


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 		_snake_case  (				lowerCAmelCase__							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  **_lowerCamelCase ,  ):
       super().__init__(features=_SCREAMING_SNAKE_CASE ,  cache_dir=_SCREAMING_SNAKE_CASE ,  keep_in_memory=_SCREAMING_SNAKE_CASE ,  **_SCREAMING_SNAKE_CASE   )
       a						:Tuple										=	Sql(
           cache_dir=_SCREAMING_SNAKE_CASE ,  features=_SCREAMING_SNAKE_CASE ,  sql=_SCREAMING_SNAKE_CASE ,  con=_SCREAMING_SNAKE_CASE ,  **_SCREAMING_SNAKE_CASE ,  )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	None
       a						:Union[str, Any]										=	None
       a						:Any										=	None
       a						:List[str]										=	None

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

       # Build dataset for splits
       a						:Any										=	self.builder.as_dataset(
           split='''train''' ,  verification_mode=_SCREAMING_SNAKE_CASE ,  in_memory=self.keep_in_memory   )
       return dataset







class 		_snake_case  :




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       if num_proc is not None and num_proc <= 0:
             raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''   )

       a						:Optional[int]										=	dataset
       a						:str										=	name
       a						:Optional[Any]										=	con
       a						:Any										=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       a						:List[Any]										=	num_proc
       a						:Union[str, Any]										=	to_sql_kwargs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.to_sql_kwargs.pop('''sql''' ,  _SCREAMING_SNAKE_CASE   )
       a						:Optional[Any]										=	self.to_sql_kwargs.pop('''con''' ,  _SCREAMING_SNAKE_CASE   )
       a						:List[Any]										=	self.to_sql_kwargs.pop('''index''' ,  _SCREAMING_SNAKE_CASE   )

       a						:Optional[Any]										=	self._write(index=_SCREAMING_SNAKE_CASE ,  **self.to_sql_kwargs   )
       return written




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a							,    a							,    a						:int										=	args
       a						:Any										=	{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
       a						:Dict										=	query_table(
           table=self.dataset.data ,  key=slice(_SCREAMING_SNAKE_CASE ,  offset + self.batch_size   ) ,  indices=self.dataset._indices ,  )
       a						:Any										=	batch.to_pandas()
       a						:Any										=	df.to_sql(self.name ,  self.con ,  index=_SCREAMING_SNAKE_CASE ,  **_SCREAMING_SNAKE_CASE   )
       return num_rows or len(_SCREAMING_SNAKE_CASE   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       a						: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:
             a							,    a						:Any										=	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 ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE   )] ,  ) ,  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









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

snake_case    :   Dict	    =					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'data2vec-vision'




 def __init__(		self ,  _lowerCamelCase=768 ,  _lowerCamelCase=12 ,  _lowerCamelCase=12 ,  _lowerCamelCase=3072 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=224 ,  _lowerCamelCase=16 ,  _lowerCamelCase=3 ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=True ,  _lowerCamelCase=[3, 5, 7, 11] ,  _lowerCamelCase=[1, 2, 3, 6] ,  _lowerCamelCase=True ,  _lowerCamelCase=0.4 ,  _lowerCamelCase=256 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Optional[int]										=	num_attention_heads
       a						:Dict										=	intermediate_size
       a						:List[Any]										=	hidden_act
       a						:List[str]										=	hidden_dropout_prob
       a						:Union[str, Any]										=	attention_probs_dropout_prob
       a						:Any										=	initializer_range
       a						:Any										=	layer_norm_eps

       a						:Union[str, Any]										=	image_size
       a						:int										=	patch_size
       a						:Optional[int]										=	num_channels
       a						:Union[str, Any]										=	use_mask_token
       a						:Optional[Any]										=	use_absolute_position_embeddings
       a						:Tuple										=	use_relative_position_bias
       a						:List[Any]										=	use_shared_relative_position_bias
       a						:Dict										=	layer_scale_init_value
       a						:Optional[int]										=	drop_path_rate
       a						:List[str]										=	use_mean_pooling
       # decode head attributes (semantic segmentation)
       a						:str										=	out_indices
       a						:Tuple										=	pool_scales
       # auxiliary head attributes (semantic segmentation)
       a						:List[Any]										=	use_auxiliary_head
       a						:List[Any]										=	auxiliary_loss_weight
       a						:Optional[int]										=	auxiliary_channels
       a						:List[str]										=	auxiliary_num_convs
       a						:str										=	auxiliary_concat_input
       a						:Union[str, Any]										=	semantic_loss_ignore_index







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4





