


'''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     (     UpperCAmelCase_       ):
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							pa.array(TypedSequence([1, 2, 3]						)						)
      self.assertEqual(arr.type      ,				pa.intaa()						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      with self.assertRaises(_lowercase						):
           _a			      =							pa.array(TypedSequence([1, 2, 3]						)      ,				type=pa.intaa()						)
 def 							__UpperCAmelCase		( self						)					->							str:
      with self.assertRaises(_lowercase						):
           _a			      =							pa.array(TypedSequence([1, 2, 3]      ,				try_type=Value('bool'						)      ,				type=Value('int64'						)						)						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							pa.array(TypedSequence([1, 2, 3]      ,				type=Value('int32'						)						)						)
      self.assertEqual(arr.type      ,				pa.intaa()						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
           _a			      =							pa.array(TypedSequence(['foo', 'bar']      ,				type=Value('int64'						)						)						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							pa.array(TypedSequence([1, 2, 3]      ,				try_type=Value('int32'						)						)						)
      self.assertEqual(arr.type      ,				pa.intaa()						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							pa.array(TypedSequence(['foo', 'bar']      ,				try_type=Value('int64'						)						)						)
      self.assertEqual(arr.type      ,				pa.string()						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							pa.array(TypedSequence([[[1, 2, 3]]]      ,				type=ArrayaD((1, 3)      ,				'int64'						)						)						)
      self.assertEqual(arr.type      ,				ArrayaDExtensionType((1, 3)      ,				'int64'						)						)
 def 							__UpperCAmelCase		( self						)					->							int:
      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
           _a			      =							pa.array(TypedSequence(['foo', 'bar']      ,				type=ArrayaD((1, 3)      ,				'int64'						)						)						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							pa.array(TypedSequence([[[1, 2, 3]]]      ,				try_type=ArrayaD((1, 3)      ,				'int64'						)						)						)
      self.assertEqual(arr.type      ,				ArrayaDExtensionType((1, 3)      ,				'int64'						)						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							pa.array(TypedSequence(['foo', 'bar']      ,				try_type=ArrayaD((1, 3)      ,				'int64'						)						)						)
      self.assertEqual(arr.type      ,				pa.string()						)




 @require_pil
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      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=_lowercase						) as mock_cast_to_python_objects:
           _a			      =							pa.array(TypedSequence([{'path': None, 'bytes': b'image_bytes'}, pil_image]      ,				type=Image()						)						)
           _a			      =							mock_cast_to_python_objects.call_args_list[-1]
           self.assertIn('optimize_list_casting'      ,				_lowercase						)
           self.assertFalse(kwargs['optimize_list_casting']						)






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :int		)   ->  int:




     '''simple docstring'''



     _a			      =							pa.BufferReader(lowerCAmelCase__		) if isinstance(lowerCAmelCase__				,						pa.Buffer		) else pa.memory_map(lowerCAmelCase__		)
     _a			      =							pa.ipc.open_stream(lowerCAmelCase__		)
     _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 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int		)   ->  str:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     _a			      =							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}		)
          _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(lowerCAmelCase__				,						metadata=writer._schema.metadata		)
     _check_output(output.getvalue()				,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)






def 	_A				()   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     _a			      =							Features({'labels': ClassLabel(names=['neg', 'pos']		)}		)
     with ArrowWriter(stream=lowerCAmelCase__				,						features=lowerCAmelCase__		) as writer:
          writer.write({'labels': 0}		)
          writer.write({'labels': 1}		)
          _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(lowerCAmelCase__		)
     _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(lowerCAmelCase__		)






@pytest.mark.parametrize('writer_batch_size'				,						[None, 1, 10]		)
def 	_A				(lowerCAmelCase__  :int		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							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]		)
               _a			      =							writer.finalize()






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




     '''simple docstring'''



     _a			      =							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		)
               _a			      =							writer.finalize()






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




     '''simple docstring'''



     _a			      =							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		)
          _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 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]		)   ->  int:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     _a			      =							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': []}		)
          _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(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 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     _a			      =							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]}		)		)
          _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(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 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :int		)   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     _a			      =							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]}		)		)
          _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(lowerCAmelCase__				,						metadata=writer._schema.metadata		)
     _check_output(output.getvalue()				,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)






def 	_A				()   ->  Optional[int]:




     '''simple docstring'''



     with tempfile.TemporaryDirectory() as tmp_dir:
          _a			      =							{'col_1': pa.string(), 'col_2': pa.intaa()}
          _a			      =							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]}		)
               _a			      =							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 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  int:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :str		)   ->  Any:




     '''simple docstring'''



     if isinstance(lst[0]				,						lowerCAmelCase__		):
          change_first_primitive_element_in_list(lst[0]				,						lowerCAmelCase__		)
     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 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Dict		)   ->  int:




     '''simple docstring'''



     _a			      =							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 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :List[Any]		)   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							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
          _a			      =							copy.deepcopy(lowerCAmelCase__		)
          _a			      =							np.iinfo(expected_dtype.to_pandas_dtype()		).max + 1
          change_first_primitive_element_in_list(lowerCAmelCase__				,						lowerCAmelCase__		)
          _a			      =							pa.array(OptimizedTypedSequence(lowerCAmelCase__				,						col=lowerCAmelCase__		)		)
          assert get_base_dtype(arr.type		) == pa.intaa()






@pytest.mark.parametrize('raise_exception'				,						[False, True]		)
def 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :Optional[int]		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							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 	_A				(lowerCAmelCase__  :Dict		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							'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}		)
          _a			      =							writer.finalize()
     assert num_examples == 2
     assert num_bytes > 0
     assert mockfs.exists(lowerCAmelCase__		)






def 	_A				()   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							pa.BufferOutputStream()
     with ParquetWriter(stream=lowerCAmelCase__		) as writer:
          writer.write({'col_1': 'foo', 'col_2': 1}		)
          writer.write({'col_1': 'bar', 'col_2': 2}		)
          _a			      =							writer.finalize()
     assert num_examples == 2
     assert num_bytes > 0
     _a			      =							pa.BufferReader(output.getvalue()		)
     _a			      =							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 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]		)   ->  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(lowerCAmelCase__				,						format='png'		)
     _a			      =							pa.BufferOutputStream()
     with ParquetWriter(
         stream=lowerCAmelCase__				,						features=Features({'image': Image()}		)				,						embed_local_files=lowerCAmelCase__		) as writer:
          writer.write({'image': image_path}		)
          writer.finalize()
     _a			      =							pa.BufferReader(output.getvalue()		)
     _a			      =							pq.read_table(lowerCAmelCase__		)
     _a			      =							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 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							pa.schema([pa.field('col_1'				,						pa.string()				,						nullable=lowerCAmelCase__		)]		)
     _a			      =							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'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

