We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Encoding Error on Windows with WandB
When syncing with `openai wandb sync`, I get a character encoding issue on one of the run files. This happens specifically on Windows which often struggles with the default encoding on a file with `open(filename)`.

One solution is `artifact.new_file(filename, "w", encoding="utf-8")` on line 279 of the `wandb_logger.py` which solves the problem locally for me. Alternatively, using a default of `"utf-8"` in the `artifact.new_file` function should work too, but might have other unintended side effects.

Here is the output:
```
wandb: ERROR Failed to open the provided file (UnicodeEncodeError: 'charmap' codec can't encode character '\u03bc' in position 205764: character maps to <undefined>). Please provide the proper encoding.
Traceback (most recent call last):
  File "C:\Users\miniconda3\lib\runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\kaiser\miniconda3\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "C:\venv\Scripts\openai.exe\__main__.py", line 7, in <module>
    sys.exit(main())
  File "C:\venv\lib\site-packages\openai\_openai_scripts.py", line 63, in main
    args.func(args)
  File "C:\venv\lib\site-packages\openai\cli.py", line 586, in sync
    resp = openai.wandb_logger.WandbLogger.sync(
  File "C:\venv\lib\site-packages\openai\wandb_logger.py", line 74, in sync
    fine_tune_logged = [
  File "C:\venv\lib\site-packages\openai\wandb_logger.py", line 75, in <listcomp>
    cls._log_fine_tune(
  File "C:\venv\lib\site-packages\openai\wandb_logger.py", line 172, in _log_fine_tune
    cls._log_artifacts(fine_tune, project, entity)
  File "C:\venv\lib\site-packages\openai\wandb_logger.py", line 236, in _log_artifacts
    cls._log_artifact_inputs(file, prefix, artifact_type, project, entity)
  File "C:\venv\lib\site-packages\openai\wandb_logger.py", line 280, in _log_artifact_inputs
    f.write(file_content)
  File "C:\Users\miniconda3\lib\encodings\cp1252.py", line 19, in encode
    return codecs.charmap_encode(input,self.errors,encoding_table)[0]
UnicodeEncodeError: 'charmap' codec can't encode character '\u03bc' in position 205764: character maps to <undefined>
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `openai/wandb_logger.py`
Content:
```
1 try:
2     import wandb
3 
4     WANDB_AVAILABLE = True
5 except:
6     WANDB_AVAILABLE = False
7 
8 
9 if WANDB_AVAILABLE:
10     import datetime
11     import io
12     import json
13     import re
14     from pathlib import Path
15 
16     import numpy as np
17     import pandas as pd
18 
19     from openai import File, FineTune
20 
21 
22 class WandbLogger:
23     """
24     Log fine-tunes to [Weights & Biases](https://wandb.me/openai-docs)
25     """
26 
27     if not WANDB_AVAILABLE:
28         print("Logging requires wandb to be installed. Run `pip install wandb`.")
29     else:
30         _wandb_api = None
31         _logged_in = False
32 
33     @classmethod
34     def sync(
35         cls,
36         id=None,
37         n_fine_tunes=None,
38         project="GPT-3",
39         entity=None,
40         force=False,
41         **kwargs_wandb_init,
42     ):
43         """
44         Sync fine-tunes to Weights & Biases.
45         :param id: The id of the fine-tune (optional)
46         :param n_fine_tunes: Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced.
47         :param project: Name of the project where you're sending runs. By default, it is "GPT-3".
48         :param entity: Username or team name where you're sending runs. By default, your default entity is used, which is usually your username.
49         :param force: Forces logging and overwrite existing wandb run of the same fine-tune.
50         """
51 
52         if not WANDB_AVAILABLE:
53             return
54 
55         if id:
56             fine_tune = FineTune.retrieve(id=id)
57             fine_tune.pop("events", None)
58             fine_tunes = [fine_tune]
59 
60         else:
61             # get list of fine_tune to log
62             fine_tunes = FineTune.list()
63             if not fine_tunes or fine_tunes.get("data") is None:
64                 print("No fine-tune has been retrieved")
65                 return
66             fine_tunes = fine_tunes["data"][
67                 -n_fine_tunes if n_fine_tunes is not None else None :
68             ]
69 
70         # log starting from oldest fine_tune
71         show_individual_warnings = (
72             False if id is None and n_fine_tunes is None else True
73         )
74         fine_tune_logged = [
75             cls._log_fine_tune(
76                 fine_tune,
77                 project,
78                 entity,
79                 force,
80                 show_individual_warnings,
81                 **kwargs_wandb_init,
82             )
83             for fine_tune in fine_tunes
84         ]
85 
86         if not show_individual_warnings and not any(fine_tune_logged):
87             print("No new successful fine-tunes were found")
88 
89         return "🎉 wandb sync completed successfully"
90 
91     @classmethod
92     def _log_fine_tune(
93         cls,
94         fine_tune,
95         project,
96         entity,
97         force,
98         show_individual_warnings,
99         **kwargs_wandb_init,
100     ):
101         fine_tune_id = fine_tune.get("id")
102         status = fine_tune.get("status")
103 
104         # check run completed successfully
105         if status != "succeeded":
106             if show_individual_warnings:
107                 print(
108                     f'Fine-tune {fine_tune_id} has the status "{status}" and will not be logged'
109                 )
110             return
111 
112         # check results are present
113         try:
114             results_id = fine_tune["result_files"][0]["id"]
115             results = File.download(id=results_id).decode("utf-8")
116         except:
117             if show_individual_warnings:
118                 print(f"Fine-tune {fine_tune_id} has no results and will not be logged")
119             return
120 
121         # check run has not been logged already
122         run_path = f"{project}/{fine_tune_id}"
123         if entity is not None:
124             run_path = f"{entity}/{run_path}"
125         wandb_run = cls._get_wandb_run(run_path)
126         if wandb_run:
127             wandb_status = wandb_run.summary.get("status")
128             if show_individual_warnings:
129                 if wandb_status == "succeeded":
130                     print(
131                         f"Fine-tune {fine_tune_id} has already been logged successfully at {wandb_run.url}"
132                     )
133                     if not force:
134                         print(
135                             'Use "--force" in the CLI or "force=True" in python if you want to overwrite previous run'
136                         )
137                 else:
138                     print(
139                         f"A run for fine-tune {fine_tune_id} was previously created but didn't end successfully"
140                     )
141                 if wandb_status != "succeeded" or force:
142                     print(
143                         f"A new wandb run will be created for fine-tune {fine_tune_id} and previous run will be overwritten"
144                     )
145             if wandb_status == "succeeded" and not force:
146                 return
147 
148         # start a wandb run
149         wandb.init(
150             job_type="fine-tune",
151             config=cls._get_config(fine_tune),
152             project=project,
153             entity=entity,
154             name=fine_tune_id,
155             id=fine_tune_id,
156             **kwargs_wandb_init,
157         )
158 
159         # log results
160         df_results = pd.read_csv(io.StringIO(results))
161         for _, row in df_results.iterrows():
162             metrics = {k: v for k, v in row.items() if not np.isnan(v)}
163             step = metrics.pop("step")
164             if step is not None:
165                 step = int(step)
166             wandb.log(metrics, step=step)
167         fine_tuned_model = fine_tune.get("fine_tuned_model")
168         if fine_tuned_model is not None:
169             wandb.summary["fine_tuned_model"] = fine_tuned_model
170 
171         # training/validation files and fine-tune details
172         cls._log_artifacts(fine_tune, project, entity)
173 
174         # mark run as complete
175         wandb.summary["status"] = "succeeded"
176 
177         wandb.finish()
178         return True
179 
180     @classmethod
181     def _ensure_logged_in(cls):
182         if not cls._logged_in:
183             if wandb.login():
184                 cls._logged_in = True
185             else:
186                 raise Exception("You need to log in to wandb")
187 
188     @classmethod
189     def _get_wandb_run(cls, run_path):
190         cls._ensure_logged_in()
191         try:
192             if cls._wandb_api is None:
193                 cls._wandb_api = wandb.Api()
194             return cls._wandb_api.run(run_path)
195         except Exception:
196             return None
197 
198     @classmethod
199     def _get_wandb_artifact(cls, artifact_path):
200         cls._ensure_logged_in()
201         try:
202             if cls._wandb_api is None:
203                 cls._wandb_api = wandb.Api()
204             return cls._wandb_api.artifact(artifact_path)
205         except Exception:
206             return None
207 
208     @classmethod
209     def _get_config(cls, fine_tune):
210         config = dict(fine_tune)
211         for key in ("training_files", "validation_files", "result_files"):
212             if config.get(key) and len(config[key]):
213                 config[key] = config[key][0]
214         if config.get("created_at"):
215             config["created_at"] = datetime.datetime.fromtimestamp(config["created_at"])
216         return config
217 
218     @classmethod
219     def _log_artifacts(cls, fine_tune, project, entity):
220         # training/validation files
221         training_file = (
222             fine_tune["training_files"][0]
223             if fine_tune.get("training_files") and len(fine_tune["training_files"])
224             else None
225         )
226         validation_file = (
227             fine_tune["validation_files"][0]
228             if fine_tune.get("validation_files") and len(fine_tune["validation_files"])
229             else None
230         )
231         for file, prefix, artifact_type in (
232             (training_file, "train", "training_files"),
233             (validation_file, "valid", "validation_files"),
234         ):
235             if file is not None:
236                 cls._log_artifact_inputs(file, prefix, artifact_type, project, entity)
237 
238         # fine-tune details
239         fine_tune_id = fine_tune.get("id")
240         artifact = wandb.Artifact(
241             "fine_tune_details",
242             type="fine_tune_details",
243             metadata=fine_tune,
244         )
245         with artifact.new_file("fine_tune_details.json") as f:
246             json.dump(fine_tune, f, indent=2)
247         wandb.run.log_artifact(
248             artifact,
249             aliases=["latest", fine_tune_id],
250         )
251 
252     @classmethod
253     def _log_artifact_inputs(cls, file, prefix, artifact_type, project, entity):
254         file_id = file["id"]
255         filename = Path(file["filename"]).name
256         stem = Path(file["filename"]).stem
257 
258         # get input artifact
259         artifact_name = f"{prefix}-{filename}"
260         # sanitize name to valid wandb artifact name
261         artifact_name = re.sub(r"[^a-zA-Z0-9_\-.]", "_", artifact_name)
262         artifact_alias = file_id
263         artifact_path = f"{project}/{artifact_name}:{artifact_alias}"
264         if entity is not None:
265             artifact_path = f"{entity}/{artifact_path}"
266         artifact = cls._get_wandb_artifact(artifact_path)
267 
268         # create artifact if file not already logged previously
269         if artifact is None:
270             # get file content
271             try:
272                 file_content = File.download(id=file_id).decode("utf-8")
273             except:
274                 print(
275                     f"File {file_id} could not be retrieved. Make sure you are allowed to download training/validation files"
276                 )
277                 return
278             artifact = wandb.Artifact(artifact_name, type=artifact_type, metadata=file)
279             with artifact.new_file(filename, mode="w") as f:
280                 f.write(file_content)
281 
282             # create a Table
283             try:
284                 table, n_items = cls._make_table(file_content)
285                 artifact.add(table, stem)
286                 wandb.config.update({f"n_{prefix}": n_items})
287                 artifact.metadata["items"] = n_items
288             except:
289                 print(f"File {file_id} could not be read as a valid JSON file")
290         else:
291             # log number of items
292             wandb.config.update({f"n_{prefix}": artifact.metadata.get("items")})
293 
294         wandb.run.use_artifact(artifact, aliases=["latest", artifact_alias])
295 
296     @classmethod
297     def _make_table(cls, file_content):
298         df = pd.read_json(io.StringIO(file_content), orient="records", lines=True)
299         return wandb.Table(dataframe=df), len(df)
300
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/openai/wandb_logger.py b/openai/wandb_logger.py
--- a/openai/wandb_logger.py
+++ b/openai/wandb_logger.py
@@ -242,7 +242,9 @@
             type="fine_tune_details",
             metadata=fine_tune,
         )
-        with artifact.new_file("fine_tune_details.json") as f:
+        with artifact.new_file(
+            "fine_tune_details.json", mode="w", encoding="utf-8"
+        ) as f:
             json.dump(fine_tune, f, indent=2)
         wandb.run.log_artifact(
             artifact,
@@ -276,7 +278,7 @@
                 )
                 return
             artifact = wandb.Artifact(artifact_name, type=artifact_type, metadata=file)
-            with artifact.new_file(filename, mode="w") as f:
+            with artifact.new_file(filename, mode="w", encoding="utf-8") as f:
                 f.write(file_content)
 
             # create a Table

{"golden_diff": "diff --git a/openai/wandb_logger.py b/openai/wandb_logger.py\n--- a/openai/wandb_logger.py\n+++ b/openai/wandb_logger.py\n@@ -242,7 +242,9 @@\n             type=\"fine_tune_details\",\n             metadata=fine_tune,\n         )\n-        with artifact.new_file(\"fine_tune_details.json\") as f:\n+        with artifact.new_file(\n+            \"fine_tune_details.json\", mode=\"w\", encoding=\"utf-8\"\n+        ) as f:\n             json.dump(fine_tune, f, indent=2)\n         wandb.run.log_artifact(\n             artifact,\n@@ -276,7 +278,7 @@\n                 )\n                 return\n             artifact = wandb.Artifact(artifact_name, type=artifact_type, metadata=file)\n-            with artifact.new_file(filename, mode=\"w\") as f:\n+            with artifact.new_file(filename, mode=\"w\", encoding=\"utf-8\") as f:\n                 f.write(file_content)\n \n             # create a Table\n", "issue": "Encoding Error on Windows with WandB\nWhen syncing with `openai wandb sync`, I get a character encoding issue on one of the run files. This happens specifically on Windows which often struggles with the default encoding on a file with `open(filename)`.\r\n\r\nOne solution is `artifact.new_file(filename, \"w\", encoding=\"utf-8\")` on line 279 of the `wandb_logger.py` which solves the problem locally for me. Alternatively, using a default of `\"utf-8\"` in the `artifact.new_file` function should work too, but might have other unintended side effects.\r\n\r\nHere is the output:\r\n```\r\nwandb: ERROR Failed to open the provided file (UnicodeEncodeError: 'charmap' codec can't encode character '\\u03bc' in position 205764: character maps to <undefined>). Please provide the proper encoding.\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\miniconda3\\lib\\runpy.py\", line 197, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"C:\\Users\\kaiser\\miniconda3\\lib\\runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\venv\\Scripts\\openai.exe\\__main__.py\", line 7, in <module>\r\n    sys.exit(main())\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\_openai_scripts.py\", line 63, in main\r\n    args.func(args)\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\cli.py\", line 586, in sync\r\n    resp = openai.wandb_logger.WandbLogger.sync(\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\wandb_logger.py\", line 74, in sync\r\n    fine_tune_logged = [\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\wandb_logger.py\", line 75, in <listcomp>\r\n    cls._log_fine_tune(\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\wandb_logger.py\", line 172, in _log_fine_tune\r\n    cls._log_artifacts(fine_tune, project, entity)\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\wandb_logger.py\", line 236, in _log_artifacts\r\n    cls._log_artifact_inputs(file, prefix, artifact_type, project, entity)\r\n  File \"C:\\venv\\lib\\site-packages\\openai\\wandb_logger.py\", line 280, in _log_artifact_inputs\r\n    f.write(file_content)\r\n  File \"C:\\Users\\miniconda3\\lib\\encodings\\cp1252.py\", line 19, in encode\r\n    return codecs.charmap_encode(input,self.errors,encoding_table)[0]\r\nUnicodeEncodeError: 'charmap' codec can't encode character '\\u03bc' in position 205764: character maps to <undefined>\r\n```\n", "before_files": [{"content": "try:\n    import wandb\n\n    WANDB_AVAILABLE = True\nexcept:\n    WANDB_AVAILABLE = False\n\n\nif WANDB_AVAILABLE:\n    import datetime\n    import io\n    import json\n    import re\n    from pathlib import Path\n\n    import numpy as np\n    import pandas as pd\n\n    from openai import File, FineTune\n\n\nclass WandbLogger:\n    \"\"\"\n    Log fine-tunes to [Weights & Biases](https://wandb.me/openai-docs)\n    \"\"\"\n\n    if not WANDB_AVAILABLE:\n        print(\"Logging requires wandb to be installed. Run `pip install wandb`.\")\n    else:\n        _wandb_api = None\n        _logged_in = False\n\n    @classmethod\n    def sync(\n        cls,\n        id=None,\n        n_fine_tunes=None,\n        project=\"GPT-3\",\n        entity=None,\n        force=False,\n        **kwargs_wandb_init,\n    ):\n        \"\"\"\n        Sync fine-tunes to Weights & Biases.\n        :param id: The id of the fine-tune (optional)\n        :param n_fine_tunes: Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced.\n        :param project: Name of the project where you're sending runs. By default, it is \"GPT-3\".\n        :param entity: Username or team name where you're sending runs. By default, your default entity is used, which is usually your username.\n        :param force: Forces logging and overwrite existing wandb run of the same fine-tune.\n        \"\"\"\n\n        if not WANDB_AVAILABLE:\n            return\n\n        if id:\n            fine_tune = FineTune.retrieve(id=id)\n            fine_tune.pop(\"events\", None)\n            fine_tunes = [fine_tune]\n\n        else:\n            # get list of fine_tune to log\n            fine_tunes = FineTune.list()\n            if not fine_tunes or fine_tunes.get(\"data\") is None:\n                print(\"No fine-tune has been retrieved\")\n                return\n            fine_tunes = fine_tunes[\"data\"][\n                -n_fine_tunes if n_fine_tunes is not None else None :\n            ]\n\n        # log starting from oldest fine_tune\n        show_individual_warnings = (\n            False if id is None and n_fine_tunes is None else True\n        )\n        fine_tune_logged = [\n            cls._log_fine_tune(\n                fine_tune,\n                project,\n                entity,\n                force,\n                show_individual_warnings,\n                **kwargs_wandb_init,\n            )\n            for fine_tune in fine_tunes\n        ]\n\n        if not show_individual_warnings and not any(fine_tune_logged):\n            print(\"No new successful fine-tunes were found\")\n\n        return \"\ud83c\udf89 wandb sync completed successfully\"\n\n    @classmethod\n    def _log_fine_tune(\n        cls,\n        fine_tune,\n        project,\n        entity,\n        force,\n        show_individual_warnings,\n        **kwargs_wandb_init,\n    ):\n        fine_tune_id = fine_tune.get(\"id\")\n        status = fine_tune.get(\"status\")\n\n        # check run completed successfully\n        if status != \"succeeded\":\n            if show_individual_warnings:\n                print(\n                    f'Fine-tune {fine_tune_id} has the status \"{status}\" and will not be logged'\n                )\n            return\n\n        # check results are present\n        try:\n            results_id = fine_tune[\"result_files\"][0][\"id\"]\n            results = File.download(id=results_id).decode(\"utf-8\")\n        except:\n            if show_individual_warnings:\n                print(f\"Fine-tune {fine_tune_id} has no results and will not be logged\")\n            return\n\n        # check run has not been logged already\n        run_path = f\"{project}/{fine_tune_id}\"\n        if entity is not None:\n            run_path = f\"{entity}/{run_path}\"\n        wandb_run = cls._get_wandb_run(run_path)\n        if wandb_run:\n            wandb_status = wandb_run.summary.get(\"status\")\n            if show_individual_warnings:\n                if wandb_status == \"succeeded\":\n                    print(\n                        f\"Fine-tune {fine_tune_id} has already been logged successfully at {wandb_run.url}\"\n                    )\n                    if not force:\n                        print(\n                            'Use \"--force\" in the CLI or \"force=True\" in python if you want to overwrite previous run'\n                        )\n                else:\n                    print(\n                        f\"A run for fine-tune {fine_tune_id} was previously created but didn't end successfully\"\n                    )\n                if wandb_status != \"succeeded\" or force:\n                    print(\n                        f\"A new wandb run will be created for fine-tune {fine_tune_id} and previous run will be overwritten\"\n                    )\n            if wandb_status == \"succeeded\" and not force:\n                return\n\n        # start a wandb run\n        wandb.init(\n            job_type=\"fine-tune\",\n            config=cls._get_config(fine_tune),\n            project=project,\n            entity=entity,\n            name=fine_tune_id,\n            id=fine_tune_id,\n            **kwargs_wandb_init,\n        )\n\n        # log results\n        df_results = pd.read_csv(io.StringIO(results))\n        for _, row in df_results.iterrows():\n            metrics = {k: v for k, v in row.items() if not np.isnan(v)}\n            step = metrics.pop(\"step\")\n            if step is not None:\n                step = int(step)\n            wandb.log(metrics, step=step)\n        fine_tuned_model = fine_tune.get(\"fine_tuned_model\")\n        if fine_tuned_model is not None:\n            wandb.summary[\"fine_tuned_model\"] = fine_tuned_model\n\n        # training/validation files and fine-tune details\n        cls._log_artifacts(fine_tune, project, entity)\n\n        # mark run as complete\n        wandb.summary[\"status\"] = \"succeeded\"\n\n        wandb.finish()\n        return True\n\n    @classmethod\n    def _ensure_logged_in(cls):\n        if not cls._logged_in:\n            if wandb.login():\n                cls._logged_in = True\n            else:\n                raise Exception(\"You need to log in to wandb\")\n\n    @classmethod\n    def _get_wandb_run(cls, run_path):\n        cls._ensure_logged_in()\n        try:\n            if cls._wandb_api is None:\n                cls._wandb_api = wandb.Api()\n            return cls._wandb_api.run(run_path)\n        except Exception:\n            return None\n\n    @classmethod\n    def _get_wandb_artifact(cls, artifact_path):\n        cls._ensure_logged_in()\n        try:\n            if cls._wandb_api is None:\n                cls._wandb_api = wandb.Api()\n            return cls._wandb_api.artifact(artifact_path)\n        except Exception:\n            return None\n\n    @classmethod\n    def _get_config(cls, fine_tune):\n        config = dict(fine_tune)\n        for key in (\"training_files\", \"validation_files\", \"result_files\"):\n            if config.get(key) and len(config[key]):\n                config[key] = config[key][0]\n        if config.get(\"created_at\"):\n            config[\"created_at\"] = datetime.datetime.fromtimestamp(config[\"created_at\"])\n        return config\n\n    @classmethod\n    def _log_artifacts(cls, fine_tune, project, entity):\n        # training/validation files\n        training_file = (\n            fine_tune[\"training_files\"][0]\n            if fine_tune.get(\"training_files\") and len(fine_tune[\"training_files\"])\n            else None\n        )\n        validation_file = (\n            fine_tune[\"validation_files\"][0]\n            if fine_tune.get(\"validation_files\") and len(fine_tune[\"validation_files\"])\n            else None\n        )\n        for file, prefix, artifact_type in (\n            (training_file, \"train\", \"training_files\"),\n            (validation_file, \"valid\", \"validation_files\"),\n        ):\n            if file is not None:\n                cls._log_artifact_inputs(file, prefix, artifact_type, project, entity)\n\n        # fine-tune details\n        fine_tune_id = fine_tune.get(\"id\")\n        artifact = wandb.Artifact(\n            \"fine_tune_details\",\n            type=\"fine_tune_details\",\n            metadata=fine_tune,\n        )\n        with artifact.new_file(\"fine_tune_details.json\") as f:\n            json.dump(fine_tune, f, indent=2)\n        wandb.run.log_artifact(\n            artifact,\n            aliases=[\"latest\", fine_tune_id],\n        )\n\n    @classmethod\n    def _log_artifact_inputs(cls, file, prefix, artifact_type, project, entity):\n        file_id = file[\"id\"]\n        filename = Path(file[\"filename\"]).name\n        stem = Path(file[\"filename\"]).stem\n\n        # get input artifact\n        artifact_name = f\"{prefix}-{filename}\"\n        # sanitize name to valid wandb artifact name\n        artifact_name = re.sub(r\"[^a-zA-Z0-9_\\-.]\", \"_\", artifact_name)\n        artifact_alias = file_id\n        artifact_path = f\"{project}/{artifact_name}:{artifact_alias}\"\n        if entity is not None:\n            artifact_path = f\"{entity}/{artifact_path}\"\n        artifact = cls._get_wandb_artifact(artifact_path)\n\n        # create artifact if file not already logged previously\n        if artifact is None:\n            # get file content\n            try:\n                file_content = File.download(id=file_id).decode(\"utf-8\")\n            except:\n                print(\n                    f\"File {file_id} could not be retrieved. Make sure you are allowed to download training/validation files\"\n                )\n                return\n            artifact = wandb.Artifact(artifact_name, type=artifact_type, metadata=file)\n            with artifact.new_file(filename, mode=\"w\") as f:\n                f.write(file_content)\n\n            # create a Table\n            try:\n                table, n_items = cls._make_table(file_content)\n                artifact.add(table, stem)\n                wandb.config.update({f\"n_{prefix}\": n_items})\n                artifact.metadata[\"items\"] = n_items\n            except:\n                print(f\"File {file_id} could not be read as a valid JSON file\")\n        else:\n            # log number of items\n            wandb.config.update({f\"n_{prefix}\": artifact.metadata.get(\"items\")})\n\n        wandb.run.use_artifact(artifact, aliases=[\"latest\", artifact_alias])\n\n    @classmethod\n    def _make_table(cls, file_content):\n        df = pd.read_json(io.StringIO(file_content), orient=\"records\", lines=True)\n        return wandb.Table(dataframe=df), len(df)\n", "path": "openai/wandb_logger.py"}], "after_files": [{"content": "try:\n    import wandb\n\n    WANDB_AVAILABLE = True\nexcept:\n    WANDB_AVAILABLE = False\n\n\nif WANDB_AVAILABLE:\n    import datetime\n    import io\n    import json\n    import re\n    from pathlib import Path\n\n    import numpy as np\n    import pandas as pd\n\n    from openai import File, FineTune\n\n\nclass WandbLogger:\n    \"\"\"\n    Log fine-tunes to [Weights & Biases](https://wandb.me/openai-docs)\n    \"\"\"\n\n    if not WANDB_AVAILABLE:\n        print(\"Logging requires wandb to be installed. Run `pip install wandb`.\")\n    else:\n        _wandb_api = None\n        _logged_in = False\n\n    @classmethod\n    def sync(\n        cls,\n        id=None,\n        n_fine_tunes=None,\n        project=\"GPT-3\",\n        entity=None,\n        force=False,\n        **kwargs_wandb_init,\n    ):\n        \"\"\"\n        Sync fine-tunes to Weights & Biases.\n        :param id: The id of the fine-tune (optional)\n        :param n_fine_tunes: Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced.\n        :param project: Name of the project where you're sending runs. By default, it is \"GPT-3\".\n        :param entity: Username or team name where you're sending runs. By default, your default entity is used, which is usually your username.\n        :param force: Forces logging and overwrite existing wandb run of the same fine-tune.\n        \"\"\"\n\n        if not WANDB_AVAILABLE:\n            return\n\n        if id:\n            fine_tune = FineTune.retrieve(id=id)\n            fine_tune.pop(\"events\", None)\n            fine_tunes = [fine_tune]\n\n        else:\n            # get list of fine_tune to log\n            fine_tunes = FineTune.list()\n            if not fine_tunes or fine_tunes.get(\"data\") is None:\n                print(\"No fine-tune has been retrieved\")\n                return\n            fine_tunes = fine_tunes[\"data\"][\n                -n_fine_tunes if n_fine_tunes is not None else None :\n            ]\n\n        # log starting from oldest fine_tune\n        show_individual_warnings = (\n            False if id is None and n_fine_tunes is None else True\n        )\n        fine_tune_logged = [\n            cls._log_fine_tune(\n                fine_tune,\n                project,\n                entity,\n                force,\n                show_individual_warnings,\n                **kwargs_wandb_init,\n            )\n            for fine_tune in fine_tunes\n        ]\n\n        if not show_individual_warnings and not any(fine_tune_logged):\n            print(\"No new successful fine-tunes were found\")\n\n        return \"\ud83c\udf89 wandb sync completed successfully\"\n\n    @classmethod\n    def _log_fine_tune(\n        cls,\n        fine_tune,\n        project,\n        entity,\n        force,\n        show_individual_warnings,\n        **kwargs_wandb_init,\n    ):\n        fine_tune_id = fine_tune.get(\"id\")\n        status = fine_tune.get(\"status\")\n\n        # check run completed successfully\n        if status != \"succeeded\":\n            if show_individual_warnings:\n                print(\n                    f'Fine-tune {fine_tune_id} has the status \"{status}\" and will not be logged'\n                )\n            return\n\n        # check results are present\n        try:\n            results_id = fine_tune[\"result_files\"][0][\"id\"]\n            results = File.download(id=results_id).decode(\"utf-8\")\n        except:\n            if show_individual_warnings:\n                print(f\"Fine-tune {fine_tune_id} has no results and will not be logged\")\n            return\n\n        # check run has not been logged already\n        run_path = f\"{project}/{fine_tune_id}\"\n        if entity is not None:\n            run_path = f\"{entity}/{run_path}\"\n        wandb_run = cls._get_wandb_run(run_path)\n        if wandb_run:\n            wandb_status = wandb_run.summary.get(\"status\")\n            if show_individual_warnings:\n                if wandb_status == \"succeeded\":\n                    print(\n                        f\"Fine-tune {fine_tune_id} has already been logged successfully at {wandb_run.url}\"\n                    )\n                    if not force:\n                        print(\n                            'Use \"--force\" in the CLI or \"force=True\" in python if you want to overwrite previous run'\n                        )\n                else:\n                    print(\n                        f\"A run for fine-tune {fine_tune_id} was previously created but didn't end successfully\"\n                    )\n                if wandb_status != \"succeeded\" or force:\n                    print(\n                        f\"A new wandb run will be created for fine-tune {fine_tune_id} and previous run will be overwritten\"\n                    )\n            if wandb_status == \"succeeded\" and not force:\n                return\n\n        # start a wandb run\n        wandb.init(\n            job_type=\"fine-tune\",\n            config=cls._get_config(fine_tune),\n            project=project,\n            entity=entity,\n            name=fine_tune_id,\n            id=fine_tune_id,\n            **kwargs_wandb_init,\n        )\n\n        # log results\n        df_results = pd.read_csv(io.StringIO(results))\n        for _, row in df_results.iterrows():\n            metrics = {k: v for k, v in row.items() if not np.isnan(v)}\n            step = metrics.pop(\"step\")\n            if step is not None:\n                step = int(step)\n            wandb.log(metrics, step=step)\n        fine_tuned_model = fine_tune.get(\"fine_tuned_model\")\n        if fine_tuned_model is not None:\n            wandb.summary[\"fine_tuned_model\"] = fine_tuned_model\n\n        # training/validation files and fine-tune details\n        cls._log_artifacts(fine_tune, project, entity)\n\n        # mark run as complete\n        wandb.summary[\"status\"] = \"succeeded\"\n\n        wandb.finish()\n        return True\n\n    @classmethod\n    def _ensure_logged_in(cls):\n        if not cls._logged_in:\n            if wandb.login():\n                cls._logged_in = True\n            else:\n                raise Exception(\"You need to log in to wandb\")\n\n    @classmethod\n    def _get_wandb_run(cls, run_path):\n        cls._ensure_logged_in()\n        try:\n            if cls._wandb_api is None:\n                cls._wandb_api = wandb.Api()\n            return cls._wandb_api.run(run_path)\n        except Exception:\n            return None\n\n    @classmethod\n    def _get_wandb_artifact(cls, artifact_path):\n        cls._ensure_logged_in()\n        try:\n            if cls._wandb_api is None:\n                cls._wandb_api = wandb.Api()\n            return cls._wandb_api.artifact(artifact_path)\n        except Exception:\n            return None\n\n    @classmethod\n    def _get_config(cls, fine_tune):\n        config = dict(fine_tune)\n        for key in (\"training_files\", \"validation_files\", \"result_files\"):\n            if config.get(key) and len(config[key]):\n                config[key] = config[key][0]\n        if config.get(\"created_at\"):\n            config[\"created_at\"] = datetime.datetime.fromtimestamp(config[\"created_at\"])\n        return config\n\n    @classmethod\n    def _log_artifacts(cls, fine_tune, project, entity):\n        # training/validation files\n        training_file = (\n            fine_tune[\"training_files\"][0]\n            if fine_tune.get(\"training_files\") and len(fine_tune[\"training_files\"])\n            else None\n        )\n        validation_file = (\n            fine_tune[\"validation_files\"][0]\n            if fine_tune.get(\"validation_files\") and len(fine_tune[\"validation_files\"])\n            else None\n        )\n        for file, prefix, artifact_type in (\n            (training_file, \"train\", \"training_files\"),\n            (validation_file, \"valid\", \"validation_files\"),\n        ):\n            if file is not None:\n                cls._log_artifact_inputs(file, prefix, artifact_type, project, entity)\n\n        # fine-tune details\n        fine_tune_id = fine_tune.get(\"id\")\n        artifact = wandb.Artifact(\n            \"fine_tune_details\",\n            type=\"fine_tune_details\",\n            metadata=fine_tune,\n        )\n        with artifact.new_file(\n            \"fine_tune_details.json\", mode=\"w\", encoding=\"utf-8\"\n        ) as f:\n            json.dump(fine_tune, f, indent=2)\n        wandb.run.log_artifact(\n            artifact,\n            aliases=[\"latest\", fine_tune_id],\n        )\n\n    @classmethod\n    def _log_artifact_inputs(cls, file, prefix, artifact_type, project, entity):\n        file_id = file[\"id\"]\n        filename = Path(file[\"filename\"]).name\n        stem = Path(file[\"filename\"]).stem\n\n        # get input artifact\n        artifact_name = f\"{prefix}-{filename}\"\n        # sanitize name to valid wandb artifact name\n        artifact_name = re.sub(r\"[^a-zA-Z0-9_\\-.]\", \"_\", artifact_name)\n        artifact_alias = file_id\n        artifact_path = f\"{project}/{artifact_name}:{artifact_alias}\"\n        if entity is not None:\n            artifact_path = f\"{entity}/{artifact_path}\"\n        artifact = cls._get_wandb_artifact(artifact_path)\n\n        # create artifact if file not already logged previously\n        if artifact is None:\n            # get file content\n            try:\n                file_content = File.download(id=file_id).decode(\"utf-8\")\n            except:\n                print(\n                    f\"File {file_id} could not be retrieved. Make sure you are allowed to download training/validation files\"\n                )\n                return\n            artifact = wandb.Artifact(artifact_name, type=artifact_type, metadata=file)\n            with artifact.new_file(filename, mode=\"w\", encoding=\"utf-8\") as f:\n                f.write(file_content)\n\n            # create a Table\n            try:\n                table, n_items = cls._make_table(file_content)\n                artifact.add(table, stem)\n                wandb.config.update({f\"n_{prefix}\": n_items})\n                artifact.metadata[\"items\"] = n_items\n            except:\n                print(f\"File {file_id} could not be read as a valid JSON file\")\n        else:\n            # log number of items\n            wandb.config.update({f\"n_{prefix}\": artifact.metadata.get(\"items\")})\n\n        wandb.run.use_artifact(artifact, aliases=[\"latest\", artifact_alias])\n\n    @classmethod\n    def _make_table(cls, file_content):\n        df = pd.read_json(io.StringIO(file_content), orient=\"records\", lines=True)\n        return wandb.Table(dataframe=df), len(df)\n", "path": "openai/wandb_logger.py"}]}