






"""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
A_     =				'''\
@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
A_     =				'''\
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
A_     =				''''''

# TODO: Add the licence for the dataset here if you can find it
A_     =				''''''

# 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)
A_     =				{
    '''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		):
							snake_case_  =				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')
							snake_case_  =				[
							    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'		),
							]

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


							def     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





													if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
																			A__    :	Optional[int]			=	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"
																			A__    :	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     _UpperCamelCase							(      self   :    Optional[Any]      ,     snake_case   :    Optional[Any]				):
													'''simple docstring'''





													A__    :	Dict			=	_URLS[self.config.name]
													A__    :	Optional[Any]			=	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     _UpperCamelCase							(      self   :    Union[str, Any]      ,     snake_case   :    Optional[Any]      ,     snake_case   :    Any				):
													'''simple docstring'''





													with open(snake_case      ,     encoding="""utf-8"""				) as f:
																			for key, row in enumerate(snake_case				):
																									A__    :	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"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_     =				{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				['''DeformableDetrFeatureExtractor''']
						A_     =				['''DeformableDetrImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				[
						    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DeformableDetrForObjectDetection''',
						    '''DeformableDetrModel''',
						    '''DeformableDetrPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

						A_     =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






