





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class       __A					(					unittest.TestCase				):

   '''simple docstring'''







   @slow
   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''							)
    lowerCamelCase__              =	AutoTokenizer.from_pretrained('''xlm-roberta-base'''							)
    lowerCamelCase__              =	'''The dog is cute and lives in the garden house'''
    lowerCamelCase__              =	jnp.array([tokenizer.encode(A_							)]							)

    lowerCamelCase__              =	(1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
    lowerCamelCase__              =	jnp.array(
        [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)

    lowerCamelCase__              =	model(A_							)['''last_hidden_state''']
    self.assertEqual(output.shape ,						A_							)
    # compare the actual values for a slice of last dim
    self.assertTrue(jnp.allclose(output[:, :, -1] ,						A_ ,						atol=1E-3							)							)










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				if text is None and audios is None:
					raise ValueError('''You have to specify either text or audios. Both cannot be none.'''							)

				if text is not None:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if audios is not None:
					lowerCamelCase__              =	self.feature_extractor(
					    __lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if text is not None and audios is not None:
					lowerCamelCase__              =	audio_features.input_features
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**__lowerCAmelCase							) ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



