

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,
)


A_		:			Any										=   {
    'configuration_switch_transformers': [
        'SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwitchTransformersConfig',
        'SwitchTransformersOnnxConfig',
    ]
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       A_		:			Dict										=   [
           '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

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







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_		:			Any										=   '\\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
A_		:			Optional[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
A_		:			Dict										=   ''

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

# 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_		:			Union[str, Any]										=   {
    '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						):


    '''simple docstring'''






    a__     							=       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__     							=       [
        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"						),
    ]

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


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        # 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
            __UpperCAmelCase									=			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"
            __UpperCAmelCase									=			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      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        # 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
        __UpperCAmelCase									=			_URLS[self.config.name]
        __UpperCAmelCase									=			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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
        # 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(lowercase__     ,      encoding='''utf-8'''  ) as f:
            for key, row in enumerate(lowercase__  ):
                __UpperCAmelCase									=			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"],
                    }




