: %s" %
(driver_id, function_descriptor, e))
raise KeyError(message)
return info | [
"Get",
"the",
"FunctionExecutionInfo",
"of",
"a",
"remote",
"function",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L455-L489 | [
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"# Load function from local code.",
"# Currently, we don't support isolating code by drivers,",
"# thus always set d... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._wait_for_function | Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a problem somewhere and we will push an error message to the user.
If this worker is an actor, then this will wait until the actor has
been defined.
Args:
function_descriptor : The FunctionDescriptor of the function that
we want to execute.
driver_id (str): The ID of the driver to push the error message to
if this times out. | python/ray/function_manager.py | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
"""Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a problem somewhere and we will push an error message to the user.
If this worker is an actor, then this will wait until the actor has
been defined.
Args:
function_descriptor : The FunctionDescriptor of the function that
we want to execute.
driver_id (str): The ID of the driver to push the error message to
if this times out.
"""
start_time = time.time()
# Only send the warning once.
warning_sent = False
while True:
with self.lock:
if (self._worker.actor_id.is_nil()
and (function_descriptor.function_id in
self._function_execution_info[driver_id])):
break
elif not self._worker.actor_id.is_nil() and (
self._worker.actor_id in self._worker.actors):
break
if time.time() - start_time > timeout:
warning_message = ("This worker was asked to execute a "
"function that it does not have "
"registered. You may have to restart "
"Ray.")
if not warning_sent:
ray.utils.push_error_to_driver(
self._worker,
ray_constants.WAIT_FOR_FUNCTION_PUSH_ERROR,
warning_message,
driver_id=driver_id)
warning_sent = True
time.sleep(0.001) | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
"""Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a problem somewhere and we will push an error message to the user.
If this worker is an actor, then this will wait until the actor has
been defined.
Args:
function_descriptor : The FunctionDescriptor of the function that
we want to execute.
driver_id (str): The ID of the driver to push the error message to
if this times out.
"""
start_time = time.time()
# Only send the warning once.
warning_sent = False
while True:
with self.lock:
if (self._worker.actor_id.is_nil()
and (function_descriptor.function_id in
self._function_execution_info[driver_id])):
break
elif not self._worker.actor_id.is_nil() and (
self._worker.actor_id in self._worker.actors):
break
if time.time() - start_time > timeout:
warning_message = ("This worker was asked to execute a "
"function that it does not have "
"registered. You may have to restart "
"Ray.")
if not warning_sent:
ray.utils.push_error_to_driver(
self._worker,
ray_constants.WAIT_FOR_FUNCTION_PUSH_ERROR,
warning_message,
driver_id=driver_id)
warning_sent = True
time.sleep(0.001) | [
"Wait",
"until",
"the",
"function",
"to",
"be",
"executed",
"is",
"present",
"on",
"this",
"worker",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L518-L558 | [
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"=",
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"True",
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._publish_actor_class_to_key | Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
on cached actor class definitions when a worker connects for the first
time.
Args:
key: The key to store the actor class info at.
actor_class_info: Information about the actor class. | python/ray/function_manager.py | def _publish_actor_class_to_key(self, key, actor_class_info):
"""Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
on cached actor class definitions when a worker connects for the first
time.
Args:
key: The key to store the actor class info at.
actor_class_info: Information about the actor class.
"""
# We set the driver ID here because it may not have been available when
# the actor class was defined.
self._worker.redis_client.hmset(key, actor_class_info)
self._worker.redis_client.rpush("Exports", key) | def _publish_actor_class_to_key(self, key, actor_class_info):
"""Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
on cached actor class definitions when a worker connects for the first
time.
Args:
key: The key to store the actor class info at.
actor_class_info: Information about the actor class.
"""
# We set the driver ID here because it may not have been available when
# the actor class was defined.
self._worker.redis_client.hmset(key, actor_class_info)
self._worker.redis_client.rpush("Exports", key) | [
"Push",
"an",
"actor",
"class",
"definition",
"to",
"Redis",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L560-L574 | [
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"_worker",
".",
"redis_client",
".",
"hmset",
"(",
"key"... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager.load_actor_class | Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class. | python/ray/function_manager.py | def load_actor_class(self, driver_id, function_descriptor):
"""Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
"""
function_id = function_descriptor.function_id
# Check if the actor class already exists in the cache.
actor_class = self._loaded_actor_classes.get(function_id, None)
if actor_class is None:
# Load actor class.
if self._worker.load_code_from_local:
driver_id = ray.DriverID.nil()
# Load actor class from local code.
actor_class = self._load_actor_from_local(
driver_id, function_descriptor)
else:
# Load actor class from GCS.
actor_class = self._load_actor_class_from_gcs(
driver_id, function_descriptor)
# Save the loaded actor class in cache.
self._loaded_actor_classes[function_id] = actor_class
# Generate execution info for the methods of this actor class.
module_name = function_descriptor.module_name
actor_class_name = function_descriptor.class_name
actor_methods = inspect.getmembers(
actor_class, predicate=is_function_or_method)
for actor_method_name, actor_method in actor_methods:
method_descriptor = FunctionDescriptor(
module_name, actor_method_name, actor_class_name)
method_id = method_descriptor.function_id
executor = self._make_actor_method_executor(
actor_method_name,
actor_method,
actor_imported=True,
)
self._function_execution_info[driver_id][method_id] = (
FunctionExecutionInfo(
function=executor,
function_name=actor_method_name,
max_calls=0,
))
self._num_task_executions[driver_id][method_id] = 0
self._num_task_executions[driver_id][function_id] = 0
return actor_class | def load_actor_class(self, driver_id, function_descriptor):
"""Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
"""
function_id = function_descriptor.function_id
# Check if the actor class already exists in the cache.
actor_class = self._loaded_actor_classes.get(function_id, None)
if actor_class is None:
# Load actor class.
if self._worker.load_code_from_local:
driver_id = ray.DriverID.nil()
# Load actor class from local code.
actor_class = self._load_actor_from_local(
driver_id, function_descriptor)
else:
# Load actor class from GCS.
actor_class = self._load_actor_class_from_gcs(
driver_id, function_descriptor)
# Save the loaded actor class in cache.
self._loaded_actor_classes[function_id] = actor_class
# Generate execution info for the methods of this actor class.
module_name = function_descriptor.module_name
actor_class_name = function_descriptor.class_name
actor_methods = inspect.getmembers(
actor_class, predicate=is_function_or_method)
for actor_method_name, actor_method in actor_methods:
method_descriptor = FunctionDescriptor(
module_name, actor_method_name, actor_class_name)
method_id = method_descriptor.function_id
executor = self._make_actor_method_executor(
actor_method_name,
actor_method,
actor_imported=True,
)
self._function_execution_info[driver_id][method_id] = (
FunctionExecutionInfo(
function=executor,
function_name=actor_method_name,
max_calls=0,
))
self._num_task_executions[driver_id][method_id] = 0
self._num_task_executions[driver_id][function_id] = 0
return actor_class | [
"Load",
"the",
"actor",
"class",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L619-L668 | [
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"actor_class",
"=",
"self",
".",
"_loaded_actor_classes",
... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._load_actor_from_local | Load actor class from local code. | python/ray/function_manager.py | def _load_actor_from_local(self, driver_id, function_descriptor):
"""Load actor class from local code."""
module_name, class_name = (function_descriptor.module_name,
function_descriptor.class_name)
try:
module = importlib.import_module(module_name)
actor_class = getattr(module, class_name)
if isinstance(actor_class, ray.actor.ActorClass):
return actor_class._modified_class
else:
return actor_class
except Exception:
logger.exception(
"Failed to load actor_class %s.".format(class_name))
raise Exception(
"Actor {} failed to be imported from local code.".format(
class_name)) | def _load_actor_from_local(self, driver_id, function_descriptor):
"""Load actor class from local code."""
module_name, class_name = (function_descriptor.module_name,
function_descriptor.class_name)
try:
module = importlib.import_module(module_name)
actor_class = getattr(module, class_name)
if isinstance(actor_class, ray.actor.ActorClass):
return actor_class._modified_class
else:
return actor_class
except Exception:
logger.exception(
"Failed to load actor_class %s.".format(class_name))
raise Exception(
"Actor {} failed to be imported from local code.".format(
class_name)) | [
"Load",
"actor",
"class",
"from",
"local",
"code",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L670-L686 | [
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"... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._load_actor_class_from_gcs | Load actor class from GCS. | python/ray/function_manager.py | def _load_actor_class_from_gcs(self, driver_id, function_descriptor):
"""Load actor class from GCS."""
key = (b"ActorClass:" + driver_id.binary() + b":" +
function_descriptor.function_id.binary())
# Wait for the actor class key to have been imported by the
# import thread. TODO(rkn): It shouldn't be possible to end
# up in an infinite loop here, but we should push an error to
# the driver if too much time is spent here.
while key not in self.imported_actor_classes:
time.sleep(0.001)
# Fetch raw data from GCS.
(driver_id_str, class_name, module, pickled_class,
actor_method_names) = self._worker.redis_client.hmget(
key, [
"driver_id", "class_name", "module", "class",
"actor_method_names"
])
class_name = ensure_str(class_name)
module_name = ensure_str(module)
driver_id = ray.DriverID(driver_id_str)
actor_method_names = json.loads(ensure_str(actor_method_names))
actor_class = None
try:
with self.lock:
actor_class = pickle.loads(pickled_class)
except Exception:
logger.exception(
"Failed to load actor class %s.".format(class_name))
# The actor class failed to be unpickled, create a fake actor
# class instead (just to produce error messages and to prevent
# the driver from hanging).
actor_class = self._create_fake_actor_class(
class_name, actor_method_names)
# If an exception was thrown when the actor was imported, we record
# the traceback and notify the scheduler of the failure.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
# Log the error message.
push_error_to_driver(
self._worker, ray_constants.REGISTER_ACTOR_PUSH_ERROR,
"Failed to unpickle actor class '{}' for actor ID {}. "
"Traceback:\n{}".format(class_name,
self._worker.actor_id.hex(),
traceback_str), driver_id)
# TODO(rkn): In the future, it might make sense to have the worker
# exit here. However, currently that would lead to hanging if
# someone calls ray.get on a method invoked on the actor.
# The below line is necessary. Because in the driver process,
# if the function is defined in the file where the python script
# was started from, its module is `__main__`.
# However in the worker process, the `__main__` module is a
# different module, which is `default_worker.py`
actor_class.__module__ = module_name
return actor_class | def _load_actor_class_from_gcs(self, driver_id, function_descriptor):
"""Load actor class from GCS."""
key = (b"ActorClass:" + driver_id.binary() + b":" +
function_descriptor.function_id.binary())
# Wait for the actor class key to have been imported by the
# import thread. TODO(rkn): It shouldn't be possible to end
# up in an infinite loop here, but we should push an error to
# the driver if too much time is spent here.
while key not in self.imported_actor_classes:
time.sleep(0.001)
# Fetch raw data from GCS.
(driver_id_str, class_name, module, pickled_class,
actor_method_names) = self._worker.redis_client.hmget(
key, [
"driver_id", "class_name", "module", "class",
"actor_method_names"
])
class_name = ensure_str(class_name)
module_name = ensure_str(module)
driver_id = ray.DriverID(driver_id_str)
actor_method_names = json.loads(ensure_str(actor_method_names))
actor_class = None
try:
with self.lock:
actor_class = pickle.loads(pickled_class)
except Exception:
logger.exception(
"Failed to load actor class %s.".format(class_name))
# The actor class failed to be unpickled, create a fake actor
# class instead (just to produce error messages and to prevent
# the driver from hanging).
actor_class = self._create_fake_actor_class(
class_name, actor_method_names)
# If an exception was thrown when the actor was imported, we record
# the traceback and notify the scheduler of the failure.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
# Log the error message.
push_error_to_driver(
self._worker, ray_constants.REGISTER_ACTOR_PUSH_ERROR,
"Failed to unpickle actor class '{}' for actor ID {}. "
"Traceback:\n{}".format(class_name,
self._worker.actor_id.hex(),
traceback_str), driver_id)
# TODO(rkn): In the future, it might make sense to have the worker
# exit here. However, currently that would lead to hanging if
# someone calls ray.get on a method invoked on the actor.
# The below line is necessary. Because in the driver process,
# if the function is defined in the file where the python script
# was started from, its module is `__main__`.
# However in the worker process, the `__main__` module is a
# different module, which is `default_worker.py`
actor_class.__module__ = module_name
return actor_class | [
"Load",
"actor",
"class",
"from",
"GCS",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L702-L759 | [
"def",
"_load_actor_class_from_gcs",
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"b\":\"",
"+",
"function_descriptor",
".",
"function_id",
".",
"binary"... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._make_actor_method_executor | Make an executor that wraps a user-defined actor method.
The wrapped method updates the worker's internal state and performs any
necessary checkpointing operations.
Args:
method_name (str): The name of the actor method.
method (instancemethod): The actor method to wrap. This should be a
method defined on the actor class and should therefore take an
instance of the actor as the first argument.
actor_imported (bool): Whether the actor has been imported.
Checkpointing operations will not be run if this is set to
False.
Returns:
A function that executes the given actor method on the worker's
stored instance of the actor. The function also updates the
worker's internal state to record the executed method. | python/ray/function_manager.py | def _make_actor_method_executor(self, method_name, method, actor_imported):
"""Make an executor that wraps a user-defined actor method.
The wrapped method updates the worker's internal state and performs any
necessary checkpointing operations.
Args:
method_name (str): The name of the actor method.
method (instancemethod): The actor method to wrap. This should be a
method defined on the actor class and should therefore take an
instance of the actor as the first argument.
actor_imported (bool): Whether the actor has been imported.
Checkpointing operations will not be run if this is set to
False.
Returns:
A function that executes the given actor method on the worker's
stored instance of the actor. The function also updates the
worker's internal state to record the executed method.
"""
def actor_method_executor(dummy_return_id, actor, *args):
# Update the actor's task counter to reflect the task we're about
# to execute.
self._worker.actor_task_counter += 1
# Execute the assigned method and save a checkpoint if necessary.
try:
if is_class_method(method):
method_returns = method(*args)
else:
method_returns = method(actor, *args)
except Exception as e:
# Save the checkpoint before allowing the method exception
# to be thrown, but don't save the checkpoint for actor
# creation task.
if (isinstance(actor, ray.actor.Checkpointable)
and self._worker.actor_task_counter != 1):
self._save_and_log_checkpoint(actor)
raise e
else:
# Handle any checkpointing operations before storing the
# method's return values.
# NOTE(swang): If method_returns is a pointer to the actor's
# state and the checkpointing operations can modify the return
# values if they mutate the actor's state. Is this okay?
if isinstance(actor, ray.actor.Checkpointable):
# If this is the first task to execute on the actor, try to
# resume from a checkpoint.
if self._worker.actor_task_counter == 1:
if actor_imported:
self._restore_and_log_checkpoint(actor)
else:
# Save the checkpoint before returning the method's
# return values.
self._save_and_log_checkpoint(actor)
return method_returns
return actor_method_executor | def _make_actor_method_executor(self, method_name, method, actor_imported):
"""Make an executor that wraps a user-defined actor method.
The wrapped method updates the worker's internal state and performs any
necessary checkpointing operations.
Args:
method_name (str): The name of the actor method.
method (instancemethod): The actor method to wrap. This should be a
method defined on the actor class and should therefore take an
instance of the actor as the first argument.
actor_imported (bool): Whether the actor has been imported.
Checkpointing operations will not be run if this is set to
False.
Returns:
A function that executes the given actor method on the worker's
stored instance of the actor. The function also updates the
worker's internal state to record the executed method.
"""
def actor_method_executor(dummy_return_id, actor, *args):
# Update the actor's task counter to reflect the task we're about
# to execute.
self._worker.actor_task_counter += 1
# Execute the assigned method and save a checkpoint if necessary.
try:
if is_class_method(method):
method_returns = method(*args)
else:
method_returns = method(actor, *args)
except Exception as e:
# Save the checkpoint before allowing the method exception
# to be thrown, but don't save the checkpoint for actor
# creation task.
if (isinstance(actor, ray.actor.Checkpointable)
and self._worker.actor_task_counter != 1):
self._save_and_log_checkpoint(actor)
raise e
else:
# Handle any checkpointing operations before storing the
# method's return values.
# NOTE(swang): If method_returns is a pointer to the actor's
# state and the checkpointing operations can modify the return
# values if they mutate the actor's state. Is this okay?
if isinstance(actor, ray.actor.Checkpointable):
# If this is the first task to execute on the actor, try to
# resume from a checkpoint.
if self._worker.actor_task_counter == 1:
if actor_imported:
self._restore_and_log_checkpoint(actor)
else:
# Save the checkpoint before returning the method's
# return values.
self._save_and_log_checkpoint(actor)
return method_returns
return actor_method_executor | [
"Make",
"an",
"executor",
"that",
"wraps",
"a",
"user",
"-",
"defined",
"actor",
"method",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L761-L819 | [
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")",
":",
"# Update the actor's task counter to reflect the tas... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._save_and_log_checkpoint | Save an actor checkpoint if necessary and log any errors.
Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method. | python/ray/function_manager.py | def _save_and_log_checkpoint(self, actor):
"""Save an actor checkpoint if necessary and log any errors.
Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method.
"""
actor_id = self._worker.actor_id
checkpoint_info = self._worker.actor_checkpoint_info[actor_id]
checkpoint_info.num_tasks_since_last_checkpoint += 1
now = int(1000 * time.time())
checkpoint_context = ray.actor.CheckpointContext(
actor_id, checkpoint_info.num_tasks_since_last_checkpoint,
now - checkpoint_info.last_checkpoint_timestamp)
# If we should take a checkpoint, notify raylet to prepare a checkpoint
# and then call `save_checkpoint`.
if actor.should_checkpoint(checkpoint_context):
try:
now = int(1000 * time.time())
checkpoint_id = (self._worker.raylet_client.
prepare_actor_checkpoint(actor_id))
checkpoint_info.checkpoint_ids.append(checkpoint_id)
actor.save_checkpoint(actor_id, checkpoint_id)
if (len(checkpoint_info.checkpoint_ids) >
ray._config.num_actor_checkpoints_to_keep()):
actor.checkpoint_expired(
actor_id,
checkpoint_info.checkpoint_ids.pop(0),
)
checkpoint_info.num_tasks_since_last_checkpoint = 0
checkpoint_info.last_checkpoint_timestamp = now
except Exception:
# Checkpoint save or reload failed. Notify the driver.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
ray.utils.push_error_to_driver(
self._worker,
ray_constants.CHECKPOINT_PUSH_ERROR,
traceback_str,
driver_id=self._worker.task_driver_id) | def _save_and_log_checkpoint(self, actor):
"""Save an actor checkpoint if necessary and log any errors.
Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method.
"""
actor_id = self._worker.actor_id
checkpoint_info = self._worker.actor_checkpoint_info[actor_id]
checkpoint_info.num_tasks_since_last_checkpoint += 1
now = int(1000 * time.time())
checkpoint_context = ray.actor.CheckpointContext(
actor_id, checkpoint_info.num_tasks_since_last_checkpoint,
now - checkpoint_info.last_checkpoint_timestamp)
# If we should take a checkpoint, notify raylet to prepare a checkpoint
# and then call `save_checkpoint`.
if actor.should_checkpoint(checkpoint_context):
try:
now = int(1000 * time.time())
checkpoint_id = (self._worker.raylet_client.
prepare_actor_checkpoint(actor_id))
checkpoint_info.checkpoint_ids.append(checkpoint_id)
actor.save_checkpoint(actor_id, checkpoint_id)
if (len(checkpoint_info.checkpoint_ids) >
ray._config.num_actor_checkpoints_to_keep()):
actor.checkpoint_expired(
actor_id,
checkpoint_info.checkpoint_ids.pop(0),
)
checkpoint_info.num_tasks_since_last_checkpoint = 0
checkpoint_info.last_checkpoint_timestamp = now
except Exception:
# Checkpoint save or reload failed. Notify the driver.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
ray.utils.push_error_to_driver(
self._worker,
ray_constants.CHECKPOINT_PUSH_ERROR,
traceback_str,
driver_id=self._worker.task_driver_id) | [
"Save",
"an",
"actor",
"checkpoint",
"if",
"necessary",
"and",
"log",
"any",
"errors",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L821-L862 | [
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"_worker",
".",
"actor_checkpoint_info",
"[",
"actor_id",
"]",
"checkpoint_info",
".",
"num_tas... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | FunctionActorManager._restore_and_log_checkpoint | Restore an actor from a checkpoint if available and log any errors.
This should only be called on workers that have just executed an actor
creation task.
Args:
actor: The actor to restore from a checkpoint. | python/ray/function_manager.py | def _restore_and_log_checkpoint(self, actor):
"""Restore an actor from a checkpoint if available and log any errors.
This should only be called on workers that have just executed an actor
creation task.
Args:
actor: The actor to restore from a checkpoint.
"""
actor_id = self._worker.actor_id
try:
checkpoints = ray.actor.get_checkpoints_for_actor(actor_id)
if len(checkpoints) > 0:
# If we found previously saved checkpoints for this actor,
# call the `load_checkpoint` callback.
checkpoint_id = actor.load_checkpoint(actor_id, checkpoints)
if checkpoint_id is not None:
# Check that the returned checkpoint id is in the
# `available_checkpoints` list.
msg = (
"`load_checkpoint` must return a checkpoint id that " +
"exists in the `available_checkpoints` list, or eone.")
assert any(checkpoint_id == checkpoint.checkpoint_id
for checkpoint in checkpoints), msg
# Notify raylet that this actor has been resumed from
# a checkpoint.
(self._worker.raylet_client.
notify_actor_resumed_from_checkpoint(
actor_id, checkpoint_id))
except Exception:
# Checkpoint save or reload failed. Notify the driver.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
ray.utils.push_error_to_driver(
self._worker,
ray_constants.CHECKPOINT_PUSH_ERROR,
traceback_str,
driver_id=self._worker.task_driver_id) | def _restore_and_log_checkpoint(self, actor):
"""Restore an actor from a checkpoint if available and log any errors.
This should only be called on workers that have just executed an actor
creation task.
Args:
actor: The actor to restore from a checkpoint.
"""
actor_id = self._worker.actor_id
try:
checkpoints = ray.actor.get_checkpoints_for_actor(actor_id)
if len(checkpoints) > 0:
# If we found previously saved checkpoints for this actor,
# call the `load_checkpoint` callback.
checkpoint_id = actor.load_checkpoint(actor_id, checkpoints)
if checkpoint_id is not None:
# Check that the returned checkpoint id is in the
# `available_checkpoints` list.
msg = (
"`load_checkpoint` must return a checkpoint id that " +
"exists in the `available_checkpoints` list, or eone.")
assert any(checkpoint_id == checkpoint.checkpoint_id
for checkpoint in checkpoints), msg
# Notify raylet that this actor has been resumed from
# a checkpoint.
(self._worker.raylet_client.
notify_actor_resumed_from_checkpoint(
actor_id, checkpoint_id))
except Exception:
# Checkpoint save or reload failed. Notify the driver.
traceback_str = ray.utils.format_error_message(
traceback.format_exc())
ray.utils.push_error_to_driver(
self._worker,
ray_constants.CHECKPOINT_PUSH_ERROR,
traceback_str,
driver_id=self._worker.task_driver_id) | [
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"an",
"actor",
"from",
"a",
"checkpoint",
"if",
"available",
"and",
"log",
"any",
"errors",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/function_manager.py#L864-L901 | [
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"("... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | _env_runner | This implements the common experience collection logic.
Args:
base_env (BaseEnv): env implementing BaseEnv.
extra_batch_callback (fn): function to send extra batch data to.
policies (dict): Map of policy ids to PolicyGraph instances.
policy_mapping_fn (func): Function that maps agent ids to policy ids.
This is called when an agent first enters the environment. The
agent is then "bound" to the returned policy for the episode.
unroll_length (int): Number of episode steps before `SampleBatch` is
yielded. Set to infinity to yield complete episodes.
horizon (int): Horizon of the episode.
preprocessors (dict): Map of policy id to preprocessor for the
observations prior to filtering.
obs_filters (dict): Map of policy id to filter used to process
observations for the policy.
clip_rewards (bool): Whether to clip rewards before postprocessing.
pack (bool): Whether to pack multiple episodes into each batch. This
guarantees batches will be exactly `unroll_length` in size.
clip_actions (bool): Whether to clip actions to the space range.
callbacks (dict): User callbacks to run on episode events.
tf_sess (Session|None): Optional tensorflow session to use for batching
TF policy evaluations.
perf_stats (PerfStats): Record perf stats into this object.
soft_horizon (bool): Calculate rewards but don't reset the
environment when the horizon is hit.
Yields:
rollout (SampleBatch): Object containing state, action, reward,
terminal condition, and other fields as dictated by `policy`. | python/ray/rllib/evaluation/sampler.py | def _env_runner(base_env, extra_batch_callback, policies, policy_mapping_fn,
unroll_length, horizon, preprocessors, obs_filters,
clip_rewards, clip_actions, pack, callbacks, tf_sess,
perf_stats, soft_horizon):
"""This implements the common experience collection logic.
Args:
base_env (BaseEnv): env implementing BaseEnv.
extra_batch_callback (fn): function to send extra batch data to.
policies (dict): Map of policy ids to PolicyGraph instances.
policy_mapping_fn (func): Function that maps agent ids to policy ids.
This is called when an agent first enters the environment. The
agent is then "bound" to the returned policy for the episode.
unroll_length (int): Number of episode steps before `SampleBatch` is
yielded. Set to infinity to yield complete episodes.
horizon (int): Horizon of the episode.
preprocessors (dict): Map of policy id to preprocessor for the
observations prior to filtering.
obs_filters (dict): Map of policy id to filter used to process
observations for the policy.
clip_rewards (bool): Whether to clip rewards before postprocessing.
pack (bool): Whether to pack multiple episodes into each batch. This
guarantees batches will be exactly `unroll_length` in size.
clip_actions (bool): Whether to clip actions to the space range.
callbacks (dict): User callbacks to run on episode events.
tf_sess (Session|None): Optional tensorflow session to use for batching
TF policy evaluations.
perf_stats (PerfStats): Record perf stats into this object.
soft_horizon (bool): Calculate rewards but don't reset the
environment when the horizon is hit.
Yields:
rollout (SampleBatch): Object containing state, action, reward,
terminal condition, and other fields as dictated by `policy`.
"""
try:
if not horizon:
horizon = (base_env.get_unwrapped()[0].spec.max_episode_steps)
except Exception:
logger.debug("no episode horizon specified, assuming inf")
if not horizon:
horizon = float("inf")
# Pool of batch builders, which can be shared across episodes to pack
# trajectory data.
batch_builder_pool = []
def get_batch_builder():
if batch_builder_pool:
return batch_builder_pool.pop()
else:
return MultiAgentSampleBatchBuilder(
policies, clip_rewards, callbacks.get("on_postprocess_traj"))
def new_episode():
episode = MultiAgentEpisode(policies, policy_mapping_fn,
get_batch_builder, extra_batch_callback)
if callbacks.get("on_episode_start"):
callbacks["on_episode_start"]({
"env": base_env,
"policy": policies,
"episode": episode,
})
return episode
active_episodes = defaultdict(new_episode)
while True:
perf_stats.iters += 1
t0 = time.time()
# Get observations from all ready agents
unfiltered_obs, rewards, dones, infos, off_policy_actions = \
base_env.poll()
perf_stats.env_wait_time += time.time() - t0
if log_once("env_returns"):
logger.info("Raw obs from env: {}".format(
summarize(unfiltered_obs)))
logger.info("Info return from env: {}".format(summarize(infos)))
# Process observations and prepare for policy evaluation
t1 = time.time()
active_envs, to_eval, outputs = _process_observations(
base_env, policies, batch_builder_pool, active_episodes,
unfiltered_obs, rewards, dones, infos, off_policy_actions, horizon,
preprocessors, obs_filters, unroll_length, pack, callbacks,
soft_horizon)
perf_stats.processing_time += time.time() - t1
for o in outputs:
yield o
# Do batched policy eval
t2 = time.time()
eval_results = _do_policy_eval(tf_sess, to_eval, policies,
active_episodes)
perf_stats.inference_time += time.time() - t2
# Process results and update episode state
t3 = time.time()
actions_to_send = _process_policy_eval_results(
to_eval, eval_results, active_episodes, active_envs,
off_policy_actions, policies, clip_actions)
perf_stats.processing_time += time.time() - t3
# Return computed actions to ready envs. We also send to envs that have
# taken off-policy actions; those envs are free to ignore the action.
t4 = time.time()
base_env.send_actions(actions_to_send)
perf_stats.env_wait_time += time.time() - t4 | def _env_runner(base_env, extra_batch_callback, policies, policy_mapping_fn,
unroll_length, horizon, preprocessors, obs_filters,
clip_rewards, clip_actions, pack, callbacks, tf_sess,
perf_stats, soft_horizon):
"""This implements the common experience collection logic.
Args:
base_env (BaseEnv): env implementing BaseEnv.
extra_batch_callback (fn): function to send extra batch data to.
policies (dict): Map of policy ids to PolicyGraph instances.
policy_mapping_fn (func): Function that maps agent ids to policy ids.
This is called when an agent first enters the environment. The
agent is then "bound" to the returned policy for the episode.
unroll_length (int): Number of episode steps before `SampleBatch` is
yielded. Set to infinity to yield complete episodes.
horizon (int): Horizon of the episode.
preprocessors (dict): Map of policy id to preprocessor for the
observations prior to filtering.
obs_filters (dict): Map of policy id to filter used to process
observations for the policy.
clip_rewards (bool): Whether to clip rewards before postprocessing.
pack (bool): Whether to pack multiple episodes into each batch. This
guarantees batches will be exactly `unroll_length` in size.
clip_actions (bool): Whether to clip actions to the space range.
callbacks (dict): User callbacks to run on episode events.
tf_sess (Session|None): Optional tensorflow session to use for batching
TF policy evaluations.
perf_stats (PerfStats): Record perf stats into this object.
soft_horizon (bool): Calculate rewards but don't reset the
environment when the horizon is hit.
Yields:
rollout (SampleBatch): Object containing state, action, reward,
terminal condition, and other fields as dictated by `policy`.
"""
try:
if not horizon:
horizon = (base_env.get_unwrapped()[0].spec.max_episode_steps)
except Exception:
logger.debug("no episode horizon specified, assuming inf")
if not horizon:
horizon = float("inf")
# Pool of batch builders, which can be shared across episodes to pack
# trajectory data.
batch_builder_pool = []
def get_batch_builder():
if batch_builder_pool:
return batch_builder_pool.pop()
else:
return MultiAgentSampleBatchBuilder(
policies, clip_rewards, callbacks.get("on_postprocess_traj"))
def new_episode():
episode = MultiAgentEpisode(policies, policy_mapping_fn,
get_batch_builder, extra_batch_callback)
if callbacks.get("on_episode_start"):
callbacks["on_episode_start"]({
"env": base_env,
"policy": policies,
"episode": episode,
})
return episode
active_episodes = defaultdict(new_episode)
while True:
perf_stats.iters += 1
t0 = time.time()
# Get observations from all ready agents
unfiltered_obs, rewards, dones, infos, off_policy_actions = \
base_env.poll()
perf_stats.env_wait_time += time.time() - t0
if log_once("env_returns"):
logger.info("Raw obs from env: {}".format(
summarize(unfiltered_obs)))
logger.info("Info return from env: {}".format(summarize(infos)))
# Process observations and prepare for policy evaluation
t1 = time.time()
active_envs, to_eval, outputs = _process_observations(
base_env, policies, batch_builder_pool, active_episodes,
unfiltered_obs, rewards, dones, infos, off_policy_actions, horizon,
preprocessors, obs_filters, unroll_length, pack, callbacks,
soft_horizon)
perf_stats.processing_time += time.time() - t1
for o in outputs:
yield o
# Do batched policy eval
t2 = time.time()
eval_results = _do_policy_eval(tf_sess, to_eval, policies,
active_episodes)
perf_stats.inference_time += time.time() - t2
# Process results and update episode state
t3 = time.time()
actions_to_send = _process_policy_eval_results(
to_eval, eval_results, active_episodes, active_envs,
off_policy_actions, policies, clip_actions)
perf_stats.processing_time += time.time() - t3
# Return computed actions to ready envs. We also send to envs that have
# taken off-policy actions; those envs are free to ignore the action.
t4 = time.time()
base_env.send_actions(actions_to_send)
perf_stats.env_wait_time += time.time() - t4 | [
"This",
"implements",
"the",
"common",
"experience",
"collection",
"logic",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/rllib/evaluation/sampler.py#L230-L339 | [
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | _process_observations | Record new data from the environment and prepare for policy evaluation.
Returns:
active_envs: set of non-terminated env ids
to_eval: map of policy_id to list of agent PolicyEvalData
outputs: list of metrics and samples to return from the sampler | python/ray/rllib/evaluation/sampler.py | def _process_observations(base_env, policies, batch_builder_pool,
active_episodes, unfiltered_obs, rewards, dones,
infos, off_policy_actions, horizon, preprocessors,
obs_filters, unroll_length, pack, callbacks,
soft_horizon):
"""Record new data from the environment and prepare for policy evaluation.
Returns:
active_envs: set of non-terminated env ids
to_eval: map of policy_id to list of agent PolicyEvalData
outputs: list of metrics and samples to return from the sampler
"""
active_envs = set()
to_eval = defaultdict(list)
outputs = []
# For each environment
for env_id, agent_obs in unfiltered_obs.items():
new_episode = env_id not in active_episodes
episode = active_episodes[env_id]
if not new_episode:
episode.length += 1
episode.batch_builder.count += 1
episode._add_agent_rewards(rewards[env_id])
if (episode.batch_builder.total() > max(1000, unroll_length * 10)
and log_once("large_batch_warning")):
logger.warning(
"More than {} observations for {} env steps ".format(
episode.batch_builder.total(),
episode.batch_builder.count) + "are buffered in "
"the sampler. If this is more than you expected, check that "
"that you set a horizon on your environment correctly. Note "
"that in multi-agent environments, `sample_batch_size` sets "
"the batch size based on environment steps, not the steps of "
"individual agents, which can result in unexpectedly large "
"batches.")
# Check episode termination conditions
if dones[env_id]["__all__"] or episode.length >= horizon:
hit_horizon = (episode.length >= horizon
and not dones[env_id]["__all__"])
all_done = True
atari_metrics = _fetch_atari_metrics(base_env)
if atari_metrics is not None:
for m in atari_metrics:
outputs.append(
m._replace(custom_metrics=episode.custom_metrics))
else:
outputs.append(
RolloutMetrics(episode.length, episode.total_reward,
dict(episode.agent_rewards),
episode.custom_metrics, {}))
else:
hit_horizon = False
all_done = False
active_envs.add(env_id)
# For each agent in the environment
for agent_id, raw_obs in agent_obs.items():
policy_id = episode.policy_for(agent_id)
prep_obs = _get_or_raise(preprocessors,
policy_id).transform(raw_obs)
if log_once("prep_obs"):
logger.info("Preprocessed obs: {}".format(summarize(prep_obs)))
filtered_obs = _get_or_raise(obs_filters, policy_id)(prep_obs)
if log_once("filtered_obs"):
logger.info("Filtered obs: {}".format(summarize(filtered_obs)))
agent_done = bool(all_done or dones[env_id].get(agent_id))
if not agent_done:
to_eval[policy_id].append(
PolicyEvalData(env_id, agent_id, filtered_obs,
infos[env_id].get(agent_id, {}),
episode.rnn_state_for(agent_id),
episode.last_action_for(agent_id),
rewards[env_id][agent_id] or 0.0))
last_observation = episode.last_observation_for(agent_id)
episode._set_last_observation(agent_id, filtered_obs)
episode._set_last_raw_obs(agent_id, raw_obs)
episode._set_last_info(agent_id, infos[env_id].get(agent_id, {}))
# Record transition info if applicable
if (last_observation is not None and infos[env_id].get(
agent_id, {}).get("training_enabled", True)):
episode.batch_builder.add_values(
agent_id,
policy_id,
t=episode.length - 1,
eps_id=episode.episode_id,
agent_index=episode._agent_index(agent_id),
obs=last_observation,
actions=episode.last_action_for(agent_id),
rewards=rewards[env_id][agent_id],
prev_actions=episode.prev_action_for(agent_id),
prev_rewards=episode.prev_reward_for(agent_id),
dones=(False
if (hit_horizon and soft_horizon) else agent_done),
infos=infos[env_id].get(agent_id, {}),
new_obs=filtered_obs,
**episode.last_pi_info_for(agent_id))
# Invoke the step callback after the step is logged to the episode
if callbacks.get("on_episode_step"):
callbacks["on_episode_step"]({"env": base_env, "episode": episode})
# Cut the batch if we're not packing multiple episodes into one,
# or if we've exceeded the requested batch size.
if episode.batch_builder.has_pending_data():
if dones[env_id]["__all__"]:
episode.batch_builder.check_missing_dones()
if (all_done and not pack) or \
episode.batch_builder.count >= unroll_length:
outputs.append(episode.batch_builder.build_and_reset(episode))
elif all_done:
# Make sure postprocessor stays within one episode
episode.batch_builder.postprocess_batch_so_far(episode)
if all_done:
# Handle episode termination
batch_builder_pool.append(episode.batch_builder)
if callbacks.get("on_episode_end"):
callbacks["on_episode_end"]({
"env": base_env,
"policy": policies,
"episode": episode
})
if hit_horizon and soft_horizon:
episode.soft_reset()
resetted_obs = agent_obs
else:
del active_episodes[env_id]
resetted_obs = base_env.try_reset(env_id)
if resetted_obs is None:
# Reset not supported, drop this env from the ready list
if horizon != float("inf"):
raise ValueError(
"Setting episode horizon requires reset() support "
"from the environment.")
elif resetted_obs != ASYNC_RESET_RETURN:
# Creates a new episode if this is not async return
# If reset is async, we will get its result in some future poll
episode = active_episodes[env_id]
for agent_id, raw_obs in resetted_obs.items():
policy_id = episode.policy_for(agent_id)
policy = _get_or_raise(policies, policy_id)
prep_obs = _get_or_raise(preprocessors,
policy_id).transform(raw_obs)
filtered_obs = _get_or_raise(obs_filters,
policy_id)(prep_obs)
episode._set_last_observation(agent_id, filtered_obs)
to_eval[policy_id].append(
PolicyEvalData(
env_id, agent_id, filtered_obs,
episode.last_info_for(agent_id) or {},
episode.rnn_state_for(agent_id),
np.zeros_like(
_flatten_action(policy.action_space.sample())),
0.0))
return active_envs, to_eval, outputs | def _process_observations(base_env, policies, batch_builder_pool,
active_episodes, unfiltered_obs, rewards, dones,
infos, off_policy_actions, horizon, preprocessors,
obs_filters, unroll_length, pack, callbacks,
soft_horizon):
"""Record new data from the environment and prepare for policy evaluation.
Returns:
active_envs: set of non-terminated env ids
to_eval: map of policy_id to list of agent PolicyEvalData
outputs: list of metrics and samples to return from the sampler
"""
active_envs = set()
to_eval = defaultdict(list)
outputs = []
# For each environment
for env_id, agent_obs in unfiltered_obs.items():
new_episode = env_id not in active_episodes
episode = active_episodes[env_id]
if not new_episode:
episode.length += 1
episode.batch_builder.count += 1
episode._add_agent_rewards(rewards[env_id])
if (episode.batch_builder.total() > max(1000, unroll_length * 10)
and log_once("large_batch_warning")):
logger.warning(
"More than {} observations for {} env steps ".format(
episode.batch_builder.total(),
episode.batch_builder.count) + "are buffered in "
"the sampler. If this is more than you expected, check that "
"that you set a horizon on your environment correctly. Note "
"that in multi-agent environments, `sample_batch_size` sets "
"the batch size based on environment steps, not the steps of "
"individual agents, which can result in unexpectedly large "
"batches.")
# Check episode termination conditions
if dones[env_id]["__all__"] or episode.length >= horizon:
hit_horizon = (episode.length >= horizon
and not dones[env_id]["__all__"])
all_done = True
atari_metrics = _fetch_atari_metrics(base_env)
if atari_metrics is not None:
for m in atari_metrics:
outputs.append(
m._replace(custom_metrics=episode.custom_metrics))
else:
outputs.append(
RolloutMetrics(episode.length, episode.total_reward,
dict(episode.agent_rewards),
episode.custom_metrics, {}))
else:
hit_horizon = False
all_done = False
active_envs.add(env_id)
# For each agent in the environment
for agent_id, raw_obs in agent_obs.items():
policy_id = episode.policy_for(agent_id)
prep_obs = _get_or_raise(preprocessors,
policy_id).transform(raw_obs)
if log_once("prep_obs"):
logger.info("Preprocessed obs: {}".format(summarize(prep_obs)))
filtered_obs = _get_or_raise(obs_filters, policy_id)(prep_obs)
if log_once("filtered_obs"):
logger.info("Filtered obs: {}".format(summarize(filtered_obs)))
agent_done = bool(all_done or dones[env_id].get(agent_id))
if not agent_done:
to_eval[policy_id].append(
PolicyEvalData(env_id, agent_id, filtered_obs,
infos[env_id].get(agent_id, {}),
episode.rnn_state_for(agent_id),
episode.last_action_for(agent_id),
rewards[env_id][agent_id] or 0.0))
last_observation = episode.last_observation_for(agent_id)
episode._set_last_observation(agent_id, filtered_obs)
episode._set_last_raw_obs(agent_id, raw_obs)
episode._set_last_info(agent_id, infos[env_id].get(agent_id, {}))
# Record transition info if applicable
if (last_observation is not None and infos[env_id].get(
agent_id, {}).get("training_enabled", True)):
episode.batch_builder.add_values(
agent_id,
policy_id,
t=episode.length - 1,
eps_id=episode.episode_id,
agent_index=episode._agent_index(agent_id),
obs=last_observation,
actions=episode.last_action_for(agent_id),
rewards=rewards[env_id][agent_id],
prev_actions=episode.prev_action_for(agent_id),
prev_rewards=episode.prev_reward_for(agent_id),
dones=(False
if (hit_horizon and soft_horizon) else agent_done),
infos=infos[env_id].get(agent_id, {}),
new_obs=filtered_obs,
**episode.last_pi_info_for(agent_id))
# Invoke the step callback after the step is logged to the episode
if callbacks.get("on_episode_step"):
callbacks["on_episode_step"]({"env": base_env, "episode": episode})
# Cut the batch if we're not packing multiple episodes into one,
# or if we've exceeded the requested batch size.
if episode.batch_builder.has_pending_data():
if dones[env_id]["__all__"]:
episode.batch_builder.check_missing_dones()
if (all_done and not pack) or \
episode.batch_builder.count >= unroll_length:
outputs.append(episode.batch_builder.build_and_reset(episode))
elif all_done:
# Make sure postprocessor stays within one episode
episode.batch_builder.postprocess_batch_so_far(episode)
if all_done:
# Handle episode termination
batch_builder_pool.append(episode.batch_builder)
if callbacks.get("on_episode_end"):
callbacks["on_episode_end"]({
"env": base_env,
"policy": policies,
"episode": episode
})
if hit_horizon and soft_horizon:
episode.soft_reset()
resetted_obs = agent_obs
else:
del active_episodes[env_id]
resetted_obs = base_env.try_reset(env_id)
if resetted_obs is None:
# Reset not supported, drop this env from the ready list
if horizon != float("inf"):
raise ValueError(
"Setting episode horizon requires reset() support "
"from the environment.")
elif resetted_obs != ASYNC_RESET_RETURN:
# Creates a new episode if this is not async return
# If reset is async, we will get its result in some future poll
episode = active_episodes[env_id]
for agent_id, raw_obs in resetted_obs.items():
policy_id = episode.policy_for(agent_id)
policy = _get_or_raise(policies, policy_id)
prep_obs = _get_or_raise(preprocessors,
policy_id).transform(raw_obs)
filtered_obs = _get_or_raise(obs_filters,
policy_id)(prep_obs)
episode._set_last_observation(agent_id, filtered_obs)
to_eval[policy_id].append(
PolicyEvalData(
env_id, agent_id, filtered_obs,
episode.last_info_for(agent_id) or {},
episode.rnn_state_for(agent_id),
np.zeros_like(
_flatten_action(policy.action_space.sample())),
0.0))
return active_envs, to_eval, outputs | [
"Record",
"new",
"data",
"from",
"the",
"environment",
"and",
"prepare",
"for",
"policy",
"evaluation",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/rllib/evaluation/sampler.py#L342-L505 | [
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",",
"infos",
",",
"off_policy_actions",
",",
"horizon",
",",
"preprocessors",
",",
"obs_filters... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | _do_policy_eval | Call compute actions on observation batches to get next actions.
Returns:
eval_results: dict of policy to compute_action() outputs. | python/ray/rllib/evaluation/sampler.py | def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
"""Call compute actions on observation batches to get next actions.
Returns:
eval_results: dict of policy to compute_action() outputs.
"""
eval_results = {}
if tf_sess:
builder = TFRunBuilder(tf_sess, "policy_eval")
pending_fetches = {}
else:
builder = None
if log_once("compute_actions_input"):
logger.info("Inputs to compute_actions():\n\n{}\n".format(
summarize(to_eval)))
for policy_id, eval_data in to_eval.items():
rnn_in_cols = _to_column_format([t.rnn_state for t in eval_data])
policy = _get_or_raise(policies, policy_id)
if builder and (policy.compute_actions.__code__ is
TFPolicyGraph.compute_actions.__code__):
# TODO(ekl): how can we make info batch available to TF code?
pending_fetches[policy_id] = policy._build_compute_actions(
builder, [t.obs for t in eval_data],
rnn_in_cols,
prev_action_batch=[t.prev_action for t in eval_data],
prev_reward_batch=[t.prev_reward for t in eval_data])
else:
eval_results[policy_id] = policy.compute_actions(
[t.obs for t in eval_data],
rnn_in_cols,
prev_action_batch=[t.prev_action for t in eval_data],
prev_reward_batch=[t.prev_reward for t in eval_data],
info_batch=[t.info for t in eval_data],
episodes=[active_episodes[t.env_id] for t in eval_data])
if builder:
for k, v in pending_fetches.items():
eval_results[k] = builder.get(v)
if log_once("compute_actions_result"):
logger.info("Outputs of compute_actions():\n\n{}\n".format(
summarize(eval_results)))
return eval_results | def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
"""Call compute actions on observation batches to get next actions.
Returns:
eval_results: dict of policy to compute_action() outputs.
"""
eval_results = {}
if tf_sess:
builder = TFRunBuilder(tf_sess, "policy_eval")
pending_fetches = {}
else:
builder = None
if log_once("compute_actions_input"):
logger.info("Inputs to compute_actions():\n\n{}\n".format(
summarize(to_eval)))
for policy_id, eval_data in to_eval.items():
rnn_in_cols = _to_column_format([t.rnn_state for t in eval_data])
policy = _get_or_raise(policies, policy_id)
if builder and (policy.compute_actions.__code__ is
TFPolicyGraph.compute_actions.__code__):
# TODO(ekl): how can we make info batch available to TF code?
pending_fetches[policy_id] = policy._build_compute_actions(
builder, [t.obs for t in eval_data],
rnn_in_cols,
prev_action_batch=[t.prev_action for t in eval_data],
prev_reward_batch=[t.prev_reward for t in eval_data])
else:
eval_results[policy_id] = policy.compute_actions(
[t.obs for t in eval_data],
rnn_in_cols,
prev_action_batch=[t.prev_action for t in eval_data],
prev_reward_batch=[t.prev_reward for t in eval_data],
info_batch=[t.info for t in eval_data],
episodes=[active_episodes[t.env_id] for t in eval_data])
if builder:
for k, v in pending_fetches.items():
eval_results[k] = builder.get(v)
if log_once("compute_actions_result"):
logger.info("Outputs of compute_actions():\n\n{}\n".format(
summarize(eval_results)))
return eval_results | [
"Call",
"compute",
"actions",
"on",
"observation",
"batches",
"to",
"get",
"next",
"actions",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/rllib/evaluation/sampler.py#L508-L554 | [
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"=... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | _process_policy_eval_results | Process the output of policy neural network evaluation.
Records policy evaluation results into the given episode objects and
returns replies to send back to agents in the env.
Returns:
actions_to_send: nested dict of env id -> agent id -> agent replies. | python/ray/rllib/evaluation/sampler.py | def _process_policy_eval_results(to_eval, eval_results, active_episodes,
active_envs, off_policy_actions, policies,
clip_actions):
"""Process the output of policy neural network evaluation.
Records policy evaluation results into the given episode objects and
returns replies to send back to agents in the env.
Returns:
actions_to_send: nested dict of env id -> agent id -> agent replies.
"""
actions_to_send = defaultdict(dict)
for env_id in active_envs:
actions_to_send[env_id] = {} # at minimum send empty dict
for policy_id, eval_data in to_eval.items():
rnn_in_cols = _to_column_format([t.rnn_state for t in eval_data])
actions, rnn_out_cols, pi_info_cols = eval_results[policy_id]
if len(rnn_in_cols) != len(rnn_out_cols):
raise ValueError("Length of RNN in did not match RNN out, got: "
"{} vs {}".format(rnn_in_cols, rnn_out_cols))
# Add RNN state info
for f_i, column in enumerate(rnn_in_cols):
pi_info_cols["state_in_{}".format(f_i)] = column
for f_i, column in enumerate(rnn_out_cols):
pi_info_cols["state_out_{}".format(f_i)] = column
# Save output rows
actions = _unbatch_tuple_actions(actions)
policy = _get_or_raise(policies, policy_id)
for i, action in enumerate(actions):
env_id = eval_data[i].env_id
agent_id = eval_data[i].agent_id
if clip_actions:
actions_to_send[env_id][agent_id] = clip_action(
action, policy.action_space)
else:
actions_to_send[env_id][agent_id] = action
episode = active_episodes[env_id]
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
episode._set_last_pi_info(
agent_id, {k: v[i]
for k, v in pi_info_cols.items()})
if env_id in off_policy_actions and \
agent_id in off_policy_actions[env_id]:
episode._set_last_action(agent_id,
off_policy_actions[env_id][agent_id])
else:
episode._set_last_action(agent_id, action)
return actions_to_send | def _process_policy_eval_results(to_eval, eval_results, active_episodes,
active_envs, off_policy_actions, policies,
clip_actions):
"""Process the output of policy neural network evaluation.
Records policy evaluation results into the given episode objects and
returns replies to send back to agents in the env.
Returns:
actions_to_send: nested dict of env id -> agent id -> agent replies.
"""
actions_to_send = defaultdict(dict)
for env_id in active_envs:
actions_to_send[env_id] = {} # at minimum send empty dict
for policy_id, eval_data in to_eval.items():
rnn_in_cols = _to_column_format([t.rnn_state for t in eval_data])
actions, rnn_out_cols, pi_info_cols = eval_results[policy_id]
if len(rnn_in_cols) != len(rnn_out_cols):
raise ValueError("Length of RNN in did not match RNN out, got: "
"{} vs {}".format(rnn_in_cols, rnn_out_cols))
# Add RNN state info
for f_i, column in enumerate(rnn_in_cols):
pi_info_cols["state_in_{}".format(f_i)] = column
for f_i, column in enumerate(rnn_out_cols):
pi_info_cols["state_out_{}".format(f_i)] = column
# Save output rows
actions = _unbatch_tuple_actions(actions)
policy = _get_or_raise(policies, policy_id)
for i, action in enumerate(actions):
env_id = eval_data[i].env_id
agent_id = eval_data[i].agent_id
if clip_actions:
actions_to_send[env_id][agent_id] = clip_action(
action, policy.action_space)
else:
actions_to_send[env_id][agent_id] = action
episode = active_episodes[env_id]
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
episode._set_last_pi_info(
agent_id, {k: v[i]
for k, v in pi_info_cols.items()})
if env_id in off_policy_actions and \
agent_id in off_policy_actions[env_id]:
episode._set_last_action(agent_id,
off_policy_actions[env_id][agent_id])
else:
episode._set_last_action(agent_id, action)
return actions_to_send | [
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train | _fetch_atari_metrics | Atari games have multiple logical episodes, one per life.
However for metrics reporting we count full episodes all lives included. | python/ray/rllib/evaluation/sampler.py | def _fetch_atari_metrics(base_env):
"""Atari games have multiple logical episodes, one per life.
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unwrapped = base_env.get_unwrapped()
if not unwrapped:
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if not monitor:
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for eps_rew, eps_len in monitor.next_episode_results():
atari_out.append(RolloutMetrics(eps_len, eps_rew, {}, {}, {}))
return atari_out | def _fetch_atari_metrics(base_env):
"""Atari games have multiple logical episodes, one per life.
However for metrics reporting we count full episodes all lives included.
"""
unwrapped = base_env.get_unwrapped()
if not unwrapped:
return None
atari_out = []
for u in unwrapped:
monitor = get_wrapper_by_cls(u, MonitorEnv)
if not monitor:
return None
for eps_rew, eps_len in monitor.next_episode_results():
atari_out.append(RolloutMetrics(eps_len, eps_rew, {}, {}, {}))
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train | compare_version | Compare two version number strings of the form W.X.Y.Z.
The numbers are compared most-significant to least-significant.
For example, 12.345.67.89 > 2.987.88.99.
Args:
a: First version number string to compare
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0 if the numbers are identical, a positive number if 'a' is larger, and
a negative number if 'b' is larger. | android/jni/msbuild.py | def compare_version(a, b):
"""Compare two version number strings of the form W.X.Y.Z.
The numbers are compared most-significant to least-significant.
For example, 12.345.67.89 > 2.987.88.99.
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"""
aa = string.split(a, ".")
bb = string.split(b, ".")
for i in range(0, 4):
if aa[i] != bb[i]:
return cmp(int(aa[i]), int(bb[i]))
return 0 | def compare_version(a, b):
"""Compare two version number strings of the form W.X.Y.Z.
The numbers are compared most-significant to least-significant.
For example, 12.345.67.89 > 2.987.88.99.
Args:
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b: Second version number string to compare
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bb = string.split(b, ".")
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train | FlatbuffersConan.configure_cmake | Create CMake instance and execute configure step | conanfile.py | def configure_cmake(self):
"""Create CMake instance and execute configure step
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cmake = CMake(self)
cmake.definitions["FLATBUFFERS_BUILD_TESTS"] = False
cmake.definitions["FLATBUFFERS_BUILD_SHAREDLIB"] = self.options.shared
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cmake.configure()
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cmake = CMake(self)
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train | FlatbuffersConan.package | Copy Flatbuffers' artifacts to package folder | conanfile.py | def package(self):
"""Copy Flatbuffers' artifacts to package folder
"""
cmake = self.configure_cmake()
cmake.install()
self.copy(pattern="LICENSE.txt", dst="licenses")
self.copy(pattern="FindFlatBuffers.cmake", dst=os.path.join("lib", "cmake", "flatbuffers"), src="CMake")
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cmake = self.configure_cmake()
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train | FlatbuffersConan.package_info | Collect built libraries names and solve flatc path. | conanfile.py | def package_info(self):
"""Collect built libraries names and solve flatc path.
"""
self.cpp_info.libs = tools.collect_libs(self)
self.user_info.flatc = os.path.join(self.package_folder, "bin", "flatc") | def package_info(self):
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train | Table.Offset | Offset provides access into the Table's vtable.
Deprecated fields are ignored by checking the vtable's length. | python/flatbuffers/table.py | def Offset(self, vtableOffset):
"""Offset provides access into the Table's vtable.
Deprecated fields are ignored by checking the vtable's length."""
vtable = self.Pos - self.Get(N.SOffsetTFlags, self.Pos)
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train | Table.String | String gets a string from data stored inside the flatbuffer. | python/flatbuffers/table.py | def String(self, off):
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N.enforce_number(off, N.UOffsetTFlags)
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train | Table.VectorLen | VectorLen retrieves the length of the vector whose offset is stored
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"""VectorLen retrieves the length of the vector whose offset is stored
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N.enforce_number(off, N.UOffsetTFlags)
off += self.Pos
off += encode.Get(N.UOffsetTFlags.packer_type, self.Bytes, off)
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off += self.Pos
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train | Table.Vector | Vector retrieves the start of data of the vector whose offset is
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"""Vector retrieves the start of data of the vector whose offset is
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off += self.Pos
x = off + self.Get(N.UOffsetTFlags, off)
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train | Table.Union | Union initializes any Table-derived type to point to the union at
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"""Union initializes any Table-derived type to point to the union at
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assert type(t2) is Table
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off += self.Pos
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t2.Bytes = self.Bytes | def Union(self, t2, off):
"""Union initializes any Table-derived type to point to the union at
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assert type(t2) is Table
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off += self.Pos
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train | Table.Get | Get retrieves a value of the type specified by `flags` at the
given offset. | python/flatbuffers/table.py | def Get(self, flags, off):
"""
Get retrieves a value of the type specified by `flags` at the
given offset.
"""
N.enforce_number(off, N.UOffsetTFlags)
return flags.py_type(encode.Get(flags.packer_type, self.Bytes, off)) | def Get(self, flags, off):
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Get retrieves a value of the type specified by `flags` at the
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train | Table.GetVectorAsNumpy | GetVectorAsNumpy returns the vector that starts at `Vector(off)`
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a `view` into Bytes, so modifying the returned array will
modify Bytes in place. | python/flatbuffers/table.py | def GetVectorAsNumpy(self, flags, off):
"""
GetVectorAsNumpy returns the vector that starts at `Vector(off)`
as a numpy array with the type specified by `flags`. The array is
a `view` into Bytes, so modifying the returned array will
modify Bytes in place.
"""
offset = self.Vector(off)
length = self.VectorLen(off) # TODO: length accounts for bytewidth, right?
numpy_dtype = N.to_numpy_type(flags)
return encode.GetVectorAsNumpy(numpy_dtype, self.Bytes, length, offset) | def GetVectorAsNumpy(self, flags, off):
"""
GetVectorAsNumpy returns the vector that starts at `Vector(off)`
as a numpy array with the type specified by `flags`. The array is
a `view` into Bytes, so modifying the returned array will
modify Bytes in place.
"""
offset = self.Vector(off)
length = self.VectorLen(off) # TODO: length accounts for bytewidth, right?
numpy_dtype = N.to_numpy_type(flags)
return encode.GetVectorAsNumpy(numpy_dtype, self.Bytes, length, offset) | [
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train | Table.GetVOffsetTSlot | GetVOffsetTSlot retrieves the VOffsetT that the given vtable location
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"""
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train | GetVectorAsNumpy | GetVecAsNumpy decodes values starting at buf[head] as
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if np is not None:
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train | Write | Write encodes `n` at buf[head] using `packer_type`. | python/flatbuffers/encode.py | def Write(packer_type, buf, head, n):
""" Write encodes `n` at buf[head] using `packer_type`. """
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train | main | Script that finds and runs flatc built from source. | android/jni/run_flatc.py | def main():
"""Script that finds and runs flatc built from source."""
if len(sys.argv) < 2:
sys.stderr.write('Usage: run_flatc.py flatbuffers_dir [flatc_args]\n')
return 1
cwd = os.getcwd()
flatc = ''
flatbuffers_dir = sys.argv[1]
for path in FLATC_SEARCH_PATHS:
current = os.path.join(flatbuffers_dir, path,
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if os.path.exists(current):
flatc = current
break
if not flatc:
sys.stderr.write('flatc not found\n')
return 1
command = [flatc] + sys.argv[2:]
return subprocess.call(command) | def main():
"""Script that finds and runs flatc built from source."""
if len(sys.argv) < 2:
sys.stderr.write('Usage: run_flatc.py flatbuffers_dir [flatc_args]\n')
return 1
cwd = os.getcwd()
flatc = ''
flatbuffers_dir = sys.argv[1]
for path in FLATC_SEARCH_PATHS:
current = os.path.join(flatbuffers_dir, path,
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if os.path.exists(current):
flatc = current
break
if not flatc:
sys.stderr.write('flatc not found\n')
return 1
command = [flatc] + sys.argv[2:]
return subprocess.call(command) | [
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train | import_numpy | Returns the numpy module if it exists on the system,
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"""
Returns the numpy module if it exists on the system,
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"""
try:
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numpy_exists = False
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import numpy as np
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Returns the numpy module if it exists on the system,
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numpy_exists = False
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import numpy as np
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train | vtableEqual | vtableEqual compares an unwritten vtable to a written vtable. | python/flatbuffers/builder.py | def vtableEqual(a, objectStart, b):
"""vtableEqual compares an unwritten vtable to a written vtable."""
N.enforce_number(objectStart, N.UOffsetTFlags)
if len(a) * N.VOffsetTFlags.bytewidth != len(b):
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# Skip vtable entries that indicate a default value.
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pass
else:
y = objectStart - elem
if x != y:
return False
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N.enforce_number(objectStart, N.UOffsetTFlags)
if len(a) * N.VOffsetTFlags.bytewidth != len(b):
return False
for i, elem in enumerate(a):
x = encode.Get(packer.voffset, b, i * N.VOffsetTFlags.bytewidth)
# Skip vtable entries that indicate a default value.
if x == 0 and elem == 0:
pass
else:
y = objectStart - elem
if x != y:
return False
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train | Builder.StartObject | StartObject initializes bookkeeping for writing a new object. | python/flatbuffers/builder.py | def StartObject(self, numfields):
"""StartObject initializes bookkeeping for writing a new object."""
self.assertNotNested()
# use 32-bit offsets so that arithmetic doesn't overflow.
self.current_vtable = [0 for _ in range_func(numfields)]
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train | Builder.WriteVtable | WriteVtable serializes the vtable for the current object, if needed.
Before writing out the vtable, this checks pre-existing vtables for
equality to this one. If an equal vtable is found, point the object to
the existing vtable and return.
Because vtable values are sensitive to alignment of object data, not
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An object has the following format:
<SOffsetT: offset to this object's vtable (may be negative)>
<byte: data>+ | python/flatbuffers/builder.py | def WriteVtable(self):
"""
WriteVtable serializes the vtable for the current object, if needed.
Before writing out the vtable, this checks pre-existing vtables for
equality to this one. If an equal vtable is found, point the object to
the existing vtable and return.
Because vtable values are sensitive to alignment of object data, not
all logically-equal vtables will be deduplicated.
A vtable has the following format:
<VOffsetT: size of the vtable in bytes, including this value>
<VOffsetT: size of the object in bytes, including the vtable offset>
<VOffsetT: offset for a field> * N, where N is the number of fields
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An object has the following format:
<SOffsetT: offset to this object's vtable (may be negative)>
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"""
# Prepend a zero scalar to the object. Later in this function we'll
# write an offset here that points to the object's vtable:
self.PrependSOffsetTRelative(0)
objectOffset = self.Offset()
existingVtable = None
# Trim trailing 0 offsets.
while self.current_vtable and self.current_vtable[-1] == 0:
self.current_vtable.pop()
# Search backwards through existing vtables, because similar vtables
# are likely to have been recently appended. See
# BenchmarkVtableDeduplication for a case in which this heuristic
# saves about 30% of the time used in writing objects with duplicate
# tables.
i = len(self.vtables) - 1
while i >= 0:
# Find the other vtable, which is associated with `i`:
vt2Offset = self.vtables[i]
vt2Start = len(self.Bytes) - vt2Offset
vt2Len = encode.Get(packer.voffset, self.Bytes, vt2Start)
metadata = VtableMetadataFields * N.VOffsetTFlags.bytewidth
vt2End = vt2Start + vt2Len
vt2 = self.Bytes[vt2Start+metadata:vt2End]
# Compare the other vtable to the one under consideration.
# If they are equal, store the offset and break:
if vtableEqual(self.current_vtable, objectOffset, vt2):
existingVtable = vt2Offset
break
i -= 1
if existingVtable is None:
# Did not find a vtable, so write this one to the buffer.
# Write out the current vtable in reverse , because
# serialization occurs in last-first order:
i = len(self.current_vtable) - 1
while i >= 0:
off = 0
if self.current_vtable[i] != 0:
# Forward reference to field;
# use 32bit number to ensure no overflow:
off = objectOffset - self.current_vtable[i]
self.PrependVOffsetT(off)
i -= 1
# The two metadata fields are written last.
# First, store the object bytesize:
objectSize = UOffsetTFlags.py_type(objectOffset - self.objectEnd)
self.PrependVOffsetT(VOffsetTFlags.py_type(objectSize))
# Second, store the vtable bytesize:
vBytes = len(self.current_vtable) + VtableMetadataFields
vBytes *= N.VOffsetTFlags.bytewidth
self.PrependVOffsetT(VOffsetTFlags.py_type(vBytes))
# Next, write the offset to the new vtable in the
# already-allocated SOffsetT at the beginning of this object:
objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset)
encode.Write(packer.soffset, self.Bytes, objectStart,
SOffsetTFlags.py_type(self.Offset() - objectOffset))
# Finally, store this vtable in memory for future
# deduplication:
self.vtables.append(self.Offset())
else:
# Found a duplicate vtable.
objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset)
self.head = UOffsetTFlags.py_type(objectStart)
# Write the offset to the found vtable in the
# already-allocated SOffsetT at the beginning of this object:
encode.Write(packer.soffset, self.Bytes, self.Head(),
SOffsetTFlags.py_type(existingVtable - objectOffset))
self.current_vtable = None
return objectOffset | def WriteVtable(self):
"""
WriteVtable serializes the vtable for the current object, if needed.
Before writing out the vtable, this checks pre-existing vtables for
equality to this one. If an equal vtable is found, point the object to
the existing vtable and return.
Because vtable values are sensitive to alignment of object data, not
all logically-equal vtables will be deduplicated.
A vtable has the following format:
<VOffsetT: size of the vtable in bytes, including this value>
<VOffsetT: size of the object in bytes, including the vtable offset>
<VOffsetT: offset for a field> * N, where N is the number of fields
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Thus, a vtable is made of 2 + N elements, each VOffsetT bytes wide.
An object has the following format:
<SOffsetT: offset to this object's vtable (may be negative)>
<byte: data>+
"""
# Prepend a zero scalar to the object. Later in this function we'll
# write an offset here that points to the object's vtable:
self.PrependSOffsetTRelative(0)
objectOffset = self.Offset()
existingVtable = None
# Trim trailing 0 offsets.
while self.current_vtable and self.current_vtable[-1] == 0:
self.current_vtable.pop()
# Search backwards through existing vtables, because similar vtables
# are likely to have been recently appended. See
# BenchmarkVtableDeduplication for a case in which this heuristic
# saves about 30% of the time used in writing objects with duplicate
# tables.
i = len(self.vtables) - 1
while i >= 0:
# Find the other vtable, which is associated with `i`:
vt2Offset = self.vtables[i]
vt2Start = len(self.Bytes) - vt2Offset
vt2Len = encode.Get(packer.voffset, self.Bytes, vt2Start)
metadata = VtableMetadataFields * N.VOffsetTFlags.bytewidth
vt2End = vt2Start + vt2Len
vt2 = self.Bytes[vt2Start+metadata:vt2End]
# Compare the other vtable to the one under consideration.
# If they are equal, store the offset and break:
if vtableEqual(self.current_vtable, objectOffset, vt2):
existingVtable = vt2Offset
break
i -= 1
if existingVtable is None:
# Did not find a vtable, so write this one to the buffer.
# Write out the current vtable in reverse , because
# serialization occurs in last-first order:
i = len(self.current_vtable) - 1
while i >= 0:
off = 0
if self.current_vtable[i] != 0:
# Forward reference to field;
# use 32bit number to ensure no overflow:
off = objectOffset - self.current_vtable[i]
self.PrependVOffsetT(off)
i -= 1
# The two metadata fields are written last.
# First, store the object bytesize:
objectSize = UOffsetTFlags.py_type(objectOffset - self.objectEnd)
self.PrependVOffsetT(VOffsetTFlags.py_type(objectSize))
# Second, store the vtable bytesize:
vBytes = len(self.current_vtable) + VtableMetadataFields
vBytes *= N.VOffsetTFlags.bytewidth
self.PrependVOffsetT(VOffsetTFlags.py_type(vBytes))
# Next, write the offset to the new vtable in the
# already-allocated SOffsetT at the beginning of this object:
objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset)
encode.Write(packer.soffset, self.Bytes, objectStart,
SOffsetTFlags.py_type(self.Offset() - objectOffset))
# Finally, store this vtable in memory for future
# deduplication:
self.vtables.append(self.Offset())
else:
# Found a duplicate vtable.
objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset)
self.head = UOffsetTFlags.py_type(objectStart)
# Write the offset to the found vtable in the
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encode.Write(packer.soffset, self.Bytes, self.Head(),
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self.current_vtable = None
return objectOffset | [
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train | Builder.growByteBuffer | Doubles the size of the byteslice, and copies the old data towards
the end of the new buffer (since we build the buffer backwards). | python/flatbuffers/builder.py | def growByteBuffer(self):
"""Doubles the size of the byteslice, and copies the old data towards
the end of the new buffer (since we build the buffer backwards)."""
if len(self.Bytes) == Builder.MAX_BUFFER_SIZE:
msg = "flatbuffers: cannot grow buffer beyond 2 gigabytes"
raise BuilderSizeError(msg)
newSize = min(len(self.Bytes) * 2, Builder.MAX_BUFFER_SIZE)
if newSize == 0:
newSize = 1
bytes2 = bytearray(newSize)
bytes2[newSize-len(self.Bytes):] = self.Bytes
self.Bytes = bytes2 | def growByteBuffer(self):
"""Doubles the size of the byteslice, and copies the old data towards
the end of the new buffer (since we build the buffer backwards)."""
if len(self.Bytes) == Builder.MAX_BUFFER_SIZE:
msg = "flatbuffers: cannot grow buffer beyond 2 gigabytes"
raise BuilderSizeError(msg)
newSize = min(len(self.Bytes) * 2, Builder.MAX_BUFFER_SIZE)
if newSize == 0:
newSize = 1
bytes2 = bytearray(newSize)
bytes2[newSize-len(self.Bytes):] = self.Bytes
self.Bytes = bytes2 | [
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train | Builder.Pad | Pad places zeros at the current offset. | python/flatbuffers/builder.py | def Pad(self, n):
"""Pad places zeros at the current offset."""
for i in range_func(n):
self.Place(0, N.Uint8Flags) | def Pad(self, n):
"""Pad places zeros at the current offset."""
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train | Builder.Prep | Prep prepares to write an element of `size` after `additional_bytes`
have been written, e.g. if you write a string, you need to align
such the int length field is aligned to SizeInt32, and the string
data follows it directly.
If all you need to do is align, `additionalBytes` will be 0. | python/flatbuffers/builder.py | def Prep(self, size, additionalBytes):
"""
Prep prepares to write an element of `size` after `additional_bytes`
have been written, e.g. if you write a string, you need to align
such the int length field is aligned to SizeInt32, and the string
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"""
# Track the biggest thing we've ever aligned to.
if size > self.minalign:
self.minalign = size
# Find the amount of alignment needed such that `size` is properly
# aligned after `additionalBytes`:
alignSize = (~(len(self.Bytes) - self.Head() + additionalBytes)) + 1
alignSize &= (size - 1)
# Reallocate the buffer if needed:
while self.Head() < alignSize+size+additionalBytes:
oldBufSize = len(self.Bytes)
self.growByteBuffer()
updated_head = self.head + len(self.Bytes) - oldBufSize
self.head = UOffsetTFlags.py_type(updated_head)
self.Pad(alignSize) | def Prep(self, size, additionalBytes):
"""
Prep prepares to write an element of `size` after `additional_bytes`
have been written, e.g. if you write a string, you need to align
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"""
# Track the biggest thing we've ever aligned to.
if size > self.minalign:
self.minalign = size
# Find the amount of alignment needed such that `size` is properly
# aligned after `additionalBytes`:
alignSize = (~(len(self.Bytes) - self.Head() + additionalBytes)) + 1
alignSize &= (size - 1)
# Reallocate the buffer if needed:
while self.Head() < alignSize+size+additionalBytes:
oldBufSize = len(self.Bytes)
self.growByteBuffer()
updated_head = self.head + len(self.Bytes) - oldBufSize
self.head = UOffsetTFlags.py_type(updated_head)
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train | Builder.PrependSOffsetTRelative | PrependSOffsetTRelative prepends an SOffsetT, relative to where it
will be written. | python/flatbuffers/builder.py | def PrependSOffsetTRelative(self, off):
"""
PrependSOffsetTRelative prepends an SOffsetT, relative to where it
will be written.
"""
# Ensure alignment is already done:
self.Prep(N.SOffsetTFlags.bytewidth, 0)
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msg = "flatbuffers: Offset arithmetic error."
raise OffsetArithmeticError(msg)
off2 = self.Offset() - off + N.SOffsetTFlags.bytewidth
self.PlaceSOffsetT(off2) | def PrependSOffsetTRelative(self, off):
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PrependSOffsetTRelative prepends an SOffsetT, relative to where it
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"""
# Ensure alignment is already done:
self.Prep(N.SOffsetTFlags.bytewidth, 0)
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raise OffsetArithmeticError(msg)
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train | Builder.PrependUOffsetTRelative | Prepends an unsigned offset into vector data, relative to where it
will be written. | python/flatbuffers/builder.py | def PrependUOffsetTRelative(self, off):
"""Prepends an unsigned offset into vector data, relative to where it
will be written.
"""
# Ensure alignment is already done:
self.Prep(N.UOffsetTFlags.bytewidth, 0)
if not (off <= self.Offset()):
msg = "flatbuffers: Offset arithmetic error."
raise OffsetArithmeticError(msg)
off2 = self.Offset() - off + N.UOffsetTFlags.bytewidth
self.PlaceUOffsetT(off2) | def PrependUOffsetTRelative(self, off):
"""Prepends an unsigned offset into vector data, relative to where it
will be written.
"""
# Ensure alignment is already done:
self.Prep(N.UOffsetTFlags.bytewidth, 0)
if not (off <= self.Offset()):
msg = "flatbuffers: Offset arithmetic error."
raise OffsetArithmeticError(msg)
off2 = self.Offset() - off + N.UOffsetTFlags.bytewidth
self.PlaceUOffsetT(off2) | [
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train | Builder.StartVector | StartVector initializes bookkeeping for writing a new vector.
A vector has the following format:
- <UOffsetT: number of elements in this vector>
- <T: data>+, where T is the type of elements of this vector. | python/flatbuffers/builder.py | def StartVector(self, elemSize, numElems, alignment):
"""
StartVector initializes bookkeeping for writing a new vector.
A vector has the following format:
- <UOffsetT: number of elements in this vector>
- <T: data>+, where T is the type of elements of this vector.
"""
self.assertNotNested()
self.nested = True
self.Prep(N.Uint32Flags.bytewidth, elemSize*numElems)
self.Prep(alignment, elemSize*numElems) # In case alignment > int.
return self.Offset() | def StartVector(self, elemSize, numElems, alignment):
"""
StartVector initializes bookkeeping for writing a new vector.
A vector has the following format:
- <UOffsetT: number of elements in this vector>
- <T: data>+, where T is the type of elements of this vector.
"""
self.assertNotNested()
self.nested = True
self.Prep(N.Uint32Flags.bytewidth, elemSize*numElems)
self.Prep(alignment, elemSize*numElems) # In case alignment > int.
return self.Offset() | [
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train | Builder.EndVector | EndVector writes data necessary to finish vector construction. | python/flatbuffers/builder.py | def EndVector(self, vectorNumElems):
"""EndVector writes data necessary to finish vector construction."""
self.assertNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = False
## @endcond
# we already made space for this, so write without PrependUint32
self.PlaceUOffsetT(vectorNumElems)
return self.Offset() | def EndVector(self, vectorNumElems):
"""EndVector writes data necessary to finish vector construction."""
self.assertNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = False
## @endcond
# we already made space for this, so write without PrependUint32
self.PlaceUOffsetT(vectorNumElems)
return self.Offset() | [
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train | Builder.CreateString | CreateString writes a null-terminated byte string as a vector. | python/flatbuffers/builder.py | def CreateString(self, s, encoding='utf-8', errors='strict'):
"""CreateString writes a null-terminated byte string as a vector."""
self.assertNotNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = True
## @endcond
if isinstance(s, compat.string_types):
x = s.encode(encoding, errors)
elif isinstance(s, compat.binary_types):
x = s
else:
raise TypeError("non-string passed to CreateString")
self.Prep(N.UOffsetTFlags.bytewidth, (len(x)+1)*N.Uint8Flags.bytewidth)
self.Place(0, N.Uint8Flags)
l = UOffsetTFlags.py_type(len(s))
## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
## @endcond
self.Bytes[self.Head():self.Head()+l] = x
return self.EndVector(len(x)) | def CreateString(self, s, encoding='utf-8', errors='strict'):
"""CreateString writes a null-terminated byte string as a vector."""
self.assertNotNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = True
## @endcond
if isinstance(s, compat.string_types):
x = s.encode(encoding, errors)
elif isinstance(s, compat.binary_types):
x = s
else:
raise TypeError("non-string passed to CreateString")
self.Prep(N.UOffsetTFlags.bytewidth, (len(x)+1)*N.Uint8Flags.bytewidth)
self.Place(0, N.Uint8Flags)
l = UOffsetTFlags.py_type(len(s))
## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
## @endcond
self.Bytes[self.Head():self.Head()+l] = x
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train | Builder.CreateByteVector | CreateString writes a byte vector. | python/flatbuffers/builder.py | def CreateByteVector(self, x):
"""CreateString writes a byte vector."""
self.assertNotNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = True
## @endcond
if not isinstance(x, compat.binary_types):
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self.Prep(N.UOffsetTFlags.bytewidth, len(x)*N.Uint8Flags.bytewidth)
l = UOffsetTFlags.py_type(len(x))
## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
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self.Bytes[self.Head():self.Head()+l] = x
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self.assertNotNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = True
## @endcond
if not isinstance(x, compat.binary_types):
raise TypeError("non-byte vector passed to CreateByteVector")
self.Prep(N.UOffsetTFlags.bytewidth, len(x)*N.Uint8Flags.bytewidth)
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## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
## @endcond
self.Bytes[self.Head():self.Head()+l] = x
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train | Builder.CreateNumpyVector | CreateNumpyVector writes a numpy array into the buffer. | python/flatbuffers/builder.py | def CreateNumpyVector(self, x):
"""CreateNumpyVector writes a numpy array into the buffer."""
if np is None:
# Numpy is required for this feature
raise NumpyRequiredForThisFeature("Numpy was not found.")
if not isinstance(x, np.ndarray):
raise TypeError("non-numpy-ndarray passed to CreateNumpyVector")
if x.dtype.kind not in ['b', 'i', 'u', 'f']:
raise TypeError("numpy-ndarray holds elements of unsupported datatype")
if x.ndim > 1:
raise TypeError("multidimensional-ndarray passed to CreateNumpyVector")
self.StartVector(x.itemsize, x.size, x.dtype.alignment)
# Ensure little endian byte ordering
if x.dtype.str[0] == "<":
x_lend = x
else:
x_lend = x.byteswap(inplace=False)
# Calculate total length
l = UOffsetTFlags.py_type(x_lend.itemsize * x_lend.size)
## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
## @endcond
# tobytes ensures c_contiguous ordering
self.Bytes[self.Head():self.Head()+l] = x_lend.tobytes(order='C')
return self.EndVector(x.size) | def CreateNumpyVector(self, x):
"""CreateNumpyVector writes a numpy array into the buffer."""
if np is None:
# Numpy is required for this feature
raise NumpyRequiredForThisFeature("Numpy was not found.")
if not isinstance(x, np.ndarray):
raise TypeError("non-numpy-ndarray passed to CreateNumpyVector")
if x.dtype.kind not in ['b', 'i', 'u', 'f']:
raise TypeError("numpy-ndarray holds elements of unsupported datatype")
if x.ndim > 1:
raise TypeError("multidimensional-ndarray passed to CreateNumpyVector")
self.StartVector(x.itemsize, x.size, x.dtype.alignment)
# Ensure little endian byte ordering
if x.dtype.str[0] == "<":
x_lend = x
else:
x_lend = x.byteswap(inplace=False)
# Calculate total length
l = UOffsetTFlags.py_type(x_lend.itemsize * x_lend.size)
## @cond FLATBUFFERS_INTERNAL
self.head = UOffsetTFlags.py_type(self.Head() - l)
## @endcond
# tobytes ensures c_contiguous ordering
self.Bytes[self.Head():self.Head()+l] = x_lend.tobytes(order='C')
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train | Builder.assertStructIsInline | Structs are always stored inline, so need to be created right
where they are used. You'll get this error if you created it
elsewhere. | python/flatbuffers/builder.py | def assertStructIsInline(self, obj):
"""
Structs are always stored inline, so need to be created right
where they are used. You'll get this error if you created it
elsewhere.
"""
N.enforce_number(obj, N.UOffsetTFlags)
if obj != self.Offset():
msg = ("flatbuffers: Tried to write a Struct at an Offset that "
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raise StructIsNotInlineError(msg) | def assertStructIsInline(self, obj):
"""
Structs are always stored inline, so need to be created right
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elsewhere.
"""
N.enforce_number(obj, N.UOffsetTFlags)
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msg = ("flatbuffers: Tried to write a Struct at an Offset that "
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raise StructIsNotInlineError(msg) | [
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train | Builder.Slot | Slot sets the vtable key `voffset` to the current location in the
buffer. | python/flatbuffers/builder.py | def Slot(self, slotnum):
"""
Slot sets the vtable key `voffset` to the current location in the
buffer.
"""
self.assertNested()
self.current_vtable[slotnum] = self.Offset() | def Slot(self, slotnum):
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Slot sets the vtable key `voffset` to the current location in the
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self.assertNested()
self.current_vtable[slotnum] = self.Offset() | [
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train | Builder.PrependUOffsetTRelativeSlot | PrependUOffsetTRelativeSlot prepends an UOffsetT onto the object at
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train | Builder.Place | Place prepends a value specified by `flags` to the Builder,
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Place prepends a value specified by `flags` to the Builder,
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Place prepends a value specified by `flags` to the Builder,
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train | Builder.PlaceVOffsetT | PlaceVOffsetT prepends a VOffsetT to the Builder, without checking
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train | Builder.PlaceSOffsetT | PlaceSOffsetT prepends a SOffsetT to the Builder, without checking
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"""PlaceSOffsetT prepends a SOffsetT to the Builder, without checking
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"""PlaceUOffsetT prepends a UOffsetT to the Builder, without checking
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train | site_data_dir | r"""Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
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Only applied when appname is present.
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returned, or '/usr/local/share/<AppName>',
if XDG_DATA_DIRS is not set
Typical site data directories are:
Mac OS X: /Library/Application Support/<AppName>
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
For Unix, this is using the $XDG_DATA_DIRS[0] default.
WARNING: Do not use this on Windows. See the Vista-Fail note above for why. | pipenv/vendor/appdirs.py | def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
r"""Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
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Mac OS X: /Library/Application Support/<AppName>
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
For Unix, this is using the $XDG_DATA_DIRS[0] default.
WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
"""
if system == "win32":
if appauthor is None:
appauthor = appname
path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
if appname:
if appauthor is not False:
path = os.path.join(path, appauthor, appname)
else:
path = os.path.join(path, appname)
elif system == 'darwin':
path = os.path.expanduser('/Library/Application Support')
if appname:
path = os.path.join(path, appname)
else:
# XDG default for $XDG_DATA_DIRS
# only first, if multipath is False
path = os.getenv('XDG_DATA_DIRS',
os.pathsep.join(['/usr/local/share', '/usr/share']))
pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
if appname:
if version:
appname = os.path.join(appname, version)
pathlist = [os.sep.join([x, appname]) for x in pathlist]
if multipath:
path = os.pathsep.join(pathlist)
else:
path = pathlist[0]
return path
if appname and version:
path = os.path.join(path, version)
return path | def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
r"""Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
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"version" is an optional version path element to append to the
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"multipath" is an optional parameter only applicable to *nix
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Mac OS X: /Library/Application Support/<AppName>
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
For Unix, this is using the $XDG_DATA_DIRS[0] default.
WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
"""
if system == "win32":
if appauthor is None:
appauthor = appname
path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
if appname:
if appauthor is not False:
path = os.path.join(path, appauthor, appname)
else:
path = os.path.join(path, appname)
elif system == 'darwin':
path = os.path.expanduser('/Library/Application Support')
if appname:
path = os.path.join(path, appname)
else:
# XDG default for $XDG_DATA_DIRS
# only first, if multipath is False
path = os.getenv('XDG_DATA_DIRS',
os.pathsep.join(['/usr/local/share', '/usr/share']))
pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
if appname:
if version:
appname = os.path.join(appname, version)
pathlist = [os.sep.join([x, appname]) for x in pathlist]
if multipath:
path = os.pathsep.join(pathlist)
else:
path = pathlist[0]
return path
if appname and version:
path = os.path.join(path, version)
return path | [
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train | user_config_dir | r"""Return full path to the user-specific config dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
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"version" is an optional version path element to append to the
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Only applied when appname is present.
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network setup for roaming profiles, this user data will be
sync'd on login. See
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
for a discussion of issues.
Typical user config directories are:
Mac OS X: same as user_data_dir
Unix: ~/.config/<AppName> # or in $XDG_CONFIG_HOME, if defined
Win *: same as user_data_dir
For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
That means, by default "~/.config/<AppName>". | pipenv/vendor/appdirs.py | def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
r"""Return full path to the user-specific config dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
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Only applied when appname is present.
"roaming" (boolean, default False) can be set True to use the Windows
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network setup for roaming profiles, this user data will be
sync'd on login. See
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
for a discussion of issues.
Typical user config directories are:
Mac OS X: same as user_data_dir
Unix: ~/.config/<AppName> # or in $XDG_CONFIG_HOME, if defined
Win *: same as user_data_dir
For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
That means, by default "~/.config/<AppName>".
"""
if system in ["win32", "darwin"]:
path = user_data_dir(appname, appauthor, None, roaming)
else:
path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config"))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path | def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
r"""Return full path to the user-specific config dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
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Only applied when appname is present.
"roaming" (boolean, default False) can be set True to use the Windows
roaming appdata directory. That means that for users on a Windows
network setup for roaming profiles, this user data will be
sync'd on login. See
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
for a discussion of issues.
Typical user config directories are:
Mac OS X: same as user_data_dir
Unix: ~/.config/<AppName> # or in $XDG_CONFIG_HOME, if defined
Win *: same as user_data_dir
For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
That means, by default "~/.config/<AppName>".
"""
if system in ["win32", "darwin"]:
path = user_data_dir(appname, appauthor, None, roaming)
else:
path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config"))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path | [
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train | request | Constructs and sends a :class:`Request <Request>`.
:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
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:param data: (optional) Dictionary, list of tuples, bytes, or file-like
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:param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
:param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
:param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
:param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
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:param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
:param timeout: (optional) How many seconds to wait for the server to send data
before giving up, as a float, or a :ref:`(connect timeout, read
timeout) <timeouts>` tuple.
:type timeout: float or tuple
:param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.
:type allow_redirects: bool
:param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
:param verify: (optional) Either a boolean, in which case it controls whether we verify
the server's TLS certificate, or a string, in which case it must be a path
to a CA bundle to use. Defaults to ``True``.
:param stream: (optional) if ``False``, the response content will be immediately downloaded.
:param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
:return: :class:`Response <Response>` object
:rtype: requests.Response
Usage::
>>> import requests
>>> req = requests.request('GET', 'https://httpbin.org/get')
<Response [200]> | pipenv/vendor/requests/api.py | def request(method, url, **kwargs):
"""Constructs and sends a :class:`Request <Request>`.
:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
in the body of the :class:`Request`.
:param data: (optional) Dictionary, list of tuples, bytes, or file-like
object to send in the body of the :class:`Request`.
:param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
:param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
:param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
:param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string
defining the content type of the given file and ``custom_headers`` a dict-like object containing additional headers
to add for the file.
:param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
:param timeout: (optional) How many seconds to wait for the server to send data
before giving up, as a float, or a :ref:`(connect timeout, read
timeout) <timeouts>` tuple.
:type timeout: float or tuple
:param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.
:type allow_redirects: bool
:param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
:param verify: (optional) Either a boolean, in which case it controls whether we verify
the server's TLS certificate, or a string, in which case it must be a path
to a CA bundle to use. Defaults to ``True``.
:param stream: (optional) if ``False``, the response content will be immediately downloaded.
:param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
:return: :class:`Response <Response>` object
:rtype: requests.Response
Usage::
>>> import requests
>>> req = requests.request('GET', 'https://httpbin.org/get')
<Response [200]>
"""
# By using the 'with' statement we are sure the session is closed, thus we
# avoid leaving sockets open which can trigger a ResourceWarning in some
# cases, and look like a memory leak in others.
with sessions.Session() as session:
return session.request(method=method, url=url, **kwargs) | def request(method, url, **kwargs):
"""Constructs and sends a :class:`Request <Request>`.
:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
in the body of the :class:`Request`.
:param data: (optional) Dictionary, list of tuples, bytes, or file-like
object to send in the body of the :class:`Request`.
:param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
:param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
:param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
:param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string
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:param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
:param timeout: (optional) How many seconds to wait for the server to send data
before giving up, as a float, or a :ref:`(connect timeout, read
timeout) <timeouts>` tuple.
:type timeout: float or tuple
:param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.
:type allow_redirects: bool
:param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
:param verify: (optional) Either a boolean, in which case it controls whether we verify
the server's TLS certificate, or a string, in which case it must be a path
to a CA bundle to use. Defaults to ``True``.
:param stream: (optional) if ``False``, the response content will be immediately downloaded.
:param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
:return: :class:`Response <Response>` object
:rtype: requests.Response
Usage::
>>> import requests
>>> req = requests.request('GET', 'https://httpbin.org/get')
<Response [200]>
"""
# By using the 'with' statement we are sure the session is closed, thus we
# avoid leaving sockets open which can trigger a ResourceWarning in some
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with sessions.Session() as session:
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train | get | r"""Sends a GET request.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
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:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
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train | dump | Writes out dict as toml to a file
Args:
o: Object to dump into toml
f: File descriptor where the toml should be stored
Returns:
String containing the toml corresponding to dictionary
Raises:
TypeError: When anything other than file descriptor is passed | pipenv/vendor/toml/encoder.py | def dump(o, f):
"""Writes out dict as toml to a file
Args:
o: Object to dump into toml
f: File descriptor where the toml should be stored
Returns:
String containing the toml corresponding to dictionary
Raises:
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"""
if not f.write:
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f.write(d)
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o: Object to dump into toml
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String containing the toml corresponding to dictionary
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train | dumps | Stringifies input dict as toml
Args:
o: Object to dump into toml
preserve: Boolean parameter. If true, preserve inline tables.
Returns:
String containing the toml corresponding to dict | pipenv/vendor/toml/encoder.py | def dumps(o, encoder=None):
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o: Object to dump into toml
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String containing the toml corresponding to dict
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train | TomlEncoder.dump_inline_table | Preserve inline table in its compact syntax instead of expanding
into subsection.
https://github.com/toml-lang/toml#user-content-inline-table | pipenv/vendor/toml/encoder.py | def dump_inline_table(self, section):
"""Preserve inline table in its compact syntax instead of expanding
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https://github.com/toml-lang/toml#user-content-inline-table
"""
retval = ""
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train | _is_env_truthy | An environment variable is truthy if it exists and isn't one of (0, false, no, off) | pipenv/environments.py | def _is_env_truthy(name):
"""An environment variable is truthy if it exists and isn't one of (0, false, no, off)
"""
if name not in os.environ:
return False
return os.environ.get(name).lower() not in ("0", "false", "no", "off") | def _is_env_truthy(name):
"""An environment variable is truthy if it exists and isn't one of (0, false, no, off)
"""
if name not in os.environ:
return False
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train | is_in_virtualenv | Check virtualenv membership dynamically
:return: True or false depending on whether we are in a regular virtualenv or not
:rtype: bool | pipenv/environments.py | def is_in_virtualenv():
"""
Check virtualenv membership dynamically
:return: True or false depending on whether we are in a regular virtualenv or not
:rtype: bool
"""
pipenv_active = os.environ.get("PIPENV_ACTIVE", False)
virtual_env = None
use_system = False
ignore_virtualenvs = bool(os.environ.get("PIPENV_IGNORE_VIRTUALENVS", False))
if not pipenv_active and not ignore_virtualenvs:
virtual_env = os.environ.get("VIRTUAL_ENV")
use_system = bool(virtual_env)
return (use_system or virtual_env) and not (pipenv_active or ignore_virtualenvs) | def is_in_virtualenv():
"""
Check virtualenv membership dynamically
:return: True or false depending on whether we are in a regular virtualenv or not
:rtype: bool
"""
pipenv_active = os.environ.get("PIPENV_ACTIVE", False)
virtual_env = None
use_system = False
ignore_virtualenvs = bool(os.environ.get("PIPENV_IGNORE_VIRTUALENVS", False))
if not pipenv_active and not ignore_virtualenvs:
virtual_env = os.environ.get("VIRTUAL_ENV")
use_system = bool(virtual_env)
return (use_system or virtual_env) and not (pipenv_active or ignore_virtualenvs) | [
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train | unpackb | Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options. | pipenv/patched/notpip/_vendor/msgpack/fallback.py | def unpackb(packed, **kwargs):
"""
Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
"""
unpacker = Unpacker(None, **kwargs)
unpacker.feed(packed)
try:
ret = unpacker._unpack()
except OutOfData:
raise UnpackValueError("Data is not enough.")
if unpacker._got_extradata():
raise ExtraData(ret, unpacker._get_extradata())
return ret | def unpackb(packed, **kwargs):
"""
Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
"""
unpacker = Unpacker(None, **kwargs)
unpacker.feed(packed)
try:
ret = unpacker._unpack()
except OutOfData:
raise UnpackValueError("Data is not enough.")
if unpacker._got_extradata():
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train | Unpacker._consume | Gets rid of the used parts of the buffer. | pipenv/patched/notpip/_vendor/msgpack/fallback.py | def _consume(self):
""" Gets rid of the used parts of the buffer. """
self._stream_offset += self._buff_i - self._buf_checkpoint
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/patched/notpip/_vendor/msgpack/fallback.py#L300-L303 | [
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train | HTTPConnection._new_conn | Establish a socket connection and set nodelay settings on it.
:return: New socket connection. | pipenv/vendor/urllib3/connection.py | def _new_conn(self):
""" Establish a socket connection and set nodelay settings on it.
:return: New socket connection.
"""
extra_kw = {}
if self.source_address:
extra_kw['source_address'] = self.source_address
if self.socket_options:
extra_kw['socket_options'] = self.socket_options
try:
conn = connection.create_connection(
(self._dns_host, self.port), self.timeout, **extra_kw)
except SocketTimeout as e:
raise ConnectTimeoutError(
self, "Connection to %s timed out. (connect timeout=%s)" %
(self.host, self.timeout))
except SocketError as e:
raise NewConnectionError(
self, "Failed to establish a new connection: %s" % e)
return conn | def _new_conn(self):
""" Establish a socket connection and set nodelay settings on it.
:return: New socket connection.
"""
extra_kw = {}
if self.source_address:
extra_kw['source_address'] = self.source_address
if self.socket_options:
extra_kw['socket_options'] = self.socket_options
try:
conn = connection.create_connection(
(self._dns_host, self.port), self.timeout, **extra_kw)
except SocketTimeout as e:
raise ConnectTimeoutError(
self, "Connection to %s timed out. (connect timeout=%s)" %
(self.host, self.timeout))
except SocketError as e:
raise NewConnectionError(
self, "Failed to establish a new connection: %s" % e)
return conn | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/urllib3/connection.py#L145-L170 | [
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train | HTTPConnection.request_chunked | Alternative to the common request method, which sends the
body with chunked encoding and not as one block | pipenv/vendor/urllib3/connection.py | def request_chunked(self, method, url, body=None, headers=None):
"""
Alternative to the common request method, which sends the
body with chunked encoding and not as one block
"""
headers = HTTPHeaderDict(headers if headers is not None else {})
skip_accept_encoding = 'accept-encoding' in headers
skip_host = 'host' in headers
self.putrequest(
method,
url,
skip_accept_encoding=skip_accept_encoding,
skip_host=skip_host
)
for header, value in headers.items():
self.putheader(header, value)
if 'transfer-encoding' not in headers:
self.putheader('Transfer-Encoding', 'chunked')
self.endheaders()
if body is not None:
stringish_types = six.string_types + (bytes,)
if isinstance(body, stringish_types):
body = (body,)
for chunk in body:
if not chunk:
continue
if not isinstance(chunk, bytes):
chunk = chunk.encode('utf8')
len_str = hex(len(chunk))[2:]
self.send(len_str.encode('utf-8'))
self.send(b'\r\n')
self.send(chunk)
self.send(b'\r\n')
# After the if clause, to always have a closed body
self.send(b'0\r\n\r\n') | def request_chunked(self, method, url, body=None, headers=None):
"""
Alternative to the common request method, which sends the
body with chunked encoding and not as one block
"""
headers = HTTPHeaderDict(headers if headers is not None else {})
skip_accept_encoding = 'accept-encoding' in headers
skip_host = 'host' in headers
self.putrequest(
method,
url,
skip_accept_encoding=skip_accept_encoding,
skip_host=skip_host
)
for header, value in headers.items():
self.putheader(header, value)
if 'transfer-encoding' not in headers:
self.putheader('Transfer-Encoding', 'chunked')
self.endheaders()
if body is not None:
stringish_types = six.string_types + (bytes,)
if isinstance(body, stringish_types):
body = (body,)
for chunk in body:
if not chunk:
continue
if not isinstance(chunk, bytes):
chunk = chunk.encode('utf8')
len_str = hex(len(chunk))[2:]
self.send(len_str.encode('utf-8'))
self.send(b'\r\n')
self.send(chunk)
self.send(b'\r\n')
# After the if clause, to always have a closed body
self.send(b'0\r\n\r\n') | [
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train | VerifiedHTTPSConnection.set_cert | This method should only be called once, before the connection is used. | pipenv/vendor/urllib3/connection.py | def set_cert(self, key_file=None, cert_file=None,
cert_reqs=None, ca_certs=None,
assert_hostname=None, assert_fingerprint=None,
ca_cert_dir=None):
"""
This method should only be called once, before the connection is used.
"""
# If cert_reqs is not provided, we can try to guess. If the user gave
# us a cert database, we assume they want to use it: otherwise, if
# they gave us an SSL Context object we should use whatever is set for
# it.
if cert_reqs is None:
if ca_certs or ca_cert_dir:
cert_reqs = 'CERT_REQUIRED'
elif self.ssl_context is not None:
cert_reqs = self.ssl_context.verify_mode
self.key_file = key_file
self.cert_file = cert_file
self.cert_reqs = cert_reqs
self.assert_hostname = assert_hostname
self.assert_fingerprint = assert_fingerprint
self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir) | def set_cert(self, key_file=None, cert_file=None,
cert_reqs=None, ca_certs=None,
assert_hostname=None, assert_fingerprint=None,
ca_cert_dir=None):
"""
This method should only be called once, before the connection is used.
"""
# If cert_reqs is not provided, we can try to guess. If the user gave
# us a cert database, we assume they want to use it: otherwise, if
# they gave us an SSL Context object we should use whatever is set for
# it.
if cert_reqs is None:
if ca_certs or ca_cert_dir:
cert_reqs = 'CERT_REQUIRED'
elif self.ssl_context is not None:
cert_reqs = self.ssl_context.verify_mode
self.key_file = key_file
self.cert_file = cert_file
self.cert_reqs = cert_reqs
self.assert_hostname = assert_hostname
self.assert_fingerprint = assert_fingerprint
self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir) | [
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train | prettify_exc | Catch known errors and prettify them instead of showing the
entire traceback, for better UX | pipenv/exceptions.py | def prettify_exc(error):
"""Catch known errors and prettify them instead of showing the
entire traceback, for better UX"""
matched_exceptions = [k for k in KNOWN_EXCEPTIONS.keys() if k in error]
if not matched_exceptions:
return "{}".format(vistir.misc.decode_for_output(error))
errors = []
for match in matched_exceptions:
_, error, info = error.rpartition(KNOWN_EXCEPTIONS[match])
errors.append("{} {}".format(error, info))
return "\n".join(errors) | def prettify_exc(error):
"""Catch known errors and prettify them instead of showing the
entire traceback, for better UX"""
matched_exceptions = [k for k in KNOWN_EXCEPTIONS.keys() if k in error]
if not matched_exceptions:
return "{}".format(vistir.misc.decode_for_output(error))
errors = []
for match in matched_exceptions:
_, error, info = error.rpartition(KNOWN_EXCEPTIONS[match])
errors.append("{} {}".format(error, info))
return "\n".join(errors) | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/exceptions.py#L412-L423 | [
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train | get_stream_handle | Get the OS appropriate handle for the corresponding output stream.
:param str stream: The the stream to get the handle for
:return: A handle to the appropriate stream, either a ctypes buffer
or **sys.stdout** or **sys.stderr**. | pipenv/vendor/vistir/cursor.py | def get_stream_handle(stream=sys.stdout):
"""
Get the OS appropriate handle for the corresponding output stream.
:param str stream: The the stream to get the handle for
:return: A handle to the appropriate stream, either a ctypes buffer
or **sys.stdout** or **sys.stderr**.
"""
handle = stream
if os.name == "nt":
from ctypes import windll
handle_id = WIN_STDOUT_HANDLE_ID
handle = windll.kernel32.GetStdHandle(handle_id)
return handle | def get_stream_handle(stream=sys.stdout):
"""
Get the OS appropriate handle for the corresponding output stream.
:param str stream: The the stream to get the handle for
:return: A handle to the appropriate stream, either a ctypes buffer
or **sys.stdout** or **sys.stderr**.
"""
handle = stream
if os.name == "nt":
from ctypes import windll
handle_id = WIN_STDOUT_HANDLE_ID
handle = windll.kernel32.GetStdHandle(handle_id)
return handle | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/cursor.py#L19-L33 | [
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train | hide_cursor | Hide the console cursor on the given stream
:param stream: The name of the stream to get the handle for
:return: None
:rtype: None | pipenv/vendor/vistir/cursor.py | def hide_cursor(stream=sys.stdout):
"""
Hide the console cursor on the given stream
:param stream: The name of the stream to get the handle for
:return: None
:rtype: None
"""
handle = get_stream_handle(stream=stream)
if os.name == "nt":
from ctypes import windll
cursor_info = CONSOLE_CURSOR_INFO()
windll.kernel32.GetConsoleCursorInfo(handle, ctypes.byref(cursor_info))
cursor_info.visible = False
windll.kernel32.SetConsoleCursorInfo(handle, ctypes.byref(cursor_info))
else:
handle.write("\033[?25l")
handle.flush() | def hide_cursor(stream=sys.stdout):
"""
Hide the console cursor on the given stream
:param stream: The name of the stream to get the handle for
:return: None
:rtype: None
"""
handle = get_stream_handle(stream=stream)
if os.name == "nt":
from ctypes import windll
cursor_info = CONSOLE_CURSOR_INFO()
windll.kernel32.GetConsoleCursorInfo(handle, ctypes.byref(cursor_info))
cursor_info.visible = False
windll.kernel32.SetConsoleCursorInfo(handle, ctypes.byref(cursor_info))
else:
handle.write("\033[?25l")
handle.flush() | [
"Hide",
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/cursor.py#L36-L55 | [
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"CONS... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
train | choice_complete | Returns the completion results for click.core.Choice
Parameters
----------
ctx : click.core.Context
The current context
incomplete :
The string to complete
Returns
-------
[(str, str)]
A list of completion results | pipenv/vendor/click_completion/patch.py | def choice_complete(self, ctx, incomplete):
"""Returns the completion results for click.core.Choice
Parameters
----------
ctx : click.core.Context
The current context
incomplete :
The string to complete
Returns
-------
[(str, str)]
A list of completion results
"""
return [
(c, None) for c in self.choices
if completion_configuration.match_incomplete(c, incomplete)
] | def choice_complete(self, ctx, incomplete):
"""Returns the completion results for click.core.Choice
Parameters
----------
ctx : click.core.Context
The current context
incomplete :
The string to complete
Returns
-------
[(str, str)]
A list of completion results
"""
return [
(c, None) for c in self.choices
if completion_configuration.match_incomplete(c, incomplete)
] | [
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train | _shellcomplete | Internal handler for the bash completion support.
Parameters
----------
cli : click.Command
The main click Command of the program
prog_name : str
The program name on the command line
complete_var : str
The environment variable name used to control the completion behavior (Default value = None) | pipenv/vendor/click_completion/patch.py | def _shellcomplete(cli, prog_name, complete_var=None):
"""Internal handler for the bash completion support.
Parameters
----------
cli : click.Command
The main click Command of the program
prog_name : str
The program name on the command line
complete_var : str
The environment variable name used to control the completion behavior (Default value = None)
"""
if complete_var is None:
complete_var = '_%s_COMPLETE' % (prog_name.replace('-', '_')).upper()
complete_instr = os.environ.get(complete_var)
if not complete_instr:
return
if complete_instr == 'source':
echo(get_code(prog_name=prog_name, env_name=complete_var))
elif complete_instr == 'source-bash':
echo(get_code('bash', prog_name, complete_var))
elif complete_instr == 'source-fish':
echo(get_code('fish', prog_name, complete_var))
elif complete_instr == 'source-powershell':
echo(get_code('powershell', prog_name, complete_var))
elif complete_instr == 'source-zsh':
echo(get_code('zsh', prog_name, complete_var))
elif complete_instr in ['complete', 'complete-bash']:
# keep 'complete' for bash for backward compatibility
do_bash_complete(cli, prog_name)
elif complete_instr == 'complete-fish':
do_fish_complete(cli, prog_name)
elif complete_instr == 'complete-powershell':
do_powershell_complete(cli, prog_name)
elif complete_instr == 'complete-zsh':
do_zsh_complete(cli, prog_name)
elif complete_instr == 'install':
shell, path = install(prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-bash':
shell, path = install(shell='bash', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-fish':
shell, path = install(shell='fish', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-zsh':
shell, path = install(shell='zsh', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-powershell':
shell, path = install(shell='powershell', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
sys.exit() | def _shellcomplete(cli, prog_name, complete_var=None):
"""Internal handler for the bash completion support.
Parameters
----------
cli : click.Command
The main click Command of the program
prog_name : str
The program name on the command line
complete_var : str
The environment variable name used to control the completion behavior (Default value = None)
"""
if complete_var is None:
complete_var = '_%s_COMPLETE' % (prog_name.replace('-', '_')).upper()
complete_instr = os.environ.get(complete_var)
if not complete_instr:
return
if complete_instr == 'source':
echo(get_code(prog_name=prog_name, env_name=complete_var))
elif complete_instr == 'source-bash':
echo(get_code('bash', prog_name, complete_var))
elif complete_instr == 'source-fish':
echo(get_code('fish', prog_name, complete_var))
elif complete_instr == 'source-powershell':
echo(get_code('powershell', prog_name, complete_var))
elif complete_instr == 'source-zsh':
echo(get_code('zsh', prog_name, complete_var))
elif complete_instr in ['complete', 'complete-bash']:
# keep 'complete' for bash for backward compatibility
do_bash_complete(cli, prog_name)
elif complete_instr == 'complete-fish':
do_fish_complete(cli, prog_name)
elif complete_instr == 'complete-powershell':
do_powershell_complete(cli, prog_name)
elif complete_instr == 'complete-zsh':
do_zsh_complete(cli, prog_name)
elif complete_instr == 'install':
shell, path = install(prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-bash':
shell, path = install(shell='bash', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-fish':
shell, path = install(shell='fish', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-zsh':
shell, path = install(shell='zsh', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
elif complete_instr == 'install-powershell':
shell, path = install(shell='powershell', prog_name=prog_name, env_name=complete_var)
click.echo('%s completion installed in %s' % (shell, path))
sys.exit() | [
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"."
] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/click_completion/patch.py#L81-L133 | [
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train | patch | Patch click | pipenv/vendor/click_completion/patch.py | def patch():
"""Patch click"""
import click
click.types.ParamType.complete = param_type_complete
click.types.Choice.complete = choice_complete
click.core.MultiCommand.get_command_short_help = multicommand_get_command_short_help
click.core._bashcomplete = _shellcomplete | def patch():
"""Patch click"""
import click
click.types.ParamType.complete = param_type_complete
click.types.Choice.complete = choice_complete
click.core.MultiCommand.get_command_short_help = multicommand_get_command_short_help
click.core._bashcomplete = _shellcomplete | [
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train | parse_expr | expr ::= seq ( '|' seq )* ; | pipenv/vendor/docopt.py | def parse_expr(tokens, options):
"""expr ::= seq ( '|' seq )* ;"""
seq = parse_seq(tokens, options)
if tokens.current() != '|':
return seq
result = [Required(*seq)] if len(seq) > 1 else seq
while tokens.current() == '|':
tokens.move()
seq = parse_seq(tokens, options)
result += [Required(*seq)] if len(seq) > 1 else seq
return [Either(*result)] if len(result) > 1 else result | def parse_expr(tokens, options):
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seq = parse_seq(tokens, options)
if tokens.current() != '|':
return seq
result = [Required(*seq)] if len(seq) > 1 else seq
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return [Either(*result)] if len(result) > 1 else result | [
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train | parse_seq | seq ::= ( atom [ '...' ] )* ; | pipenv/vendor/docopt.py | def parse_seq(tokens, options):
"""seq ::= ( atom [ '...' ] )* ;"""
result = []
while tokens.current() not in [None, ']', ')', '|']:
atom = parse_atom(tokens, options)
if tokens.current() == '...':
atom = [OneOrMore(*atom)]
tokens.move()
result += atom
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"""seq ::= ( atom [ '...' ] )* ;"""
result = []
while tokens.current() not in [None, ']', ')', '|']:
atom = parse_atom(tokens, options)
if tokens.current() == '...':
atom = [OneOrMore(*atom)]
tokens.move()
result += atom
return result | [
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train | parse_argv | Parse command-line argument vector.
If options_first:
argv ::= [ long | shorts ]* [ argument ]* [ '--' [ argument ]* ] ;
else:
argv ::= [ long | shorts | argument ]* [ '--' [ argument ]* ] ; | pipenv/vendor/docopt.py | def parse_argv(tokens, options, options_first=False):
"""Parse command-line argument vector.
If options_first:
argv ::= [ long | shorts ]* [ argument ]* [ '--' [ argument ]* ] ;
else:
argv ::= [ long | shorts | argument ]* [ '--' [ argument ]* ] ;
"""
parsed = []
while tokens.current() is not None:
if tokens.current() == '--':
return parsed + [Argument(None, v) for v in tokens]
elif tokens.current().startswith('--'):
parsed += parse_long(tokens, options)
elif tokens.current().startswith('-') and tokens.current() != '-':
parsed += parse_shorts(tokens, options)
elif options_first:
return parsed + [Argument(None, v) for v in tokens]
else:
parsed.append(Argument(None, tokens.move()))
return parsed | def parse_argv(tokens, options, options_first=False):
"""Parse command-line argument vector.
If options_first:
argv ::= [ long | shorts ]* [ argument ]* [ '--' [ argument ]* ] ;
else:
argv ::= [ long | shorts | argument ]* [ '--' [ argument ]* ] ;
"""
parsed = []
while tokens.current() is not None:
if tokens.current() == '--':
return parsed + [Argument(None, v) for v in tokens]
elif tokens.current().startswith('--'):
parsed += parse_long(tokens, options)
elif tokens.current().startswith('-') and tokens.current() != '-':
parsed += parse_shorts(tokens, options)
elif options_first:
return parsed + [Argument(None, v) for v in tokens]
else:
parsed.append(Argument(None, tokens.move()))
return parsed | [
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train | unnest | Flatten an arbitrarily nested iterable
:param elem: An iterable to flatten
:type elem: :class:`~collections.Iterable`
>>> nested_iterable = (1234, (3456, 4398345, (234234)), (2396, (23895750, 9283798, 29384, (289375983275, 293759, 2347, (2098, 7987, 27599)))))
>>> list(vistir.misc.unnest(nested_iterable))
[1234, 3456, 4398345, 234234, 2396, 23895750, 9283798, 29384, 289375983275, 293759, 2347, 2098, 7987, 27599] | pipenv/vendor/vistir/misc.py | def unnest(elem):
"""Flatten an arbitrarily nested iterable
:param elem: An iterable to flatten
:type elem: :class:`~collections.Iterable`
>>> nested_iterable = (1234, (3456, 4398345, (234234)), (2396, (23895750, 9283798, 29384, (289375983275, 293759, 2347, (2098, 7987, 27599)))))
>>> list(vistir.misc.unnest(nested_iterable))
[1234, 3456, 4398345, 234234, 2396, 23895750, 9283798, 29384, 289375983275, 293759, 2347, 2098, 7987, 27599]
"""
if isinstance(elem, Iterable) and not isinstance(elem, six.string_types):
elem, target = tee(elem, 2)
else:
target = elem
for el in target:
if isinstance(el, Iterable) and not isinstance(el, six.string_types):
el, el_copy = tee(el, 2)
for sub in unnest(el_copy):
yield sub
else:
yield el | def unnest(elem):
"""Flatten an arbitrarily nested iterable
:param elem: An iterable to flatten
:type elem: :class:`~collections.Iterable`
>>> nested_iterable = (1234, (3456, 4398345, (234234)), (2396, (23895750, 9283798, 29384, (289375983275, 293759, 2347, (2098, 7987, 27599)))))
>>> list(vistir.misc.unnest(nested_iterable))
[1234, 3456, 4398345, 234234, 2396, 23895750, 9283798, 29384, 289375983275, 293759, 2347, 2098, 7987, 27599]
"""
if isinstance(elem, Iterable) and not isinstance(elem, six.string_types):
elem, target = tee(elem, 2)
else:
target = elem
for el in target:
if isinstance(el, Iterable) and not isinstance(el, six.string_types):
el, el_copy = tee(el, 2)
for sub in unnest(el_copy):
yield sub
else:
yield el | [
"Flatten",
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"iterable"
] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L74-L95 | [
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train | run | Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd: A list representing the command you want to run.
:param dict env: Additional environment settings to pass through to the subprocess.
:param bool return_object: When True, returns the whole subprocess instance
:param bool block: When False, returns a potentially still-running :class:`subprocess.Popen` instance
:param str cwd: Current working directory contect to use for spawning the subprocess.
:param bool verbose: Whether to print stdout in real time when non-blocking.
:param bool nospin: Whether to disable the cli spinner.
:param str spinner_name: The name of the spinner to use if enabled, defaults to bouncingBar
:param bool combine_stderr: Optionally merge stdout and stderr in the subprocess, false if nonblocking.
:param int dispay_limit: The max width of output lines to display when using a spinner.
:param bool write_to_stdout: Whether to write to stdout when using a spinner, default True.
:returns: A 2-tuple of (output, error) or a :class:`subprocess.Popen` object.
.. Warning:: Merging standard out and standarad error in a nonblocking subprocess
can cause errors in some cases and may not be ideal. Consider disabling
this functionality. | pipenv/vendor/vistir/misc.py | def run(
cmd,
env=None,
return_object=False,
block=True,
cwd=None,
verbose=False,
nospin=False,
spinner_name=None,
combine_stderr=True,
display_limit=200,
write_to_stdout=True,
):
"""Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd: A list representing the command you want to run.
:param dict env: Additional environment settings to pass through to the subprocess.
:param bool return_object: When True, returns the whole subprocess instance
:param bool block: When False, returns a potentially still-running :class:`subprocess.Popen` instance
:param str cwd: Current working directory contect to use for spawning the subprocess.
:param bool verbose: Whether to print stdout in real time when non-blocking.
:param bool nospin: Whether to disable the cli spinner.
:param str spinner_name: The name of the spinner to use if enabled, defaults to bouncingBar
:param bool combine_stderr: Optionally merge stdout and stderr in the subprocess, false if nonblocking.
:param int dispay_limit: The max width of output lines to display when using a spinner.
:param bool write_to_stdout: Whether to write to stdout when using a spinner, default True.
:returns: A 2-tuple of (output, error) or a :class:`subprocess.Popen` object.
.. Warning:: Merging standard out and standarad error in a nonblocking subprocess
can cause errors in some cases and may not be ideal. Consider disabling
this functionality.
"""
_env = os.environ.copy()
if env:
_env.update(env)
if six.PY2:
fs_encode = partial(to_bytes, encoding=locale_encoding)
_env = {fs_encode(k): fs_encode(v) for k, v in _env.items()}
else:
_env = {k: fs_str(v) for k, v in _env.items()}
if not spinner_name:
spinner_name = "bouncingBar"
if six.PY2:
if isinstance(cmd, six.string_types):
cmd = cmd.encode("utf-8")
elif isinstance(cmd, (list, tuple)):
cmd = [c.encode("utf-8") for c in cmd]
if not isinstance(cmd, Script):
cmd = Script.parse(cmd)
if block or not return_object:
combine_stderr = False
start_text = ""
with spinner(
spinner_name=spinner_name,
start_text=start_text,
nospin=nospin,
write_to_stdout=write_to_stdout,
) as sp:
return _create_subprocess(
cmd,
env=_env,
return_object=return_object,
block=block,
cwd=cwd,
verbose=verbose,
spinner=sp,
combine_stderr=combine_stderr,
start_text=start_text,
write_to_stdout=True,
) | def run(
cmd,
env=None,
return_object=False,
block=True,
cwd=None,
verbose=False,
nospin=False,
spinner_name=None,
combine_stderr=True,
display_limit=200,
write_to_stdout=True,
):
"""Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd: A list representing the command you want to run.
:param dict env: Additional environment settings to pass through to the subprocess.
:param bool return_object: When True, returns the whole subprocess instance
:param bool block: When False, returns a potentially still-running :class:`subprocess.Popen` instance
:param str cwd: Current working directory contect to use for spawning the subprocess.
:param bool verbose: Whether to print stdout in real time when non-blocking.
:param bool nospin: Whether to disable the cli spinner.
:param str spinner_name: The name of the spinner to use if enabled, defaults to bouncingBar
:param bool combine_stderr: Optionally merge stdout and stderr in the subprocess, false if nonblocking.
:param int dispay_limit: The max width of output lines to display when using a spinner.
:param bool write_to_stdout: Whether to write to stdout when using a spinner, default True.
:returns: A 2-tuple of (output, error) or a :class:`subprocess.Popen` object.
.. Warning:: Merging standard out and standarad error in a nonblocking subprocess
can cause errors in some cases and may not be ideal. Consider disabling
this functionality.
"""
_env = os.environ.copy()
if env:
_env.update(env)
if six.PY2:
fs_encode = partial(to_bytes, encoding=locale_encoding)
_env = {fs_encode(k): fs_encode(v) for k, v in _env.items()}
else:
_env = {k: fs_str(v) for k, v in _env.items()}
if not spinner_name:
spinner_name = "bouncingBar"
if six.PY2:
if isinstance(cmd, six.string_types):
cmd = cmd.encode("utf-8")
elif isinstance(cmd, (list, tuple)):
cmd = [c.encode("utf-8") for c in cmd]
if not isinstance(cmd, Script):
cmd = Script.parse(cmd)
if block or not return_object:
combine_stderr = False
start_text = ""
with spinner(
spinner_name=spinner_name,
start_text=start_text,
nospin=nospin,
write_to_stdout=write_to_stdout,
) as sp:
return _create_subprocess(
cmd,
env=_env,
return_object=return_object,
block=block,
cwd=cwd,
verbose=verbose,
spinner=sp,
combine_stderr=combine_stderr,
start_text=start_text,
write_to_stdout=True,
) | [
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train | load_path | Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenvs/requirementslib-5MhGuG3C/bin/python")
['', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python37.zip', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/lib-dynload', '/home/user/.pyenv/versions/3.7.0/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/site-packages', '/home/user/git/requirementslib/src'] | pipenv/vendor/vistir/misc.py | def load_path(python):
"""Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenvs/requirementslib-5MhGuG3C/bin/python")
['', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python37.zip', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/lib-dynload', '/home/user/.pyenv/versions/3.7.0/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/site-packages', '/home/user/git/requirementslib/src']
"""
python = Path(python).as_posix()
out, err = run(
[python, "-c", "import json, sys; print(json.dumps(sys.path))"], nospin=True
)
if out:
return json.loads(out)
else:
return [] | def load_path(python):
"""Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenvs/requirementslib-5MhGuG3C/bin/python")
['', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python37.zip', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/lib-dynload', '/home/user/.pyenv/versions/3.7.0/lib/python3.7', '/home/user/.virtualenvs/requirementslib-5MhGuG3C/lib/python3.7/site-packages', '/home/user/git/requirementslib/src']
"""
python = Path(python).as_posix()
out, err = run(
[python, "-c", "import json, sys; print(json.dumps(sys.path))"], nospin=True
)
if out:
return json.loads(out)
else:
return [] | [
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train | to_bytes | Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: Corresponding byte representation (for use in filesystem operations)
:rtype: bytes | pipenv/vendor/vistir/misc.py | def to_bytes(string, encoding="utf-8", errors="ignore"):
"""Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: Corresponding byte representation (for use in filesystem operations)
:rtype: bytes
"""
if not errors:
if encoding.lower() == "utf-8":
errors = "surrogateescape" if six.PY3 else "ignore"
else:
errors = "strict"
if isinstance(string, bytes):
if encoding.lower() == "utf-8":
return string
else:
return string.decode("utf-8").encode(encoding, errors)
elif isinstance(string, memoryview):
return bytes(string)
elif not isinstance(string, six.string_types):
try:
if six.PY3:
return six.text_type(string).encode(encoding, errors)
else:
return bytes(string)
except UnicodeEncodeError:
if isinstance(string, Exception):
return b" ".join(to_bytes(arg, encoding, errors) for arg in string)
return six.text_type(string).encode(encoding, errors)
else:
return string.encode(encoding, errors) | def to_bytes(string, encoding="utf-8", errors="ignore"):
"""Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: Corresponding byte representation (for use in filesystem operations)
:rtype: bytes
"""
if not errors:
if encoding.lower() == "utf-8":
errors = "surrogateescape" if six.PY3 else "ignore"
else:
errors = "strict"
if isinstance(string, bytes):
if encoding.lower() == "utf-8":
return string
else:
return string.decode("utf-8").encode(encoding, errors)
elif isinstance(string, memoryview):
return bytes(string)
elif not isinstance(string, six.string_types):
try:
if six.PY3:
return six.text_type(string).encode(encoding, errors)
else:
return bytes(string)
except UnicodeEncodeError:
if isinstance(string, Exception):
return b" ".join(to_bytes(arg, encoding, errors) for arg in string)
return six.text_type(string).encode(encoding, errors)
else:
return string.encode(encoding, errors) | [
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train | to_text | Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: The unicode representation of the string
:rtype: str | pipenv/vendor/vistir/misc.py | def to_text(string, encoding="utf-8", errors=None):
"""Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: The unicode representation of the string
:rtype: str
"""
if not errors:
if encoding.lower() == "utf-8":
errors = "surrogateescape" if six.PY3 else "ignore"
else:
errors = "strict"
if issubclass(type(string), six.text_type):
return string
try:
if not issubclass(type(string), six.string_types):
if six.PY3:
if isinstance(string, bytes):
string = six.text_type(string, encoding, errors)
else:
string = six.text_type(string)
elif hasattr(string, "__unicode__"):
string = six.text_type(string)
else:
string = six.text_type(bytes(string), encoding, errors)
else:
string = string.decode(encoding, errors)
except UnicodeDecodeError:
string = " ".join(to_text(arg, encoding, errors) for arg in string)
return string | def to_text(string, encoding="utf-8", errors=None):
"""Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: The unicode representation of the string
:rtype: str
"""
if not errors:
if encoding.lower() == "utf-8":
errors = "surrogateescape" if six.PY3 else "ignore"
else:
errors = "strict"
if issubclass(type(string), six.text_type):
return string
try:
if not issubclass(type(string), six.string_types):
if six.PY3:
if isinstance(string, bytes):
string = six.text_type(string, encoding, errors)
else:
string = six.text_type(string)
elif hasattr(string, "__unicode__"):
string = six.text_type(string)
else:
string = six.text_type(bytes(string), encoding, errors)
else:
string = string.decode(encoding, errors)
except UnicodeDecodeError:
string = " ".join(to_text(arg, encoding, errors) for arg in string)
return string | [
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train | divide | split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
:rtype: list | pipenv/vendor/vistir/misc.py | def divide(n, iterable):
"""
split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
:rtype: list
"""
seq = tuple(iterable)
q, r = divmod(len(seq), n)
ret = []
for i in range(n):
start = (i * q) + (i if i < r else r)
stop = ((i + 1) * q) + (i + 1 if i + 1 < r else r)
ret.append(iter(seq[start:stop]))
return ret | def divide(n, iterable):
"""
split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
:rtype: list
"""
seq = tuple(iterable)
q, r = divmod(len(seq), n)
ret = []
for i in range(n):
start = (i * q) + (i if i < r else r)
stop = ((i + 1) * q) + (i + 1 if i + 1 < r else r)
ret.append(iter(seq[start:stop]))
return ret | [
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train | getpreferredencoding | Determine the proper output encoding for terminal rendering | pipenv/vendor/vistir/misc.py | def getpreferredencoding():
"""Determine the proper output encoding for terminal rendering"""
# Borrowed from Invoke
# (see https://github.com/pyinvoke/invoke/blob/93af29d/invoke/runners.py#L881)
_encoding = locale.getpreferredencoding(False)
if six.PY2 and not sys.platform == "win32":
_default_encoding = locale.getdefaultlocale()[1]
if _default_encoding is not None:
_encoding = _default_encoding
return _encoding | def getpreferredencoding():
"""Determine the proper output encoding for terminal rendering"""
# Borrowed from Invoke
# (see https://github.com/pyinvoke/invoke/blob/93af29d/invoke/runners.py#L881)
_encoding = locale.getpreferredencoding(False)
if six.PY2 and not sys.platform == "win32":
_default_encoding = locale.getdefaultlocale()[1]
if _default_encoding is not None:
_encoding = _default_encoding
return _encoding | [
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train | decode_for_output | Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A mapping of unicode character ordinals to replacement strings.
:return: A re-encoded string using the preferred encoding
:rtype: str | pipenv/vendor/vistir/misc.py | def decode_for_output(output, target_stream=None, translation_map=None):
"""Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A mapping of unicode character ordinals to replacement strings.
:return: A re-encoded string using the preferred encoding
:rtype: str
"""
if not isinstance(output, six.string_types):
return output
encoding = None
if target_stream is not None:
encoding = getattr(target_stream, "encoding", None)
encoding = get_output_encoding(encoding)
try:
output = _encode(output, encoding=encoding, translation_map=translation_map)
except (UnicodeDecodeError, UnicodeEncodeError):
output = to_native_string(output)
output = _encode(
output, encoding=encoding, errors="replace", translation_map=translation_map
)
return to_text(output, encoding=encoding, errors="replace") | def decode_for_output(output, target_stream=None, translation_map=None):
"""Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A mapping of unicode character ordinals to replacement strings.
:return: A re-encoded string using the preferred encoding
:rtype: str
"""
if not isinstance(output, six.string_types):
return output
encoding = None
if target_stream is not None:
encoding = getattr(target_stream, "encoding", None)
encoding = get_output_encoding(encoding)
try:
output = _encode(output, encoding=encoding, translation_map=translation_map)
except (UnicodeDecodeError, UnicodeEncodeError):
output = to_native_string(output)
output = _encode(
output, encoding=encoding, errors="replace", translation_map=translation_map
)
return to_text(output, encoding=encoding, errors="replace") | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L574-L597 | [
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train | get_canonical_encoding_name | Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str | pipenv/vendor/vistir/misc.py | def get_canonical_encoding_name(name):
# type: (str) -> str
"""
Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str
"""
import codecs
try:
codec = codecs.lookup(name)
except LookupError:
return name
else:
return codec.name | def get_canonical_encoding_name(name):
# type: (str) -> str
"""
Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str
"""
import codecs
try:
codec = codecs.lookup(name)
except LookupError:
return name
else:
return codec.name | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L600-L617 | [
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... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
train | get_wrapped_stream | Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper` | pipenv/vendor/vistir/misc.py | def get_wrapped_stream(stream):
"""
Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper`
"""
if stream is None:
raise TypeError("must provide a stream to wrap")
encoding = getattr(stream, "encoding", None)
encoding = get_output_encoding(encoding)
return StreamWrapper(stream, encoding, "replace", line_buffering=True) | def get_wrapped_stream(stream):
"""
Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper`
"""
if stream is None:
raise TypeError("must provide a stream to wrap")
encoding = getattr(stream, "encoding", None)
encoding = get_output_encoding(encoding)
return StreamWrapper(stream, encoding, "replace", line_buffering=True) | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L620-L633 | [
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train | is_connection_dropped | Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us. | pipenv/vendor/urllib3/util/connection.py | def is_connection_dropped(conn): # Platform-specific
"""
Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us.
"""
sock = getattr(conn, 'sock', False)
if sock is False: # Platform-specific: AppEngine
return False
if sock is None: # Connection already closed (such as by httplib).
return True
try:
# Returns True if readable, which here means it's been dropped
return wait_for_read(sock, timeout=0.0)
except NoWayToWaitForSocketError: # Platform-specific: AppEngine
return False | def is_connection_dropped(conn): # Platform-specific
"""
Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us.
"""
sock = getattr(conn, 'sock', False)
if sock is False: # Platform-specific: AppEngine
return False
if sock is None: # Connection already closed (such as by httplib).
return True
try:
# Returns True if readable, which here means it's been dropped
return wait_for_read(sock, timeout=0.0)
except NoWayToWaitForSocketError: # Platform-specific: AppEngine
return False | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/urllib3/util/connection.py#L7-L26 | [
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"Non... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
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