INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Replace weights of models in the pipeline with those provided in the params dictionary. Can be used as a contextmanager in which case models go back to their original weights after the block. | def use_params(self, params, **cfg):
"""Replace weights of models in the pipeline with those provided in the
params dictionary. Can be used as a contextmanager, in which case,
models go back to their original weights after the block.
params (dict): A dictionary of parameters keyed by model ID.
**cfg: Config parameters.
EXAMPLE:
>>> with nlp.use_params(optimizer.averages):
>>> nlp.to_disk('/tmp/checkpoint')
"""
contexts = [
pipe.use_params(params)
for name, pipe in self.pipeline
if hasattr(pipe, "use_params")
]
# TODO: Having trouble with contextlib
# Workaround: these aren't actually context managers atm.
for context in contexts:
try:
next(context)
except StopIteration:
pass
yield
for context in contexts:
try:
next(context)
except StopIteration:
pass |
Process texts as a stream and yield Doc objects in order. | def pipe(
self,
texts,
as_tuples=False,
n_threads=-1,
batch_size=1000,
disable=[],
cleanup=False,
component_cfg=None,
):
"""Process texts as a stream, and yield `Doc` objects in order.
texts (iterator): A sequence of texts to process.
as_tuples (bool): If set to True, inputs should be a sequence of
(text, context) tuples. Output will then be a sequence of
(doc, context) tuples. Defaults to False.
batch_size (int): The number of texts to buffer.
disable (list): Names of the pipeline components to disable.
cleanup (bool): If True, unneeded strings are freed to control memory
use. Experimental.
component_cfg (dict): An optional dictionary with extra keyword
arguments for specific components.
YIELDS (Doc): Documents in the order of the original text.
DOCS: https://spacy.io/api/language#pipe
"""
if n_threads != -1:
deprecation_warning(Warnings.W016)
if as_tuples:
text_context1, text_context2 = itertools.tee(texts)
texts = (tc[0] for tc in text_context1)
contexts = (tc[1] for tc in text_context2)
docs = self.pipe(
texts,
batch_size=batch_size,
disable=disable,
component_cfg=component_cfg,
)
for doc, context in izip(docs, contexts):
yield (doc, context)
return
docs = (self.make_doc(text) for text in texts)
if component_cfg is None:
component_cfg = {}
for name, proc in self.pipeline:
if name in disable:
continue
kwargs = component_cfg.get(name, {})
# Allow component_cfg to overwrite the top-level kwargs.
kwargs.setdefault("batch_size", batch_size)
if hasattr(proc, "pipe"):
docs = proc.pipe(docs, **kwargs)
else:
# Apply the function, but yield the doc
docs = _pipe(proc, docs, kwargs)
# Track weakrefs of "recent" documents, so that we can see when they
# expire from memory. When they do, we know we don't need old strings.
# This way, we avoid maintaining an unbounded growth in string entries
# in the string store.
recent_refs = weakref.WeakSet()
old_refs = weakref.WeakSet()
# Keep track of the original string data, so that if we flush old strings,
# we can recover the original ones. However, we only want to do this if we're
# really adding strings, to save up-front costs.
original_strings_data = None
nr_seen = 0
for doc in docs:
yield doc
if cleanup:
recent_refs.add(doc)
if nr_seen < 10000:
old_refs.add(doc)
nr_seen += 1
elif len(old_refs) == 0:
old_refs, recent_refs = recent_refs, old_refs
if original_strings_data is None:
original_strings_data = list(self.vocab.strings)
else:
keys, strings = self.vocab.strings._cleanup_stale_strings(
original_strings_data
)
self.vocab._reset_cache(keys, strings)
self.tokenizer._reset_cache(keys)
nr_seen = 0 |
Save the current state to a directory. If a model is loaded this will include the model. | def to_disk(self, path, exclude=tuple(), disable=None):
"""Save the current state to a directory. If a model is loaded, this
will include the model.
path (unicode or Path): Path to a directory, which will be created if
it doesn't exist.
exclude (list): Names of components or serialization fields to exclude.
DOCS: https://spacy.io/api/language#to_disk
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
path = util.ensure_path(path)
serializers = OrderedDict()
serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(p, exclude=["vocab"])
serializers["meta.json"] = lambda p: p.open("w").write(srsly.json_dumps(self.meta))
for name, proc in self.pipeline:
if not hasattr(proc, "name"):
continue
if name in exclude:
continue
if not hasattr(proc, "to_disk"):
continue
serializers[name] = lambda p, proc=proc: proc.to_disk(p, exclude=["vocab"])
serializers["vocab"] = lambda p: self.vocab.to_disk(p)
util.to_disk(path, serializers, exclude) |
Loads state from a directory. Modifies the object in place and returns it. If the saved Language object contains a model the model will be loaded. | def from_disk(self, path, exclude=tuple(), disable=None):
"""Loads state from a directory. Modifies the object in place and
returns it. If the saved `Language` object contains a model, the
model will be loaded.
path (unicode or Path): A path to a directory.
exclude (list): Names of components or serialization fields to exclude.
RETURNS (Language): The modified `Language` object.
DOCS: https://spacy.io/api/language#from_disk
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
path = util.ensure_path(path)
deserializers = OrderedDict()
deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
deserializers["vocab"] = lambda p: self.vocab.from_disk(p) and _fix_pretrained_vectors_name(self)
deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk(p, exclude=["vocab"])
for name, proc in self.pipeline:
if name in exclude:
continue
if not hasattr(proc, "from_disk"):
continue
deserializers[name] = lambda p, proc=proc: proc.from_disk(p, exclude=["vocab"])
if not (path / "vocab").exists() and "vocab" not in exclude:
# Convert to list here in case exclude is (default) tuple
exclude = list(exclude) + ["vocab"]
util.from_disk(path, deserializers, exclude)
self._path = path
return self |
Serialize the current state to a binary string. | def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
"""Serialize the current state to a binary string.
exclude (list): Names of components or serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Language` object.
DOCS: https://spacy.io/api/language#to_bytes
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
serializers = OrderedDict()
serializers["vocab"] = lambda: self.vocab.to_bytes()
serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"])
serializers["meta.json"] = lambda: srsly.json_dumps(self.meta)
for name, proc in self.pipeline:
if name in exclude:
continue
if not hasattr(proc, "to_bytes"):
continue
serializers[name] = lambda proc=proc: proc.to_bytes(exclude=["vocab"])
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
return util.to_bytes(serializers, exclude) |
Load state from a binary string. | def from_bytes(self, bytes_data, exclude=tuple(), disable=None, **kwargs):
"""Load state from a binary string.
bytes_data (bytes): The data to load from.
exclude (list): Names of components or serialization fields to exclude.
RETURNS (Language): The `Language` object.
DOCS: https://spacy.io/api/language#from_bytes
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
deserializers = OrderedDict()
deserializers["meta.json"] = lambda b: self.meta.update(srsly.json_loads(b))
deserializers["vocab"] = lambda b: self.vocab.from_bytes(b) and _fix_pretrained_vectors_name(self)
deserializers["tokenizer"] = lambda b: self.tokenizer.from_bytes(b, exclude=["vocab"])
for name, proc in self.pipeline:
if name in exclude:
continue
if not hasattr(proc, "from_bytes"):
continue
deserializers[name] = lambda b, proc=proc: proc.from_bytes(b, exclude=["vocab"])
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
util.from_bytes(bytes_data, deserializers, exclude)
return self |
Restore the pipeline to its state when DisabledPipes was created. | def restore(self):
"""Restore the pipeline to its state when DisabledPipes was created."""
current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline
unexpected = [name for name, pipe in current if not self.nlp.has_pipe(name)]
if unexpected:
# Don't change the pipeline if we're raising an error.
self.nlp.pipeline = current
raise ValueError(Errors.E008.format(names=unexpected))
self[:] = [] |
Yields all available rules. | def get_loaded_rules(rules_paths):
"""Yields all available rules.
:type rules_paths: [Path]
:rtype: Iterable[Rule]
"""
for path in rules_paths:
if path.name != '__init__.py':
rule = Rule.from_path(path)
if rule.is_enabled:
yield rule |
Yields all rules import paths. | def get_rules_import_paths():
"""Yields all rules import paths.
:rtype: Iterable[Path]
"""
# Bundled rules:
yield Path(__file__).parent.joinpath('rules')
# Rules defined by user:
yield settings.user_dir.joinpath('rules')
# Packages with third-party rules:
for path in sys.path:
for contrib_module in Path(path).glob('thefuck_contrib_*'):
contrib_rules = contrib_module.joinpath('rules')
if contrib_rules.is_dir():
yield contrib_rules |
Returns all enabled rules. | def get_rules():
"""Returns all enabled rules.
:rtype: [Rule]
"""
paths = [rule_path for path in get_rules_import_paths()
for rule_path in sorted(path.glob('*.py'))]
return sorted(get_loaded_rules(paths),
key=lambda rule: rule.priority) |
Yields sorted commands without duplicates. | def organize_commands(corrected_commands):
"""Yields sorted commands without duplicates.
:type corrected_commands: Iterable[thefuck.types.CorrectedCommand]
:rtype: Iterable[thefuck.types.CorrectedCommand]
"""
try:
first_command = next(corrected_commands)
yield first_command
except StopIteration:
return
without_duplicates = {
command for command in sorted(
corrected_commands, key=lambda command: command.priority)
if command != first_command}
sorted_commands = sorted(
without_duplicates,
key=lambda corrected_command: corrected_command.priority)
logs.debug('Corrected commands: '.format(
', '.join(u'{}'.format(cmd) for cmd in [first_command] + sorted_commands)))
for command in sorted_commands:
yield command |
Returns generator with sorted and unique corrected commands. | def get_corrected_commands(command):
"""Returns generator with sorted and unique corrected commands.
:type command: thefuck.types.Command
:rtype: Iterable[thefuck.types.CorrectedCommand]
"""
corrected_commands = (
corrected for rule in get_rules()
if rule.is_match(command)
for corrected in rule.get_corrected_commands(command))
return organize_commands(corrected_commands) |
Fixes previous command. Used when thefuck called without arguments. | def fix_command(known_args):
"""Fixes previous command. Used when `thefuck` called without arguments."""
settings.init(known_args)
with logs.debug_time('Total'):
logs.debug(u'Run with settings: {}'.format(pformat(settings)))
raw_command = _get_raw_command(known_args)
try:
command = types.Command.from_raw_script(raw_command)
except EmptyCommand:
logs.debug('Empty command, nothing to do')
return
corrected_commands = get_corrected_commands(command)
selected_command = select_command(corrected_commands)
if selected_command:
selected_command.run(command)
else:
sys.exit(1) |
Gets command output from shell logger. | def get_output(script):
"""Gets command output from shell logger."""
with logs.debug_time(u'Read output from external shell logger'):
commands = _get_last_n(const.SHELL_LOGGER_LIMIT)
for command in commands:
if command['command'] == script:
lines = _get_output_lines(command['output'])
output = '\n'.join(lines).strip()
return output
else:
logs.warn("Output isn't available in shell logger")
return None |
Returns list of history entries. | def _get_history_lines(self):
"""Returns list of history entries."""
history_file_name = self._get_history_file_name()
if os.path.isfile(history_file_name):
with io.open(history_file_name, 'r',
encoding='utf-8', errors='ignore') as history_file:
lines = history_file.readlines()
if settings.history_limit:
lines = lines[-settings.history_limit:]
for line in lines:
prepared = self._script_from_history(line) \
.strip()
if prepared:
yield prepared |
Split the command using shell - like syntax. | def split_command(self, command):
"""Split the command using shell-like syntax."""
encoded = self.encode_utf8(command)
try:
splitted = [s.replace("??", "\\ ") for s in shlex.split(encoded.replace('\\ ', '??'))]
except ValueError:
splitted = encoded.split(' ')
return self.decode_utf8(splitted) |
Return a shell - escaped version of the string s. | def quote(self, s):
"""Return a shell-escaped version of the string s."""
if six.PY2:
from pipes import quote
else:
from shlex import quote
return quote(s) |
Returns the name and version of the current shell | def info(self):
"""Returns the name and version of the current shell"""
proc = Popen(['fish', '--version'],
stdout=PIPE, stderr=DEVNULL)
version = proc.stdout.read().decode('utf-8').split()[-1]
return u'Fish Shell {}'.format(version) |
Puts command script to shell history. | def _put_to_history(self, command_script):
"""Puts command script to shell history."""
history_file_name = self._get_history_file_name()
if os.path.isfile(history_file_name):
with open(history_file_name, 'a') as history:
entry = self._get_history_line(command_script)
if six.PY2:
history.write(entry.encode('utf-8'))
else:
history.write(entry) |
To get brew default commands on local environment | def _get_brew_commands(brew_path_prefix):
"""To get brew default commands on local environment"""
brew_cmd_path = brew_path_prefix + BREW_CMD_PATH
return [name[:-3] for name in os.listdir(brew_cmd_path)
if name.endswith(('.rb', '.sh'))] |
To get tap s specific commands https:// github. com/ Homebrew/ homebrew/ blob/ master/ Library/ brew. rb#L115 | def _get_brew_tap_specific_commands(brew_path_prefix):
"""To get tap's specific commands
https://github.com/Homebrew/homebrew/blob/master/Library/brew.rb#L115"""
commands = []
brew_taps_path = brew_path_prefix + TAP_PATH
for user in _get_directory_names_only(brew_taps_path):
taps = _get_directory_names_only(brew_taps_path + '/%s' % user)
# Brew Taps's naming rule
# https://github.com/Homebrew/homebrew/blob/master/share/doc/homebrew/brew-tap.md#naming-conventions-and-limitations
taps = (tap for tap in taps if tap.startswith('homebrew-'))
for tap in taps:
tap_cmd_path = brew_taps_path + TAP_CMD_PATH % (user, tap)
if os.path.isdir(tap_cmd_path):
commands += (name.replace('brew-', '').replace('.rb', '')
for name in os.listdir(tap_cmd_path)
if _is_brew_tap_cmd_naming(name))
return commands |
Returns the name and version of the current shell | def info(self):
"""Returns the name and version of the current shell"""
proc = Popen(['zsh', '-c', 'echo $ZSH_VERSION'],
stdout=PIPE, stderr=DEVNULL)
version = proc.stdout.read().decode('utf-8').strip()
return u'ZSH {}'.format(version) |
Resolves git aliases and supports testing for both git and hub. | def git_support(fn, command):
"""Resolves git aliases and supports testing for both git and hub."""
# supports GitHub's `hub` command
# which is recommended to be used with `alias git=hub`
# but at this point, shell aliases have already been resolved
if not is_app(command, 'git', 'hub'):
return False
# perform git aliases expansion
if 'trace: alias expansion:' in command.output:
search = re.search("trace: alias expansion: ([^ ]*) => ([^\n]*)",
command.output)
alias = search.group(1)
# by default git quotes everything, for example:
# 'commit' '--amend'
# which is surprising and does not allow to easily test for
# eg. 'git commit'
expansion = ' '.join(shell.quote(part)
for part in shell.split_command(search.group(2)))
new_script = command.script.replace(alias, expansion)
command = command.update(script=new_script)
return fn(command) |
Yields actions for pressed keys. | def read_actions():
"""Yields actions for pressed keys."""
while True:
key = get_key()
# Handle arrows, j/k (qwerty), and n/e (colemak)
if key in (const.KEY_UP, const.KEY_CTRL_N, 'k', 'e'):
yield const.ACTION_PREVIOUS
elif key in (const.KEY_DOWN, const.KEY_CTRL_P, 'j', 'n'):
yield const.ACTION_NEXT
elif key in (const.KEY_CTRL_C, 'q'):
yield const.ACTION_ABORT
elif key in ('\n', '\r'):
yield const.ACTION_SELECT |
Returns: | def select_command(corrected_commands):
"""Returns:
- the first command when confirmation disabled;
- None when ctrl+c pressed;
- selected command.
:type corrected_commands: Iterable[thefuck.types.CorrectedCommand]
:rtype: thefuck.types.CorrectedCommand | None
"""
try:
selector = CommandSelector(corrected_commands)
except NoRuleMatched:
logs.failed('No fucks given' if get_alias() == 'fuck'
else 'Nothing found')
return
if not settings.require_confirmation:
logs.show_corrected_command(selector.value)
return selector.value
logs.confirm_text(selector.value)
for action in read_actions():
if action == const.ACTION_SELECT:
sys.stderr.write('\n')
return selector.value
elif action == const.ACTION_ABORT:
logs.failed('\nAborted')
return
elif action == const.ACTION_PREVIOUS:
selector.previous()
logs.confirm_text(selector.value)
elif action == const.ACTION_NEXT:
selector.next()
logs.confirm_text(selector.value) |
Create a spawned process. | def _spawn(shell, master_read):
"""Create a spawned process.
Modified version of pty.spawn with terminal size support.
"""
pid, master_fd = pty.fork()
if pid == pty.CHILD:
os.execlp(shell, shell)
try:
mode = tty.tcgetattr(pty.STDIN_FILENO)
tty.setraw(pty.STDIN_FILENO)
restore = True
except tty.error: # This is the same as termios.error
restore = False
_set_pty_size(master_fd)
signal.signal(signal.SIGWINCH, lambda *_: _set_pty_size(master_fd))
try:
pty._copy(master_fd, master_read, pty._read)
except OSError:
if restore:
tty.tcsetattr(pty.STDIN_FILENO, tty.TCSAFLUSH, mode)
os.close(master_fd)
return os.waitpid(pid, 0)[1] |
Logs shell output to the output. | def shell_logger(output):
"""Logs shell output to the `output`.
Works like unix script command with `-f` flag.
"""
if not os.environ.get('SHELL'):
logs.warn("Shell logger doesn't support your platform.")
sys.exit(1)
fd = os.open(output, os.O_CREAT | os.O_TRUNC | os.O_RDWR)
os.write(fd, b'\x00' * const.LOG_SIZE_IN_BYTES)
buffer = mmap.mmap(fd, const.LOG_SIZE_IN_BYTES, mmap.MAP_SHARED, mmap.PROT_WRITE)
return_code = _spawn(os.environ['SHELL'], partial(_read, buffer))
sys.exit(return_code) |
Get output of the script. | def get_output(script, expanded):
"""Get output of the script.
:param script: Console script.
:type script: str
:param expanded: Console script with expanded aliases.
:type expanded: str
:rtype: str
"""
if shell_logger.is_available():
return shell_logger.get_output(script)
if settings.instant_mode:
return read_log.get_output(script)
else:
return rerun.get_output(script, expanded) |
Adds arguments to parser. | def _add_arguments(self):
"""Adds arguments to parser."""
self._parser.add_argument(
'-v', '--version',
action='store_true',
help="show program's version number and exit")
self._parser.add_argument(
'-a', '--alias',
nargs='?',
const=get_alias(),
help='[custom-alias-name] prints alias for current shell')
self._parser.add_argument(
'-l', '--shell-logger',
action='store',
help='log shell output to the file')
self._parser.add_argument(
'--enable-experimental-instant-mode',
action='store_true',
help='enable experimental instant mode, use on your own risk')
self._parser.add_argument(
'-h', '--help',
action='store_true',
help='show this help message and exit')
self._add_conflicting_arguments()
self._parser.add_argument(
'-d', '--debug',
action='store_true',
help='enable debug output')
self._parser.add_argument(
'--force-command',
action='store',
help=SUPPRESS)
self._parser.add_argument(
'command',
nargs='*',
help='command that should be fixed') |
It s too dangerous to use - y and - r together. | def _add_conflicting_arguments(self):
"""It's too dangerous to use `-y` and `-r` together."""
group = self._parser.add_mutually_exclusive_group()
group.add_argument(
'-y', '--yes', '--yeah',
action='store_true',
help='execute fixed command without confirmation')
group.add_argument(
'-r', '--repeat',
action='store_true',
help='repeat on failure') |
Prepares arguments by: | def _prepare_arguments(self, argv):
"""Prepares arguments by:
- removing placeholder and moving arguments after it to beginning,
we need this to distinguish arguments from `command` with ours;
- adding `--` before `command`, so our parse would ignore arguments
of `command`.
"""
if ARGUMENT_PLACEHOLDER in argv:
index = argv.index(ARGUMENT_PLACEHOLDER)
return argv[index + 1:] + ['--'] + argv[:index]
elif argv and not argv[0].startswith('-') and argv[0] != '--':
return ['--'] + argv
else:
return argv |
Get custom npm scripts. | def get_scripts():
"""Get custom npm scripts."""
proc = Popen(['npm', 'run-script'], stdout=PIPE)
should_yeild = False
for line in proc.stdout.readlines():
line = line.decode()
if 'available via `npm run-script`:' in line:
should_yeild = True
continue
if should_yeild and re.match(r'^ [^ ]+', line):
yield line.strip().split(' ')[0] |
Fills settings with values from settings. py and env. | def init(self, args=None):
"""Fills `settings` with values from `settings.py` and env."""
from .logs import exception
self._setup_user_dir()
self._init_settings_file()
try:
self.update(self._settings_from_file())
except Exception:
exception("Can't load settings from file", sys.exc_info())
try:
self.update(self._settings_from_env())
except Exception:
exception("Can't load settings from env", sys.exc_info())
self.update(self._settings_from_args(args)) |
Returns Path object representing the user config resource | def _get_user_dir_path(self):
"""Returns Path object representing the user config resource"""
xdg_config_home = os.environ.get('XDG_CONFIG_HOME', '~/.config')
user_dir = Path(xdg_config_home, 'thefuck').expanduser()
legacy_user_dir = Path('~', '.thefuck').expanduser()
# For backward compatibility use legacy '~/.thefuck' if it exists:
if legacy_user_dir.is_dir():
warn(u'Config path {} is deprecated. Please move to {}'.format(
legacy_user_dir, user_dir))
return legacy_user_dir
else:
return user_dir |
Returns user config dir create it when it doesn t exist. | def _setup_user_dir(self):
"""Returns user config dir, create it when it doesn't exist."""
user_dir = self._get_user_dir_path()
rules_dir = user_dir.joinpath('rules')
if not rules_dir.is_dir():
rules_dir.mkdir(parents=True)
self.user_dir = user_dir |
Loads settings from file. | def _settings_from_file(self):
"""Loads settings from file."""
settings = load_source(
'settings', text_type(self.user_dir.joinpath('settings.py')))
return {key: getattr(settings, key)
for key in const.DEFAULT_SETTINGS.keys()
if hasattr(settings, key)} |
Transforms rules list from env - string to python. | def _rules_from_env(self, val):
"""Transforms rules list from env-string to python."""
val = val.split(':')
if 'DEFAULT_RULES' in val:
val = const.DEFAULT_RULES + [rule for rule in val if rule != 'DEFAULT_RULES']
return val |
Gets priority pairs from env. | def _priority_from_env(self, val):
"""Gets priority pairs from env."""
for part in val.split(':'):
try:
rule, priority = part.split('=')
yield rule, int(priority)
except ValueError:
continue |
Transforms env - strings to python. | def _val_from_env(self, env, attr):
"""Transforms env-strings to python."""
val = os.environ[env]
if attr in ('rules', 'exclude_rules'):
return self._rules_from_env(val)
elif attr == 'priority':
return dict(self._priority_from_env(val))
elif attr in ('wait_command', 'history_limit', 'wait_slow_command',
'num_close_matches'):
return int(val)
elif attr in ('require_confirmation', 'no_colors', 'debug',
'alter_history', 'instant_mode'):
return val.lower() == 'true'
elif attr == 'slow_commands':
return val.split(':')
else:
return val |
Loads settings from env. | def _settings_from_env(self):
"""Loads settings from env."""
return {attr: self._val_from_env(env, attr)
for env, attr in const.ENV_TO_ATTR.items()
if env in os.environ} |
Loads settings from args. | def _settings_from_args(self, args):
"""Loads settings from args."""
if not args:
return {}
from_args = {}
if args.yes:
from_args['require_confirmation'] = not args.yes
if args.debug:
from_args['debug'] = args.debug
if args.repeat:
from_args['repeat'] = args.repeat
return from_args |
When arguments order is wrong first argument will be destination. | def _get_destination(script_parts):
"""When arguments order is wrong first argument will be destination."""
for part in script_parts:
if part not in {'ln', '-s', '--symbolic'} and os.path.exists(part):
return part |
Removes sudo before calling fn and adds it after. | def sudo_support(fn, command):
"""Removes sudo before calling fn and adds it after."""
if not command.script.startswith('sudo '):
return fn(command)
result = fn(command.update(script=command.script[5:]))
if result and isinstance(result, six.string_types):
return u'sudo {}'.format(result)
elif isinstance(result, list):
return [u'sudo {}'.format(x) for x in result]
else:
return result |
Tries to kill the process otherwise just logs a debug message the process will be killed when thefuck terminates. | def _kill_process(proc):
"""Tries to kill the process otherwise just logs a debug message, the
process will be killed when thefuck terminates.
:type proc: Process
"""
try:
proc.kill()
except AccessDenied:
logs.debug(u'Rerun: process PID {} ({}) could not be terminated'.format(
proc.pid, proc.exe())) |
Returns True if we can get output of the command in the settings. wait_command time. | def _wait_output(popen, is_slow):
"""Returns `True` if we can get output of the command in the
`settings.wait_command` time.
Command will be killed if it wasn't finished in the time.
:type popen: Popen
:rtype: bool
"""
proc = Process(popen.pid)
try:
proc.wait(settings.wait_slow_command if is_slow
else settings.wait_command)
return True
except TimeoutExpired:
for child in proc.children(recursive=True):
_kill_process(child)
_kill_process(proc)
return False |
Runs the script and obtains stdin/ stderr. | def get_output(script, expanded):
"""Runs the script and obtains stdin/stderr.
:type script: str
:type expanded: str
:rtype: str | None
"""
env = dict(os.environ)
env.update(settings.env)
is_slow = shlex.split(expanded) in settings.slow_commands
with logs.debug_time(u'Call: {}; with env: {}; is slow: '.format(
script, env, is_slow)):
result = Popen(expanded, shell=True, stdin=PIPE,
stdout=PIPE, stderr=STDOUT, env=env)
if _wait_output(result, is_slow):
output = result.stdout.read().decode('utf-8')
logs.debug(u'Received output: {}'.format(output))
return output
else:
logs.debug(u'Execution timed out!')
return None |
Reads script output from log. | def get_output(script):
"""Reads script output from log.
:type script: str
:rtype: str | None
"""
if six.PY2:
logs.warn('Experimental instant mode is Python 3+ only')
return None
if 'THEFUCK_OUTPUT_LOG' not in os.environ:
logs.warn("Output log isn't specified")
return None
if const.USER_COMMAND_MARK not in os.environ.get('PS1', ''):
logs.warn(
"PS1 doesn't contain user command mark, please ensure "
"that PS1 is not changed after The Fuck alias initialization")
return None
try:
with logs.debug_time(u'Read output from log'):
fd = os.open(os.environ['THEFUCK_OUTPUT_LOG'], os.O_RDONLY)
buffer = mmap.mmap(fd, const.LOG_SIZE_IN_BYTES, mmap.MAP_SHARED, mmap.PROT_READ)
_skip_old_lines(buffer)
lines = _get_output_lines(script, buffer)
output = '\n'.join(lines).strip()
logs.debug(u'Received output: {}'.format(output))
return output
except OSError:
logs.warn("Can't read output log")
return None
except ScriptNotInLog:
logs.warn("Script not found in output log")
return None |
Gets the packages that provide the given command using pkgfile. | def get_pkgfile(command):
""" Gets the packages that provide the given command using `pkgfile`.
If the command is of the form `sudo foo`, searches for the `foo` command
instead.
"""
try:
command = command.strip()
if command.startswith('sudo '):
command = command[5:]
command = command.split(" ")[0]
packages = subprocess.check_output(
['pkgfile', '-b', '-v', command],
universal_newlines=True, stderr=utils.DEVNULL
).splitlines()
return [package.split()[0] for package in packages]
except subprocess.CalledProcessError as err:
if err.returncode == 1 and err.output == "":
return []
else:
raise err |
Returns a list of the child directories of the given parent directory | def _get_sub_dirs(parent):
"""Returns a list of the child directories of the given parent directory"""
return [child for child in os.listdir(parent) if os.path.isdir(os.path.join(parent, child))] |
Attempt to rebuild the path string by spellchecking the directories. If it fails ( i. e. no directories are a close enough match ) then it defaults to the rules of cd_mkdir. Change sensitivity by changing MAX_ALLOWED_DIFF. Default value is 0. 6 | def get_new_command(command):
"""
Attempt to rebuild the path string by spellchecking the directories.
If it fails (i.e. no directories are a close enough match), then it
defaults to the rules of cd_mkdir.
Change sensitivity by changing MAX_ALLOWED_DIFF. Default value is 0.6
"""
dest = command.script_parts[1].split(os.sep)
if dest[-1] == '':
dest = dest[:-1]
if dest[0] == '':
cwd = os.sep
dest = dest[1:]
elif six.PY2:
cwd = os.getcwdu()
else:
cwd = os.getcwd()
for directory in dest:
if directory == ".":
continue
elif directory == "..":
cwd = os.path.split(cwd)[0]
continue
best_matches = get_close_matches(directory, _get_sub_dirs(cwd), cutoff=MAX_ALLOWED_DIFF)
if best_matches:
cwd = os.path.join(cwd, best_matches[0])
else:
return cd_mkdir.get_new_command(command)
return u'cd "{0}"'.format(cwd) |
Returns new command with replaced fields. | def update(self, **kwargs):
"""Returns new command with replaced fields.
:rtype: Command
"""
kwargs.setdefault('script', self.script)
kwargs.setdefault('output', self.output)
return Command(**kwargs) |
Creates instance of Command from a list of script parts. | def from_raw_script(cls, raw_script):
"""Creates instance of `Command` from a list of script parts.
:type raw_script: [basestring]
:rtype: Command
:raises: EmptyCommand
"""
script = format_raw_script(raw_script)
if not script:
raise EmptyCommand
expanded = shell.from_shell(script)
output = get_output(script, expanded)
return cls(expanded, output) |
Creates rule instance from path. | def from_path(cls, path):
"""Creates rule instance from path.
:type path: pathlib.Path
:rtype: Rule
"""
name = path.name[:-3]
with logs.debug_time(u'Importing rule: {};'.format(name)):
rule_module = load_source(name, str(path))
priority = getattr(rule_module, 'priority', DEFAULT_PRIORITY)
return cls(name, rule_module.match,
rule_module.get_new_command,
getattr(rule_module, 'enabled_by_default', True),
getattr(rule_module, 'side_effect', None),
settings.priority.get(name, priority),
getattr(rule_module, 'requires_output', True)) |
Returns True when rule enabled. | def is_enabled(self):
"""Returns `True` when rule enabled.
:rtype: bool
"""
if self.name in settings.exclude_rules:
return False
elif self.name in settings.rules:
return True
elif self.enabled_by_default and ALL_ENABLED in settings.rules:
return True
else:
return False |
Returns True if rule matches the command. | def is_match(self, command):
"""Returns `True` if rule matches the command.
:type command: Command
:rtype: bool
"""
if command.output is None and self.requires_output:
return False
try:
with logs.debug_time(u'Trying rule: {};'.format(self.name)):
if self.match(command):
return True
except Exception:
logs.rule_failed(self, sys.exc_info()) |
Returns generator with corrected commands. | def get_corrected_commands(self, command):
"""Returns generator with corrected commands.
:type command: Command
:rtype: Iterable[CorrectedCommand]
"""
new_commands = self.get_new_command(command)
if not isinstance(new_commands, list):
new_commands = (new_commands,)
for n, new_command in enumerate(new_commands):
yield CorrectedCommand(script=new_command,
side_effect=self.side_effect,
priority=(n + 1) * self.priority) |
Returns fixed commands script. | def _get_script(self):
"""Returns fixed commands script.
If `settings.repeat` is `True`, appends command with second attempt
of running fuck in case fixed command fails again.
"""
if settings.repeat:
repeat_fuck = '{} --repeat {}--force-command {}'.format(
get_alias(),
'--debug ' if settings.debug else '',
shell.quote(self.script))
return shell.or_(self.script, repeat_fuck)
else:
return self.script |
Runs command from rule for passed command. | def run(self, old_cmd):
"""Runs command from rule for passed command.
:type old_cmd: Command
"""
if self.side_effect:
self.side_effect(old_cmd, self.script)
if settings.alter_history:
shell.put_to_history(self.script)
# This depends on correct setting of PYTHONIOENCODING by the alias:
logs.debug(u'PYTHONIOENCODING: {}'.format(
os.environ.get('PYTHONIOENCODING', '!!not-set!!')))
print(self._get_script()) |
Returns parent process pid. | def _get_shell_pid():
"""Returns parent process pid."""
proc = Process(os.getpid())
try:
return proc.parent().pid
except TypeError:
return proc.parent.pid |
Records shell pid to tracker file. | def _record_first_run():
"""Records shell pid to tracker file."""
info = {'pid': _get_shell_pid(),
'time': time.time()}
mode = 'wb' if six.PY2 else 'w'
with _get_not_configured_usage_tracker_path().open(mode) as tracker:
json.dump(info, tracker) |
Returns True when we know that fuck called second time. | def _is_second_run():
"""Returns `True` when we know that `fuck` called second time."""
tracker_path = _get_not_configured_usage_tracker_path()
if not tracker_path.exists():
return False
current_pid = _get_shell_pid()
with tracker_path.open('r') as tracker:
try:
info = json.load(tracker)
except ValueError:
return False
if not (isinstance(info, dict) and info.get('pid') == current_pid):
return False
return (_get_previous_command() == 'fuck' or
time.time() - info.get('time', 0) < const.CONFIGURATION_TIMEOUT) |
Returns True when alias already in shell config. | def _is_already_configured(configuration_details):
"""Returns `True` when alias already in shell config."""
path = Path(configuration_details.path).expanduser()
with path.open('r') as shell_config:
return configuration_details.content in shell_config.read() |
Adds alias to shell config. | def _configure(configuration_details):
"""Adds alias to shell config."""
path = Path(configuration_details.path).expanduser()
with path.open('a') as shell_config:
shell_config.write(u'\n')
shell_config.write(configuration_details.content)
shell_config.write(u'\n') |
Shows useful information about how - to configure alias on a first run and configure automatically on a second. | def main():
"""Shows useful information about how-to configure alias on a first run
and configure automatically on a second.
It'll be only visible when user type fuck and when alias isn't configured.
"""
settings.init()
configuration_details = shell.how_to_configure()
if (
configuration_details and
configuration_details.can_configure_automatically
):
if _is_already_configured(configuration_details):
logs.already_configured(configuration_details)
return
elif _is_second_run():
_configure(configuration_details)
logs.configured_successfully(configuration_details)
return
else:
_record_first_run()
logs.how_to_configure_alias(configuration_details) |
Caches previous calls to the function. | def memoize(fn):
"""Caches previous calls to the function."""
memo = {}
@wraps(fn)
def wrapper(*args, **kwargs):
if not memoize.disabled:
key = pickle.dumps((args, kwargs))
if key not in memo:
memo[key] = fn(*args, **kwargs)
value = memo[key]
else:
# Memoize is disabled, call the function
value = fn(*args, **kwargs)
return value
return wrapper |
Adds default values to settings if it not presented. | def default_settings(params):
"""Adds default values to settings if it not presented.
Usage:
@default_settings({'apt': '/usr/bin/apt'})
def match(command):
print(settings.apt)
"""
def _default_settings(fn, command):
for k, w in params.items():
settings.setdefault(k, w)
return fn(command)
return decorator(_default_settings) |
Returns closest match or just first from possibilities. | def get_closest(word, possibilities, cutoff=0.6, fallback_to_first=True):
"""Returns closest match or just first from possibilities."""
possibilities = list(possibilities)
try:
return difflib_get_close_matches(word, possibilities, 1, cutoff)[0]
except IndexError:
if fallback_to_first:
return possibilities[0] |
Overrides difflib. get_close_match to controle argument n. | def get_close_matches(word, possibilities, n=None, cutoff=0.6):
"""Overrides `difflib.get_close_match` to controle argument `n`."""
if n is None:
n = settings.num_close_matches
return difflib_get_close_matches(word, possibilities, n, cutoff) |
Replaces command line argument. | def replace_argument(script, from_, to):
"""Replaces command line argument."""
replaced_in_the_end = re.sub(u' {}$'.format(re.escape(from_)), u' {}'.format(to),
script, count=1)
if replaced_in_the_end != script:
return replaced_in_the_end
else:
return script.replace(
u' {} '.format(from_), u' {} '.format(to), 1) |
Helper for * _no_command rules. | def replace_command(command, broken, matched):
"""Helper for *_no_command rules."""
new_cmds = get_close_matches(broken, matched, cutoff=0.1)
return [replace_argument(command.script, broken, new_cmd.strip())
for new_cmd in new_cmds] |
Returns True if command is call to one of passed app names. | def is_app(command, *app_names, **kwargs):
"""Returns `True` if command is call to one of passed app names."""
at_least = kwargs.pop('at_least', 0)
if kwargs:
raise TypeError("got an unexpected keyword argument '{}'".format(kwargs.keys()))
if len(command.script_parts) > at_least:
return command.script_parts[0] in app_names
return False |
Specifies that matching script is for on of app names. | def for_app(*app_names, **kwargs):
"""Specifies that matching script is for on of app names."""
def _for_app(fn, command):
if is_app(command, *app_names, **kwargs):
return fn(command)
else:
return False
return decorator(_for_app) |
Caches function result in temporary file. | def cache(*depends_on):
"""Caches function result in temporary file.
Cache will be expired when modification date of files from `depends_on`
will be changed.
Only functions should be wrapped in `cache`, not methods.
"""
def cache_decorator(fn):
@memoize
@wraps(fn)
def wrapper(*args, **kwargs):
if cache.disabled:
return fn(*args, **kwargs)
else:
return _cache.get_value(fn, depends_on, args, kwargs)
return wrapper
return cache_decorator |
Creates single script from a list of script parts. | def format_raw_script(raw_script):
"""Creates single script from a list of script parts.
:type raw_script: [basestring]
:rtype: basestring
"""
if six.PY2:
script = ' '.join(arg.decode('utf-8') for arg in raw_script)
else:
script = ' '.join(raw_script)
return script.strip() |
Decides actions given observations information and takes them in environment.: param brain_info: A dictionary of brain names and BrainInfo from environment.: return: an ActionInfo containing action memories values and an object to be passed to add experiences | def get_action(self, brain_info: BrainInfo) -> ActionInfo:
"""
Decides actions given observations information, and takes them in environment.
:param brain_info: A dictionary of brain names and BrainInfo from environment.
:return: an ActionInfo containing action, memories, values and an object
to be passed to add experiences
"""
if len(brain_info.agents) == 0:
return ActionInfo([], [], [], None, None)
run_out = self.evaluate(brain_info)
return ActionInfo(
action=run_out.get('action'),
memory=run_out.get('memory_out'),
text=None,
value=run_out.get('value'),
outputs=run_out
) |
Executes model.: param feed_dict: Input dictionary mapping nodes to input data.: param out_dict: Output dictionary mapping names to nodes.: return: Dictionary mapping names to input data. | def _execute_model(self, feed_dict, out_dict):
"""
Executes model.
:param feed_dict: Input dictionary mapping nodes to input data.
:param out_dict: Output dictionary mapping names to nodes.
:return: Dictionary mapping names to input data.
"""
network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict)
run_out = dict(zip(list(out_dict.keys()), network_out))
return run_out |
Gets current model step.: return: current model step. | def get_current_step(self):
"""
Gets current model step.
:return: current model step.
"""
step = self.sess.run(self.model.global_step)
return step |
Saves the model: param steps: The number of steps the model was trained for: return: | def save_model(self, steps):
"""
Saves the model
:param steps: The number of steps the model was trained for
:return:
"""
with self.graph.as_default():
last_checkpoint = self.model_path + '/model-' + str(steps) + '.cptk'
self.saver.save(self.sess, last_checkpoint)
tf.train.write_graph(self.graph, self.model_path,
'raw_graph_def.pb', as_text=False) |
Exports latest saved model to. nn format for Unity embedding. | def export_model(self):
"""
Exports latest saved model to .nn format for Unity embedding.
"""
with self.graph.as_default():
target_nodes = ','.join(self._process_graph())
ckpt = tf.train.get_checkpoint_state(self.model_path)
freeze_graph.freeze_graph(
input_graph=self.model_path + '/raw_graph_def.pb',
input_binary=True,
input_checkpoint=ckpt.model_checkpoint_path,
output_node_names=target_nodes,
output_graph=(self.model_path + '/frozen_graph_def.pb'),
clear_devices=True, initializer_nodes='', input_saver='',
restore_op_name='save/restore_all',
filename_tensor_name='save/Const:0')
tf2bc.convert(self.model_path + '/frozen_graph_def.pb', self.model_path + '.nn')
logger.info('Exported ' + self.model_path + '.nn file') |
Gets the list of the output nodes present in the graph for inference: return: list of node names | def _process_graph(self):
"""
Gets the list of the output nodes present in the graph for inference
:return: list of node names
"""
all_nodes = [x.name for x in self.graph.as_graph_def().node]
nodes = [x for x in all_nodes if x in self.possible_output_nodes]
logger.info('List of nodes to export for brain :' + self.brain.brain_name)
for n in nodes:
logger.info('\t' + n)
return nodes |
Resets all the local local_buffers | def reset_local_buffers(self):
"""
Resets all the local local_buffers
"""
agent_ids = list(self.keys())
for k in agent_ids:
self[k].reset_agent() |
Appends the buffer of an agent to the update buffer.: param agent_id: The id of the agent which data will be appended: param key_list: The fields that must be added. If None: all fields will be appended.: param batch_size: The number of elements that must be appended. If None: All of them will be.: param training_length: The length of the samples that must be appended. If None: only takes one element. | def append_update_buffer(self, agent_id, key_list=None, batch_size=None, training_length=None):
"""
Appends the buffer of an agent to the update buffer.
:param agent_id: The id of the agent which data will be appended
:param key_list: The fields that must be added. If None: all fields will be appended.
:param batch_size: The number of elements that must be appended. If None: All of them will be.
:param training_length: The length of the samples that must be appended. If None: only takes one element.
"""
if key_list is None:
key_list = self[agent_id].keys()
if not self[agent_id].check_length(key_list):
raise BufferException("The length of the fields {0} for agent {1} where not of same length"
.format(key_list, agent_id))
for field_key in key_list:
self.update_buffer[field_key].extend(
self[agent_id][field_key].get_batch(batch_size=batch_size, training_length=training_length)
) |
Appends the buffer of all agents to the update buffer.: param key_list: The fields that must be added. If None: all fields will be appended.: param batch_size: The number of elements that must be appended. If None: All of them will be.: param training_length: The length of the samples that must be appended. If None: only takes one element. | def append_all_agent_batch_to_update_buffer(self, key_list=None, batch_size=None, training_length=None):
"""
Appends the buffer of all agents to the update buffer.
:param key_list: The fields that must be added. If None: all fields will be appended.
:param batch_size: The number of elements that must be appended. If None: All of them will be.
:param training_length: The length of the samples that must be appended. If None: only takes one element.
"""
for agent_id in self.keys():
self.append_update_buffer(agent_id, key_list, batch_size, training_length) |
Launches training session.: param process_queue: Queue used to send signal back to main.: param sub_id: Unique id for training session.: param run_seed: Random seed used for training.: param run_options: Command line arguments for training. | def run_training(sub_id: int, run_seed: int, run_options, process_queue):
"""
Launches training session.
:param process_queue: Queue used to send signal back to main.
:param sub_id: Unique id for training session.
:param run_seed: Random seed used for training.
:param run_options: Command line arguments for training.
"""
# Docker Parameters
docker_target_name = (run_options['--docker-target-name']
if run_options['--docker-target-name'] != 'None' else None)
# General parameters
env_path = (run_options['--env']
if run_options['--env'] != 'None' else None)
run_id = run_options['--run-id']
load_model = run_options['--load']
train_model = run_options['--train']
save_freq = int(run_options['--save-freq'])
keep_checkpoints = int(run_options['--keep-checkpoints'])
base_port = int(run_options['--base-port'])
num_envs = int(run_options['--num-envs'])
curriculum_folder = (run_options['--curriculum']
if run_options['--curriculum'] != 'None' else None)
lesson = int(run_options['--lesson'])
fast_simulation = not bool(run_options['--slow'])
no_graphics = run_options['--no-graphics']
trainer_config_path = run_options['<trainer-config-path>']
# Recognize and use docker volume if one is passed as an argument
if not docker_target_name:
model_path = './models/{run_id}-{sub_id}'.format(run_id=run_id, sub_id=sub_id)
summaries_dir = './summaries'
else:
trainer_config_path = \
'/{docker_target_name}/{trainer_config_path}'.format(
docker_target_name=docker_target_name,
trainer_config_path=trainer_config_path)
if curriculum_folder is not None:
curriculum_folder = \
'/{docker_target_name}/{curriculum_folder}'.format(
docker_target_name=docker_target_name,
curriculum_folder=curriculum_folder)
model_path = '/{docker_target_name}/models/{run_id}-{sub_id}'.format(
docker_target_name=docker_target_name,
run_id=run_id,
sub_id=sub_id)
summaries_dir = '/{docker_target_name}/summaries'.format(
docker_target_name=docker_target_name)
trainer_config = load_config(trainer_config_path)
env_factory = create_environment_factory(
env_path,
docker_target_name,
no_graphics,
run_seed,
base_port + (sub_id * num_envs)
)
env = SubprocessUnityEnvironment(env_factory, num_envs)
maybe_meta_curriculum = try_create_meta_curriculum(curriculum_folder, env)
# Create controller and begin training.
tc = TrainerController(model_path, summaries_dir, run_id + '-' + str(sub_id),
save_freq, maybe_meta_curriculum,
load_model, train_model,
keep_checkpoints, lesson, env.external_brains,
run_seed, fast_simulation)
# Signal that environment has been launched.
process_queue.put(True)
# Begin training
tc.start_learning(env, trainer_config) |
Get an action using this trainer s current policy.: param curr_info: Current BrainInfo.: return: The ActionInfo given by the policy given the BrainInfo. | def get_action(self, curr_info: BrainInfo) -> ActionInfo:
"""
Get an action using this trainer's current policy.
:param curr_info: Current BrainInfo.
:return: The ActionInfo given by the policy given the BrainInfo.
"""
self.trainer_metrics.start_experience_collection_timer()
action = self.policy.get_action(curr_info)
self.trainer_metrics.end_experience_collection_timer()
return action |
Saves training statistics to Tensorboard.: param delta_train_start: Time elapsed since training started.: param lesson_num: Current lesson number in curriculum.: param global_step: The number of steps the simulation has been going for | def write_summary(self, global_step, delta_train_start, lesson_num=0):
"""
Saves training statistics to Tensorboard.
:param delta_train_start: Time elapsed since training started.
:param lesson_num: Current lesson number in curriculum.
:param global_step: The number of steps the simulation has been going for
"""
if global_step % self.trainer_parameters['summary_freq'] == 0 and global_step != 0:
is_training = "Training." if self.is_training and self.get_step <= self.get_max_steps else "Not Training."
if len(self.stats['Environment/Cumulative Reward']) > 0:
mean_reward = np.mean(
self.stats['Environment/Cumulative Reward'])
LOGGER.info(" {}: {}: Step: {}. "
"Time Elapsed: {:0.3f} s "
"Mean "
"Reward: {"
":0.3f}. Std of Reward: {:0.3f}. {}"
.format(self.run_id, self.brain_name,
min(self.get_step, self.get_max_steps),
delta_train_start,
mean_reward, np.std(
self.stats['Environment/Cumulative Reward']),
is_training))
else:
LOGGER.info(" {}: {}: Step: {}. No episode was completed since last summary. {}"
.format(self.run_id, self.brain_name, self.get_step, is_training))
summary = tf.Summary()
for key in self.stats:
if len(self.stats[key]) > 0:
stat_mean = float(np.mean(self.stats[key]))
summary.value.add(tag='{}'.format(
key), simple_value=stat_mean)
self.stats[key] = []
summary.value.add(tag='Environment/Lesson', simple_value=lesson_num)
self.summary_writer.add_summary(summary, self.get_step)
self.summary_writer.flush() |
Saves text to Tensorboard. Note: Only works on tensorflow r1. 2 or above.: param key: The name of the text.: param input_dict: A dictionary that will be displayed in a table on Tensorboard. | def write_tensorboard_text(self, key, input_dict):
"""
Saves text to Tensorboard.
Note: Only works on tensorflow r1.2 or above.
:param key: The name of the text.
:param input_dict: A dictionary that will be displayed in a table on Tensorboard.
"""
try:
with tf.Session() as sess:
s_op = tf.summary.text(key, tf.convert_to_tensor(
([[str(x), str(input_dict[x])] for x in input_dict])))
s = sess.run(s_op)
self.summary_writer.add_summary(s, self.get_step)
except:
LOGGER.info(
"Cannot write text summary for Tensorboard. Tensorflow version must be r1.2 or above.")
pass |
A dict from brain name to the brain s curriculum s lesson number. | def lesson_nums(self):
"""A dict from brain name to the brain's curriculum's lesson number."""
lesson_nums = {}
for brain_name, curriculum in self.brains_to_curriculums.items():
lesson_nums[brain_name] = curriculum.lesson_num
return lesson_nums |
Attempts to increments all the lessons of all the curriculums in this MetaCurriculum. Note that calling this method does not guarantee the lesson of a curriculum will increment. The lesson of a curriculum will only increment if the specified measure threshold defined in the curriculum has been reached and the minimum number of episodes in the lesson have been completed. | def increment_lessons(self, measure_vals, reward_buff_sizes=None):
"""Attempts to increments all the lessons of all the curriculums in this
MetaCurriculum. Note that calling this method does not guarantee the
lesson of a curriculum will increment. The lesson of a curriculum will
only increment if the specified measure threshold defined in the
curriculum has been reached and the minimum number of episodes in the
lesson have been completed.
Args:
measure_vals (dict): A dict of brain name to measure value.
reward_buff_sizes (dict): A dict of brain names to the size of their
corresponding reward buffers.
Returns:
A dict from brain name to whether that brain's lesson number was
incremented.
"""
ret = {}
if reward_buff_sizes:
for brain_name, buff_size in reward_buff_sizes.items():
if self._lesson_ready_to_increment(brain_name, buff_size):
measure_val = measure_vals[brain_name]
ret[brain_name] = (self.brains_to_curriculums[brain_name]
.increment_lesson(measure_val))
else:
for brain_name, measure_val in measure_vals.items():
ret[brain_name] = (self.brains_to_curriculums[brain_name]
.increment_lesson(measure_val))
return ret |
Sets all the curriculums in this meta curriculum to a specified lesson number. | def set_all_curriculums_to_lesson_num(self, lesson_num):
"""Sets all the curriculums in this meta curriculum to a specified
lesson number.
Args:
lesson_num (int): The lesson number which all the curriculums will
be set to.
"""
for _, curriculum in self.brains_to_curriculums.items():
curriculum.lesson_num = lesson_num |
Get the combined configuration of all curriculums in this MetaCurriculum. | def get_config(self):
"""Get the combined configuration of all curriculums in this
MetaCurriculum.
Returns:
A dict from parameter to value.
"""
config = {}
for _, curriculum in self.brains_to_curriculums.items():
curr_config = curriculum.get_config()
config.update(curr_config)
return config |
Sends a signal to reset the unity environment.: return: AllBrainInfo: A data structure corresponding to the initial reset state of the environment. | def reset(self, config=None, train_mode=True, custom_reset_parameters=None) -> AllBrainInfo:
"""
Sends a signal to reset the unity environment.
:return: AllBrainInfo : A data structure corresponding to the initial reset state of the environment.
"""
if config is None:
config = self._resetParameters
elif config:
logger.info("Academy reset with parameters: {0}"
.format(', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))
for k in config:
if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
self._resetParameters[k] = config[k]
elif not isinstance(config[k], (int, float)):
raise UnityEnvironmentException(
"The value for parameter '{0}'' must be an Integer or a Float.".format(k))
else:
raise UnityEnvironmentException(
"The parameter '{0}' is not a valid parameter.".format(k))
if self._loaded:
outputs = self.communicator.exchange(
self._generate_reset_input(train_mode, config, custom_reset_parameters)
)
if outputs is None:
raise KeyboardInterrupt
rl_output = outputs.rl_output
s = self._get_state(rl_output)
self._global_done = s[1]
for _b in self._external_brain_names:
self._n_agents[_b] = len(s[0][_b].agents)
return s[0]
else:
raise UnityEnvironmentException("No Unity environment is loaded.") |
Provides the environment with an action moves the environment dynamics forward accordingly and returns observation state and reward information to the agent.: param value: Value estimates provided by agents.: param vector_action: Agent s vector action. Can be a scalar or vector of int/ floats.: param memory: Vector corresponding to memory used for recurrent policies.: param text_action: Text action to send to environment for.: param custom_action: Optional instance of a CustomAction protobuf message.: return: AllBrainInfo: A Data structure corresponding to the new state of the environment. | def step(self, vector_action=None, memory=None, text_action=None, value=None, custom_action=None) -> AllBrainInfo:
"""
Provides the environment with an action, moves the environment dynamics forward accordingly,
and returns observation, state, and reward information to the agent.
:param value: Value estimates provided by agents.
:param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.
:param memory: Vector corresponding to memory used for recurrent policies.
:param text_action: Text action to send to environment for.
:param custom_action: Optional instance of a CustomAction protobuf message.
:return: AllBrainInfo : A Data structure corresponding to the new state of the environment.
"""
vector_action = {} if vector_action is None else vector_action
memory = {} if memory is None else memory
text_action = {} if text_action is None else text_action
value = {} if value is None else value
custom_action = {} if custom_action is None else custom_action
# Check that environment is loaded, and episode is currently running.
if self._loaded and not self._global_done and self._global_done is not None:
if isinstance(vector_action, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
vector_action = {self._external_brain_names[0]: vector_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names a keys, "
"and vector_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a vector_action input")
if isinstance(memory, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
memory = {self._external_brain_names[0]: memory}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and memories as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a memory input")
if isinstance(text_action, self.SINGLE_BRAIN_TEXT_TYPES):
if self._num_external_brains == 1:
text_action = {self._external_brain_names[0]: text_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and text_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a value input")
if isinstance(value, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
value = {self._external_brain_names[0]: value}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and state/action value estimates as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a value input")
if isinstance(custom_action, CustomAction):
if self._num_external_brains == 1:
custom_action = {self._external_brain_names[0]: custom_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and CustomAction instances as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a custom_action input")
for brain_name in list(vector_action.keys()) + list(memory.keys()) + list(
text_action.keys()):
if brain_name not in self._external_brain_names:
raise UnityActionException(
"The name {0} does not correspond to an external brain "
"in the environment".format(brain_name))
for brain_name in self._external_brain_names:
n_agent = self._n_agents[brain_name]
if brain_name not in vector_action:
if self._brains[brain_name].vector_action_space_type == "discrete":
vector_action[brain_name] = [0.0] * n_agent * len(
self._brains[brain_name].vector_action_space_size)
else:
vector_action[brain_name] = [0.0] * n_agent * \
self._brains[
brain_name].vector_action_space_size[0]
else:
vector_action[brain_name] = self._flatten(vector_action[brain_name])
if brain_name not in memory:
memory[brain_name] = []
else:
if memory[brain_name] is None:
memory[brain_name] = []
else:
memory[brain_name] = self._flatten(memory[brain_name])
if brain_name not in text_action:
text_action[brain_name] = [""] * n_agent
else:
if text_action[brain_name] is None:
text_action[brain_name] = [""] * n_agent
if isinstance(text_action[brain_name], str):
text_action[brain_name] = [text_action[brain_name]] * n_agent
if brain_name not in custom_action:
custom_action[brain_name] = [None] * n_agent
else:
if custom_action[brain_name] is None:
custom_action[brain_name] = [None] * n_agent
if isinstance(custom_action[brain_name], CustomAction):
custom_action[brain_name] = [custom_action[brain_name]] * n_agent
number_text_actions = len(text_action[brain_name])
if not ((number_text_actions == n_agent) or number_text_actions == 0):
raise UnityActionException(
"There was a mismatch between the provided text_action and "
"the environment's expectation: "
"The brain {0} expected {1} text_action but was given {2}".format(
brain_name, n_agent, number_text_actions))
discrete_check = self._brains[brain_name].vector_action_space_type == "discrete"
expected_discrete_size = n_agent * len(
self._brains[brain_name].vector_action_space_size)
continuous_check = self._brains[brain_name].vector_action_space_type == "continuous"
expected_continuous_size = self._brains[brain_name].vector_action_space_size[
0] * n_agent
if not ((discrete_check and len(
vector_action[brain_name]) == expected_discrete_size) or
(continuous_check and len(
vector_action[brain_name]) == expected_continuous_size)):
raise UnityActionException(
"There was a mismatch between the provided action and "
"the environment's expectation: "
"The brain {0} expected {1} {2} action(s), but was provided: {3}"
.format(brain_name, str(expected_discrete_size)
if discrete_check
else str(expected_continuous_size),
self._brains[brain_name].vector_action_space_type,
str(vector_action[brain_name])))
outputs = self.communicator.exchange(
self._generate_step_input(vector_action, memory, text_action, value, custom_action))
if outputs is None:
raise KeyboardInterrupt
rl_output = outputs.rl_output
state = self._get_state(rl_output)
self._global_done = state[1]
for _b in self._external_brain_names:
self._n_agents[_b] = len(state[0][_b].agents)
return state[0]
elif not self._loaded:
raise UnityEnvironmentException("No Unity environment is loaded.")
elif self._global_done:
raise UnityActionException(
"The episode is completed. Reset the environment with 'reset()'")
elif self.global_done is None:
raise UnityActionException(
"You cannot conduct step without first calling reset. "
"Reset the environment with 'reset()'") |
Converts arrays to list.: param arr: numpy vector.: return: flattened list. | def _flatten(cls, arr) -> List[float]:
"""
Converts arrays to list.
:param arr: numpy vector.
:return: flattened list.
"""
if isinstance(arr, cls.SCALAR_ACTION_TYPES):
arr = [float(arr)]
if isinstance(arr, np.ndarray):
arr = arr.tolist()
if len(arr) == 0:
return arr
if isinstance(arr[0], np.ndarray):
arr = [item for sublist in arr for item in sublist.tolist()]
if isinstance(arr[0], list):
arr = [item for sublist in arr for item in sublist]
arr = [float(x) for x in arr]
return arr |
Collects experience information from all external brains in environment at current step.: return: a dictionary of BrainInfo objects. | def _get_state(self, output: UnityRLOutput) -> (AllBrainInfo, bool):
"""
Collects experience information from all external brains in environment at current step.
:return: a dictionary of BrainInfo objects.
"""
_data = {}
global_done = output.global_done
for brain_name in output.agentInfos:
agent_info_list = output.agentInfos[brain_name].value
_data[brain_name] = BrainInfo.from_agent_proto(agent_info_list,
self.brains[brain_name])
return _data, global_done |
Inform Metrics class that experience collection is done. | def end_experience_collection_timer(self):
"""
Inform Metrics class that experience collection is done.
"""
if self.time_start_experience_collection:
curr_delta = time() - self.time_start_experience_collection
if self.delta_last_experience_collection is None:
self.delta_last_experience_collection = curr_delta
else:
self.delta_last_experience_collection += curr_delta
self.time_start_experience_collection = None |
Inform Metrics class about time to step in environment. | def add_delta_step(self, delta: float):
"""
Inform Metrics class about time to step in environment.
"""
if self.delta_last_experience_collection:
self.delta_last_experience_collection += delta
else:
self.delta_last_experience_collection = delta |
Inform Metrics class that policy update has started.: int number_experiences: Number of experiences in Buffer at this point.: float mean_return: Return averaged across all cumulative returns since last policy update | def start_policy_update_timer(self, number_experiences: int, mean_return: float):
"""
Inform Metrics class that policy update has started.
:int number_experiences: Number of experiences in Buffer at this point.
:float mean_return: Return averaged across all cumulative returns since last policy update
"""
self.last_buffer_length = number_experiences
self.last_mean_return = mean_return
self.time_policy_update_start = time() |
Inform Metrics class that policy update has started. | def end_policy_update(self):
"""
Inform Metrics class that policy update has started.
"""
if self.time_policy_update_start:
self.delta_policy_update = time() - self.time_policy_update_start
else:
self.delta_policy_update = 0
delta_train_start = time() - self.time_training_start
LOGGER.debug(" Policy Update Training Metrics for {}: "
"\n\t\tTime to update Policy: {:0.3f} s \n"
"\t\tTime elapsed since training: {:0.3f} s \n"
"\t\tTime for experience collection: {:0.3f} s \n"
"\t\tBuffer Length: {} \n"
"\t\tReturns : {:0.3f}\n"
.format(self.brain_name, self.delta_policy_update,
delta_train_start, self.delta_last_experience_collection,
self.last_buffer_length, self.last_mean_return))
self._add_row(delta_train_start) |
Write Training Metrics to CSV | def write_training_metrics(self):
"""
Write Training Metrics to CSV
"""
with open(self.path, 'w') as file:
writer = csv.writer(file)
writer.writerow(FIELD_NAMES)
for row in self.rows:
writer.writerow(row) |
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