"""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 			UpperCamelCase__(	unittest.TestCase       ):





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





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





						def  snake_case__		(				self						,**__UpperCAmelCase   )    -> Tuple:
													return ClapFeatureExtractor.from_pretrained(self.checkpoint						,**__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													shutil.rmtree(self.tmpdirname   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.get_tokenizer()
													A__		   =							self.get_feature_extractor()

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

													processor.save_pretrained(self.tmpdirname   )
													A__		   =							ClapProcessor.from_pretrained(self.tmpdirname   )

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

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





						def  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=__UpperCAmelCase						,padding_value=1.0   )

													A__		   =							ClapProcessor.from_pretrained(
													    self.tmpdirname						,bos_token='(BOS)'						,eos_token='(EOS)'						,do_normalize=__UpperCAmelCase						,padding_value=1.0   )

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

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





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.get_feature_extractor()
													A__		   =							self.get_tokenizer()

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

													A__		   =							floats_list((3, 10_00)   )

													A__		   =							feature_extractor(__UpperCAmelCase						,return_tensors='np'   )
													A__		   =							processor(audios=__UpperCAmelCase						,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  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.get_feature_extractor()
													A__		   =							self.get_tokenizer()

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

													A__		   =							'This is a test string'

													A__		   =							processor(text=__UpperCAmelCase   )

													A__		   =							tokenizer(__UpperCAmelCase   )

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





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.get_feature_extractor()
													A__		   =							self.get_tokenizer()

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

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

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

													self.assertListEqual(__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							self.get_feature_extractor()
													A__		   =							self.get_tokenizer()

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

													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


__lowerCamelCase							 =     {
    "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:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "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

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





