


'''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 _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Optional[Any]:
			__magic_name__	:   List[Any]					     =					'laion/clap-htsat-unfused'
			__magic_name__	:   int					     =					tempfile.mkdtemp()







		def        __lowerCAmelCase		( self		:						Tuple  ,					**_A		:						str      )  ->			List[Any]:
			return RobertaTokenizer.from_pretrained(self.checkpoint  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					**_A		:						List[Any]      )  ->			Any:
			return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			int:
			shutil.rmtree(self.tmpdirname      )







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Tuple:
			__magic_name__	:   str					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_feature_extractor()

			__magic_name__	:   List[str]					     =					ClapProcessor(tokenizer=_A  ,					feature_extractor=_A      )

			processor.save_pretrained(self.tmpdirname      )
			__magic_name__	:   Tuple					     =					ClapProcessor.from_pretrained(self.tmpdirname      )

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

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







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   str					     =					ClapProcessor(tokenizer=self.get_tokenizer()  ,					feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__magic_name__	:   List[Any]					     =					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)'      )
			__magic_name__	:   Tuple					     =					self.get_feature_extractor(do_normalize=_A  ,					padding_value=1.0      )

			__magic_name__	:   Dict					     =					ClapProcessor.from_pretrained(
			    self.tmpdirname  ,					bos_token='(BOS)'  ,					eos_token='(EOS)'  ,					do_normalize=_A  ,					padding_value=1.0      )

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

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







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Dict					     =					self.get_feature_extractor()
			__magic_name__	:   List[str]					     =					self.get_tokenizer()

			__magic_name__	:   List[Any]					     =					ClapProcessor(tokenizer=_A  ,					feature_extractor=_A      )

			__magic_name__	:   List[Any]					     =					floats_list((3, 1000)      )

			__magic_name__	:   Dict					     =					feature_extractor(_A  ,					return_tensors='np'      )
			__magic_name__	:   List[Any]					     =					processor(audios=_A  ,					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		:						Optional[Any]      )  ->			Tuple:
			__magic_name__	:   List[str]					     =					self.get_feature_extractor()
			__magic_name__	:   Tuple					     =					self.get_tokenizer()

			__magic_name__	:   int					     =					ClapProcessor(tokenizer=_A  ,					feature_extractor=_A      )

			__magic_name__	:   Any					     =					'This is a test string'

			__magic_name__	:   str					     =					processor(text=_A      )

			__magic_name__	:   Optional[int]					     =					tokenizer(_A      )

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







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Dict:
			__magic_name__	:   str					     =					self.get_feature_extractor()
			__magic_name__	:   Optional[Any]					     =					self.get_tokenizer()

			__magic_name__	:   List[str]					     =					ClapProcessor(tokenizer=_A  ,					feature_extractor=_A      )

			__magic_name__	:   List[Any]					     =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__magic_name__	:   List[Any]					     =					processor.batch_decode(_A      )
			__magic_name__	:   Dict					     =					tokenizer.batch_decode(_A      )

			self.assertListEqual(_A  ,					_A      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Any:
			__magic_name__	:   Dict					     =					self.get_feature_extractor()
			__magic_name__	:   Union[str, Any]					     =					self.get_tokenizer()

			__magic_name__	:   int					     =					ClapProcessor(tokenizer=_A  ,					feature_extractor=_A      )

			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'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}