Add Windows Support and Fix Cross-Platform Compatibility Issues

README.md

@@ -46,7 +46,7 @@ If you are an advanced user looking to process data with pre-defined splits, int
 
 ## Requirements
 - Python version 3.8.10+
-- Support for Linux and Mac OS. Not tested on Windows
+- Support for Linux, Mac OS and Windows.
 
 </br>
 

generative_data_prep/__main__.py

@@ -15,9 +15,12 @@
 
 Entry point to the Text Processing Pipeline.
 """
+
 import json
 import logging
+import logging.config
 import os
+import sys
 from typing import Optional
 
 from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizerBase
@@ -46,6 +49,21 @@
 logger = logging.getLogger("generative_data_prep_logger")
 logging.config.fileConfig(get_config_file_path())
 
+# Fix Unicode encoding issues on Windows console
+# Configure stdout/stderr to handle Unicode encoding errors on Windows
+if sys.platform == "win32":
+    # Try to reconfigure streams to use UTF-8 with error replacement
+    if hasattr(sys.stdout, "reconfigure"):
+        try:
+            sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+        except (AttributeError, ValueError):
+            pass
+    if hasattr(sys.stderr, "reconfigure"):
+        try:
+            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+        except (AttributeError, ValueError):
+            pass
+
 
 def add_special_tokens_dict(tokenizer: PreTrainedTokenizerBase, special_tokens_dict: str):
     """Add the special tokens dictionary to tokenizer.
@@ -129,7 +147,7 @@ def get_categories(categories_path: str):
             _, file_extension = os.path.splitext(categories_path)
             if file_extension != ".json":
                 raise ValueError(f"Your --categories_path flag must point to a json file, you used {categories_path}")
-            with open(categories_path, "r") as categories_file:
+            with open(categories_path, "r", encoding="utf-8") as categories_file:
                 categories_list = json.load(categories_file)
                 if not isinstance(categories_list, list):
                     err_msg = (

generative_data_prep/data_prep/data_prep.py

@@ -106,20 +106,24 @@ def data_prep_main(
     dump_categories = category_to_id is not None
 
     with Hdf5FileBuffer(output_file, max_seq_length, dump_categories) as hdf5_text_buffer:
-        with open(input_file, "r") as reader:
+        total_processed = 0
+        with open(input_file, "r", encoding="utf-8", errors="replace") as reader:
             for i, line in enumerate(reader):
                 try:
                     hdf5_text_buffer.write(article_tokenizer(line))
+                    total_processed = i + 1  # Track total processed (i is 0-indexed)
+                    # Update counter every 100 articles (including the first batch)
+                    # When i+1 is a multiple of 100, we've processed exactly that many
                     if (
-                        (i != 0 and i % 100 == 0)
+                        total_processed % 100 == 0
                         and num_tokenized_articles_lock is not None
                         and num_tokenized_articles is not None
                     ):
                         with num_tokenized_articles_lock:
                             num_tokenized_articles.value += 100
                 except json.JSONDecodeError as exc:
                     if ignore_input_format_error:
-                        with open(error_log_path, "a") as f:
+                        with open(error_log_path, "a", encoding="utf-8", errors="replace") as f:
                             f.write(line)
                         if num_tokenized_articles_lock is not None and num_skipped_articles is not None:
                             with num_tokenized_articles_lock:
@@ -133,9 +137,15 @@ def data_prep_main(
                             exc.doc,
                             exc.pos,
                         ) from exc
-            if num_tokenized_articles_lock is not None and num_tokenized_articles is not None:
+            # Add remaining articles that weren't counted in the batch updates
+            if num_tokenized_articles_lock is not None and num_tokenized_articles is not None and total_processed > 0:
                 with num_tokenized_articles_lock:
-                    num_tokenized_articles.value += i % 100
+                    # Calculate remaining articles: total processed minus what we already counted
+                    # We count in batches of 100, so we need to add the remainder
+                    already_counted = (total_processed // 100) * 100  # How many we've already counted
+                    remaining = total_processed - already_counted
+                    if remaining > 0:
+                        num_tokenized_articles.value += remaining
             hdf5_text_buffer.write(article_tokenizer(None))
     article_tokenizer.metrics.dataset_type = dataset_type
     return article_tokenizer.metrics