




'''simple docstring'''

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil






class 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				pa.array(TypedSequence([1, 2, 3]					)					)
									self.assertEqual(arr.type       ,				pa.intaa()					)


				def   A_ (		self    :					Optional[int]					):
									with self.assertRaises(lowercase_					):
														snake_case_									=				pa.array(TypedSequence([1, 2, 3]					)       ,				type=pa.intaa()					)


				def   A_ (		self    :					Dict					):
									with self.assertRaises(lowercase_					):
														snake_case_									=				pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('''bool'''					)       ,				type=Value('''int64'''					)					)					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				pa.array(TypedSequence([1, 2, 3]       ,				type=Value('''int32'''					)					)					)
									self.assertEqual(arr.type       ,				pa.intaa()					)


				def   A_ (		self    :					Dict					):
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)					):
														snake_case_									=				pa.array(TypedSequence(['''foo''', '''bar''']       ,				type=Value('''int64'''					)					)					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('''int32'''					)					)					)
									self.assertEqual(arr.type       ,				pa.intaa()					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				pa.array(TypedSequence(['''foo''', '''bar''']       ,				try_type=Value('''int64'''					)					)					)
									self.assertEqual(arr.type       ,				pa.string()					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				pa.array(TypedSequence([[[1, 2, 3]]]       ,				type=ArrayaD((1, 3)       ,				'''int64'''					)					)					)
									self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'''int64'''					)					)


				def   A_ (		self    :					Dict					):
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)					):
														snake_case_									=				pa.array(TypedSequence(['''foo''', '''bar''']       ,				type=ArrayaD((1, 3)       ,				'''int64'''					)					)					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				pa.array(TypedSequence([[[1, 2, 3]]]       ,				try_type=ArrayaD((1, 3)       ,				'''int64'''					)					)					)
									self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'''int64'''					)					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				pa.array(TypedSequence(['''foo''', '''bar''']       ,				try_type=ArrayaD((1, 3)       ,				'''int64'''					)					)					)
									self.assertEqual(arr.type       ,				pa.string()					)


				@require_pil
				def   A_ (		self    :					Optional[int]					):
									import PIL.Image

									snake_case_									=				PIL.Image.fromarray(np.arange(10       ,				dtype=np.uinta					).reshape(2       ,				5					)					)
									with patch(
									    '''datasets.arrow_writer.cast_to_python_objects'''       ,				side_effect=lowercase_					) as mock_cast_to_python_objects:
														snake_case_									=				pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image]       ,				type=Image()					)					)
														snake_case_			,snake_case_									=				mock_cast_to_python_objects.call_args_list[-1]
														self.assertIn('''optimize_list_casting'''       ,				lowercase_					)
														self.assertFalse(kwargs['''optimize_list_casting''']					)







def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				pa.BufferReader(__UpperCAmelCase	) if isinstance(__UpperCAmelCase,      pa.Buffer	) else pa.memory_map(__UpperCAmelCase	)
					snake_case_									=				pa.ipc.open_stream(__UpperCAmelCase	)
					snake_case_									=				f.read_all()
					assert len(pa_table.to_batches()	) == expected_num_chunks
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
					del pa_table


@pytest.mark.parametrize('''writer_batch_size''',      [None, 1, 10]	)
@pytest.mark.parametrize(
    '''fields''',      [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					snake_case_									=				pa.schema(__UpperCAmelCase	) if fields else None
					with ArrowWriter(stream=__UpperCAmelCase,      schema=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase	) as writer:
										writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
										writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										snake_case_									=				{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__UpperCAmelCase,      metadata=writer._schema.metadata	)
					_check_output(output.getvalue(),      expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					snake_case_									=				Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']	)}	)
					with ArrowWriter(stream=__UpperCAmelCase,      features=__UpperCAmelCase	) as writer:
										writer.write({'''labels''': 0}	)
										writer.write({'''labels''': 1}	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert writer._schema == features.arrow_schema
					assert writer._schema.metadata == features.arrow_schema.metadata
					snake_case_									=				pa.BufferReader(output.getvalue()	)
					snake_case_									=				pa.ipc.open_stream(__UpperCAmelCase	)
					snake_case_									=				f.read_all()
					snake_case_									=				pa_table.schema
					assert pa_table.num_rows == 2
					assert schema == features.arrow_schema
					assert schema.metadata == features.arrow_schema.metadata
					assert features == Features.from_arrow_schema(__UpperCAmelCase	)


@pytest.mark.parametrize('''writer_batch_size''',      [None, 1, 10]	)
def 		__magic_name__    (	__UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase,      hash_salt='''split_name''',      check_duplicates=__UpperCAmelCase,      ) as writer:
										with pytest.raises(__UpperCAmelCase	):
															writer.write({'''col_1''': '''foo''', '''col_2''': 1},      key=[1, 2]	)
															snake_case_			,snake_case_									=				writer.finalize()


@pytest.mark.parametrize('''writer_batch_size''',      [None, 2, 10]	)
def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase,      hash_salt='''split_name''',      check_duplicates=__UpperCAmelCase,      ) as writer:
										with pytest.raises(__UpperCAmelCase	):
															writer.write({'''col_1''': '''foo''', '''col_2''': 1},      key=10	)
															writer.write({'''col_1''': '''bar''', '''col_2''': 2},      key=10	)
															snake_case_			,snake_case_									=				writer.finalize()


@pytest.mark.parametrize('''writer_batch_size''',      [None, 2, 10]	)
def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase,      hash_salt='''split_name''',      check_duplicates=__UpperCAmelCase,      ) as writer:
										writer.write({'''col_1''': '''foo''', '''col_2''': 1},      key=1	)
										writer.write({'''col_1''': '''bar''', '''col_2''': 2},      key=2	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					_check_output(output.getvalue(),      expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize('''writer_batch_size''',      [None, 1, 10]	)
@pytest.mark.parametrize(
    '''fields''',      [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					snake_case_									=				pa.schema(__UpperCAmelCase	) if fields else None
					with ArrowWriter(stream=__UpperCAmelCase,      schema=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase	) as writer:
										writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)
										writer.write_batch({'''col_1''': [], '''col_2''': []}	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										snake_case_									=				{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__UpperCAmelCase,      metadata=writer._schema.metadata	)
					_check_output(output.getvalue(),      expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize('''writer_batch_size''',      [None, 1, 10]	)
@pytest.mark.parametrize(
    '''fields''',      [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					snake_case_									=				pa.schema(__UpperCAmelCase	) if fields else None
					with ArrowWriter(stream=__UpperCAmelCase,      schema=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase	) as writer:
										writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										snake_case_									=				{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__UpperCAmelCase,      metadata=writer._schema.metadata	)
					_check_output(output.getvalue(),      expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize('''writer_batch_size''',      [None, 1, 10]	)
@pytest.mark.parametrize(
    '''fields''',      [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					snake_case_									=				pa.schema(__UpperCAmelCase	) if fields else None
					with ArrowWriter(stream=__UpperCAmelCase,      schema=__UpperCAmelCase,      writer_batch_size=__UpperCAmelCase	) as writer:
										writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}	)	)
										writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}	)	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										snake_case_									=				{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__UpperCAmelCase,      metadata=writer._schema.metadata	)
					_check_output(output.getvalue(),      expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


def 		__magic_name__    (	)		->					int:

					'''simple docstring'''

					with tempfile.TemporaryDirectory() as tmp_dir:
										snake_case_									=				{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
										snake_case_									=				os.path.join(__UpperCAmelCase,      '''test.arrow'''	)
										with ArrowWriter(path=__UpperCAmelCase,      schema=pa.schema(__UpperCAmelCase	)	) as writer:
															writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)
															snake_case_			,snake_case_									=				writer.finalize()
										assert num_examples == 2
										assert num_bytes > 0
										assert writer._schema == pa.schema(__UpperCAmelCase,      metadata=writer._schema.metadata	)
										_check_output(__UpperCAmelCase,      1	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if pa.types.is_list(__UpperCAmelCase	):
										return get_base_dtype(arr_type.value_type	)
					else:
										return arr_type


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if isinstance(lst[0],      __UpperCAmelCase	):
										change_first_primitive_element_in_list(lst[0],      __UpperCAmelCase	)
					else:
										snake_case_									=				value


@pytest.mark.parametrize('''optimized_int_type, expected_dtype''',      [(None, pa.intaa()), (Value('''int32'''	), pa.intaa())]	)
@pytest.mark.parametrize('''sequence''',      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				pa.array(TypedSequence(__UpperCAmelCase,      optimized_int_type=__UpperCAmelCase	)	)
					assert get_base_dtype(arr.type	) == expected_dtype


@pytest.mark.parametrize(
    '''col, expected_dtype''',      [
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ],      )
@pytest.mark.parametrize('''sequence''',      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				pa.array(OptimizedTypedSequence(__UpperCAmelCase,      col=__UpperCAmelCase	)	)
					assert get_base_dtype(arr.type	) == expected_dtype

					# not in range
					if col != "other":
										# avoids errors due to in-place modifications
										snake_case_									=				copy.deepcopy(__UpperCAmelCase	)
										snake_case_									=				np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
										change_first_primitive_element_in_list(__UpperCAmelCase,      __UpperCAmelCase	)
										snake_case_									=				pa.array(OptimizedTypedSequence(__UpperCAmelCase,      col=__UpperCAmelCase	)	)
										assert get_base_dtype(arr.type	) == pa.intaa()


@pytest.mark.parametrize('''raise_exception''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				str(tmp_path / '''dataset-train.arrow'''	)
					try:
										with ArrowWriter(path=__UpperCAmelCase	) as writer:
															if raise_exception:
																				raise pa.lib.ArrowInvalid()
															else:
																				writer.stream.close()
					except pa.lib.ArrowInvalid:
										pass
					finally:
										assert writer.stream.closed


def 		__magic_name__    (	__UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				'''mock://dataset-train.arrow'''
					with ArrowWriter(path=__UpperCAmelCase,      storage_options=mockfs.storage_options	) as writer:
										assert isinstance(writer._fs,      type(__UpperCAmelCase	)	)
										assert writer._fs.storage_options == mockfs.storage_options
										writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
										writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert mockfs.exists(__UpperCAmelCase	)


def 		__magic_name__    (	)		->					str:

					'''simple docstring'''

					snake_case_									=				pa.BufferOutputStream()
					with ParquetWriter(stream=__UpperCAmelCase	) as writer:
										writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
										writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
										snake_case_			,snake_case_									=				writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					snake_case_									=				pa.BufferReader(output.getvalue()	)
					snake_case_									=				pq.read_table(__UpperCAmelCase	)
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize('''embed_local_files''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					import PIL.Image

					snake_case_									=				str(tmp_path / '''test_image_rgb.jpg'''	)
					PIL.Image.fromarray(np.zeros((5, 5),      dtype=np.uinta	)	).save(__UpperCAmelCase,      format='''png'''	)
					snake_case_									=				pa.BufferOutputStream()
					with ParquetWriter(
					    stream=__UpperCAmelCase,      features=Features({'''image''': Image()}	),      embed_local_files=__UpperCAmelCase	) as writer:
										writer.write({'''image''': image_path}	)
										writer.finalize()
					snake_case_									=				pa.BufferReader(output.getvalue()	)
					snake_case_									=				pq.read_table(__UpperCAmelCase	)
					snake_case_									=				pa_table.to_pydict()
					if embed_local_files:
										assert isinstance(out['''image'''][0]['''path'''],      __UpperCAmelCase	)
										with open(__UpperCAmelCase,      '''rb'''	) as f:
															assert out["image"][0]["bytes"] == f.read()
					else:
										assert out["image"][0]["path"] == image_path
										assert out["image"][0]["bytes"] is None


def 		__magic_name__    (	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				pa.schema([pa.field('''col_1''',      pa.string(),      nullable=__UpperCAmelCase	)]	)
					snake_case_									=				pa.BufferOutputStream()
					with ArrowWriter(stream=__UpperCAmelCase	) as writer:
										writer._build_writer(inferred_schema=__UpperCAmelCase	)
										assert writer._schema == pa.schema([pa.field('''col_1''',      pa.string()	)]	)







'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

