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T5/dumptokens.py

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+#!/bin/env python
+
+"""
+According to model,
+vocab_size: 32128
+
+But it actually caps out at 32099
+"""
+
+
+from transformers import T5Tokenizer,T5EncoderModel
+import torch
+import charade
+
+T="mcmonkey/google_t5-v1_1-xxl_encoderonly"
+
+tokenizer = T5Tokenizer.from_pretrained(T)
+
+startword= tokenizer.convert_ids_to_tokens(3)
+#print (startword)
+
+# id should be a numeral
+def print_token_from_id(id):
+    decoded_tokens = tokenizer.convert_ids_to_tokens(id)
+    print(decoded_tokens+" : " + str(id))
+
+# print if it has the marker indicating it is a standalone word,
+# not just a building block
+def print_if_word(id):
+    decoded_tokens = tokenizer.convert_ids_to_tokens(id)
+    if decoded_tokens.startswith(startword):
+        print(decoded_tokens[1:] +" : " + str(id))
+
+# standalone word, AND doesnt have any foreign non-ascii7 chars
+def print_if_asciiword(id):
+    decoded_tokens = tokenizer.convert_ids_to_tokens(id)
+    if decoded_tokens.startswith(startword):
+        aword=decoded_tokens[1:]
+        if len(aword) <1:
+            return
+        estr=str(aword.encode())
+        if '\\x' in estr:
+            return
+
+        print(aword +" : " , id)
+
+for id in range(4,32099):
+    #print_token_from_id(id)
+    #print_if_word(id)
+    print_if_asciiword(id)
+
+
+