def encode_seq_structural_data(data : RawDataset,
context_tokenizer_type : \
Callable[[List[str], int], Tokenizer],
num_keywords : int,
num_reserved_tokens: int) -> \
Tuple[StructDataset, Tokenizer, SimpleEmbedding]:
embedding = SimpleEmbedding()
hyps_and_goals = [
hyp_or_goal for hyp_and_goal in [
zip(hyps +
[goal], itertools.repeat(embedding.encode_token(tactic)))
for prev_tactics, hyps, goal, tactic in data
] for hyp_or_goal in hyp_and_goal
]
context_tokenizer = make_keyword_tokenizer_relevance(
hyps_and_goals, context_tokenizer_type, num_keywords,
num_reserved_tokens)
encodedData = []
for prev_tactics, hyps, goal, tactic in data:
stem, rest = serapi_instance.split_tactic(tactic)
encodedData.append(
([context_tokenizer.toTokenList(hyp)
for hyp in hyps], context_tokenizer.toTokenList(goal),
(embedding.encode_token(stem),
[hyp_index(hyps, arg) for arg in get_symbols(rest)])))
return encodedData, context_tokenizer, embedding
def max_args(num_str : str,
in_data: TacticContext, tactic : str,
new_in_data : TacticContext,
arg_values : argparse.Namespace) -> bool:
stem, args_string = serapi_instance.split_tactic(tactic)
args = args_string.strip()[:-1].split()
return len(args) <= int(num_str)
def _determine_relevance(self, inter: ScrapedTactic) -> List[bool]:
stem, args_string = serapi_instance.split_tactic(inter.tactic)
args = args_string[:-1].split()
return [
any([
var.strip() in args
for var in serapi_instance.get_var_term_in_hyp(hyp).split(",")
]) for hyp in inter.hypotheses
]
def numeric_args(in_data : TacticContext, tactic : str,
next_in_data : TacticContext,
arg_values : argparse.Namespace) -> bool:
goal_words = get_symbols(in_data.goal)
stem, rest = serapi_instance.split_tactic(tactic)
args = get_subexprs(rest.strip("."))
for arg in args:
if not re.fullmatch("\d+", arg):
return False
return True
def args_vars_in_list(tactic : str,
context_list : List[str]) -> bool:
stem, args_string = serapi_instance.split_tactic(tactic)
args = args_string[:-1].split()
if not serapi_instance.tacticTakesHypArgs(stem) and len(args) > 0:
return False
var_names = serapi_instance.get_vars_in_hyps(context_list)
for arg in args:
if not arg in var_names:
return False
return True
def args_token_in_goal(in_data: ContextData, tactic: str,
next_in_data: ContextData,
arg_values: argparse.Namespace) -> bool:
goal = in_data["goal"]
goal_words = get_symbols(cast(str, goal))[:arg_values.max_length]
stem, rest = serapi_instance.split_tactic(tactic)
args = get_subexprs(rest.strip("."))
for arg in args:
if not arg in goal_words:
return False
return True
def get_arg_idx(max_length: int, inter: ScrapedTactic) -> int:
tactic_stem, tactic_rest = serapi_instance.split_tactic(inter.tactic)
symbols = tokenizer.get_symbols(inter.context.focused_goal)
arg = tactic_rest.split()[0].strip(".")
assert arg in symbols, "tactic: {}, arg: {}, goal: {}, symbols: {}"\
.format(inter.tactic, arg, inter.context.focused_goal, symbols)
idx = symbols.index(arg)
if idx >= max_length:
return 0
else:
return idx + 1
def args_vars_in_context(in_data: ContextData, tactic: str,
next_in_data: ContextData,
arg_values: argparse.Namespace) -> bool:
stem, args_string = serapi_instance.split_tactic(tactic)
args = args_string[:-1].split()
if not serapi_instance.tacticTakesHypArgs(stem) and len(args) > 0:
return False
var_names = serapi_instance.get_vars_in_hyps(
cast(List[str], in_data["hyps"]))
for arg in args:
if not arg in var_names:
return False
return True
def get_stem_and_arg_idx(max_length: int, embedding: Embedding,
inter: ScrapedTactic) -> Tuple[int, int]:
tactic_stem, tactic_rest = serapi_instance.split_tactic(inter.tactic)
stem_idx = embedding.encode_token(tactic_stem)
symbols = tokenizer.get_symbols(inter.context.focused_goal)
arg = tactic_rest.split()[0].strip(".")
assert arg in symbols, "tactic: {}, arg: {}, goal: {}, symbols: {}"\
.format(inter.tactic, arg, inter.context.focused_goal, symbols)
idx = symbols.index(arg)
if idx >= max_length:
return stem_idx, 0
else:
return stem_idx, idx + 1
def encode_tactic_structure(stem_embedding : SimpleEmbedding,
max_args : int,
hyps_and_tactic : Tuple[List[str], str]) \
-> TacticStructure:
hyps, tactic = hyps_and_tactic
tactic_stem, args_str = serapi_instance.split_tactic(tactic)
arg_strs = args_str.split()[:max_args]
stem_idx = stem_embedding.encode_token(tactic_stem)
arg_idxs = [get_arg_idx(hyps, arg.strip()) for arg in args_str.split()]
if len(arg_idxs) < max_args:
arg_idxs += [EOS_token] * (max_args - len(arg_idxs))
# If any arguments aren't hypotheses, ignore the arguments
if not all(arg_idxs):
arg_idxs = [EOS_token] * max_args
return TacticStructure(stem_idx=stem_idx, hyp_idxs=arg_idxs)
def args_token_in_goal(in_data : TacticContext, tactic : str,
next_in_data : TacticContext,
arg_values : argparse.Namespace) -> bool:
goal_words = get_symbols(in_data.goal)[:arg_values.max_length]
stem, rest = serapi_instance.split_tactic(tactic)
if len(rest) > 0 and rest[-1] == '.':
rest = rest[:-1]
args = get_subexprs(rest)
# While the arguments to an intro(s) might *look* like
# goal arguments, they are actually fresh variables
if (stem == "intros" or stem == "intro") and len(args) > 0:
return False
for arg in args:
if not any([serapi_instance.symbol_matches(goal_word, arg)
for goal_word in goal_words]):
return False
return True
def _encode_action(self, context: TacticContext, action: str) \
-> Tuple[int, int]:
stem, argument = serapi_instance.split_tactic(action)
stem_idx = emap_lookup(self.tactic_map, 32, stem)
all_premises = context.hypotheses + context.relevant_lemmas
stripped_arg = argument.strip(".").strip()
if stripped_arg == "":
arg_idx = 0
else:
index_hyp_vars = dict(
serapi_instance.get_indexed_vars_in_hyps(all_premises))
if stripped_arg in index_hyp_vars:
hyp_varw, _, rest = all_premises[index_hyp_vars[stripped_arg]]\
.partition(":")
arg_idx = emap_lookup(self.token_map, 128,
tokenizer.get_words(rest)[0]) + 2
else:
goal_symbols = tokenizer.get_symbols(context.goal)
if stripped_arg in goal_symbols:
arg_idx = emap_lookup(self.token_map, 128,
stripped_arg) + 128 + 2
else:
arg_idx = 1
return stem_idx, arg_idx
