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# Copyright 2022 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic encoder for inputs with a fixed vocabulary."""
import abc
from typing import Any, List, Optional, Sequence
from tracr.craft import bases
class Encoder(abc.ABC):
"""Encodes a list of tokens into a list of inputs for a transformer model.
The abstract class does not make assumptions on the input and output types,
and we have different encoders for different input types.
"""
@abc.abstractmethod
def encode(self, inputs: List[Any]) -> List[Any]:
return list()
@abc.abstractmethod
def decode(self, encodings: List[Any]) -> List[Any]:
return list()
@property
def pad_token(self) -> Optional[str]:
return None
@property
def bos_token(self) -> Optional[str]:
return None
@property
def pad_encoding(self) -> Optional[int]:
return None
@property
def bos_encoding(self) -> Optional[int]:
return None
class NumericalEncoder(Encoder):
"""Encodes numerical variables (simply using the identity mapping)."""
def encode(self, inputs: List[float]) -> List[float]:
return inputs
def decode(self, encodings: List[float]) -> List[float]:
return encodings
class CategoricalEncoder(Encoder):
"""Encodes categorical variables with a fixed vocabulary."""
def __init__(
self,
basis: Sequence[bases.BasisDirection],
enforce_bos: bool = False,
bos_token: Optional[str] = None,
pad_token: Optional[str] = None,
max_seq_len: Optional[int] = None,
):
"""Initialises. If enforce_bos is set, ensures inputs start with it."""
if enforce_bos and not bos_token:
raise ValueError("BOS token must be specified if enforcing BOS.")
self.encoding_map = {}
for i, direction in enumerate(basis):
val = direction.value
self.encoding_map[val] = i
if bos_token and bos_token not in self.encoding_map:
raise ValueError("BOS token missing in encoding.")
if pad_token and pad_token not in self.encoding_map:
raise ValueError("PAD token missing in encoding.")
self.enforce_bos = enforce_bos
self._bos_token = bos_token
self._pad_token = pad_token
self._max_seq_len = max_seq_len
def encode(self, inputs: List[bases.Value]) -> List[int]:
if self.enforce_bos and inputs[0] != self.bos_token:
raise ValueError("First input token must be BOS token. "
f"Should be '{self.bos_token}', but was '{inputs[0]}'.")
if missing := set(inputs) - set(self.encoding_map.keys()):
raise ValueError(f"Inputs {missing} not found in encoding ",
self.encoding_map.keys())
if self._max_seq_len is not None and len(inputs) > self._max_seq_len:
raise ValueError(f"inputs={inputs} are longer than the maximum "
f"sequence length {self._max_seq_len}")
return [self.encoding_map[x] for x in inputs]
def decode(self, encodings: List[int]) -> List[bases.Value]:
"""Recover the tokens that corresponds to `ids`. Inverse of __call__."""
decoding_map = {val: key for key, val in self.encoding_map.items()}
if missing := set(encodings) - set(decoding_map.keys()):
raise ValueError(f"Inputs {missing} not found in decoding map ",
decoding_map.keys())
return [decoding_map[x] for x in encodings]
@property
def vocab_size(self) -> int:
return len(self.encoding_map)
@property
def bos_token(self) -> Optional[str]:
return self._bos_token
@property
def pad_token(self) -> Optional[str]:
return self._pad_token
@property
def bos_encoding(self) -> Optional[int]:
return None if self.bos_token is None else self.encoding_map[self.bos_token]
@property
def pad_encoding(self) -> Optional[int]:
return None if self.pad_token is None else self.encoding_map[self.pad_token]
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