


'''simple docstring'''
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= '''laion/clap-htsat-unfused'''
							UpperCamelCase__   :Optional[int]												= tempfile.mkdtemp()




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return RobertaTokenizer.from_pretrained(self.checkpoint    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.get_tokenizer()
							UpperCamelCase__   :Tuple												= self.get_feature_extractor()

							UpperCamelCase__   :Union[str, Any]												= ClapProcessor(tokenizer=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_							)

							processor.save_pretrained(self.tmpdirname							)
							UpperCamelCase__   :Optional[int]												= ClapProcessor.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							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= ClapProcessor(tokenizer=self.get_tokenizer()    ,   feature_extractor=self.get_feature_extractor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__   :Union[str, Any]												= self.get_tokenizer(bos_token='''(BOS)'''    ,   eos_token='''(EOS)'''							)
							UpperCamelCase__   :Union[str, Any]												= self.get_feature_extractor(do_normalize=UpperCamelCase_    ,   padding_value=1.0							)

							UpperCamelCase__   :int												= ClapProcessor.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							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.get_feature_extractor()
							UpperCamelCase__   :int												= self.get_tokenizer()

							UpperCamelCase__   :List[Any]												= ClapProcessor(tokenizer=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_							)

							UpperCamelCase__   :Any												= floats_list((3, 1000)							)

							UpperCamelCase__   :Union[str, Any]												= feature_extractor(UpperCamelCase_    ,   return_tensors='''np'''							)
							UpperCamelCase__   :Dict												= processor(audios=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							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.get_feature_extractor()
							UpperCamelCase__   :Optional[int]												= self.get_tokenizer()

							UpperCamelCase__   :Tuple												= ClapProcessor(tokenizer=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= '''This is a test string'''

							UpperCamelCase__   :int												= processor(text=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= tokenizer(UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :int												= ClapProcessor(tokenizer=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_							)

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

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

							self.assertListEqual(UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Tuple												= ClapProcessor(tokenizer=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_							)

							self.assertListEqual(
							    processor.model_input_names[2:]    ,   feature_extractor.model_input_names    ,   msg='''`processor` and `feature_extractor` model input names do not match'''    ,   )



'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )