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tokenization_blenderbot.py
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R11484 ADDI
tokenization_blenderbot.py
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# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. 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.
"""Tokenization class for Blenderbot."""
from
typing
import
TYPE_CHECKING
,
List
from
...utils
import
logging
from
..roberta.tokenization_roberta
import
RobertaTokenizer
if
TYPE_CHECKING
:
from
transformers.pipelines.conversational
import
Conversation
logger
=
logging
.
get_logger
(
__name__
)
VOCAB_FILES_NAMES
=
{
"vocab_file"
:
"vocab.json"
,
"merges_file"
:
"merges.txt"
,
"tokenizer_config_file"
:
"tokenizer_config.json"
,
}
PRETRAINED_VOCAB_FILES_MAP
=
{
"vocab_file"
:
{
"facebook/blenderbot-3B"
:
"https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"
},
"merges_file"
:
{
"facebook/blenderbot-3B"
:
"https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"
},
"tokenizer_config_file"
:
{
"facebook/blenderbot-3B"
:
"https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
=
{
"facebook/blenderbot-3B"
:
128
}
class
BlenderbotTokenizer
(
RobertaTokenizer
):
r"""
Construct a Blenderbot tokenizer.
:class:`~transformers.Blenderbot` is nearly identical to :class:`~transformers.RobertaTokenizer` and runs
end-to-end tokenization: punctuation splitting and wordpiece. The only difference is that it doesn't add BOS token
to the beginning of sequences.
Refer to superclass :class:`~transformers.RobertaTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names
=
VOCAB_FILES_NAMES
pretrained_vocab_files_map
=
PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes
=
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def
build_inputs_with_special_tokens
(
self
,
token_ids_0
:
List
[
int
],
token_ids_1
:
List
[
int
]
=
None
):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Blenderbot sequence has the following format:
- single sequence: `` X </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`):
Will be ignored
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
return
token_ids_0
+
[
self
.
eos_token_id
]
def
_build_conversation_input_ids
(
self
,
conversation
:
"Conversation"
)
->
List
[
int
]:
inputs
=
[]
for
is_user
,
text
in
conversation
.
iter_texts
():
if
is_user
:
# We need to space prefix as it's being done within blenderbot
inputs
.
append
(
" "
+
text
)
else
:
# Generated responses should contain them already.
inputs
.
append
(
text
)
full_string
=
" "
.
join
(
inputs
)
input_ids
=
self
.
encode
(
full_string
)
if
len
(
input_ids
)
>
self
.
model_max_length
:
input_ids
=
input_ids
[
-
self
.
model_max_length
:]
logger
.
warning
(
f
"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."
)
return
input_ids
def
get_pairs
(
word
):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs
=
set
()
prev_char
=
word
[
0
]
for
char
in
word
[
1
:]:
pairs
.
add
((
prev_char
,
char
))
prev_char
=
char
pairs
=
set
(
pairs
)
return
pairs
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