






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		)









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}}}""")




