

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


a					:Union[str, Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class      __a							(__lowerCamelCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = """vit_mae"""

     def __init__(						self	,    _a=768	,    _a=12	,    _a=12	,    _a=3_072	,    _a="gelu"	,    _a=0.0	,    _a=0.0	,    _a=0.02	,    _a=1E-1_2	,    _a=224	,    _a=16	,    _a=3	,    _a=True	,    _a=16	,    _a=512	,    _a=8	,    _a=2_048	,    _a=0.75	,    _a=False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(**SCREAMING_SNAKE_CASE_  )

           SCREAMING_SNAKE_CASE__   :      str      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Dict      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      str      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      str      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      str      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Dict      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	qkv_bias
           SCREAMING_SNAKE_CASE__   :      Any      		=	decoder_num_attention_heads
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	decoder_hidden_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	decoder_num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	decoder_intermediate_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	mask_ratio
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	norm_pix_loss







"""simple docstring"""


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      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a = None	,    _a = None	,    _a = False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(features=_a	,    cache_dir=_a	,    keep_in_memory=_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	Sql(
               cache_dir=_a	,    features=_a	,    sql=_a	,    con=_a	,    **_a	,    )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Dict      		=	None
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	None

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

           # Build dataset for splits
           SCREAMING_SNAKE_CASE__   :      str      		=	self.builder.as_dataset(
               split="""train"""	,    verification_mode=_a	,    in_memory=self.keep_in_memory  )
           return dataset





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a = None	,    _a = None	,    **_a	,    )  ->      Any:


           """simple docstring"""


           if num_proc is not None and num_proc <= 0:
                 raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

           SCREAMING_SNAKE_CASE__   :      int      		=	dataset
           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	con
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           SCREAMING_SNAKE_CASE__   :      int      		=	num_proc
           SCREAMING_SNAKE_CASE__   :      int      		=	to_sql_kwargs

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.to_sql_kwargs.pop("""sql"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""con"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""index"""	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self._write(index=_a	,    **self.to_sql_kwargs  )
           return written

     def 					_a			(						self	,    _a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	args
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           SCREAMING_SNAKE_CASE__   :      Any      		=	query_table(
               table=self.dataset.data	,    key=slice(_a	,    offset + self.batch_size  )	,    indices=self.dataset._indices	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	batch.to_pandas()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	df.to_sql(self.name	,    self.con	,    index=_a	,    **_a  )
           return num_rows or len(_a  )





     def 					_a			(						self	,    _a	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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:
                 SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	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	,    _a	,    _a  )]	,    )	,    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




