

'''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
lowerCamelCase				:   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
lowerCamelCase				:   str      				=  "\\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
lowerCamelCase				:   Tuple      				=  ""

# TODO: Add the licence for the dataset here if you can find it
lowerCamelCase				:   Union[str, Any]      				=  ""

# 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				:   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						):
  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 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






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





  def 		A						(	self   :			Any							,      _a   :			Tuple				)			->					Optional[Any]:





       '''simple docstring'''






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





  def 		A						(	self   :			Tuple							,      _a   :			int							,      _a   :			str				)			->					List[str]:





       '''simple docstring'''






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


lowerCamelCase				:   str      				=  {"configuration_dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase				:   Optional[int]      				=  [
         "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DinatForImageClassification",
         "DinatModel",
         "DinatPreTrainedModel",
         "DinatBackbone",
     ]

if TYPE_CHECKING:
     from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dinat import (
              DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DinatBackbone,
              DinatForImageClassification,
              DinatModel,
              DinatPreTrainedModel,
          )

else:
     import sys

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