





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	_lowerCamelCase       =	'platform'

	import jax
	import jax.numpy as jnp

	from transformers import MarianTokenizer
	from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							Union[str, Any]=None    ,      UpperCamelCase__:							Tuple=None    ,      UpperCamelCase__:							Tuple=None    ,      UpperCamelCase__:							int=None    ,      UpperCamelCase__:							Union[str, Any]=None    ,      UpperCamelCase__:							List[str]=None    ,      ):
		if attention_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.where(input_ids != config.pad_token_id    ,      1    ,      0			)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.where(decoder_input_ids != config.pad_token_id    ,      1    ,      0			)
		if head_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}




class      UpperCamelCase_				:


							def __init__(							self			:Optional[Any]							, __A			:Union[str, Any]							, __A			:int=13							, __A			:Dict=7							, __A			:Tuple=True							, __A			:Optional[Any]=False							, __A			:Optional[int]=99							, __A			:int=16							, __A			:Optional[Any]=2							, __A			:List[Any]=4							, __A			:List[Any]=4							, __A			:Optional[Any]="gelu"							, __A			:List[str]=0.1							, __A			:List[Any]=0.1							, __A			:str=32							, __A			:Dict=2							, __A			:Optional[Any]=1							, __A			:List[Any]=0							, __A			:List[str]=0.0_2							, )      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					parent
									SCREAMING_SNAKE_CASE__								=					batch_size
									SCREAMING_SNAKE_CASE__								=					seq_length
									SCREAMING_SNAKE_CASE__								=					is_training
									SCREAMING_SNAKE_CASE__								=					use_labels
									SCREAMING_SNAKE_CASE__								=					vocab_size
									SCREAMING_SNAKE_CASE__								=					hidden_size
									SCREAMING_SNAKE_CASE__								=					num_hidden_layers
									SCREAMING_SNAKE_CASE__								=					num_attention_heads
									SCREAMING_SNAKE_CASE__								=					intermediate_size
									SCREAMING_SNAKE_CASE__								=					hidden_act
									SCREAMING_SNAKE_CASE__								=					hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=					attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=					max_position_embeddings
									SCREAMING_SNAKE_CASE__								=					eos_token_id
									SCREAMING_SNAKE_CASE__								=					pad_token_id
									SCREAMING_SNAKE_CASE__								=					bos_token_id
									SCREAMING_SNAKE_CASE__								=					initializer_range


							def    _snake_case     (							self			:List[Any]						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							, self.vocab_size						)							, 3							, self.vocab_size						)
									SCREAMING_SNAKE_CASE__								=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							, dtype=np.intaa						))							, -1						)

									SCREAMING_SNAKE_CASE__								=					shift_tokens_right(__A							, 1							, 2						)

									SCREAMING_SNAKE_CASE__								=					MarianConfig(
									    vocab_size=self.vocab_size							, d_model=self.hidden_size							, encoder_layers=self.num_hidden_layers							, decoder_layers=self.num_hidden_layers							, encoder_attention_heads=self.num_attention_heads							, decoder_attention_heads=self.num_attention_heads							, encoder_ffn_dim=self.intermediate_size							, decoder_ffn_dim=self.intermediate_size							, dropout=self.hidden_dropout_prob							, attention_dropout=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, eos_token_id=self.eos_token_id							, bos_token_id=self.bos_token_id							, pad_token_id=self.pad_token_id							, initializer_range=self.initializer_range							, use_cache=__A							, )
									SCREAMING_SNAKE_CASE__								=					prepare_marian_inputs_dict(__A							, __A							, __A						)
									return config, inputs_dict


							def    _snake_case     (							self			:List[Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.prepare_config_and_inputs()
									return config, inputs_dict


							def    _snake_case     (							self			:Any							, __A			:Any							, __A			:List[Any]							, __A			:Optional[int]						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					20
									SCREAMING_SNAKE_CASE__								=					model_class_name(__A						)

									SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]						)

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									SCREAMING_SNAKE_CASE__								=					model.init_cache(decoder_input_ids.shape[0]							, __A							, __A						)
									SCREAMING_SNAKE_CASE__								=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							, dtype="""i4"""						)

									SCREAMING_SNAKE_CASE__								=					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							, )
									SCREAMING_SNAKE_CASE__								=					model.decode(
									    decoder_input_ids[:, :-1]							, __A							, decoder_attention_mask=__A							, past_key_values=__A							, decoder_position_ids=__A							, )

									SCREAMING_SNAKE_CASE__								=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							, dtype="""i4"""						)
									SCREAMING_SNAKE_CASE__								=					model.decode(
									    decoder_input_ids[:, -1:]							, __A							, decoder_attention_mask=__A							, past_key_values=outputs_cache.past_key_values							, decoder_position_ids=__A							, )

									SCREAMING_SNAKE_CASE__								=					model.decode(__A							, __A						)

									SCREAMING_SNAKE_CASE__								=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1E-3							, msg=f'''Max diff is {diff}'''						)


							def    _snake_case     (							self			:Optional[int]							, __A			:Optional[int]							, __A			:List[str]							, __A			:int						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					20
									SCREAMING_SNAKE_CASE__								=					model_class_name(__A						)

									SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]						)

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									SCREAMING_SNAKE_CASE__								=					jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
									    ]							, axis=-1							, )

									SCREAMING_SNAKE_CASE__								=					model.init_cache(decoder_input_ids.shape[0]							, __A							, __A						)
									SCREAMING_SNAKE_CASE__								=					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							, )

									SCREAMING_SNAKE_CASE__								=					model.decode(
									    decoder_input_ids[:, :-1]							, __A							, decoder_attention_mask=__A							, past_key_values=__A							, decoder_position_ids=__A							, )
									SCREAMING_SNAKE_CASE__								=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							, dtype="""i4"""						)
									SCREAMING_SNAKE_CASE__								=					model.decode(
									    decoder_input_ids[:, -1:]							, __A							, past_key_values=outputs_cache.past_key_values							, decoder_attention_mask=__A							, decoder_position_ids=__A							, )

									SCREAMING_SNAKE_CASE__								=					model.decode(__A							, __A							, decoder_attention_mask=__A						)

									SCREAMING_SNAKE_CASE__								=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1E-3							, msg=f'''Max diff is {diff}'''						)




@require_flax
class      UpperCamelCase_				(   UpperCamelCase__ ,     unittest.TestCase ,     UpperCamelCase__			):
							lowerCamelCase_							=    True
							lowerCamelCase_							=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							lowerCamelCase_							=    (FlaxMarianMTModel,) if is_flax_available() else ()


							def    _snake_case     (							self			:Any						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					FlaxMarianModelTester(self						)


							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(__A							, __A							, __A						)


							def    _snake_case     (							self			:Union[str, Any]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(__A							, __A							, __A						)


							def    _snake_case     (							self			:List[Any]						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
													SCREAMING_SNAKE_CASE__								=					self._prepare_for_class(__A							, __A						)
													SCREAMING_SNAKE_CASE__								=					model_class(__A						)

													@jax.jit
													def encode_jitted(__A			:List[str]							, __A			:List[str]=None							, **__A			:Tuple						):
															return model.encode(input_ids=__A							, attention_mask=__A						)

													with self.subTest("""JIT Enabled"""						):
															SCREAMING_SNAKE_CASE__								=					encode_jitted(**__A						).to_tuple()

													with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																	SCREAMING_SNAKE_CASE__								=					encode_jitted(**__A						).to_tuple()

													self.assertEqual(len(__A						)							, len(__A						)						)
													for jitted_output, output in zip(__A							, __A						):
															self.assertEqual(jitted_output.shape							, output.shape						)


							def    _snake_case     (							self			:Tuple						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
													SCREAMING_SNAKE_CASE__								=					model_class(__A						)
													SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]							, inputs_dict["""attention_mask"""]						)

													SCREAMING_SNAKE_CASE__								=					{
													    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
													    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
													    """encoder_outputs""": encoder_outputs,
													}

													@jax.jit
													def decode_jitted(__A			:Union[str, Any]							, __A			:Optional[Any]							, __A			:List[Any]						):
															return model.decode(
															    decoder_input_ids=__A							, decoder_attention_mask=__A							, encoder_outputs=__A							, )

													with self.subTest("""JIT Enabled"""						):
															SCREAMING_SNAKE_CASE__								=					decode_jitted(**__A						).to_tuple()

													with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																	SCREAMING_SNAKE_CASE__								=					decode_jitted(**__A						).to_tuple()

													self.assertEqual(len(__A						)							, len(__A						)						)
													for jitted_output, output in zip(__A							, __A						):
															self.assertEqual(jitted_output.shape							, output.shape						)


							@slow
							def    _snake_case     (							self			:Optional[Any]						)      ->      str:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								=					model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""						)
											# FlaxMarianForSequenceClassification expects eos token in input_ids
											SCREAMING_SNAKE_CASE__								=					np.ones((1, 1)						) * model.config.eos_token_id
											SCREAMING_SNAKE_CASE__								=					model(__A						)
											self.assertIsNotNone(__A						)




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   unittest.TestCase			):
							lowerCamelCase_							=    None
							lowerCamelCase_							=    None


							@classmethod
							def    _snake_case     (							cls			:int						)      ->      None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
									return cls


							@cached_property
							def    _snake_case     (							self			:Any						)      ->      int:



									"""simple docstring"""



									return MarianTokenizer.from_pretrained(self.model_name						)


							@property
							def    _snake_case     (							self			:Optional[int]						)      ->      int:



									"""simple docstring"""



									return self.tokenizer.eos_token_id


							@cached_property
							def    _snake_case     (							self			:Optional[int]						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					FlaxMarianMTModel.from_pretrained(self.model_name						)
									self.assertEqual(model.config.decoder_start_token_id							, model.config.pad_token_id						)
									return model


							def    _snake_case     (							self			:Optional[int]							, **__A			:int						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.translate_src_text(**__A						)
									self.assertListEqual(self.expected_text							, __A						)


							def    _snake_case     (							self			:Union[str, Any]							, **__A			:List[Any]						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tokenizer(self.src_text							, padding=__A							, return_tensors="""np"""							, **__A						)
									SCREAMING_SNAKE_CASE__								=					self.model.generate(
									    model_inputs.input_ids							, attention_mask=model_inputs.attention_mask							, num_beams=2							, max_length=128							, ).sequences
									SCREAMING_SNAKE_CASE__								=					self.tokenizer.batch_decode(__A							, skip_special_tokens=__A						)
									return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    [
							    "I am a small frog.",
							    "Now I can forget the 100 words of german that I know.",
							]
							lowerCamelCase_							=    [
							    "Je suis une petite grenouille.",
							    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
							]


							@slow
							def    _snake_case     (							self			:str						)      ->      Optional[Any]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    [
							    "Donnez moi le micro.",
							    "Tom et Mary étaient assis à une table.",  # Accents
							]
							lowerCamelCase_							=    [
							    "Give me the microphone.",
							    "Tom and Mary were sitting at a table.",
							]


							@slow
							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "mt"
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
							lowerCamelCase_							=    ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


							@slow
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "de"
							lowerCamelCase_							=    [
							    "I am a small frog.",
							    "Now I can forget the 100 words of german that I know.",
							    "Tom asked his teacher for advice.",
							    "That's how I would do it.",
							    "Tom really admired Mary's courage.",
							    "Turn around and close your eyes.",
							]
							lowerCamelCase_							=    [
							    "Ich bin ein kleiner Frosch.",
							    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
							    "Tom bat seinen Lehrer um Rat.",
							    "So würde ich das machen.",
							    "Tom bewunderte Marias Mut wirklich.",
							    "Drehen Sie sich um und schließen Sie die Augen.",
							]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "zh"
							lowerCamelCase_							=    ["My name is Wolfgang and I live in Berlin"]
							lowerCamelCase_							=    ["我叫沃尔夫冈 我住在柏林"]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "ru"
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    ["Он показал мне рукопись своей новой пьесы."]
							lowerCamelCase_							=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "ROMANCE"
							lowerCamelCase_							=    [
							    ">>fr<< Don't spend so much time watching TV.",
							    ">>pt<< Your message has been sent.",
							    ">>es<< He's two years older than me.",
							]
							lowerCamelCase_							=    [
							    "Ne passez pas autant de temps à regarder la télé.",
							    "A sua mensagem foi enviada.",
							    "Es dos años más viejo que yo.",
							]


							@slow
							def    _snake_case     (							self			:str						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()






'''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    =			'''\
@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
_SCREAMING_SNAKE_CASE    =			'''\
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
_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 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				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			: List[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   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							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"
							__lowercase   =							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   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							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   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							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"],
													}





