Create akshara_tokenizer.py

akshara_tokenizer.py

@@ -0,0 +1,142 @@
+import os
+import json
+import regex as re
+from typing import Dict, List, Optional, Tuple, Union
+
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+class AksharaTokenizer(PreTrainedTokenizer):
+   """
+   Akshara tokenizer for processing Indic language text.
+   This tokenizer handles characters at the akshara (syllable) level.
+   """
+   
+   vocab_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
+   model_input_names = ["input_ids", "attention_mask"]
+   
+   def __init__(
+       self,
+       vocab_file,
+       merges_file=None,
+       unk_token="<unk>",
+       bos_token="<s>",
+       eos_token="</s>",
+       pad_token="<pad>",
+       mask_token="<mask>",
+       add_prefix_space=False,
+       **kwargs
+   ):
+       super().__init__(
+           unk_token=unk_token,
+           bos_token=bos_token,
+           eos_token=eos_token,
+           pad_token=pad_token,
+           mask_token=mask_token,
+           add_prefix_space=add_prefix_space,
+           **kwargs,
+       )
+       
+       # Load vocabulary
+       with open(vocab_file, encoding="utf-8") as vocab_handle:
+           self.encoder = json.load(vocab_handle)
+       self.decoder = {v: k for k, v in self.encoder.items()}
+       
+       # Load merges if available
+       self.merges = {}
+       if merges_file is not None and os.path.isfile(merges_file):
+           with open(merges_file, encoding="utf-8") as merges_handle:
+               merges = merges_handle.read().split("\n")
+               self.merges = {tuple(merge.split()): i for i, merge in enumerate(merges) if merge}
+       
+       # Special token handling
+       self.add_prefix_space = add_prefix_space
+       
+       # Pre-compile regex patterns for tokenization
+       self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+   
+   @property
+   def vocab_size(self):
+       return len(self.encoder)
+   
+   def get_vocab(self):
+       return dict(self.encoder, **self.added_tokens_encoder)
+   
+   def _tokenize(self, text):
+       """Tokenize text into akshara units."""
+       if self.add_prefix_space and not text.startswith(" "):
+           text = " " + text
+       tokens = re.findall(self.pat, text)
+       return tokens
+   
+   def _convert_token_to_id(self, token):
+       """Convert a token to its ID in the vocabulary."""
+       return self.encoder.get(token, self.encoder.get(self.unk_token))
+   
+   def _convert_id_to_token(self, index):
+       """Convert an ID to its token in the vocabulary."""
+       return self.decoder.get(index, self.unk_token)
+   
+   def convert_tokens_to_string(self, tokens):
+       """Convert a sequence of tokens to a single string."""
+       text = "".join(tokens)
+       text = text.replace(" ", "").replace("▁", " ").strip()
+       return text
+   
+   def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+       """Build model inputs from a sequence by appending eos_token_id."""
+       if token_ids_1 is None:
+           return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+       return [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+   
+   def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
+       """Get list where entries are [1] if a token is special and [0] otherwise."""
+       if already_has_special_tokens:
+           return super().get_special_tokens_mask(
+               token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+           )
+       
+       if token_ids_1 is None:
+           return [1] + ([0] * len(token_ids_0)) + [1]
+       return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+   
+   def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
+       """Create a mask from the two sequences for sequence classification tasks."""
+       eos = [self.eos_token_id]
+       bos = [self.bos_token_id]
+       
+       if token_ids_1 is None:
+           return len(bos + token_ids_0 + eos) * [0]
+       return len(bos + token_ids_0 + eos) * [0] + len(token_ids_1 + eos) * [1]
+   
+   def save_vocabulary(self, save_directory, filename_prefix=None):
+       """Save the vocabulary and merges files to a directory."""
+       if not os.path.isdir(save_directory):
+           logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+           return
+       
+       vocab_file = os.path.join(
+           save_directory, (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
+       )
+       
+       with open(vocab_file, "w", encoding="utf-8") as f:
+           f.write(json.dumps(self.encoder, ensure_ascii=False))
+           
+       return (vocab_file,)
+
+
+# Register the tokenizer with the AutoTokenizer class
+from transformers import AutoTokenizer
+AutoTokenizer.register("akshara", AksharaTokenizer)
+
+# Register the model configuration if needed
+from transformers.models.auto.configuration_auto import CONFIG_MAPPING
+if "akshara" not in CONFIG_MAPPING:
+   from transformers import PretrainedConfig
+   
+   class AksharaConfig(PretrainedConfig):
+       model_type = "akshara"
+   
+   CONFIG_MAPPING.register("akshara", AksharaConfig)