



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.sql.Selectable"]			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,						__lowerCAmelCase					:					Optional[Features] = None			,						__lowerCAmelCase					:					str = None			,						__lowerCAmelCase					:					bool = False			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	List[str]:
    super().__init__(features=__lowerCAmelCase			,						cache_dir=__lowerCAmelCase			,						keep_in_memory=__lowerCAmelCase			,						**__lowerCAmelCase      )
    _A      =       Sql(
        cache_dir=__lowerCAmelCase			,						features=__lowerCAmelCase			,						sql=__lowerCAmelCase			,						con=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       None
    _A      =       None
    _A      =       None
    _A      =       None

    self.builder.download_and_prepare(
        download_config=__lowerCAmelCase			,						download_mode=__lowerCAmelCase			,						verification_mode=__lowerCAmelCase			,						base_path=__lowerCAmelCase			,						)

    # Build dataset for splits
    _A      =       self.builder.as_dataset(
        split='''train'''			,						verification_mode=__lowerCAmelCase			,						in_memory=self.keep_in_memory      )
    return dataset


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					Dataset			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						**__lowerCAmelCase					:					Optional[int]			,						)   ->	str:
    if num_proc is not None and num_proc <= 0:
       raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''      )

    _A      =       dataset
    _A      =       name
    _A      =       con
    _A      =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    _A      =       num_proc
    _A      =       to_sql_kwargs

 def     snake_case_      (    self					:					str      )   ->	int:
    _A      =       self.to_sql_kwargs.pop('''sql'''			,						__lowerCAmelCase      )
    _A      =       self.to_sql_kwargs.pop('''con'''			,						__lowerCAmelCase      )
    _A      =       self.to_sql_kwargs.pop('''index'''			,						__lowerCAmelCase      )

    _A      =       self._write(index=__lowerCAmelCase			,						**self.to_sql_kwargs      )
    return written

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Dict:
    _A  ,    _A  ,    _A      =       args
    _A      =       {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
    _A      =       query_table(
        table=self.dataset.data			,						key=slice(__lowerCAmelCase			,						offset + self.batch_size      )			,						indices=self.dataset._indices			,						)
    _A      =       batch.to_pandas()
    _A      =       df.to_sql(self.name			,						self.con			,						index=__lowerCAmelCase			,						**__lowerCAmelCase      )
    return num_rows or len(__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Dict			,						**__lowerCAmelCase					:					Optional[int]      )   ->	int:
    _A      =       0

    if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0			,						len(self.dataset      )			,						self.batch_size      )			,						unit='''ba'''			,						disable=not logging.is_progress_bar_enabled()			,						desc='''Creating SQL from Arrow format'''			,						):
          written += self._batch_sql((offset, index, to_sql_kwargs)      )
    else:
       _A  ,    _A      =       len(self.dataset      ), self.batch_size
       with multiprocessing.Pool(self.num_proc      ) as pool:
          for num_rows in logging.tqdm(
              pool.imap(
                  self._batch_sql			,						[(offset, index, to_sql_kwargs) for offset in range(0			,						__lowerCAmelCase			,						__lowerCAmelCase      )]			,						)			,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,						unit='''ba'''			,						disable=not logging.is_progress_bar_enabled()			,						desc='''Creating SQL from Arrow format'''			,						):
             written += num_rows

    return written










'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


