

from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							__UpperCAmelCase      ="""mobilebert"""




							def __init__(			self       ,    _A=3_0_5_2_2       ,    _A=5_1_2       ,    _A=2_4       ,    _A=4       ,    _A=5_1_2       ,    _A="relu"       ,    _A=0.0       ,    _A=0.1       ,    _A=5_1_2       ,    _A=2       ,    _A=0.02       ,    _A=1e-1_2       ,    _A=0       ,    _A=1_2_8       ,    _A=True       ,    _A=True       ,    _A=1_2_8       ,    _A=False       ,    _A=True       ,    _A=4       ,    _A="no_norm"       ,    _A=True       ,    _A=None       ,    **_A       ,    ):
												super().__init__(pad_token_id=lowerCamelCase_       ,    **lowerCamelCase_				)

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

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

												__lowerCAmelCase       =				classifier_dropout





class      snake_case_      (					_a				):


							"""simple docstring"""





							@property
							def 							A__	(			self				):

												if self.task == "multiple-choice":
																	__lowerCAmelCase       =				{0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																	__lowerCAmelCase       =				{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_						)
