'''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
_lowercase		:					Dict													=		"\\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
_lowercase		:					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
_lowercase		:					List[str]													=		""

# TODO: Add the licence for the dataset here if you can find it
_lowercase		:					List[Any]													=		""

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





class        __magic_name__		(       datasets.GeneratorBasedBuilder):
			UpperCamelCase__							=			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__							=			[
			    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'''),
			]

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    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
																	lowercase_   :     Union[str, Any]									=			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"
																	lowercase_   :     Optional[Any]									=			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       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    str       ):
										# 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
										lowercase_   :     Dict									=			_URLS[self.config.name]
										lowercase_   :     int									=			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       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     lowercase_							:    List[Any]							,     lowercase_							:    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(lowercase_							,     encoding="""utf-8"""       ) as f:
																	for key, row in enumerate(lowercase_       ):
																								lowercase_   :     str									=			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"],
																															}






'''simple docstring'''




_lowercase		:					List[str]													=		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





