We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ASV] add read_csv benchmark with dtype/names/parse_dates parameters


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `asv_bench/benchmarks/io/csv.py`
Content:
```
1 # Licensed to Modin Development Team under one or more contributor license agreements.
2 # See the NOTICE file distributed with this work for additional information regarding
3 # copyright ownership.  The Modin Development Team licenses this file to you under the
4 # Apache License, Version 2.0 (the "License"); you may not use this file except in
5 # compliance with the License.  You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software distributed under
10 # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
11 # ANY KIND, either express or implied. See the License for the specific language
12 # governing permissions and limitations under the License.
13 
14 import modin.pandas as pd
15 import numpy as np
16 
17 from ..utils import (
18     generate_dataframe,
19     RAND_LOW,
20     RAND_HIGH,
21     ASV_USE_IMPL,
22     ASV_DATASET_SIZE,
23     UNARY_OP_DATA_SIZE,
24     IMPL,
25     execute,
26     get_shape_id,
27 )
28 
29 # ray init
30 if ASV_USE_IMPL == "modin":
31     pd.DataFrame([])
32 
33 
34 class BaseReadCsv:
35     # test data file can de created only once
36     def setup_cache(self, test_filename="io_test_file"):
37         test_filenames = {}
38         for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:
39             shape_id = get_shape_id(shape)
40             test_filenames[shape_id] = f"{test_filename}_{shape_id}.csv"
41             df = generate_dataframe("pandas", "str_int", *shape, RAND_LOW, RAND_HIGH)
42             df.to_csv(test_filenames[shape_id], index=False)
43 
44         return test_filenames
45 
46     def setup(self, test_filenames, shape, *args, **kwargs):
47         self.shape_id = get_shape_id(shape)
48 
49 
50 class TimeReadCsvSkiprows(BaseReadCsv):
51     param_names = ["shape", "skiprows"]
52     params = [
53         UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],
54         [
55             None,
56             lambda x: x % 2,
57             np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0] // 10),
58             np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0], 2),
59         ],
60     ]
61 
62     def time_skiprows(self, test_filenames, shape, skiprows):
63         execute(
64             IMPL[ASV_USE_IMPL].read_csv(
65                 test_filenames[self.shape_id], skiprows=skiprows
66             )
67         )
68
```
--- 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/asv_bench/benchmarks/io/csv.py b/asv_bench/benchmarks/io/csv.py
--- a/asv_bench/benchmarks/io/csv.py
+++ b/asv_bench/benchmarks/io/csv.py
@@ -65,3 +65,70 @@
                 test_filenames[self.shape_id], skiprows=skiprows
             )
         )
+
+
+class TimeReadCsvNamesDtype:
+    _dtypes_params = ["Int64", "Int64_Timestamp"]
+    _timestamp_columns = ["col1", "col2"]
+
+    param_names = ["shape", "names", "dtype"]
+    params = [
+        UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],
+        ["array-like"],
+        _dtypes_params,
+    ]
+
+    def _get_file_id(self, shape, dtype):
+        return get_shape_id(shape) + dtype
+
+    def _add_timestamp_columns(self, df):
+        df = df.copy()
+        date_column = IMPL["pandas"].date_range(
+            "2000",
+            periods=df.shape[0],
+            freq="ms",
+        )
+        for col in self._timestamp_columns:
+            df[col] = date_column
+        return df
+
+    def setup_cache(self, test_filename="io_test_file_csv_names_dtype"):
+        # filenames with a metadata of saved dataframes
+        cache = {}
+        for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:
+            for dtype in self._dtypes_params:
+                df = generate_dataframe("pandas", "int", *shape, RAND_LOW, RAND_HIGH)
+                if dtype == "Int64_Timestamp":
+                    df = self._add_timestamp_columns(df)
+
+                file_id = self._get_file_id(shape, dtype)
+                cache[file_id] = (
+                    f"{test_filename}_{file_id}.csv",
+                    df.columns.to_list(),
+                    df.dtypes.to_dict(),
+                )
+                df.to_csv(cache[file_id][0], index=False)
+        return cache
+
+    def setup(self, cache, shape, names, dtype):
+        file_id = self._get_file_id(shape, dtype)
+        self.filename, self.names, self.dtype = cache[file_id]
+
+        self.parse_dates = None
+        if dtype == "Int64_Timestamp":
+            # cached version of dtype should not change
+            self.dtype = self.dtype.copy()
+            for col in self._timestamp_columns:
+                del self.dtype[col]
+            self.parse_dates = self._timestamp_columns
+
+    def time_read_csv_names_dtype(self, cache, shape, names, dtype):
+        execute(
+            IMPL[ASV_USE_IMPL].read_csv(
+                self.filename,
+                names=self.names,
+                header=0,
+                dtype=self.dtype,
+                parse_dates=self.parse_dates,
+            )
+        )

{"golden_diff": "diff --git a/asv_bench/benchmarks/io/csv.py b/asv_bench/benchmarks/io/csv.py\n--- a/asv_bench/benchmarks/io/csv.py\n+++ b/asv_bench/benchmarks/io/csv.py\n@@ -65,3 +65,70 @@\n                 test_filenames[self.shape_id], skiprows=skiprows\n             )\n         )\n+\n+\n+class TimeReadCsvNamesDtype:\n+    _dtypes_params = [\"Int64\", \"Int64_Timestamp\"]\n+    _timestamp_columns = [\"col1\", \"col2\"]\n+\n+    param_names = [\"shape\", \"names\", \"dtype\"]\n+    params = [\n+        UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],\n+        [\"array-like\"],\n+        _dtypes_params,\n+    ]\n+\n+    def _get_file_id(self, shape, dtype):\n+        return get_shape_id(shape) + dtype\n+\n+    def _add_timestamp_columns(self, df):\n+        df = df.copy()\n+        date_column = IMPL[\"pandas\"].date_range(\n+            \"2000\",\n+            periods=df.shape[0],\n+            freq=\"ms\",\n+        )\n+        for col in self._timestamp_columns:\n+            df[col] = date_column\n+        return df\n+\n+    def setup_cache(self, test_filename=\"io_test_file_csv_names_dtype\"):\n+        # filenames with a metadata of saved dataframes\n+        cache = {}\n+        for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:\n+            for dtype in self._dtypes_params:\n+                df = generate_dataframe(\"pandas\", \"int\", *shape, RAND_LOW, RAND_HIGH)\n+                if dtype == \"Int64_Timestamp\":\n+                    df = self._add_timestamp_columns(df)\n+\n+                file_id = self._get_file_id(shape, dtype)\n+                cache[file_id] = (\n+                    f\"{test_filename}_{file_id}.csv\",\n+                    df.columns.to_list(),\n+                    df.dtypes.to_dict(),\n+                )\n+                df.to_csv(cache[file_id][0], index=False)\n+        return cache\n+\n+    def setup(self, cache, shape, names, dtype):\n+        file_id = self._get_file_id(shape, dtype)\n+        self.filename, self.names, self.dtype = cache[file_id]\n+\n+        self.parse_dates = None\n+        if dtype == \"Int64_Timestamp\":\n+            # cached version of dtype should not change\n+            self.dtype = self.dtype.copy()\n+            for col in self._timestamp_columns:\n+                del self.dtype[col]\n+            self.parse_dates = self._timestamp_columns\n+\n+    def time_read_csv_names_dtype(self, cache, shape, names, dtype):\n+        execute(\n+            IMPL[ASV_USE_IMPL].read_csv(\n+                self.filename,\n+                names=self.names,\n+                header=0,\n+                dtype=self.dtype,\n+                parse_dates=self.parse_dates,\n+            )\n+        )\n", "issue": "[ASV] add read_csv benchmark with dtype/names/parse_dates parameters\n\n", "before_files": [{"content": "# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership.  The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file except in\n# compliance with the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software distributed under\n# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific language\n# governing permissions and limitations under the License.\n\nimport modin.pandas as pd\nimport numpy as np\n\nfrom ..utils import (\n    generate_dataframe,\n    RAND_LOW,\n    RAND_HIGH,\n    ASV_USE_IMPL,\n    ASV_DATASET_SIZE,\n    UNARY_OP_DATA_SIZE,\n    IMPL,\n    execute,\n    get_shape_id,\n)\n\n# ray init\nif ASV_USE_IMPL == \"modin\":\n    pd.DataFrame([])\n\n\nclass BaseReadCsv:\n    # test data file can de created only once\n    def setup_cache(self, test_filename=\"io_test_file\"):\n        test_filenames = {}\n        for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:\n            shape_id = get_shape_id(shape)\n            test_filenames[shape_id] = f\"{test_filename}_{shape_id}.csv\"\n            df = generate_dataframe(\"pandas\", \"str_int\", *shape, RAND_LOW, RAND_HIGH)\n            df.to_csv(test_filenames[shape_id], index=False)\n\n        return test_filenames\n\n    def setup(self, test_filenames, shape, *args, **kwargs):\n        self.shape_id = get_shape_id(shape)\n\n\nclass TimeReadCsvSkiprows(BaseReadCsv):\n    param_names = [\"shape\", \"skiprows\"]\n    params = [\n        UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],\n        [\n            None,\n            lambda x: x % 2,\n            np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0] // 10),\n            np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0], 2),\n        ],\n    ]\n\n    def time_skiprows(self, test_filenames, shape, skiprows):\n        execute(\n            IMPL[ASV_USE_IMPL].read_csv(\n                test_filenames[self.shape_id], skiprows=skiprows\n            )\n        )\n", "path": "asv_bench/benchmarks/io/csv.py"}], "after_files": [{"content": "# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership.  The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file except in\n# compliance with the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software distributed under\n# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific language\n# governing permissions and limitations under the License.\n\nimport modin.pandas as pd\nimport numpy as np\n\nfrom ..utils import (\n    generate_dataframe,\n    RAND_LOW,\n    RAND_HIGH,\n    ASV_USE_IMPL,\n    ASV_DATASET_SIZE,\n    UNARY_OP_DATA_SIZE,\n    IMPL,\n    execute,\n    get_shape_id,\n)\n\n# ray init\nif ASV_USE_IMPL == \"modin\":\n    pd.DataFrame([])\n\n\nclass BaseReadCsv:\n    # test data file can de created only once\n    def setup_cache(self, test_filename=\"io_test_file\"):\n        test_filenames = {}\n        for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:\n            shape_id = get_shape_id(shape)\n            test_filenames[shape_id] = f\"{test_filename}_{shape_id}.csv\"\n            df = generate_dataframe(\"pandas\", \"str_int\", *shape, RAND_LOW, RAND_HIGH)\n            df.to_csv(test_filenames[shape_id], index=False)\n\n        return test_filenames\n\n    def setup(self, test_filenames, shape, *args, **kwargs):\n        self.shape_id = get_shape_id(shape)\n\n\nclass TimeReadCsvSkiprows(BaseReadCsv):\n    param_names = [\"shape\", \"skiprows\"]\n    params = [\n        UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],\n        [\n            None,\n            lambda x: x % 2,\n            np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0] // 10),\n            np.arange(1, UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE][0][0], 2),\n        ],\n    ]\n\n    def time_skiprows(self, test_filenames, shape, skiprows):\n        execute(\n            IMPL[ASV_USE_IMPL].read_csv(\n                test_filenames[self.shape_id], skiprows=skiprows\n            )\n        )\n\n\nclass TimeReadCsvNamesDtype:\n    _dtypes_params = [\"Int64\", \"Int64_Timestamp\"]\n    _timestamp_columns = [\"col1\", \"col2\"]\n\n    param_names = [\"shape\", \"names\", \"dtype\"]\n    params = [\n        UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE],\n        [\"array-like\"],\n        _dtypes_params,\n    ]\n\n    def _get_file_id(self, shape, dtype):\n        return get_shape_id(shape) + dtype\n\n    def _add_timestamp_columns(self, df):\n        df = df.copy()\n        date_column = IMPL[\"pandas\"].date_range(\n            \"2000\",\n            periods=df.shape[0],\n            freq=\"ms\",\n        )\n        for col in self._timestamp_columns:\n            df[col] = date_column\n        return df\n\n    def setup_cache(self, test_filename=\"io_test_file_csv_names_dtype\"):\n        # filenames with a metadata of saved dataframes\n        cache = {}\n        for shape in UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE]:\n            for dtype in self._dtypes_params:\n                df = generate_dataframe(\"pandas\", \"int\", *shape, RAND_LOW, RAND_HIGH)\n                if dtype == \"Int64_Timestamp\":\n                    df = self._add_timestamp_columns(df)\n\n                file_id = self._get_file_id(shape, dtype)\n                cache[file_id] = (\n                    f\"{test_filename}_{file_id}.csv\",\n                    df.columns.to_list(),\n                    df.dtypes.to_dict(),\n                )\n                df.to_csv(cache[file_id][0], index=False)\n        return cache\n\n    def setup(self, cache, shape, names, dtype):\n        file_id = self._get_file_id(shape, dtype)\n        self.filename, self.names, self.dtype = cache[file_id]\n\n        self.parse_dates = None\n        if dtype == \"Int64_Timestamp\":\n            # cached version of dtype should not change\n            self.dtype = self.dtype.copy()\n            for col in self._timestamp_columns:\n                del self.dtype[col]\n            self.parse_dates = self._timestamp_columns\n\n    def time_read_csv_names_dtype(self, cache, shape, names, dtype):\n        execute(\n            IMPL[ASV_USE_IMPL].read_csv(\n                self.filename,\n                names=self.names,\n                header=0,\n                dtype=self.dtype,\n                parse_dates=self.parse_dates,\n            )\n        )\n", "path": "asv_bench/benchmarks/io/csv.py"}]}