support wmt21 tokenizer in m2m100 tokenizer (#14376)

src/transformers/models/m2m_100/tokenization_m2m_100.py

@@ -54,7 +54,10 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
 }
 
 # fmt: off
-FAIRSEQ_LANGUAGE_CODES = ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"]
+FAIRSEQ_LANGUAGE_CODES = {
+    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
+    "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
+}
 # fmt: on
 
 
@@ -86,6 +89,8 @@ class M2M100Tokenizer(PreTrainedTokenizer):
             token instead.
         pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
+        language_codes (:obj:`str`, `optional`, defaults to :obj:`"m2m100"`):
+            What language codes to use. Should be one of :obj:`"m2m100"` or :obj:`"wmt21"`.
         sp_model_kwargs (:obj:`dict`, `optional`):
             Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece
             <https://github.com/google/sentencepiece/tree/master/python>`__ can be used, among other things, to set:
@@ -132,17 +137,21 @@ class M2M100Tokenizer(PreTrainedTokenizer):
         sep_token="</s>",
         pad_token="<pad>",
         unk_token="<unk>",
+        language_codes="m2m100",
         sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        num_madeup_words=8,
         **kwargs,
     ) -> None:
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 
-        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in FAIRSEQ_LANGUAGE_CODES}
+        self.language_codes = language_codes
+        fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
+        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
 
         kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(lang_code)
-            for lang_code in FAIRSEQ_LANGUAGE_CODES
+            for lang_code in fairseq_language_code
             if self.get_lang_token(lang_code) not in kwargs["additional_special_tokens"]
         ]
 
@@ -154,7 +163,9 @@ class M2M100Tokenizer(PreTrainedTokenizer):
             sep_token=sep_token,
             unk_token=unk_token,
             pad_token=pad_token,
+            language_codes=language_codes,
             sp_model_kwargs=self.sp_model_kwargs,
+            num_madeup_words=num_madeup_words,
             **kwargs,
         )
 
@@ -167,9 +178,9 @@ class M2M100Tokenizer(PreTrainedTokenizer):
         self.encoder_size = len(self.encoder)
 
         self.lang_token_to_id = {
-            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(FAIRSEQ_LANGUAGE_CODES)
+            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
         }
-        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(FAIRSEQ_LANGUAGE_CODES)}
+        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
         self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
 
         self._src_lang = src_lang if src_lang is not None else "en"
@@ -177,7 +188,7 @@ class M2M100Tokenizer(PreTrainedTokenizer):
         self.cur_lang_id = self.get_lang_id(self._src_lang)
         self.set_src_lang_special_tokens(self._src_lang)
 
-        self.num_madeup_words = 8
+        self.num_madeup_words = num_madeup_words
 
     @property
     def vocab_size(self) -> int: