




'''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 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence([1, 2, 3]   )   )
      self.assertEqual(arr.type  ,				pa.intaa()   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      with self.assertRaises(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence([1, 2, 3]   )  ,				type=pa.intaa()   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      with self.assertRaises(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence([1, 2, 3]  ,				try_type=Value("bool"   )  ,				type=Value("int64"   )   )   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence([1, 2, 3]  ,				type=Value("int32"   )   )   )
      self.assertEqual(arr.type  ,				pa.intaa()   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
         _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence(["foo", "bar"]  ,				type=Value("int64"   )   )   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence([1, 2, 3]  ,				try_type=Value("int32"   )   )   )
      self.assertEqual(arr.type  ,				pa.intaa()   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence(["foo", "bar"]  ,				try_type=Value("int64"   )   )   )
      self.assertEqual(arr.type  ,				pa.string()   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _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 :     int   ):

      """simple docstring"""
      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
         _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence(["foo", "bar"]  ,				type=ArrayaD((1, 3)  ,				"int64"   )   )   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _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 :     Union[str, Any]   ):

      """simple docstring"""
      _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 :     Any   ):

      """simple docstring"""
      import PIL.Image

      _SCREAMING_SNAKE_CASE	=				PIL.Image.fromarray(np.arange(1_0  ,				dtype=np.uinta   ).reshape(2  ,				5   )   )
      with patch(
          "datasets.arrow_writer.cast_to_python_objects"  ,				side_effect=__lowerCamelCase   ) 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"  ,				__lowerCamelCase   )
         self.assertFalse(kwargs["optimize_list_casting"]   )


def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			int  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				pa.BufferReader(__A  ) if isinstance(__A	,					pa.Buffer  ) else pa.memory_map(__A  )
   _SCREAMING_SNAKE_CASE	=				pa.ipc.open_stream(__A  )
   _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_			(							__A    :			int	,					__A    :			List[str]  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   _SCREAMING_SNAKE_CASE	=				pa.schema(__A  ) if fields else None
   with ArrowWriter(stream=__A	,					schema=__A	,					writer_batch_size=__A  ) 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(__A	,					metadata=writer._schema.metadata  )
   _check_output(output.getvalue()	,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			int:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   _SCREAMING_SNAKE_CASE	=				Features({"labels": ClassLabel(names=["neg", "pos"]  )}  )
   with ArrowWriter(stream=__A	,					features=__A  ) 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(__A  )
   _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(__A  )
@pytest.mark.parametrize("writer_batch_size"	,					[None, 1, 10]  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   with ArrowWriter(
       stream=__A	,					writer_batch_size=__A	,					hash_salt="split_name"	,					check_duplicates=__A	,					) as writer:
      with pytest.raises(__A  ):
         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_			(							__A    :			Union[str, Any]  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   with ArrowWriter(
       stream=__A	,					writer_batch_size=__A	,					hash_salt="split_name"	,					check_duplicates=__A	,					) as writer:
      with pytest.raises(__A  ):
         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_			(							__A    :			List[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   with ArrowWriter(
       stream=__A	,					writer_batch_size=__A	,					hash_salt="split_name"	,					check_duplicates=__A	,					) 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_			(							__A    :			Dict	,					__A    :			Dict  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   _SCREAMING_SNAKE_CASE	=				pa.schema(__A  ) if fields else None
   with ArrowWriter(stream=__A	,					schema=__A	,					writer_batch_size=__A  ) 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(__A	,					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_			(							__A    :			int	,					__A    :			str  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   _SCREAMING_SNAKE_CASE	=				pa.schema(__A  ) if fields else None
   with ArrowWriter(stream=__A	,					schema=__A	,					writer_batch_size=__A  ) 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(__A	,					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_			(							__A    :			int	,					__A    :			int  )			->			str:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   _SCREAMING_SNAKE_CASE	=				pa.schema(__A  ) if fields else None
   with ArrowWriter(stream=__A	,					schema=__A	,					writer_batch_size=__A  ) 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(__A	,					metadata=writer._schema.metadata  )
   _check_output(output.getvalue()	,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			List[Any]:
   with tempfile.TemporaryDirectory() as tmp_dir:
      _SCREAMING_SNAKE_CASE	=				{"col_1": pa.string(), "col_2": pa.intaa()}
      _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					"test.arrow"  )
      with ArrowWriter(path=__A	,					schema=pa.schema(__A  )  ) 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(__A	,					metadata=writer._schema.metadata  )
      _check_output(__A	,					1  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Optional[Any]:
   if pa.types.is_list(__A  ):
      return get_base_dtype(arr_type.value_type  )
   else:
      return arr_type
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			List[Any]  )			->			str:
   if isinstance(lst[0]	,					__A  ):
      change_first_primitive_element_in_list(lst[0]	,					__A  )
   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_			(							__A    :			Optional[Any]	,					__A    :			Optional[Any]	,					__A    :			List[Any]  )			->			List[Any]:
   _SCREAMING_SNAKE_CASE	=				pa.array(TypedSequence(__A	,					optimized_int_type=__A  )  )
   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_			(							__A    :			List[Any]	,					__A    :			Any	,					__A    :			int  )			->			Union[str, Any]:
   # in range
   _SCREAMING_SNAKE_CASE	=				pa.array(OptimizedTypedSequence(__A	,					col=__A  )  )
   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(__A  )
      _SCREAMING_SNAKE_CASE	=				np.iinfo(expected_dtype.to_pandas_dtype()  ).max + 1
      change_first_primitive_element_in_list(__A	,					__A  )
      _SCREAMING_SNAKE_CASE	=				pa.array(OptimizedTypedSequence(__A	,					col=__A  )  )
      assert get_base_dtype(arr.type  ) == pa.intaa()
@pytest.mark.parametrize("raise_exception"	,					[False, True]  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			Dict  )			->			str:
   _SCREAMING_SNAKE_CASE	=				str(tmp_path / "dataset-train.arrow"  )
   try:
      with ArrowWriter(path=__A  ) 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_			(							__A    :			Dict  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				"mock://dataset-train.arrow"
   with ArrowWriter(path=__A	,					storage_options=mockfs.storage_options  ) as writer:
      assert isinstance(writer._fs	,					type(__A  )  )
      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(__A  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			List[Any]:
   _SCREAMING_SNAKE_CASE	=				pa.BufferOutputStream()
   with ParquetWriter(stream=__A  ) 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(__A  )
   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_			(							__A    :			List[str]	,					__A    :			Optional[Any]  )			->			Optional[int]:
   import PIL.Image

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







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

