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		(      _A				,					_A       ):



 """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 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	tmp_path / "cache"
 __a							=	{"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							=	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 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	tmp_path / "cache"
 __a							=	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 __a							=	features.copy() if features else default_expected_features
 __a							=	(
     Features({feature: Value(_A       ) for feature, dtype in features.items()}       ) if features is not None else None
 )
 __a							=	SqlDatasetReader("dataset"				,					"sqlite:///" + sqlite_path				,					features=_A				,					cache_dir=_A       ).read()
 _check_sql_dataset(_A				,					_A       )



def 	__A		(      _A       ):



 """simple docstring"""


 with contextlib.closing(sqlitea.connect(_A       )       ) as con:
  __a							=	con.cursor()
  cur.execute("SELECT * FROM dataset"       )
  for row in cur:
   yield row



@require_sqlalchemy
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	tmp_path / "cache"
 __a							=	os.path.join(_A				,					"tmp.sql"       )
 __a							=	SqlDatasetReader("dataset"				,					"sqlite:///" + sqlite_path				,					cache_dir=_A       ).read()
 SqlDatasetWriter(_A				,					"dataset"				,					"sqlite:///" + output_sqlite_path				,					num_proc=1       ).write()

 __a							=	iter_sql_file(_A       )
 __a							=	iter_sql_file(_A       )

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



@require_sqlalchemy
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	tmp_path / "cache"
 __a							=	os.path.join(_A				,					"tmp.sql"       )
 __a							=	SqlDatasetReader("dataset"				,					"sqlite:///" + sqlite_path				,					cache_dir=_A       ).read()
 SqlDatasetWriter(_A				,					"dataset"				,					"sqlite:///" + output_sqlite_path				,					num_proc=2       ).write()

 __a							=	iter_sql_file(_A       )
 __a							=	iter_sql_file(_A       )

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






@require_sqlalchemy
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	tmp_path / "cache"
 __a							=	os.path.join(_A				,					"tmp.sql"       )
 __a							=	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


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """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  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	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
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


