

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 				__a							(       __A			,							__A						):
    assert isinstance(__A			,							__A						)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"			,							[False, True]						)
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__         =    CsvDatasetReader(__A			,							cache_dir=__A			,							keep_in_memory=__A						).read()
    _check_csv_dataset(__A			,							__A						)





@pytest.mark.parametrize(
    "features"			,							[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,							)
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    SCREAMING_SNAKE_CASE__         =    features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE__         =    (
        Features({feature: Value(__A						) for feature, dtype in features.items()}						) if features is not None else None
    )
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader(__A			,							features=__A			,							cache_dir=__A						).read()
    _check_csv_dataset(__A			,							__A						)





@pytest.mark.parametrize("split"			,							[None, NamedSplit("train"						), "train", "test"]						)
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader(__A			,							cache_dir=__A			,							split=__A						).read()
    _check_csv_dataset(__A			,							__A						)
    assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"			,							[str, list]						)
def 				__a							(       __A			,							__A			,							__A						):
    if issubclass(__A			,							__A						):
        SCREAMING_SNAKE_CASE__         =    csv_path
    elif issubclass(__A			,							__A						):
        SCREAMING_SNAKE_CASE__         =    [csv_path]
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader(__A			,							cache_dir=__A						).read()
    _check_csv_dataset(__A			,							__A						)





def 				__a							(       __A			,							__A			,							__A=("train",)						):
    assert isinstance(__A			,							__A						)
    for split in splits:
        SCREAMING_SNAKE_CASE__         =    dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"			,							[False, True]						)
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__         =    CsvDatasetReader({"train": csv_path}			,							cache_dir=__A			,							keep_in_memory=__A						).read()
    _check_csv_datasetdict(__A			,							__A						)





@pytest.mark.parametrize(
    "features"			,							[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,							)
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    SCREAMING_SNAKE_CASE__         =    features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE__         =    (
        Features({feature: Value(__A						) for feature, dtype in features.items()}						) if features is not None else None
    )
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader({"train": csv_path}			,							features=__A			,							cache_dir=__A						).read()
    _check_csv_datasetdict(__A			,							__A						)





@pytest.mark.parametrize("split"			,							[None, NamedSplit("train"						), "train", "test"]						)
def 				__a							(       __A			,							__A			,							__A						):
    if split:
        SCREAMING_SNAKE_CASE__         =    {split: csv_path}
    else:
        SCREAMING_SNAKE_CASE__         =    {"train": csv_path, "test": csv_path}
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader(__A			,							cache_dir=__A						).read()
    _check_csv_datasetdict(__A			,							__A			,							splits=list(path.keys()						)						)
    assert all(dataset[split].split == split for split in path.keys()						)





def 				__a							(       __A						):
    with open(__A			,							encoding="utf-8"						) as csvfile:
        yield from csv.reader(__A						)





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    os.path.join(__A			,							"tmp.csv"						)
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader({"train": csv_path}			,							cache_dir=__A						).read()
    CsvDatasetWriter(dataset["train"]			,							__A			,							num_proc=1						).write()

    SCREAMING_SNAKE_CASE__         =    iter_csv_file(__A						)
    SCREAMING_SNAKE_CASE__         =    iter_csv_file(__A						)

    for rowa, rowa in zip(__A			,							__A						):
        assert rowa == rowa





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    os.path.join(__A			,							"tmp.csv"						)
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader({"train": csv_path}			,							cache_dir=__A						).read()
    CsvDatasetWriter(dataset["train"]			,							__A			,							num_proc=2						).write()

    SCREAMING_SNAKE_CASE__         =    iter_csv_file(__A						)
    SCREAMING_SNAKE_CASE__         =    iter_csv_file(__A						)

    for rowa, rowa in zip(__A			,							__A						):
        assert rowa == rowa





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    tmp_path / "cache"
    SCREAMING_SNAKE_CASE__         =    os.path.join(__A			,							"tmp.csv"						)
    SCREAMING_SNAKE_CASE__         =    CsvDatasetReader({"train": csv_path}			,							cache_dir=__A						).read()
    with pytest.raises(__A						):
        CsvDatasetWriter(dataset["train"]			,							__A			,							num_proc=0						)





from sklearn.metrics import accuracy_score

import datasets


A__										=      "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


A__										=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


A__										=      "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,  _KWARGS_DESCRIPTION      )
class  UpperCamelCase_      (	datasets.Metric      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
              {
                  "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                  "references": datasets.Sequence(datasets.Value("int32"						)						),
              }
              if self.config_name == "multilabel"
              else {
                  "predictions": datasets.Value("int32"						),
                  "references": datasets.Value("int32"						),
              }						)  ,	reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]  ,	)







  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


