





"""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 lowerCamelCase		(__a      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([1, 2, 3]		)		)
									self.assertEqual(arr.type   , pa.intaa()		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									with self.assertRaises(snake_case__		):
													SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([1, 2, 3]		)   , type=pa.intaa()		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									with self.assertRaises(snake_case__		):
													SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([1, 2, 3]   , try_type=Value("bool"		)   , type=Value("int64"		)		)		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([1, 2, 3]   , type=Value("int32"		)		)		)
									self.assertEqual(arr.type   , pa.intaa()		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       Tuple:
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)		):
													SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence(["foo", "bar"]   , type=Value("int64"		)		)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([1, 2, 3]   , try_type=Value("int32"		)		)		)
									self.assertEqual(arr.type   , pa.intaa()		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence(["foo", "bar"]   , try_type=Value("int64"		)		)		)
									self.assertEqual(arr.type   , pa.string()		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([[[1, 2, 3]]]   , type=ArrayaD((1, 3)   , "int64"		)		)		)
									self.assertEqual(arr.type   , ArrayaDExtensionType((1, 3)   , "int64"		)		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)		):
													SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence(["foo", "bar"]   , type=ArrayaD((1, 3)   , "int64"		)		)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([[[1, 2, 3]]]   , try_type=ArrayaD((1, 3)   , "int64"		)		)		)
									self.assertEqual(arr.type   , ArrayaDExtensionType((1, 3)   , "int64"		)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence(["foo", "bar"]   , try_type=ArrayaD((1, 3)   , "int64"		)		)		)
									self.assertEqual(arr.type   , pa.string()		)



					@require_pil
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[Any]:
									import PIL.Image

									SCREAMING_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=snake_case__		) as mock_cast_to_python_objects:
													SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence([{"path": None, "bytes": B"image_bytes"}, pil_image]   , type=Image()		)		)
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				mock_cast_to_python_objects.call_args_list[-1]
													self.assertIn("optimize_list_casting"   , snake_case__		)
													self.assertFalse(kwargs["optimize_list_casting"]		)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferReader(lowerCAmelCase__       ) if isinstance(lowerCAmelCase__  ,					pa.Buffer       ) else pa.memory_map(lowerCAmelCase__       )
				SCREAMING_SNAKE_CASE__		  =				pa.ipc.open_stream(lowerCAmelCase__       )
				SCREAMING_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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				SCREAMING_SNAKE_CASE__		  =				pa.schema(lowerCAmelCase__       ) if fields else None
				with ArrowWriter(stream=lowerCAmelCase__  ,					schema=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__       ) as writer:
								writer.write({"col_1": "foo", "col_2": 1}       )
								writer.write({"col_1": "bar", "col_2": 2}       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								SCREAMING_SNAKE_CASE__		  =				{"col_1": pa.string(), "col_2": pa.intaa()}
				assert writer._schema == pa.schema(lowerCAmelCase__  ,					metadata=writer._schema.metadata       )
				_check_output(output.getvalue()  ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				SCREAMING_SNAKE_CASE__		  =				Features({"labels": ClassLabel(names=["neg", "pos"]       )}       )
				with ArrowWriter(stream=lowerCAmelCase__  ,					features=lowerCAmelCase__       ) as writer:
								writer.write({"labels": 0}       )
								writer.write({"labels": 1}       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_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
				SCREAMING_SNAKE_CASE__		  =				pa.BufferReader(output.getvalue()       )
				SCREAMING_SNAKE_CASE__		  =				pa.ipc.open_stream(lowerCAmelCase__       )
				SCREAMING_SNAKE_CASE__		  =				f.read_all()
				SCREAMING_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(lowerCAmelCase__       )




@pytest.mark.parametrize("writer_batch_size"  ,					[None, 1, 10]       )
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__  ,					hash_salt="split_name"  ,					check_duplicates=lowerCAmelCase__  ,					) as writer:
								with pytest.raises(lowerCAmelCase__       ):
												writer.write({"col_1": "foo", "col_2": 1}  ,					key=[1, 2]       )
												SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()




@pytest.mark.parametrize("writer_batch_size"  ,					[None, 2, 10]       )
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__  ,					hash_salt="split_name"  ,					check_duplicates=lowerCAmelCase__  ,					) as writer:
								with pytest.raises(lowerCAmelCase__       ):
												writer.write({"col_1": "foo", "col_2": 1}  ,					key=10       )
												writer.write({"col_1": "bar", "col_2": 2}  ,					key=10       )
												SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()




@pytest.mark.parametrize("writer_batch_size"  ,					[None, 2, 10]       )
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__  ,					hash_salt="split_name"  ,					check_duplicates=lowerCAmelCase__  ,					) as writer:
								writer.write({"col_1": "foo", "col_2": 1}  ,					key=1       )
								writer.write({"col_1": "bar", "col_2": 2}  ,					key=2       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				SCREAMING_SNAKE_CASE__		  =				pa.schema(lowerCAmelCase__       ) if fields else None
				with ArrowWriter(stream=lowerCAmelCase__  ,					schema=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__       ) as writer:
								writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}       )
								writer.write_batch({"col_1": [], "col_2": []}       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								SCREAMING_SNAKE_CASE__		  =				{"col_1": pa.string(), "col_2": pa.intaa()}
				assert writer._schema == pa.schema(lowerCAmelCase__  ,					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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				SCREAMING_SNAKE_CASE__		  =				pa.schema(lowerCAmelCase__       ) if fields else None
				with ArrowWriter(stream=lowerCAmelCase__  ,					schema=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__       ) as writer:
								writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}       )       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								SCREAMING_SNAKE_CASE__		  =				{"col_1": pa.string(), "col_2": pa.intaa()}
				assert writer._schema == pa.schema(lowerCAmelCase__  ,					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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				SCREAMING_SNAKE_CASE__		  =				pa.schema(lowerCAmelCase__       ) if fields else None
				with ArrowWriter(stream=lowerCAmelCase__  ,					schema=lowerCAmelCase__  ,					writer_batch_size=lowerCAmelCase__       ) 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]}       )       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								SCREAMING_SNAKE_CASE__		  =				{"col_1": pa.string(), "col_2": pa.intaa()}
				assert writer._schema == pa.schema(lowerCAmelCase__  ,					metadata=writer._schema.metadata       )
				_check_output(output.getvalue()  ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )




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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Dict:
				if pa.types.is_list(lowerCAmelCase__       ):
								return get_base_dtype(arr_type.value_type       )
				else:
								return arr_type




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				if isinstance(lst[0]  ,					lowerCAmelCase__       ):
								change_first_primitive_element_in_list(lst[0]  ,					lowerCAmelCase__       )
				else:
								SCREAMING_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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				pa.array(TypedSequence(lowerCAmelCase__  ,					optimized_int_type=lowerCAmelCase__       )       )
				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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				# in range
				SCREAMING_SNAKE_CASE__		  =				pa.array(OptimizedTypedSequence(lowerCAmelCase__  ,					col=lowerCAmelCase__       )       )
				assert get_base_dtype(arr.type       ) == expected_dtype

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




@pytest.mark.parametrize("raise_exception"  ,					[False, True]       )
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				str(tmp_path / "dataset-train.arrow"       )
				try:
								with ArrowWriter(path=lowerCAmelCase__       ) as writer:
												if raise_exception:
																raise pa.lib.ArrowInvalid()
												else:
																writer.stream.close()
				except pa.lib.ArrowInvalid:
								pass
				finally:
								assert writer.stream.closed




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




def 						SCREAMING_SNAKE_CASE					(  )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ParquetWriter(stream=lowerCAmelCase__       ) as writer:
								writer.write({"col_1": "foo", "col_2": 1}       )
								writer.write({"col_1": "bar", "col_2": 2}       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				SCREAMING_SNAKE_CASE__		  =				pa.BufferReader(output.getvalue()       )
				SCREAMING_SNAKE_CASE__		  =				pq.read_table(lowerCAmelCase__       )
				assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}




@require_pil
@pytest.mark.parametrize("embed_local_files"  ,					[False, True]       )
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				import PIL.Image

				SCREAMING_SNAKE_CASE__		  =				str(tmp_path / "test_image_rgb.jpg"       )
				PIL.Image.fromarray(np.zeros((5, 5)  ,					dtype=np.uinta       )       ).save(lowerCAmelCase__  ,					format="png"       )
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ParquetWriter(
				    stream=lowerCAmelCase__  ,					features=Features({"image": Image()}       )  ,					embed_local_files=lowerCAmelCase__       ) as writer:
								writer.write({"image": image_path}       )
								writer.finalize()
				SCREAMING_SNAKE_CASE__		  =				pa.BufferReader(output.getvalue()       )
				SCREAMING_SNAKE_CASE__		  =				pq.read_table(lowerCAmelCase__       )
				SCREAMING_SNAKE_CASE__		  =				pa_table.to_pydict()
				if embed_local_files:
								assert isinstance(out["image"][0]["path"]  ,					lowerCAmelCase__       )
								with open(lowerCAmelCase__  ,					"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 						SCREAMING_SNAKE_CASE					(  )							->   str:
				SCREAMING_SNAKE_CASE__		  =				pa.schema([pa.field("col_1"  ,					pa.string()  ,					nullable=lowerCAmelCase__       )]       )
				SCREAMING_SNAKE_CASE__		  =				pa.BufferOutputStream()
				with ArrowWriter(stream=lowerCAmelCase__       ) as writer:
								writer._build_writer(inferred_schema=lowerCAmelCase__       )
								assert writer._schema == pa.schema([pa.field("col_1"  ,					pa.string()       )]       )





"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_A   					=    'https://www.indeed.co.in/jobs?q=mobile+app+development&l='




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase = "mumbai"       )							->   Generator[tuple[str, str], None, None]:
				SCREAMING_SNAKE_CASE__		  =				BeautifulSoup(requests.get(url + location       ).content  ,					"html.parser"       )
				# This attribute finds out all the specifics listed in a job
				for job in soup.find_all("div"  ,					attrs={"data-tn-component": "organicJob"}       ):
								SCREAMING_SNAKE_CASE__		  =				job.find("a"  ,					attrs={"data-tn-element": "jobTitle"}       ).text.strip()
								SCREAMING_SNAKE_CASE__		  =				job.find("span"  ,					{"class": "company"}       ).text.strip()
								yield job_title, company_name


if __name__ == "__main__":
						for i, job in enumerate(fetch_jobs('Bangalore'), 1):
												print(F'Job {i:>2} is {job[0]} at {job[1]}')




