


'''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 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      """laion/clap-htsat-unfused"""
    UpperCAmelCase__	:Union[str, Any]      =      tempfile.mkdtemp()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       **__lowerCamelCase							:	str ):
    return RobertaTokenizer.from_pretrained(self.checkpoint			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       **__lowerCamelCase							:	Tuple ):
    return ClapFeatureExtractor.from_pretrained(self.checkpoint			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    UpperCAmelCase__	:Tuple      =      self.get_tokenizer()
    UpperCAmelCase__	:Dict      =      self.get_feature_extractor()

    UpperCAmelCase__	:Union[str, Any]      =      ClapProcessor(tokenizer=__lowerCamelCase			,       feature_extractor=__lowerCamelCase )

    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:List[Any]      =      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 ):
    UpperCAmelCase__	:Any      =      ClapProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor() )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:Any      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:int      =      self.get_feature_extractor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:List[Any]      =      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							:	Dict ):
    UpperCAmelCase__	:List[str]      =      self.get_feature_extractor()
    UpperCAmelCase__	:Tuple      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      ClapProcessor(tokenizer=__lowerCamelCase			,       feature_extractor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      floats_list((3, 1_0_0_0) )

    UpperCAmelCase__	:int      =      feature_extractor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:str      =      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							:	Optional[Any] ):
    UpperCAmelCase__	:Dict      =      self.get_feature_extractor()
    UpperCAmelCase__	:Tuple      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      ClapProcessor(tokenizer=__lowerCamelCase			,       feature_extractor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      """This is a test string"""

    UpperCAmelCase__	:List[str]      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      tokenizer(__lowerCamelCase )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Tuple      =      self.get_feature_extractor()
    UpperCAmelCase__	:Union[str, Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:Optional[Any]      =      ClapProcessor(tokenizer=__lowerCamelCase			,       feature_extractor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Any      =      processor.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:Union[str, Any]      =      tokenizer.batch_decode(__lowerCamelCase )

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )





 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:Dict      =      self.get_feature_extractor()
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:str      =      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"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
