




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 		lowercase__   (		__A:	Union[str, Any]							,__A:	Union[str, Any]				):





					'''simple docstring'''






					assert isinstance(__A							,__A				)
					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 		lowercase__   (		__A:	Union[str, Any]							,__A:	Optional[Any]							,__A:	Any							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							tmp_path / '''cache'''
					__magic_name__							:	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():
										__magic_name__							:	Union[str, Any]    							=							SqlDatasetReader(
										    '''dataset'''							,'''sqlite:///''' + sqlite_path							,cache_dir=__A							,keep_in_memory=__A				).read()
					_check_sql_dataset(__A							,__A				)







@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 		lowercase__   (		__A:	List[Any]							,__A:	Optional[int]							,__A:	Optional[int]							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							tmp_path / '''cache'''
					__magic_name__							:	Tuple    							=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__							:	Optional[Any]    							=							features.copy() if features else default_expected_features
					__magic_name__							:	Dict    							=							(
					    Features({feature: Value(__A				) for feature, dtype in features.items()}				) if features is not None else None
					)
					__magic_name__							:	List[Any]    							=							SqlDatasetReader('''dataset'''							,'''sqlite:///''' + sqlite_path							,features=__A							,cache_dir=__A				).read()
					_check_sql_dataset(__A							,__A				)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					with contextlib.closing(sqlitea.connect(__A				)				) as con:
										__magic_name__							:	List[str]    							=							con.cursor()
										cur.execute('''SELECT * FROM dataset'''				)
										for row in cur:
															yield row







@require_sqlalchemy
def 		lowercase__   (		__A:	List[str]							,__A:	Optional[int]							,__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							tmp_path / '''cache'''
					__magic_name__							:	int    							=							os.path.join(__A							,'''tmp.sql'''				)
					__magic_name__							:	Dict    							=							SqlDatasetReader('''dataset'''							,'''sqlite:///''' + sqlite_path							,cache_dir=__A				).read()
					SqlDatasetWriter(__A							,'''dataset'''							,'''sqlite:///''' + output_sqlite_path							,num_proc=1				).write()

					__magic_name__							:	Any    							=							iter_sql_file(__A				)
					__magic_name__							:	Tuple    							=							iter_sql_file(__A				)

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







@require_sqlalchemy
def 		lowercase__   (		__A:	Any							,__A:	Union[str, Any]							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							tmp_path / '''cache'''
					__magic_name__							:	str    							=							os.path.join(__A							,'''tmp.sql'''				)
					__magic_name__							:	Optional[int]    							=							SqlDatasetReader('''dataset'''							,'''sqlite:///''' + sqlite_path							,cache_dir=__A				).read()
					SqlDatasetWriter(__A							,'''dataset'''							,'''sqlite:///''' + output_sqlite_path							,num_proc=2				).write()

					__magic_name__							:	str    							=							iter_sql_file(__A				)
					__magic_name__							:	int    							=							iter_sql_file(__A				)

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







@require_sqlalchemy
def 		lowercase__   (		__A:	int							,__A:	str							,__A:	List[Any]				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							tmp_path / '''cache'''
					__magic_name__							:	Any    							=							os.path.join(__A							,'''tmp.sql'''				)
					__magic_name__							:	Optional[Any]    							=							SqlDatasetReader('''dataset'''							,'''sqlite:///''' + sqlite_path							,cache_dir=__A				).read()
					with pytest.raises(__A				):
										SqlDatasetWriter(__A							,'''dataset'''							,'''sqlite:///''' + output_sqlite_path							,num_proc=0				).write()









from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Tuple				=			{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''nat'''

			UpperCamelCase__								={
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict=4       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						lowerCamelCase_			:   List[str]=64       ,						lowerCamelCase_			:   Union[str, Any]=[3, 4, 6, 5]       ,						lowerCamelCase_			:   List[Any]=[2, 4, 8, 16]       ,						lowerCamelCase_			:   List[Any]=7       ,						lowerCamelCase_			:   Union[str, Any]=3.0       ,						lowerCamelCase_			:   int=True       ,						lowerCamelCase_			:   str=0.0       ,						lowerCamelCase_			:   int=0.0       ,						lowerCamelCase_			:   Dict=0.1       ,						lowerCamelCase_			:   Optional[int]="gelu"       ,						lowerCamelCase_			:   Tuple=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-5       ,						lowerCamelCase_			:   List[Any]=0.0       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Tuple=None       ,						**lowerCamelCase_			:   Any       ,						)						->   Any:
								super().__init__(**lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							patch_size
								__magic_name__							:	str    							=							num_channels
								__magic_name__							:	Union[str, Any]    							=							embed_dim
								__magic_name__							:	Dict    							=							depths
								__magic_name__							:	str    							=							len(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							num_heads
								__magic_name__							:	Optional[int]    							=							kernel_size
								__magic_name__							:	Any    							=							mlp_ratio
								__magic_name__							:	Any    							=							qkv_bias
								__magic_name__							:	Union[str, Any]    							=							hidden_dropout_prob
								__magic_name__							:	Any    							=							attention_probs_dropout_prob
								__magic_name__							:	List[str]    							=							drop_path_rate
								__magic_name__							:	str    							=							hidden_act
								__magic_name__							:	Tuple    							=							layer_norm_eps
								__magic_name__							:	Any    							=							initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__magic_name__							:	str    							=							int(embed_dim * 2 ** (len(lowerCamelCase_  ) - 1)  )
								__magic_name__							:	str    							=							layer_scale_init_value
								__magic_name__							:	Dict    							=							['''stem'''] + [F'''stage{idx}''' for idx in range(1       ,						len(lowerCamelCase_  ) + 1  )]
								__magic_name__							,    __magic_name__							:	int    							=							get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase_       ,						out_indices=lowerCamelCase_       ,						stage_names=self.stage_names  )



