code: 
  
  
  
  
  
  
  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 			lowercase__  (				A_:	Optional[Any]     ,			A_:	str		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         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 			lowercase__  (				A_:	int     ,			A_:	str     ,			A_:	str		)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
                __UpperCAmelCase    							=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 			lowercase__  (				A_:	Tuple     ,			A_:	Optional[int]     ,			A_:	Union[str, Any]		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         __UpperCAmelCase    							=features.copy() if features else default_expected_features
         __UpperCAmelCase    							=(
             Features({feature: Value(A_		) for feature, dtype in features.items()}		) if features is not None else None
         )
         __UpperCAmelCase    							=CsvDatasetReader(A_     ,			features=A_     ,			cache_dir=A_		).read()
         _check_csv_dataset(A_     ,			A_		)
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""split"""     ,			[None, NamedSplit("""train"""		), """train""", """test"""]		)
  def 			lowercase__  (				A_:	Optional[int]     ,			A_:	Tuple     ,			A_:	Optional[int]		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         __UpperCAmelCase    							=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 			lowercase__  (				A_:	Tuple     ,			A_:	List[Any]     ,			A_:	Optional[Any]		)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if issubclass(A_     ,			A_		):
                __UpperCAmelCase    							=csv_path
         elif issubclass(A_     ,			A_		):
                __UpperCAmelCase    							=[csv_path]
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         __UpperCAmelCase    							=CsvDatasetReader(A_     ,			cache_dir=A_		).read()
         _check_csv_dataset(A_     ,			A_		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[Any]     ,			A_:	str     ,			A_:	Optional[int]=("train",)		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         assert isinstance(A_     ,			A_		)
         for split in splits:
                __UpperCAmelCase    							=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 			lowercase__  (				A_:	List[str]     ,			A_:	List[str]     ,			A_:	str		)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
                __UpperCAmelCase    							=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 			lowercase__  (				A_:	str     ,			A_:	Dict     ,			A_:	Tuple		)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         __UpperCAmelCase    							=features.copy() if features else default_expected_features
         __UpperCAmelCase    							=(
             Features({feature: Value(A_		) for feature, dtype in features.items()}		) if features is not None else None
         )
         __UpperCAmelCase    							=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 			lowercase__  (				A_:	List[Any]     ,			A_:	Any     ,			A_:	Tuple		)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if split:
                __UpperCAmelCase    							={split: csv_path}
         else:
                __UpperCAmelCase    							={"""train""": csv_path, """test""": csv_path}
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
         __UpperCAmelCase    							=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 			lowercase__  (				A_:	Tuple		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         with open(A_     ,			encoding="""utf-8"""		) as csvfile:
                yield from csv.reader(A_		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Dict     ,			A_:	Tuple		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							=os.path.join(A_     ,			"""tmp.csv"""		)
         __UpperCAmelCase    							=CsvDatasetReader({"""train""": csv_path}     ,			cache_dir=A_		).read()
         CsvDatasetWriter(dataset["""train"""]     ,			A_     ,			num_proc=1		).write()
  
         __UpperCAmelCase    							=iter_csv_file(A_		)
         __UpperCAmelCase    							=iter_csv_file(A_		)
  
         for rowa, rowa in zip(A_     ,			A_		):
                assert rowa == rowa
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	List[Any]		)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							=os.path.join(A_     ,			"""tmp.csv"""		)
         __UpperCAmelCase    							=CsvDatasetReader({"""train""": csv_path}     ,			cache_dir=A_		).read()
         CsvDatasetWriter(dataset["""train"""]     ,			A_     ,			num_proc=2		).write()
  
         __UpperCAmelCase    							=iter_csv_file(A_		)
         __UpperCAmelCase    							=iter_csv_file(A_		)
  
         for rowa, rowa in zip(A_     ,			A_		):
                assert rowa == rowa
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Any		)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tmp_path / """cache"""
         __UpperCAmelCase    							=os.path.join(A_     ,			"""tmp.csv"""		)
         __UpperCAmelCase    							=CsvDatasetReader({"""train""": csv_path}     ,			cache_dir=A_		).read()
         with pytest.raises(A_		):
                CsvDatasetWriter(dataset["""train"""]     ,			A_     ,			num_proc=0		)
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  