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


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

__snake_case					:							Optional[Any]           =							{
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Tuple												=			"""videomae"""


						def __init__(  self  , A=2_24  , A=16  , A=3  , A=16  , A=2  , A=7_68  , A=12  , A=12  , A=30_72  , A="gelu"  , A=0.0  , A=0.0  , A=0.02  , A=1e-12  , A=True  , A=True  , A=6  , A=3_84  , A=4  , A=15_36  , A=True  , **A  , )  ->Dict:
									super().__init__(**lowerCamelCase_	)

									UpperCAmelCase__		:Optional[Any] 		=    image_size
									UpperCAmelCase__		:str 		=    patch_size
									UpperCAmelCase__		:int 		=    num_channels
									UpperCAmelCase__		:int 		=    num_frames
									UpperCAmelCase__		:List[Any] 		=    tubelet_size

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

									UpperCAmelCase__		:Any 		=    decoder_num_attention_heads
									UpperCAmelCase__		:Optional[int] 		=    decoder_hidden_size
									UpperCAmelCase__		:Optional[int] 		=    decoder_num_hidden_layers
									UpperCAmelCase__		:Optional[Any] 		=    decoder_intermediate_size
									UpperCAmelCase__		:List[str] 		=    norm_pix_loss










import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


_lowercase:					Optional[Any]      =						datasets.utils.logging.get_logger(__name__)



@dataclass
class       _lowercase				(  datasets.BuilderConfig      ):





	"""simple docstring"""
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				True
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				10000
	__A		      =				None




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.sql is None:
											raise ValueError("sql must be specified"						)
						if self.con is None:
											raise ValueError("con must be specified"						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  config_kwargs.copy()
						# We need to stringify the Selectable object to make its hash deterministic

						# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
						a   	=  config_kwargs["sql"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																import sqlalchemy

																if isinstance(lowerCamelCase_						,			sqlalchemy.sql.Selectable						):
																					a   	=  sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
																					a   	=  str(sql.compile(dialect=engine.dialect						)						)
																					a   	=  sql_str
																else:
																					raise TypeError(
																					    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
											else:
																raise TypeError(
																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
						a   	=  config_kwargs["con"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  id(lowerCamelCase_						)
											logger.info(
											    F'''SQL connection \'con\' of type {type(lowerCamelCase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''						)

						return super().create_config_id(lowerCamelCase_						,			custom_features=lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "index_col": self.index_col,
						    "columns": self.columns,
						    "params": self.params,
						    "coerce_float": self.coerce_float,
						    "parse_dates": self.parse_dates,
						}
						return pd_read_sql_kwargs




class       _lowercase				(  datasets.ArrowBasedBuilder      ):





	"""simple docstring"""
	__A		      =				SqlConfig




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return datasets.DatasetInfo(features=self.config.features						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={}						)]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if self.config.features is not None:
											a   	=  self.config.features.arrow_schema
											if all(not require_storage_cast(lowerCamelCase_						) for feature in self.config.features.values()						):
																# cheaper cast
																a   	=  pa.Table.from_arrays([pa_table[field.name] for field in schema]						,			schema=lowerCamelCase_						)
											else:
																# more expensive cast; allows str <-> int/float or str to Audio for example
																a   	=  table_cast(lowerCamelCase_						,			lowerCamelCase_						)
						return pa_table




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.config.chunksize
						a   	=  pd.read_sql(
						    self.config.sql						,			self.config.con						,			chunksize=lowerCamelCase_						,			**self.config.pd_read_sql_kwargs						)
						a   	=  [sql_reader] if chunksize is None else sql_reader
						for chunk_idx, df in enumerate(lowerCamelCase_						):
											a   	=  pa.Table.from_pandas(lowerCamelCase_						)
											yield chunk_idx, self._cast_table(lowerCamelCase_						)
