




"""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
SCREAMING_SNAKE_CASE_      =					'\\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
SCREAMING_SNAKE_CASE_      =					'\\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
SCREAMING_SNAKE_CASE_      =					''

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

# 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)
SCREAMING_SNAKE_CASE_      =					{
    '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 ):







   """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=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      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       # 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=a_					,				homepage=_HOMEPAGE					,				license=_LICENSE					,				citation=_CITATION					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      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
       UpperCamelCase										=  _URLS[self.config.name]
       UpperCamelCase										=  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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_)							->      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(a_					,				encoding='''utf-8''') as f:
           for key, row in enumerate(a_):
               UpperCamelCase										=  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"""







import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					CustomConfig







   def __init__(		self					,				lowerCamelCase_)							->      List[Any]:
       super().__init__(lowerCamelCase_)
       UpperCamelCase										=  torch.nn.Linear(config.hidden_size					,				config.hidden_size)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       return self.linear(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      List[str]:
       pass

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					NoSuperInitConfig







   def __init__(		self					,				lowerCamelCase_)							->      Optional[int]:
       super().__init__(lowerCamelCase_)
       UpperCamelCase										=  torch.nn.Linear(config.attribute					,				config.attribute)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      int:
       return self.linear(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Any:
       pass