






import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy



def  a__ (__lowercase							:str					,  __lowercase							:List[Any]   )		-> List[str]:
				assert isinstance(__lowercase					,  __lowercase   )
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype



@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''					,  [False, True]   )
def  a__ (__lowercase							:Any					,  __lowercase							:List[str]					,  __lowercase							:int					,  __lowercase							:Union[str, Any]   )		-> str:
				_A	:				Optional[Any]   =      tmp_path / '''cache'''
				_A	:				Union[str, Any]   =      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_A	:				List[Any]   =      SqlDatasetReader(
								    '''dataset'''					,  '''sqlite:///''' + sqlite_path					,  cache_dir=__lowercase					,  keep_in_memory=__lowercase   ).read()
				_check_sql_dataset(__lowercase					,  __lowercase   )



@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''					,  [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]					,  )
def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]					,  __lowercase							:Union[str, Any]					,  __lowercase							:List[Any]   )		-> List[Any]:
				_A	:				List[Any]   =      tmp_path / '''cache'''
				_A	:				Any   =      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_A	:				Optional[int]   =      features.copy() if features else default_expected_features
				_A	:				Any   =      (
				    Features({feature: Value(__lowercase   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				_A	:				Optional[Any]   =      SqlDatasetReader('''dataset'''					,  '''sqlite:///''' + sqlite_path					,  features=__lowercase					,  cache_dir=__lowercase   ).read()
				_check_sql_dataset(__lowercase					,  __lowercase   )



def  a__ (__lowercase							:Union[str, Any]   )		-> Union[str, Any]:
				with contextlib.closing(sqlitea.connect(__lowercase   )   ) as con:
								_A	:				List[str]   =      con.cursor()
								cur.execute('''SELECT * FROM dataset'''   )
								for row in cur:
												yield row



@require_sqlalchemy
def  a__ (__lowercase							:Optional[Any]					,  __lowercase							:List[Any]					,  __lowercase							:Union[str, Any]   )		-> Union[str, Any]:
				_A	:				Tuple   =      tmp_path / '''cache'''
				_A	:				Any   =      os.path.join(__lowercase					,  '''tmp.sql'''   )
				_A	:				Optional[Any]   =      SqlDatasetReader('''dataset'''					,  '''sqlite:///''' + sqlite_path					,  cache_dir=__lowercase   ).read()
				SqlDatasetWriter(__lowercase					,  '''dataset'''					,  '''sqlite:///''' + output_sqlite_path					,  num_proc=1   ).write()

				_A	:				Union[str, Any]   =      iter_sql_file(__lowercase   )
				_A	:				Tuple   =      iter_sql_file(__lowercase   )

				for rowa, rowa in zip(__lowercase					,  __lowercase   ):
								assert rowa == rowa



@require_sqlalchemy
def  a__ (__lowercase							:int					,  __lowercase							:Union[str, Any]					,  __lowercase							:Dict   )		-> List[str]:
				_A	:				Tuple   =      tmp_path / '''cache'''
				_A	:				Optional[Any]   =      os.path.join(__lowercase					,  '''tmp.sql'''   )
				_A	:				List[str]   =      SqlDatasetReader('''dataset'''					,  '''sqlite:///''' + sqlite_path					,  cache_dir=__lowercase   ).read()
				SqlDatasetWriter(__lowercase					,  '''dataset'''					,  '''sqlite:///''' + output_sqlite_path					,  num_proc=2   ).write()

				_A	:				List[str]   =      iter_sql_file(__lowercase   )
				_A	:				List[Any]   =      iter_sql_file(__lowercase   )

				for rowa, rowa in zip(__lowercase					,  __lowercase   ):
								assert rowa == rowa






@require_sqlalchemy
def  a__ (__lowercase							:str					,  __lowercase							:Dict					,  __lowercase							:Dict   )		-> Dict:
				_A	:				str   =      tmp_path / '''cache'''
				_A	:				Optional[int]   =      os.path.join(__lowercase					,  '''tmp.sql'''   )
				_A	:				Union[str, Any]   =      SqlDatasetReader('''dataset'''					,  '''sqlite:///''' + sqlite_path					,  cache_dir=__lowercase   ).read()
				with pytest.raises(__lowercase   ):
								SqlDatasetWriter(__lowercase					,  '''dataset'''					,  '''sqlite:///''' + output_sqlite_path					,  num_proc=0   ).write()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

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



