
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		):




							def 			A__				(    self):
												lowercase						     =						'''laion/clap-htsat-unfused'''
												lowercase						     =						tempfile.mkdtemp()




							def 			A__				(    self       ,**A__):
												return RobertaTokenizer.from_pretrained(self.checkpoint       ,**A__)




							def 			A__				(    self       ,**A__):
												return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,**A__)




							def 			A__				(    self):
												shutil.rmtree(self.tmpdirname)




							def 			A__				(    self):
												lowercase						     =						self.get_tokenizer()
												lowercase						     =						self.get_feature_extractor()

												lowercase						     =						ClapProcessor(tokenizer=A__       ,feature_extractor=A__)

												processor.save_pretrained(self.tmpdirname)
												lowercase						     =						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 			A__				(    self):
												lowercase						     =						ClapProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor())
												processor.save_pretrained(self.tmpdirname)

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

												lowercase						     =						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 			A__				(    self):
												lowercase						     =						self.get_feature_extractor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						ClapProcessor(tokenizer=A__       ,feature_extractor=A__)

												lowercase						     =						floats_list((3, 1_0_0_0))

												lowercase						     =						feature_extractor(A__       ,return_tensors='''np''')
												lowercase						     =						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 			A__				(    self):
												lowercase						     =						self.get_feature_extractor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						ClapProcessor(tokenizer=A__       ,feature_extractor=A__)

												lowercase						     =						'''This is a test string'''

												lowercase						     =						processor(text=A__)

												lowercase						     =						tokenizer(A__)

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




							def 			A__				(    self):
												lowercase						     =						self.get_feature_extractor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						ClapProcessor(tokenizer=A__       ,feature_extractor=A__)

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

												lowercase						     =						processor.batch_decode(A__)
												lowercase						     =						tokenizer.batch_decode(A__)

												self.assertListEqual(A__       ,A__)





							def 			A__				(    self):
												lowercase						     =						self.get_feature_extractor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						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'''       ,)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




