

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


__A	:       int        =						logging.get_logger(__name__)

__A	:       Union[str, Any]        =						{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		SCREAMING_SNAKE_CASE														=       "mobilebert"


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=3_0_5_2_2	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=5_1_2	,	__SCREAMING_SNAKE_CASE					:						Any=2_4	,	__SCREAMING_SNAKE_CASE					:						Dict=4	,	__SCREAMING_SNAKE_CASE					:						Tuple=5_1_2	,	__SCREAMING_SNAKE_CASE					:						Any="relu"	,	__SCREAMING_SNAKE_CASE					:						str=0.0	,	__SCREAMING_SNAKE_CASE					:						Tuple=0.1	,	__SCREAMING_SNAKE_CASE					:						Dict=5_1_2	,	__SCREAMING_SNAKE_CASE					:						Any=2	,	__SCREAMING_SNAKE_CASE					:						int=0.0_2	,	__SCREAMING_SNAKE_CASE					:						List[Any]=1E-12	,	__SCREAMING_SNAKE_CASE					:						str=0	,	__SCREAMING_SNAKE_CASE					:						int=1_2_8	,	__SCREAMING_SNAKE_CASE					:						List[Any]=True	,	__SCREAMING_SNAKE_CASE					:						List[Any]=True	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=1_2_8	,	__SCREAMING_SNAKE_CASE					:						Any=False	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=True	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=4	,	__SCREAMING_SNAKE_CASE					:						Tuple="no_norm"	,	__SCREAMING_SNAKE_CASE					:						Tuple=True	,	__SCREAMING_SNAKE_CASE					:						int=None	,	**__SCREAMING_SNAKE_CASE					:						Any	,	):
							super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							A     =   vocab_size
							A     =   hidden_size
							A     =   num_hidden_layers
							A     =   num_attention_heads
							A     =   hidden_act
							A     =   intermediate_size
							A     =   hidden_dropout_prob
							A     =   attention_probs_dropout_prob
							A     =   max_position_embeddings
							A     =   type_vocab_size
							A     =   initializer_range
							A     =   layer_norm_eps
							A     =   embedding_size
							A     =   trigram_input
							A     =   use_bottleneck
							A     =   intra_bottleneck_size
							A     =   use_bottleneck_attention
							A     =   key_query_shared_bottleneck
							A     =   num_feedforward_networks
							A     =   normalization_type
							A     =   classifier_activation

							if self.use_bottleneck:
												A     =   intra_bottleneck_size
							else:
												A     =   hidden_size

							A     =   classifier_dropout





class      __UpperCamelCase					(  _A		):


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							if self.task == "multiple-choice":
												A     =   {0: "batch", 1: "choice", 2: "sequence"}
							else:
												A     =   {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ])










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_						)
