
'''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
_snake_case					: Dict     			=					"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

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

# TODO: Add a link to an official homepage for the dataset here
_snake_case					: Union[str, Any]     			=					""""""

# TODO: Add the licence for the dataset here if you can find it
_snake_case					: Optional[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)
_snake_case					: List[Any]     			=					{
    """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      ):
		a				:       int							=       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')
		a				:       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"""      ),
		]

		a				:       Tuple							=       """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
														_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=UpperCamelCase       ,    homepage=_HOMEPAGE       ,    license=_LICENSE       ,    citation=_CITATION       ,    )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								# 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(UpperCamelCase   )
								return [
								    datasets.SplitGenerator(
								        name=datasets.Split.TRAIN       ,    gen_kwargs={
								            "filepath": os.path.join(UpperCamelCase       ,    "train.jsonl"   ),
								            "split": "train",
								        }       ,    ),
								    datasets.SplitGenerator(
								        name=datasets.Split.VALIDATION       ,    gen_kwargs={
								            "filepath": os.path.join(UpperCamelCase       ,    "dev.jsonl"   ),
								            "split": "dev",
								        }       ,    ),
								    datasets.SplitGenerator(
								        name=datasets.Split.TEST       ,    gen_kwargs={
								            "filepath": os.path.join(UpperCamelCase       ,    "test.jsonl"   ),
								            "split": "test"
								        }       ,    ),
								]




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								# 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(UpperCamelCase       ,    encoding="utf-8"   ) as f:
														for key, row in enumerate(UpperCamelCase   ):
																				_SCREAMING_SNAKE_CASE					=			json.loads(UpperCamelCase   )
																				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'''







from ..utils import DummyObject, requires_backends


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Any							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Optional[Any]							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )