
"""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        _UpperCAmelCase(       lowerCamelCase      ):



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3])							,						type=pa.intaa())



		def 		UpperCAmelCase				(   self)			->			Any:






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



		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		def 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([[[1, 2, 3]]]							,						type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						ArrayaDExtensionType((1, 3)							,						'''int64'''))



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([[[1, 2, 3]]]							,						try_type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						ArrayaDExtensionType((1, 3)							,						'''int64'''))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence(['''foo''', '''bar''']							,						try_type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						pa.string())




		@require_pil
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							import PIL.Image

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





def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferReader(__snake_case      ) if isinstance(__snake_case,	pa.Buffer      ) else pa.memory_map(__snake_case      )
					_UpperCamelCase									=    pa.ipc.open_stream(__snake_case      )
					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    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}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''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  lowerCamelCase__						(     )				->						List[Any]:


					"""simple docstring"""





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


					"""simple docstring"""





					_UpperCamelCase									=    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]      )
															_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()



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


					"""simple docstring"""





					_UpperCamelCase									=    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      )
															_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()



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


					"""simple docstring"""





					_UpperCamelCase									=    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      )
										_UpperCamelCase					,    _UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    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''': []}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    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]}      )      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    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]}      )      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''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  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





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


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					if isinstance(lst[0],	__snake_case      ):
										change_first_primitive_element_in_list(lst[0],	__snake_case      )
					else:
										_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





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



@pytest.mark.parametrize('''raise_exception''',	[False, True]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''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}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert mockfs.exists(__snake_case      )



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    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}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					_UpperCamelCase									=    pa.BufferReader(output.getvalue()      )
					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					import PIL.Image

					_UpperCamelCase									=    str(tmp_path / '''test_image_rgb.jpg'''      )
					PIL.Image.fromarray(np.zeros((5, 5),	dtype=np.uinta      )      ).save(__snake_case,	format='''png'''      )
					_UpperCamelCase									=    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()
					_UpperCamelCase									=    pa.BufferReader(output.getvalue()      )
					_UpperCamelCase									=    pq.read_table(__snake_case      )
					_UpperCamelCase									=    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  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    pa.schema([pa.field('''col_1''',	pa.string(),	nullable=__snake_case      )]      )
					_UpperCamelCase									=    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()      )]      )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
