


"""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          = """\
@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
__lowercase          = """\
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
__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 		_A    (    datasets.GeneratorBasedBuilder    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[Any]      =			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')
    UpperCAmelCase			:				Optional[int]      =			[
        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"""    ),
    ]

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






    def       __snake_case				(   self							:       List[str]):
      # 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
        a		:						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		:						Optional[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=__UpperCAmelCase							,					homepage=_HOMEPAGE							,					license=_LICENSE							,					citation=_CITATION							,					)






    def       __snake_case				(   self							:       Any							,					__UpperCAmelCase							:       Any):
      # 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
      a		:						str 				=     _URLS[self.config.name]
      a		:						Optional[int] 				=     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       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       Tuple							,					__UpperCAmelCase							:       Optional[int]):
      # 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(__UpperCAmelCase							,					encoding="utf-8") as f:
        for key, row in enumerate(__UpperCAmelCase):
          a		:						List[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"],
            }










"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






