




"""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_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  pa.array(TypedSequence([1, 2, 3]						)						)
       self.assertEqual(arr.type       ,				pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  pa.array(TypedSequence([1, 2, 3]						)       ,				type=pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			str						):
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('bool'						)       ,				type=Value('int64'						)						)						)

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

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
             _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				type=Value('int64'						)						)						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('int32'						)						)						)
       self.assertEqual(arr.type       ,				pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				try_type=Value('int64'						)						)						)
       self.assertEqual(arr.type       ,				pa.string()						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  pa.array(TypedSequence([[[1, 2, 3]]]       ,				type=ArrayaD((1, 3)       ,				'int64'						)						)						)
       self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'int64'						)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
             _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				type=ArrayaD((1, 3)       ,				'int64'						)						)						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  pa.array(TypedSequence([[[1, 2, 3]]]       ,				try_type=ArrayaD((1, 3)       ,				'int64'						)						)						)
       self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'int64'						)						)

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



 @require_pil
 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       import PIL.Image

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






def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   int				)       ->   List[str]:






      '''simple docstring'''


      _A     =  pa.BufferReader(_snake_case				) if isinstance(_snake_case							,    pa.Buffer				) else pa.memory_map(_snake_case				)
      _A     =  pa.ipc.open_stream(_snake_case				)
      _A     =  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 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Dict				)       ->   Optional[int]:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


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






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
def 		_snake_case	(   _snake_case				:   Union[str, Any]				)       ->   Optional[int]:






      '''simple docstring'''


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






@pytest.mark.parametrize('writer_batch_size'							,    [None, 2, 10]				)
def 		_snake_case	(   _snake_case				:   str				)       ->   Dict:






      '''simple docstring'''


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






@pytest.mark.parametrize('writer_batch_size'							,    [None, 2, 10]				)
def 		_snake_case	(   _snake_case				:   Dict				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      with ArrowWriter(
          stream=_snake_case							,    writer_batch_size=_snake_case							,    hash_salt='split_name'							,    check_duplicates=_snake_case							,    ) as writer:
            writer.write({'col_1': 'foo', 'col_2': 1}							,    key=1				)
            writer.write({'col_1': 'bar', 'col_2': 2}							,    key=2				)
            _A  ,       _A     =  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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Tuple				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}				)
            writer.write_batch({'col_1': [], 'col_2': []}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    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 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   Union[str, Any]				)       ->   Dict:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}				)				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    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 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   Union[str, Any]				)       ->   Optional[int]:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Optional[int]				)       ->   Tuple:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   List[Any]				)       ->   Any:






      '''simple docstring'''


      if isinstance(lst[0]							,    _snake_case				):
            change_first_primitive_element_in_list(lst[0]							,    _snake_case				)
      else:
            _A     =  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 		_snake_case	(   _snake_case				:   List[Any]							,    _snake_case				:   List[str]							,    _snake_case				:   Optional[int]				)       ->   Any:






      '''simple docstring'''


      _A     =  pa.array(TypedSequence(_snake_case							,    optimized_int_type=_snake_case				)				)
      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 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Any							,    _snake_case				:   Optional[Any]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  pa.array(OptimizedTypedSequence(_snake_case							,    col=_snake_case				)				)
      assert get_base_dtype(arr.type				) == expected_dtype

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






@pytest.mark.parametrize('raise_exception'							,    [False, True]				)
def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   Optional[int]				)       ->   str:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   int				)       ->   Any:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


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






@require_pil
@pytest.mark.parametrize('embed_local_files'							,    [False, True]				)
def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   List[str]				)       ->   Dict:






      '''simple docstring'''


      import PIL.Image

      _A     =  str(tmp_path / 'test_image_rgb.jpg'				)
      PIL.Image.fromarray(np.zeros((5, 5)							,    dtype=np.uinta				)				).save(_snake_case							,    format='png'				)
      _A     =  pa.BufferOutputStream()
      with ParquetWriter(
          stream=_snake_case							,    features=Features({'image': Image()}				)							,    embed_local_files=_snake_case				) as writer:
            writer.write({'image': image_path}				)
            writer.finalize()
      _A     =  pa.BufferReader(output.getvalue()				)
      _A     =  pq.read_table(_snake_case				)
      _A     =  pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out['image'][0]['path']							,    _snake_case				)
            with open(_snake_case							,    '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 		_snake_case	(   )       ->   int:






      '''simple docstring'''


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







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      return " ".join(
          ''.join(word[::-1]				) if len(_snake_case				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('''Hey wollef sroirraw'''))

