


'''simple docstring'''




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
_lowerCAmelCase	:		List[str]		  =						'\\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
_lowerCAmelCase	:		Optional[int]		  =						'\\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
_lowerCAmelCase	:		Optional[int]		  =						''

# TODO: Add the licence for the dataset here if you can find it
_lowerCAmelCase	:		List[str]		  =						''

# 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)
_lowerCAmelCase	:		Tuple		  =						{
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}



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

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



	def 					lowercase  (       self     ):
		# 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
			lowerCAmelCase       :     List[str]   				=			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"
			lowerCAmelCase       :     Optional[int]   				=			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=snake_case__			,       homepage=_HOMEPAGE			,       license=_LICENSE			,       citation=_CITATION			,       )



	def 					lowercase  (       self			,       snake_case__     ):
		# 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
		lowerCAmelCase       :     List[Any]   				=			_URLS[self.config.name]
		lowerCAmelCase       :     int   				=			dl_manager.download_and_extract(snake_case__     )
		return [
		    datasets.SplitGenerator(
		        name=datasets.Split.TRAIN			,       gen_kwargs={
		            'filepath': os.path.join(snake_case__			,       'train.jsonl'     ),
		            'split': 'train',
		        }			,       ),
		    datasets.SplitGenerator(
		        name=datasets.Split.VALIDATION			,       gen_kwargs={
		            'filepath': os.path.join(snake_case__			,       'dev.jsonl'     ),
		            'split': 'dev',
		        }			,       ),
		    datasets.SplitGenerator(
		        name=datasets.Split.TEST			,       gen_kwargs={
		            'filepath': os.path.join(snake_case__			,       'test.jsonl'     ),
		            'split': 'test'
		        }			,       ),
		]



	def 					lowercase  (       self			,       snake_case__			,       snake_case__     ):
		# 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(snake_case__			,       encoding='utf-8'     ) as f:
			for key, row in enumerate(snake_case__     ):
				lowerCAmelCase       :     Optional[int]   				=			json.loads(snake_case__     )
				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"],
					}




'''simple docstring'''




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_lowerCAmelCase	:		Optional[int]		  =						importlib.metadata.version('numpy')
_lowerCAmelCase	:		List[Any]		  =						'.'.join([str(x) for x in sys.version_info[:3]])



class 			lowerCAmelCase					(					a				):



	def 					lowercase  (       self     ):
		# lt + different version strings
		require_version_core('numpy<1000.4.5'     )
		require_version_core('numpy<1000.4'     )
		require_version_core('numpy<1000'     )

		# le
		require_version_core('numpy<=1000.4.5'     )
		require_version_core(f"numpy<={numpy_ver}"     )

		# eq
		require_version_core(f"numpy=={numpy_ver}"     )

		# ne
		require_version_core('numpy!=1000.4.5'     )

		# ge
		require_version_core('numpy>=1.0'     )
		require_version_core('numpy>=1.0.0'     )
		require_version_core(f"numpy>={numpy_ver}"     )

		# gt
		require_version_core('numpy>1.0.0'     )

		# mix
		require_version_core('numpy>1.0.0,<1000'     )

		# requirement w/o version
		require_version_core('numpy'     )

		# unmet requirements due to version conflict
		for req in ["numpy==1.0.0", "numpy>=1000.0.0", f"numpy<{numpy_ver}"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn('but found'			,       str(snake_case__     )     )

        # unmet requirements due to missing module
		for req in ["numpipypie>1", "numpipypie2"]:
			try:
				require_version_core(snake_case__     )
			except importlib.metadata.PackageNotFoundError as e:
				self.assertIn(f"The '{req}' distribution was not found and is required by this application"			,       str(snake_case__     )     )
				self.assertIn('Try: pip install transformers -U'			,       str(snake_case__     )     )

        # bogus requirements formats:
        # 1. whole thing
		for req in ["numpy??1.0.0", "numpy1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('requirement needs to be in the pip package format'			,       str(snake_case__     )     )
        # 2. only operators
		for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('need one of '			,       str(snake_case__     )     )



	def 					lowercase  (       self     ):
		# matching requirement
		require_version('python>=3.6.0'     )

		# not matching requirements
		for req in ["python>9.9.9", "python<3.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn(f"but found python=={python_ver}"			,       str(snake_case__     )     )
