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
lowerCAmelCase__	:  int						=  '''\
@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
lowerCAmelCase__	:  Optional[Any]						=  '''\
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
lowerCAmelCase__	:  Optional[Any]						=  ''''''

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

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





class 					__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      __a     (  self						)	->    int:



									'''simple docstring'''







									if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
													snake_case__   :      List[str]   				= 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"
													snake_case__   :      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=__UpperCamelCase       ,			homepage=_HOMEPAGE       ,			license=_LICENSE       ,			citation=_CITATION       ,			)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= _URLS[self.config.name]
									snake_case__   :      Dict   				= 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







									with open(__UpperCamelCase       ,			encoding='utf-8'						) as f:
													for key, row in enumerate(__UpperCamelCase						):
																	snake_case__   :      Union[str, Any]   				= 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"],
																					}



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowerCAmelCase__	:  Union[str, Any]						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


