






"""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						):


   '''simple docstring'''

   A_						:     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')
   A_						:     Union[str, Any]									=			[
       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_						:     Dict									=			"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     # 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						      =			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						      =			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=_lowerCamelCase,	homepage=_HOMEPAGE,	license=_LICENSE,	citation=_CITATION,	)


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Any  ):

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






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     # 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(_lowerCamelCase,	encoding='''utf-8'''  ) as f:
       for key, row in enumerate(_lowerCamelCase  ):
         __A						      =			json.loads(_lowerCamelCase  )
         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"""


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


lowercase_    					=					logging.get_logger(__name__)


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       from transformers import Trainer


       class  snake_case		(							_lowerCAmelCase						):


          '''simple docstring'''

          def __init__(     self			:					List[Any],	_lowerCamelCase			:					int = 1_01  ):

            '''simple docstring'''
            __A						      =			length


          def __len__(     self			:					Optional[int]  ):

            '''simple docstring'''
            return self.length






          def __getitem__(     self			:					List[str],	_lowerCamelCase			:					Union[str, Any]  ):

            '''simple docstring'''
            return i





       class  snake_case		:


          '''simple docstring'''

          def __call__(     self			:					Tuple,	_lowerCamelCase			:					Dict  ):

            '''simple docstring'''
            return {"input_ids": torch.tensor(_lowerCamelCase  ), "labels": torch.tensor(_lowerCamelCase  )}





       class  snake_case		(							nn.Module						):


          '''simple docstring'''

          def __init__(     self			:					List[str]  ):

            '''simple docstring'''
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __A						      =			nn.Linear(1_20,	80  )






          def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Optional[Any]=None  ):

            '''simple docstring'''
            if labels is not None:
              return torch.tensor(0.0,	device=input_ids.device  ), input_ids
            else:
              return input_ids



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			HfArgumentParser((TrainingArguments,)			)
  sys.argv += ["--output_dir", "./examples"]
  __A						      =			parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f'Process rank: {training_args.local_rank}, device: {training_args.device}, '
      f'tpu_num_cores: {training_args.tpu_num_cores}'		,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
    __A						      =			DummyDataset(__UpperCamelCase			)

    def compute_metrics(__UpperCamelCase			) -> Dict:
      __A						      =			list(range(len(__UpperCamelCase			)			)			)
      __A						      =			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    __A						      =			Trainer(
        model=DummyModel()		,	args=__UpperCamelCase		,	data_collator=DummyDataCollator()		,	eval_dataset=__UpperCamelCase		,	compute_metrics=__UpperCamelCase		,	)
    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			2

    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			None

  logger.info('''🔥 All distributed tests successful'''			)



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  main()


if __name__ == "__main__":
       main()




