

import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
@require_flax
class 					A_   :


    '''simple docstring'''






    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.prepare_init_args_and_inputs_for_common()

        __UpperCAmelCase									=			self.model_class(**lowercase__  )
        __UpperCAmelCase									=			model.init(inputs_dict['''prng_key''']     ,      inputs_dict['''sample''']  )
        jax.lax.stop_gradient(lowercase__  )

        __UpperCAmelCase									=			model.apply(lowercase__     ,      inputs_dict['''sample''']  )

        if isinstance(lowercase__     ,      lowercase__  ):
            __UpperCAmelCase									=			output.sample

        self.assertIsNotNone(lowercase__  )
        __UpperCAmelCase									=			inputs_dict['''sample'''].shape
        self.assertEqual(output.shape     ,      lowercase__     ,      '''Input and output shapes do not match'''  )


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.prepare_init_args_and_inputs_for_common()

        __UpperCAmelCase									=			16
        __UpperCAmelCase									=			(16, 32)

        __UpperCAmelCase									=			self.model_class(**lowercase__  )
        __UpperCAmelCase									=			model.init(inputs_dict['''prng_key''']     ,      inputs_dict['''sample''']  )
        jax.lax.stop_gradient(lowercase__  )

        __UpperCAmelCase									=			model.apply(lowercase__     ,      inputs_dict['''sample''']  )

        if isinstance(lowercase__     ,      lowercase__  ):
            __UpperCAmelCase									=			output.sample

        self.assertIsNotNone(lowercase__  )
        __UpperCAmelCase									=			inputs_dict['''sample'''].shape
        self.assertEqual(output.shape     ,      lowercase__     ,      '''Input and output shapes do not match'''  )






    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			'''kwargs''' in inspect.signature(self.model_class.__init__  ).parameters
        __UpperCAmelCase									=			len(self.model_class._deprecated_kwargs  ) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
            raise ValueError(
                F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
                ''' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'''
                ''' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='''
                ''' [<deprecated_argument>]`'''  )

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
            raise ValueError(
                F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
                ''' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'''
                F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
                ''' from `_deprecated_kwargs = [<deprecated_argument>]`'''  )







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
A_		:			Any										=   '\\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
A_		:			Optional[Any]										=   '\\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
A_		:			Dict										=   ''

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

# 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)
A_		:			Union[str, Any]										=   {
    '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'''






    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      lowerCAmelCase_						(self  )     ->      Tuple:
        # 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=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        # 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(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__  )     ->      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(lowercase__     ,      encoding='''utf-8'''  ) as f:
            for key, row in enumerate(lowercase__  ):
                __UpperCAmelCase									=			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"],
                    }




