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     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Any    						=	'''laion/clap-htsat-unfused'''
        __UpperCamelCase  :Any    						=	tempfile.mkdtemp()







    def     UpperCamelCase__     (			self				,				**__lowercase) ->			List[Any]:
        return RobertaTokenizer.from_pretrained(self.checkpoint				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				**__lowercase) ->			List[str]:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint				,				**__lowercase)







    def     UpperCamelCase__     (			self) ->			str:
        shutil.rmtree(self.tmpdirname)







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :List[Any]    						=	self.get_tokenizer()
        __UpperCamelCase  :List[Any]    						=	self.get_feature_extractor()

        __UpperCamelCase  :List[Any]    						=	ClapProcessor(tokenizer=__lowercase				,				feature_extractor=__lowercase)

        processor.save_pretrained(self.tmpdirname)
        __UpperCamelCase  :int    						=	ClapProcessor.from_pretrained(self.tmpdirname)

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

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







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :int    						=	ClapProcessor(tokenizer=self.get_tokenizer()				,				feature_extractor=self.get_feature_extractor())
        processor.save_pretrained(self.tmpdirname)

        __UpperCamelCase  :Optional[int]    						=	self.get_tokenizer(bos_token='''(BOS)'''				,				eos_token='''(EOS)''')
        __UpperCamelCase  :Tuple    						=	self.get_feature_extractor(do_normalize=__lowercase				,				padding_value=1.0)

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

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

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







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	self.get_feature_extractor()
        __UpperCamelCase  :Dict    						=	self.get_tokenizer()

        __UpperCamelCase  :Tuple    						=	ClapProcessor(tokenizer=__lowercase				,				feature_extractor=__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	floats_list((3, 1_000))

        __UpperCamelCase  :Optional[Any]    						=	feature_extractor(__lowercase				,				return_tensors='''np''')
        __UpperCamelCase  :int    						=	processor(audios=__lowercase				,				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) ->			int:
        __UpperCamelCase  :List[str]    						=	self.get_feature_extractor()
        __UpperCamelCase  :str    						=	self.get_tokenizer()

        __UpperCamelCase  :Dict    						=	ClapProcessor(tokenizer=__lowercase				,				feature_extractor=__lowercase)

        __UpperCamelCase  :Dict    						=	'''This is a test string'''

        __UpperCamelCase  :Tuple    						=	processor(text=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	tokenizer(__lowercase)

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







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	self.get_feature_extractor()
        __UpperCamelCase  :int    						=	self.get_tokenizer()

        __UpperCamelCase  :Dict    						=	ClapProcessor(tokenizer=__lowercase				,				feature_extractor=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCamelCase  :List[Any]    						=	processor.batch_decode(__lowercase)
        __UpperCamelCase  :int    						=	tokenizer.batch_decode(__lowercase)

        self.assertListEqual(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	self.get_feature_extractor()
        __UpperCamelCase  :Tuple    						=	self.get_tokenizer()

        __UpperCamelCase  :List[str]    						=	ClapProcessor(tokenizer=__lowercase				,				feature_extractor=__lowercase)

        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_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





