




'''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      :      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
_snake_case      :      List[Any]   							=		'\\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
_snake_case      :      Union[str, Any]   							=		''

# TODO: Add the licence for the dataset here if you can find it
_snake_case      :      Optional[int]   							=		''

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

class 			A    (		datasets.GeneratorBasedBuilder    ):
							lowercase_							=				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')
							lowercase_							=				[
							    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'    ),
							]

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







							def    __lowerCAmelCase			(  self       :  int					)			->							List[Any]:


														"""simple docstring"""


														if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
																					_a	     =				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	     =				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=lowerCAmelCase_							,			homepage=_HOMEPAGE							,			license=_LICENSE							,			citation=_CITATION							,			)







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  Tuple					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				_URLS[self.config.name]
														_a	     =				dl_manager.download_and_extract(lowerCAmelCase_					)
														return [
														    datasets.SplitGenerator(
														        name=datasets.Split.TRAIN							,			gen_kwargs={
														            '''filepath''': os.path.join(lowerCAmelCase_							,			'''train.jsonl'''					),
														            '''split''': '''train''',
														        }							,			),
														    datasets.SplitGenerator(
														        name=datasets.Split.VALIDATION							,			gen_kwargs={
														            '''filepath''': os.path.join(lowerCAmelCase_							,			'''dev.jsonl'''					),
														            '''split''': '''dev''',
														        }							,			),
														    datasets.SplitGenerator(
														        name=datasets.Split.TEST							,			gen_kwargs={
														            '''filepath''': os.path.join(lowerCAmelCase_							,			'''test.jsonl'''					),
														            '''split''': '''test'''
														        }							,			),
														]







							def    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Union[str, Any]					)			->							Tuple:


														"""simple docstring"""


														with open(lowerCAmelCase_							,			encoding='''utf-8'''					) as f:
																					for key, row in enumerate(lowerCAmelCase_					):
																												_a	     =				json.loads(lowerCAmelCase_					)
																												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'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

