

"""simple docstring"""


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					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      assert isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							)
      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					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      int      		=	tmp_path / """cache"""
      SCREAMING_SNAKE_CASE__   :      Optional[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():
            SCREAMING_SNAKE_CASE__   :      int      		=	SqlDatasetReader(
                """dataset"""  ,					"""sqlite:///""" + sqlite_path  ,					cache_dir=__lowerCAmelCase  ,					keep_in_memory=__lowerCAmelCase							).read()
      _check_sql_dataset(__lowerCAmelCase  ,					__lowerCAmelCase							)




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




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      with contextlib.closing(sqlitea.connect(__lowerCAmelCase							)							) as con:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	con.cursor()
            cur.execute("""SELECT * FROM dataset"""							)
            for row in cur:
                  yield row




@require_sqlalchemy
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	tmp_path / """cache"""
      SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(__lowerCAmelCase  ,					"""tmp.sql"""							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	SqlDatasetReader("""dataset"""  ,					"""sqlite:///""" + sqlite_path  ,					cache_dir=__lowerCAmelCase							).read()
      SqlDatasetWriter(__lowerCAmelCase  ,					"""dataset"""  ,					"""sqlite:///""" + output_sqlite_path  ,					num_proc=1							).write()

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	iter_sql_file(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	iter_sql_file(__lowerCAmelCase							)

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




@require_sqlalchemy
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      Any      		=	tmp_path / """cache"""
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					"""tmp.sql"""							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	SqlDatasetReader("""dataset"""  ,					"""sqlite:///""" + sqlite_path  ,					cache_dir=__lowerCAmelCase							).read()
      SqlDatasetWriter(__lowerCAmelCase  ,					"""dataset"""  ,					"""sqlite:///""" + output_sqlite_path  ,					num_proc=2							).write()

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	iter_sql_file(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	iter_sql_file(__lowerCAmelCase							)

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




@require_sqlalchemy
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[Any]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tmp_path / """cache"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					"""tmp.sql"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	SqlDatasetReader("""dataset"""  ,					"""sqlite:///""" + sqlite_path  ,					cache_dir=__lowerCAmelCase							).read()
      with pytest.raises(__lowerCAmelCase							):
            SqlDatasetWriter(__lowerCAmelCase  ,					"""dataset"""  ,					"""sqlite:///""" + output_sqlite_path  ,					num_proc=0							).write()







"""simple docstring"""


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


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




