


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
snake_case			:      Any 							=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
snake_case			:      List[str] 							=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
snake_case			:      List[str] 							=   ''

# TODO: Add the licence for the dataset here if you can find it
snake_case			:      Dict 							=   ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
snake_case			:      Any 							=   {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}



class    __lowercase  (       datasets.GeneratorBasedBuilder							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[str]        =						datasets.Version("1.1.0"							)

							# This is an example of a dataset with multiple configurations.
							# If you don't want/need to define several sub-sets in your dataset,
							# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

							# If you need to make complex sub-parts in the datasets with configurable options
							# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
							# BUILDER_CONFIG_CLASS = MyBuilderConfig

							# You will be able to load one or the other configurations in the following list with
							# data = datasets.load_dataset('my_dataset', 'first_domain')
							# data = datasets.load_dataset('my_dataset', 'second_domain')
							SCREAMING_SNAKE_CASE   :					Tuple        =						[
							    datasets.BuilderConfig(name="first_domain"      ,     version=UpperCamelCase      ,     description="This part of my dataset covers a first domain"							),
							    datasets.BuilderConfig(name="second_domain"      ,     version=UpperCamelCase      ,     description="This part of my dataset covers a second domain"							),
							]

							SCREAMING_SNAKE_CASE   :					int        =						'''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
											if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
															_SCREAMING_SNAKE_CASE      = datasets.Features(
															    {
															        'sentence': datasets.Value('string'	),
															        'option1': datasets.Value('string'	),
															        'answer': datasets.Value('string'	)
															        # These are the features of your dataset like images, labels ...
															    }	)
											else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
															_SCREAMING_SNAKE_CASE      = datasets.Features(
															    {
															        'sentence': datasets.Value('string'	),
															        'option2': datasets.Value('string'	),
															        'second_domain_answer': datasets.Value('string'	)
															        # These are the features of your dataset like images, labels ...
															    }	)
											return datasets.DatasetInfo(
											    # This is the description that will appear on the datasets page.
											    description=_DESCRIPTION				, features=a_				, homepage=_HOMEPAGE				, license=_LICENSE				, citation=_CITATION				, )





							def 		__magic_name__			(    self				, A_	)->	Any:
											# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
											# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

											# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
											# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
											# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
											_SCREAMING_SNAKE_CASE      = _URLS[self.config.name]
											_SCREAMING_SNAKE_CASE      = dl_manager.download_and_extract(a_	)
											return [
											    datasets.SplitGenerator(
											        name=datasets.Split.TRAIN				, gen_kwargs={
											            'filepath': os.path.join(a_				, 'train.jsonl'	),
											            'split': 'train',
											        }				, ),
											    datasets.SplitGenerator(
											        name=datasets.Split.VALIDATION				, gen_kwargs={
											            'filepath': os.path.join(a_				, 'dev.jsonl'	),
											            'split': 'dev',
											        }				, ),
											    datasets.SplitGenerator(
											        name=datasets.Split.TEST				, gen_kwargs={
											            'filepath': os.path.join(a_				, 'test.jsonl'	),
											            'split': 'test'
											        }				, ),
											]





							def 		__magic_name__			(    self				, A_				, A_	)->	Tuple:
											# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
											# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
											with open(a_				, encoding='utf-8'	) as f:
															for key, row in enumerate(a_	):
																			_SCREAMING_SNAKE_CASE      = json.loads(a_	)
																			if self.config.name == "first_domain":
																							# Yields examples as (key, example) tuples
																							yield key, {
																							    "sentence": data["sentence"],
																							    "option1": data["option1"],
																							    "answer": "" if split == "test" else data["answer"],
																							}
																			else:
																							yield key, {
																							    "sentence": data["sentence"],
																							    "option2": data["option2"],
																							    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
																							}




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


snake_case			:      List[str] 							=   datasets.utils.logging.get_logger(__name__)



@dataclass
class    __lowercase  (       datasets.BuilderConfig							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Union[str, "sqlalchemy.sql.Selectable"]        =						None
							SCREAMING_SNAKE_CASE   :					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[Union[str, List[str]]]        =						None
							SCREAMING_SNAKE_CASE   :					bool        =						True
							SCREAMING_SNAKE_CASE   :					Optional[Union[List, Tuple, Dict]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[Union[List, Dict]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[List[str]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						1_00_00
							SCREAMING_SNAKE_CASE   :					Optional[datasets.Features]        =						None





							def 		__magic_name__			(    self	)->	int:
											if self.sql is None:
															raise ValueError('sql must be specified'	)
											if self.con is None:
															raise ValueError('con must be specified'	)





							def 		__magic_name__			(    self				, A_				, A_ = None				, )->	str:
											_SCREAMING_SNAKE_CASE      = 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
											_SCREAMING_SNAKE_CASE      = config_kwargs['sql']
											if not isinstance(A_				, A_	):
															if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																			import sqlalchemy

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

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





							@property
							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      = {
											    '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"""


							SCREAMING_SNAKE_CASE   :					Any        =						SqlConfig





							def 		__magic_name__			(    self	)->	Dict:
											return datasets.DatasetInfo(features=self.config.features	)





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={}	)]





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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = self.config.chunksize
											_SCREAMING_SNAKE_CASE      = pd.read_sql(
											    self.config.sql				, self.config.con				, chunksize=A_				, **self.config.pd_read_sql_kwargs	)
											_SCREAMING_SNAKE_CASE      = [sql_reader] if chunksize is None else sql_reader
											for chunk_idx, df in enumerate(A_	):
															_SCREAMING_SNAKE_CASE      = pa.Table.from_pandas(A_	)
															yield chunk_idx, self._cast_table(A_	)
