"""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
lowercase__   			=      '\\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
lowercase__   			=      '\\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
lowercase__   			=      ''

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

# 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)
lowercase__   			=      {
    '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							):
      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=__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"""							),
      ]

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



      def     lowerCamelCase_  (		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__:	Any     				=      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)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Tuple     				=      _URLS[self.config.name]
        a__:	List[str]     				=      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)      ->     List[str]:





        '''simple docstring'''





        with open(lowercase       ,     encoding='utf-8') as f:
          for key, row in enumerate(lowercase):
            a__:	List[Any]     				=      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"],
              }



"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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


