


'''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 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      '''laion/clap-htsat-unfused'''
														lowerCAmelCase__			  =      tempfile.mkdtemp()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return RobertaTokenizer.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														shutil.rmtree(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_feature_extractor()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														processor.save_pretrained(self.tmpdirname  )
														lowerCAmelCase__			  =      ClapProcessor.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     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      ClapProcessor(tokenizer=self.get_tokenizer()   ,       feature_extractor=self.get_feature_extractor()  )
														processor.save_pretrained(self.tmpdirname  )

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

														lowerCAmelCase__			  =      ClapProcessor.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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      floats_list((3, 10_00)  )

														lowerCAmelCase__			  =      feature_extractor(lowerCamelCase_   ,       return_tensors='''np'''  )
														lowerCAmelCase__			  =      processor(audios=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     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''This is a test string'''

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

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

														lowerCAmelCase__			  =      processor.batch_decode(lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.batch_decode(lowerCamelCase_  )

														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														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 sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase       							=				'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def    _snake_case  (	A					,       A=None			)		->       Optional[Any]:
							require_version(deps[pkg]					,       A			)