






'''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  _snake_case (					unittest.TestCase       ):

					"""simple docstring"""





					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Optional[int]:
										a_						= 'laion/clap-htsat-unfused'
										a_						= tempfile.mkdtemp()




					def 			__SCREAMING_SNAKE_CASE			(  self ,			**UpperCAmelCase__  )   ->						List[str]:
										return RobertaTokenizer.from_pretrained(self.checkpoint ,			**__lowerCAmelCase  )




					def 			__SCREAMING_SNAKE_CASE			(  self ,			**UpperCAmelCase__  )   ->						Optional[Any]:
										return ClapFeatureExtractor.from_pretrained(self.checkpoint ,			**__lowerCAmelCase  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Tuple:
										shutil.rmtree(self.tmpdirname  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						str:
										a_						= self.get_tokenizer()
										a_						= self.get_feature_extractor()

										a_						= ClapProcessor(tokenizer=__lowerCAmelCase ,			feature_extractor=__lowerCAmelCase  )

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

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

										a_						= 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  )   ->						str:
										a_						= self.get_feature_extractor()
										a_						= self.get_tokenizer()

										a_						= ClapProcessor(tokenizer=__lowerCAmelCase ,			feature_extractor=__lowerCAmelCase  )

										a_						= floats_list((3, 1000)  )

										a_						= feature_extractor(__lowerCAmelCase ,			return_tensors='np'  )
										a_						= 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  )   ->						Tuple:
										a_						= self.get_feature_extractor()
										a_						= self.get_tokenizer()

										a_						= ClapProcessor(tokenizer=__lowerCAmelCase ,			feature_extractor=__lowerCAmelCase  )

										a_						= 'This is a test string'

										a_						= processor(text=__lowerCAmelCase  )

										a_						= tokenizer(__lowerCAmelCase  )

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




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						List[str]:
										a_						= self.get_feature_extractor()
										a_						= self.get_tokenizer()

										a_						= ClapProcessor(tokenizer=__lowerCAmelCase ,			feature_extractor=__lowerCAmelCase  )

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

										a_						= processor.batch_decode(__lowerCAmelCase  )
										a_						= tokenizer.batch_decode(__lowerCAmelCase  )

										self.assertListEqual(__lowerCAmelCase ,			__lowerCAmelCase  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						int:
										a_						= self.get_feature_extractor()
										a_						= self.get_tokenizer()

										a_						= 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'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase        =    {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    ['''ChineseCLIPFeatureExtractor''']
						__lowercase        =    ['''ChineseCLIPImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    [
						    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ChineseCLIPModel''',
						    '''ChineseCLIPPreTrainedModel''',
						    '''ChineseCLIPTextModel''',
						    '''ChineseCLIPVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_chinese_clip import (
						    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ChineseCLIPConfig,
						    ChineseCLIPOnnxConfig,
						    ChineseCLIPTextConfig,
						    ChineseCLIPVisionConfig,
						)
						from .processing_chinese_clip import ChineseCLIPProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_chinese_clip import (
												    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ChineseCLIPModel,
												    ChineseCLIPPreTrainedModel,
												    ChineseCLIPTextModel,
												    ChineseCLIPVisionModel,
												)

else:
						import sys

						__lowercase        =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




