





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			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   "laion/clap-htsat-unfused"
       UpperCamelCase	:					Union[str, Any]       	=   tempfile.mkdtemp()





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.get_tokenizer()
       UpperCamelCase	:					int       	=   self.get_feature_extractor()

       UpperCamelCase	:					Optional[int]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       processor.save_pretrained(self.tmpdirname   )
       UpperCamelCase	:					List[str]       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   ClapProcessor(tokenizer=self.get_tokenizer()       ,							feature_extractor=self.get_feature_extractor()   )
       processor.save_pretrained(self.tmpdirname   )

       UpperCamelCase	:					int       	=   self.get_tokenizer(bos_token="(BOS)"       ,							eos_token="(EOS)"   )
       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor(do_normalize=A_       ,							padding_value=1.0   )

       UpperCamelCase	:					Any       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor()
       UpperCamelCase	:					List[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					Tuple       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					Optional[Any]       	=   floats_list((3, 1000)   )

       UpperCamelCase	:					Any       	=   feature_extractor(A_       ,							return_tensors="np"   )
       UpperCamelCase	:					Union[str, 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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.get_feature_extractor()
       UpperCamelCase	:					Optional[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					List[Any]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					List[Any]       	=   "This is a test string"

       UpperCamelCase	:					List[Any]       	=   processor(text=A_   )

       UpperCamelCase	:					List[str]       	=   tokenizer(A_   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor()
       UpperCamelCase	:					List[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					Optional[int]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					Dict       	=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase	:					Optional[Any]       	=   processor.batch_decode(A_   )
       UpperCamelCase	:					List[str]       	=   tokenizer.batch_decode(A_   )

       self.assertListEqual(A_       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.get_feature_extractor()
       UpperCamelCase	:					Dict       	=   self.get_tokenizer()

       UpperCamelCase	:					Union[str, Any]       	=   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"       ,							)





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Union[str, Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      int     		=	get_activation('''gelu'''  )
						self.assertTrue(torch.allclose(gelu_python(__lowercase  )   ,torch_builtin(__lowercase  )  )  )
						self.assertFalse(torch.allclose(gelu_python(__lowercase  )   ,gelu_new(__lowercase  )  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      Union[str, Any]     		=	get_activation('''gelu'''  )
						snake_case__	:      int     		=	get_activation('''gelu_10'''  )

						snake_case__	:      Optional[int]     		=	torch_builtin(__lowercase  )
						snake_case__	:      str     		=	geluaa(__lowercase  )

						snake_case__	:      Tuple     		=	torch.where(y_gelu_aa < 10.0   ,1   ,0  )

						self.assertTrue(torch.max(__lowercase  ).item() == 10.0  )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,y_gelu_aa * clipped_mask  )  )



		def 	__lowerCamelCase			(     self     :Any  ):
						get_activation('''gelu'''  )
						get_activation('''gelu_10'''  )
						get_activation('''gelu_fast'''  )
						get_activation('''gelu_new'''  )
						get_activation('''gelu_python'''  )
						get_activation('''gelu_pytorch_tanh'''  )
						get_activation('''linear'''  )
						get_activation('''mish'''  )
						get_activation('''quick_gelu'''  )
						get_activation('''relu'''  )
						get_activation('''sigmoid'''  )
						get_activation('''silu'''  )
						get_activation('''swish'''  )
						get_activation('''tanh'''  )
						with self.assertRaises(__lowercase  ):
										get_activation('''bogus'''  )
						with self.assertRaises(__lowercase  ):
										get_activation(__lowercase  )






		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	get_activation('''gelu'''  )
						snake_case__	:      List[Any]     		=	1
						snake_case__	:      Optional[Any]     		=	get_activation('''gelu'''  )
						self.assertEqual(acta.a   ,1  )
						with self.assertRaises(__lowercase  ):
										snake_case__	:      str     		=	acta.a

