






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_		:		int			= '\\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_		:		Dict			= '\\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_		:		int			= ''

# TODO: Add the licence for the dataset here if you can find it
A_		:		List[Any]			= ''

# 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_		:		Optional[Any]			= {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}




class 				_lowerCAmelCase(       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				:		int									=[
           datasets.BuilderConfig(name='''first_domain'''							,	version=__SCREAMING_SNAKE_CASE							,	description='''This part of my dataset covers a first domain'''					),
           datasets.BuilderConfig(name='''second_domain'''							,	version=__SCREAMING_SNAKE_CASE							,	description='''This part of my dataset covers a second domain'''					),
       ]

       a				:		List[Any]									='''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.





       def 			_a    (					self							):







         if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
           UpperCamelCase_:					Union[str, Any]          =				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_:					Tuple          =				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 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					Optional[Any]          =				_URLS[self.config.name]
         UpperCamelCase_:					Optional[Any]          =				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 			_a    (					self  ,			_lowerCamelCase  ,			_lowerCamelCase							):







         with open(UpperCamelCase__  ,			encoding='utf-8'							) as f:
           for key, row in enumerate(UpperCamelCase__							):
             UpperCamelCase_:					Tuple          =				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 json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		os.path.join(self.tmpdirname   ,     UpperCamelCase__	)
				with open(self.feature_extraction_file   ,     '''w'''   ,     encoding='''utf-8'''	) as fp:
						fp.write(json.dumps(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_tokenizer()
				snake_case   :    List[str]			       =		self.get_feature_extractor()

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		SpeechTaProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		SpeechTaProcessor.from_pretrained(
				    self.tmpdirname   ,     bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''   ,     do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    List[str]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		processor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		processor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    Union[str, Any]			       =		self.get_tokenizer()

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[int]			       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    Tuple			       =		self.get_tokenizer()

				snake_case   :    List[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				self.assertListEqual(
				    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg='''`processor` and `feature_extractor` model input names do not match'''   ,     )


