prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [ASV] add read_csv benchmark with dtype/names/parse_dates parameters
  
  
  </issue>
  <code>
  [start of asv_bench/benchmarks/io/csv.py]
  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 of asv_bench/benchmarks/io/csv.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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,
  +            )
  +        )
  
verification_info: {"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"}]}