




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 							__SCREAMING_SNAKE_CASE				(						datasets.GeneratorBasedBuilder    ):
					_lowerCamelCase						    =					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						    =					[
					    datasets.BuilderConfig(name="""first_domain"""  ,	version=lowerCamelCase  ,	description="""This part of my dataset covers a first domain"""    ),
					    datasets.BuilderConfig(name="""second_domain"""  ,	version=lowerCamelCase  ,	description="""This part of my dataset covers a second domain"""    ),
					]

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							lowerCamelCase_				:						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"
							lowerCamelCase_				:						List[str]				  =				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       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				_URLS[self.config.name]
						lowerCamelCase_				:						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       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						with open(__lowercase					,			encoding='utf-8'   ) as f:
							for key, row in enumerate(__lowercase   ):
								lowerCamelCase_				:						Optional[int]				  =				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 typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
		import torch

__magic_name__       =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
		from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):





		def 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

					if generator is not None:
										lowercase						   =       generator.device.type if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															lowercase						   =       'cpu'
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

					if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				):
										lowercase						   =       (1,) + shape[1:]
										lowercase						   =       [
										    torch.randn(_UpperCAmelCase       ,			generator=generator[i]       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				)
										    for i in range(_UpperCAmelCase				)
										]
										lowercase						   =       torch.cat(_UpperCAmelCase       ,			dim=0				).to(_UpperCAmelCase				)
					else:
										lowercase						   =       torch.randn(_UpperCAmelCase       ,			generator=_UpperCAmelCase       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				).to(_UpperCAmelCase				)

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if is_torch_version('<'       ,			'2.0.0'				) or not hasattr(_UpperCAmelCase       ,			'_dynamo'				):
										return False
					return isinstance(_UpperCAmelCase       ,			torch._dynamo.eval_frame.OptimizedModule				)






