



'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


A_							: Tuple						 =	logging.get_logger(__name__)



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """upernet"""







 def __init__(							self  ,a_=None  ,a_=512  ,a_=0.02  ,a_=[1, 2, 3, 6]  ,a_=True  ,a_=0.4  ,a_=384  ,a_=256  ,a_=1  ,a_=False  ,a_=255  ,**a_  ,)		->   Any:
        super().__init__(**a_							)

        if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               _UpperCAmelCase  :						int			     = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							)
        elif isinstance(a_  ,a_							):
               _UpperCAmelCase  :						List[Any]			     = backbone_config.get("""model_type"""							)
               _UpperCAmelCase  :						Union[str, Any]			     = CONFIG_MAPPING[backbone_model_type]
               _UpperCAmelCase  :						Union[str, Any]			     = config_class.from_dict(a_							)

        _UpperCAmelCase  :						List[str]			     = backbone_config
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = pool_scales
        _UpperCAmelCase  :						str			     = use_auxiliary_head
        _UpperCAmelCase  :						Any			     = auxiliary_loss_weight
        _UpperCAmelCase  :						List[str]			     = auxiliary_in_channels
        _UpperCAmelCase  :						List[str]			     = auxiliary_channels
        _UpperCAmelCase  :						List[Any]			     = auxiliary_num_convs
        _UpperCAmelCase  :						Optional[Any]			     = auxiliary_concat_input
        _UpperCAmelCase  :						str			     = loss_ignore_index







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Union[str, Any]			     = copy.deepcopy(self.__dict__							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.backbone_config.to_dict()
        _UpperCAmelCase  :						Optional[Any]			     = self.__class__.model_type
        return output








'''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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """laion/clap-htsat-unfused"""
        _UpperCAmelCase  :						int			     = tempfile.mkdtemp()







 def _snake_case (							self  ,**a_							)		->   str:
        return RobertaTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = self.get_tokenizer()
        _UpperCAmelCase  :						Any			     = self.get_feature_extractor()

        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = 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 _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor(do_normalize=a_  ,padding_value=1.0							)

        _UpperCAmelCase  :						Optional[Any]			     = 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 _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()

        _UpperCAmelCase  :						str			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Tuple			     = floats_list((3, 1_000)							)

        _UpperCAmelCase  :						int			     = feature_extractor(a_  ,return_tensors="""np"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = 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 _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Any			     = self.get_tokenizer()

        _UpperCAmelCase  :						Optional[int]			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test string"""

        _UpperCAmelCase  :						Optional[Any]			     = processor(text=a_							)

        _UpperCAmelCase  :						Any			     = tokenizer(a_							)

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







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = self.get_feature_extractor()
        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Any			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						List[str]			     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase  :						Dict			     = processor.batch_decode(a_							)
        _UpperCAmelCase  :						Any			     = tokenizer.batch_decode(a_							)

        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[str]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						int			     = self.get_tokenizer()

        _UpperCAmelCase  :						Dict			     = 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"""  ,)



