Upload tokenizer.py

tokenizer.py

@@ -0,0 +1,69 @@
+import os
+import re
+import pandas as pd
+import numpy as np
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+import datasets
+
+URL = "https://huggingface.co/datasets/thewall/tokenizer/resolve/main"
+
+
+class TokenizerConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(TokenizerConfig, self).__init__(**kwargs)
+
+class Tokenizer(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        TokenizerConfig(name=key) for key in ["esm", "progen2"]
+    ]
+
+    DEFAULT_CONFIG_NAME = "esm"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "name": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "tokenizer": datasets.Value("string"),
+                    "special_tokens_map": datasets.Value("string"),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        file = dl_manager.download_and_extract(f"{URL}/{self.config.name}.tar.gz")
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        name = self.config.name
+        tokenizer_file = os.path.join(filepath, name, "tokenizer.json")
+        tokens_map_file = os.path.join(filepath, name, "special_tokens_map.json")
+        with open(tokenizer_file) as f:
+            tokenizer = "".join(f.readlines())
+        with open(tokens_map_file) as f:
+            special_tokens = "".join(f.readlines())
+        yield 0, {"id": 0,
+                  "path": os.path.join(filepath, name),
+                  "name": name,
+                  "tokenizer": tokenizer,
+                  "special_tokens_map": special_tokens,}
+
+
+if __name__=="__main__":
+    from datasets import load_dataset
+    from tokenizers import Tokenizer
+    from transformers import PreTrainedTokenizerFast
+    import json
+    dataset = load_dataset("tokenizer.py", split="all")
+    tokenizer = Tokenizer.from_str(dataset[0]['tokenizer'])
+    token_map = json.loads(dataset[0]['special_tokens_map'])
+    fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer, **token_map)