We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Not all training tasks need commits DB
Indeed I think none of the ones we currently run as part of the data pipeline need the commits.
We should:
- Make the trainer script only download the DBs which are necessary;
- Remove the dependency on the commit retrieval task in the data-pipeline.yml.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `scripts/trainer.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 
3 import argparse
4 import lzma
5 import os
6 import shutil
7 from logging import INFO, basicConfig, getLogger
8 from urllib.request import urlretrieve
9 
10 from bugbug.models.component import ComponentModel
11 from bugbug.models.defect_enhancement_task import DefectEnhancementTaskModel
12 from bugbug.models.regression import RegressionModel
13 from bugbug.models.tracking import TrackingModel
14 
15 basicConfig(level=INFO)
16 logger = getLogger(__name__)
17 
18 BASE_URL = "https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public"
19 
20 
21 class Trainer(object):
22     def decompress_file(self, path):
23         with lzma.open(f"{path}.xz", "rb") as input_f:
24             with open(path, "wb") as output_f:
25                 shutil.copyfileobj(input_f, output_f)
26 
27     def compress_file(self, path):
28         with open(path, "rb") as input_f:
29             with lzma.open(f"{path}.xz", "wb") as output_f:
30                 shutil.copyfileobj(input_f, output_f)
31 
32     def train_defect_enhancement_task(self):
33         logger.info("Training *defect vs enhancement vs task* model")
34         model = DefectEnhancementTaskModel()
35         model.train()
36         self.compress_file("defectenhancementtaskmodel")
37 
38     def train_component(self):
39         logger.info("Training *component* model")
40         model = ComponentModel()
41         model.train()
42         self.compress_file("componentmodel")
43 
44     def train_regression(self):
45         logger.info("Training *regression vs non-regression* model")
46         model = RegressionModel()
47         model.train()
48         self.compress_file("regressionmodel")
49 
50     def train_tracking(self):
51         logger.info("Training *tracking* model")
52         model = TrackingModel()
53         model.train()
54         self.compress_file("trackingmodel")
55 
56     def go(self, model):
57         # TODO: Stop hard-coding them
58         valid_models = ["defect", "component", "regression", "tracking"]
59 
60         if model not in valid_models:
61             exception = (
62                 f"Invalid model {model!r} name, use one of {valid_models!r} instead"
63             )
64             raise ValueError(exception)
65 
66         # Download datasets that were built by bugbug_data.
67         os.makedirs("data", exist_ok=True)
68 
69         # Bugs.json
70         logger.info("Downloading bugs database")
71         bugs_url = BASE_URL.format("bugs")
72         urlretrieve(f"{bugs_url}/bugs.json.xz", "data/bugs.json.xz")
73         logger.info("Decompressing bugs database")
74         self.decompress_file("data/bugs.json")
75 
76         # Commits.json
77         logger.info("Downloading commits database")
78         commits_url = BASE_URL.format("commits")
79         urlretrieve(f"{commits_url}/commits.json.xz", "data/commits.json.xz")
80         logger.info("Decompressing commits database")
81         self.decompress_file("data/commits.json")
82 
83         if model == "defect":
84             # Train classifier for defect-vs-enhancement-vs-task.
85             self.train_defect_enhancement_task()
86         elif model == "component":
87             # Train classifier for the component of a bug.
88             self.train_component()
89         elif model == "regression":
90             # Train classifier for regression-vs-nonregression.
91             self.train_regression()
92         elif model == "tracking":
93             # Train classifier for tracking bugs.
94             self.train_tracking()
95         else:
96             # We shouldn't be here
97             raise Exception("valid_models is likely not up-to-date anymore")
98 
99 
100 def main():
101     description = "Train the models"
102     parser = argparse.ArgumentParser(description=description)
103 
104     parser.add_argument("model", help="Which model to train.")
105 
106     args = parser.parse_args()
107 
108     retriever = Trainer()
109     retriever.go(args.model)
110
```
--- 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/scripts/trainer.py b/scripts/trainer.py
--- a/scripts/trainer.py
+++ b/scripts/trainer.py
@@ -73,13 +73,6 @@
         logger.info("Decompressing bugs database")
         self.decompress_file("data/bugs.json")
 
-        # Commits.json
-        logger.info("Downloading commits database")
-        commits_url = BASE_URL.format("commits")
-        urlretrieve(f"{commits_url}/commits.json.xz", "data/commits.json.xz")
-        logger.info("Decompressing commits database")
-        self.decompress_file("data/commits.json")
-
         if model == "defect":
             # Train classifier for defect-vs-enhancement-vs-task.
             self.train_defect_enhancement_task()

{"golden_diff": "diff --git a/scripts/trainer.py b/scripts/trainer.py\n--- a/scripts/trainer.py\n+++ b/scripts/trainer.py\n@@ -73,13 +73,6 @@\n         logger.info(\"Decompressing bugs database\")\n         self.decompress_file(\"data/bugs.json\")\n \n-        # Commits.json\n-        logger.info(\"Downloading commits database\")\n-        commits_url = BASE_URL.format(\"commits\")\n-        urlretrieve(f\"{commits_url}/commits.json.xz\", \"data/commits.json.xz\")\n-        logger.info(\"Decompressing commits database\")\n-        self.decompress_file(\"data/commits.json\")\n-\n         if model == \"defect\":\n             # Train classifier for defect-vs-enhancement-vs-task.\n             self.train_defect_enhancement_task()\n", "issue": "Not all training tasks need commits DB\nIndeed I think none of the ones we currently run as part of the data pipeline need the commits.\r\nWe should:\r\n- Make the trainer script only download the DBs which are necessary;\r\n- Remove the dependency on the commit retrieval task in the data-pipeline.yml.\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\nimport argparse\nimport lzma\nimport os\nimport shutil\nfrom logging import INFO, basicConfig, getLogger\nfrom urllib.request import urlretrieve\n\nfrom bugbug.models.component import ComponentModel\nfrom bugbug.models.defect_enhancement_task import DefectEnhancementTaskModel\nfrom bugbug.models.regression import RegressionModel\nfrom bugbug.models.tracking import TrackingModel\n\nbasicConfig(level=INFO)\nlogger = getLogger(__name__)\n\nBASE_URL = \"https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public\"\n\n\nclass Trainer(object):\n    def decompress_file(self, path):\n        with lzma.open(f\"{path}.xz\", \"rb\") as input_f:\n            with open(path, \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def compress_file(self, path):\n        with open(path, \"rb\") as input_f:\n            with lzma.open(f\"{path}.xz\", \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def train_defect_enhancement_task(self):\n        logger.info(\"Training *defect vs enhancement vs task* model\")\n        model = DefectEnhancementTaskModel()\n        model.train()\n        self.compress_file(\"defectenhancementtaskmodel\")\n\n    def train_component(self):\n        logger.info(\"Training *component* model\")\n        model = ComponentModel()\n        model.train()\n        self.compress_file(\"componentmodel\")\n\n    def train_regression(self):\n        logger.info(\"Training *regression vs non-regression* model\")\n        model = RegressionModel()\n        model.train()\n        self.compress_file(\"regressionmodel\")\n\n    def train_tracking(self):\n        logger.info(\"Training *tracking* model\")\n        model = TrackingModel()\n        model.train()\n        self.compress_file(\"trackingmodel\")\n\n    def go(self, model):\n        # TODO: Stop hard-coding them\n        valid_models = [\"defect\", \"component\", \"regression\", \"tracking\"]\n\n        if model not in valid_models:\n            exception = (\n                f\"Invalid model {model!r} name, use one of {valid_models!r} instead\"\n            )\n            raise ValueError(exception)\n\n        # Download datasets that were built by bugbug_data.\n        os.makedirs(\"data\", exist_ok=True)\n\n        # Bugs.json\n        logger.info(\"Downloading bugs database\")\n        bugs_url = BASE_URL.format(\"bugs\")\n        urlretrieve(f\"{bugs_url}/bugs.json.xz\", \"data/bugs.json.xz\")\n        logger.info(\"Decompressing bugs database\")\n        self.decompress_file(\"data/bugs.json\")\n\n        # Commits.json\n        logger.info(\"Downloading commits database\")\n        commits_url = BASE_URL.format(\"commits\")\n        urlretrieve(f\"{commits_url}/commits.json.xz\", \"data/commits.json.xz\")\n        logger.info(\"Decompressing commits database\")\n        self.decompress_file(\"data/commits.json\")\n\n        if model == \"defect\":\n            # Train classifier for defect-vs-enhancement-vs-task.\n            self.train_defect_enhancement_task()\n        elif model == \"component\":\n            # Train classifier for the component of a bug.\n            self.train_component()\n        elif model == \"regression\":\n            # Train classifier for regression-vs-nonregression.\n            self.train_regression()\n        elif model == \"tracking\":\n            # Train classifier for tracking bugs.\n            self.train_tracking()\n        else:\n            # We shouldn't be here\n            raise Exception(\"valid_models is likely not up-to-date anymore\")\n\n\ndef main():\n    description = \"Train the models\"\n    parser = argparse.ArgumentParser(description=description)\n\n    parser.add_argument(\"model\", help=\"Which model to train.\")\n\n    args = parser.parse_args()\n\n    retriever = Trainer()\n    retriever.go(args.model)\n", "path": "scripts/trainer.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\nimport argparse\nimport lzma\nimport os\nimport shutil\nfrom logging import INFO, basicConfig, getLogger\nfrom urllib.request import urlretrieve\n\nfrom bugbug.models.component import ComponentModel\nfrom bugbug.models.defect_enhancement_task import DefectEnhancementTaskModel\nfrom bugbug.models.regression import RegressionModel\nfrom bugbug.models.tracking import TrackingModel\n\nbasicConfig(level=INFO)\nlogger = getLogger(__name__)\n\nBASE_URL = \"https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public\"\n\n\nclass Trainer(object):\n    def decompress_file(self, path):\n        with lzma.open(f\"{path}.xz\", \"rb\") as input_f:\n            with open(path, \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def compress_file(self, path):\n        with open(path, \"rb\") as input_f:\n            with lzma.open(f\"{path}.xz\", \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def train_defect_enhancement_task(self):\n        logger.info(\"Training *defect vs enhancement vs task* model\")\n        model = DefectEnhancementTaskModel()\n        model.train()\n        self.compress_file(\"defectenhancementtaskmodel\")\n\n    def train_component(self):\n        logger.info(\"Training *component* model\")\n        model = ComponentModel()\n        model.train()\n        self.compress_file(\"componentmodel\")\n\n    def train_regression(self):\n        logger.info(\"Training *regression vs non-regression* model\")\n        model = RegressionModel()\n        model.train()\n        self.compress_file(\"regressionmodel\")\n\n    def train_tracking(self):\n        logger.info(\"Training *tracking* model\")\n        model = TrackingModel()\n        model.train()\n        self.compress_file(\"trackingmodel\")\n\n    def go(self, model):\n        # TODO: Stop hard-coding them\n        valid_models = [\"defect\", \"component\", \"regression\", \"tracking\"]\n\n        if model not in valid_models:\n            exception = (\n                f\"Invalid model {model!r} name, use one of {valid_models!r} instead\"\n            )\n            raise ValueError(exception)\n\n        # Download datasets that were built by bugbug_data.\n        os.makedirs(\"data\", exist_ok=True)\n\n        # Bugs.json\n        logger.info(\"Downloading bugs database\")\n        bugs_url = BASE_URL.format(\"bugs\")\n        urlretrieve(f\"{bugs_url}/bugs.json.xz\", \"data/bugs.json.xz\")\n        logger.info(\"Decompressing bugs database\")\n        self.decompress_file(\"data/bugs.json\")\n\n        if model == \"defect\":\n            # Train classifier for defect-vs-enhancement-vs-task.\n            self.train_defect_enhancement_task()\n        elif model == \"component\":\n            # Train classifier for the component of a bug.\n            self.train_component()\n        elif model == \"regression\":\n            # Train classifier for regression-vs-nonregression.\n            self.train_regression()\n        elif model == \"tracking\":\n            # Train classifier for tracking bugs.\n            self.train_tracking()\n        else:\n            # We shouldn't be here\n            raise Exception(\"valid_models is likely not up-to-date anymore\")\n\n\ndef main():\n    description = \"Train the models\"\n    parser = argparse.ArgumentParser(description=description)\n\n    parser.add_argument(\"model\", help=\"Which model to train.\")\n\n    args = parser.parse_args()\n\n    retriever = Trainer()\n    retriever.go(args.model)\n", "path": "scripts/trainer.py"}]}