





'''simple docstring'''



import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_SCREAMING_SNAKE_CASE    =			get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							tempfile.mkdtemp()

				__lowercase   =							['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
				__lowercase   =							dict(zip(_lowerCamelCase   ,range(len(_lowerCamelCase       )       )       )       )
				__lowercase   =							Path(self.tmpdirname       )
				save_json(_lowerCamelCase   ,save_dir / VOCAB_FILES_NAMES['''vocab_file''']       )
				if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
							copyfile(_lowerCamelCase   ,save_dir / VOCAB_FILES_NAMES['''spm_file''']       )

				__lowercase   =							SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
				tokenizer.save_pretrained(self.tmpdirname       )

				__lowercase   =							{
				    '''feature_size''': 24,
				    '''num_mel_bins''': 24,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}
				save_json(_lowerCamelCase   ,save_dir / FEATURE_EXTRACTOR_NAME       )


	def   _UpperCAmelCase   (self   ,**_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,**_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,**_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,**_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				shutil.rmtree(self.tmpdirname       )


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				__lowercase   =							self.get_tokenizer()
				__lowercase   =							self.get_feature_extractor()

				__lowercase   =							SpeechaTextProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				processor.save_pretrained(self.tmpdirname       )
				__lowercase   =							SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

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

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


	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							SpeechaTextProcessor(
				    tokenizer=self.get_tokenizer()   ,feature_extractor=self.get_feature_extractor()       )
				processor.save_pretrained(self.tmpdirname       )

				__lowercase   =							self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''       )
				__lowercase   =							self.get_feature_extractor(do_normalize=_lowerCamelCase   ,padding_value=1.0       )

				__lowercase   =							SpeechaTextProcessor.from_pretrained(
				    self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=_lowerCamelCase   ,padding_value=1.0       )

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

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


	def   _UpperCAmelCase   (self       )							->							Any:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechaTextProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							floats_list((3, 1000)       )

				__lowercase   =							feature_extractor(_lowerCamelCase   ,return_tensors='''np'''       )
				__lowercase   =							processor(_lowerCamelCase   ,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   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechaTextProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							'''This is a test string'''

				__lowercase   =							processor(text=_lowerCamelCase       )

				__lowercase   =							tokenizer(_lowerCamelCase       )

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


	def   _UpperCAmelCase   (self       )							->							Any:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechaTextProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				__lowercase   =							processor.batch_decode(_lowerCamelCase       )
				__lowercase   =							tokenizer.batch_decode(_lowerCamelCase       )

				self.assertListEqual(_lowerCamelCase   ,_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechaTextProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

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












'''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"],
													}





