



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
UpperCamelCase_	       =   "\\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
UpperCamelCase_	       =   "\\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
UpperCamelCase_	       =   ""

# TODO: Add the licence for the dataset here if you can find it
UpperCamelCase_	       =   ""

# 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)
UpperCamelCase_	       =   {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						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')
						UpperCamelCase__      :						List[str]				       =[
						    datasets.BuilderConfig(name="first_domain"					, version=_snake_case					, description="This part of my dataset covers a first domain"				),
						    datasets.BuilderConfig(name="second_domain"					, version=_snake_case					, description="This part of my dataset covers a second domain"				),
						]

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

						def 	__a       (    self	:Dict)		->					List[str]:
												# 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
																		UpperCAmelCase_     							=     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"
																		UpperCAmelCase_     							=     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=_lowercase   ,						homepage=_HOMEPAGE   ,						license=_LICENSE   ,						citation=_CITATION   ,						)

						def 	__a       (    self	:List[Any]   ,						_lowercase	:str)		->					List[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
												UpperCAmelCase_     							=     _URLS[self.config.name]
												UpperCAmelCase_     							=     dl_manager.download_and_extract(_lowercase)
												return [
												    datasets.SplitGenerator(
												        name=datasets.Split.TRAIN   ,						gen_kwargs={
												            '''filepath''': os.path.join(_lowercase   ,						'''train.jsonl'''),
												            '''split''': '''train''',
												        }   ,						),
												    datasets.SplitGenerator(
												        name=datasets.Split.VALIDATION   ,						gen_kwargs={
												            '''filepath''': os.path.join(_lowercase   ,						'''dev.jsonl'''),
												            '''split''': '''dev''',
												        }   ,						),
												    datasets.SplitGenerator(
												        name=datasets.Split.TEST   ,						gen_kwargs={
												            '''filepath''': os.path.join(_lowercase   ,						'''test.jsonl'''),
												            '''split''': '''test'''
												        }   ,						),
												]




						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:Any   ,						_lowercase	:Optional[int])		->					str:
												# 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(_lowercase   ,						encoding='''utf-8''') as f:
																		for key, row in enumerate(_lowercase):
																								UpperCAmelCase_     							=     json.loads(_lowercase)
																								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"],
																														}










from __future__ import annotations

from pathlib import Path







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       bool:



						'''simple docstring'''






						UpperCAmelCase_     							=     (xa, ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     -vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)
						UpperCAmelCase_     							=     +vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)

						return a > 0 and b > 0 and a + b < 1







def 				A			(		__UpperCAmelCase = "p102_triangles.txt"	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     Path(__UpperCAmelCase	).parent.joinpath(__UpperCAmelCase	).read_text(encoding='''utf-8'''	)

						UpperCAmelCase_     							=     []
						for line in data.strip().split('''\n'''	):
												triangles.append([int(__UpperCAmelCase	) for number in line.split(''','''	)]	)

						UpperCAmelCase_     							=     0
						UpperCAmelCase_     							=     42

						for triangle in triangles:
												ret += contains_origin(*__UpperCAmelCase	)

						return ret


if __name__ == "__main__":
							print(f"{solution() = }")





