






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 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:List[Any]          =						"""laion/clap-htsat-unfused"""
            __snake_case			:Optional[Any]          =						tempfile.mkdtemp()



       def 					__lowercase						(							self			,						**a__    ) ->      List[str]:






            '''simple docstring'''





            return RobertaTokenizer.from_pretrained(self.checkpoint			,						**a__    )



       def 					__lowercase						(							self			,						**a__    ) ->      Optional[int]:






            '''simple docstring'''





            return ClapFeatureExtractor.from_pretrained(self.checkpoint			,						**a__    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            shutil.rmtree(self.tmpdirname    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						self.get_tokenizer()
            __snake_case			:Optional[Any]          =						self.get_feature_extractor()

            __snake_case			:Union[str, Any]          =						ClapProcessor(tokenizer=a__			,						feature_extractor=a__    )

            processor.save_pretrained(self.tmpdirname    )
            __snake_case			:Optional[int]          =						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 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						ClapProcessor(tokenizer=self.get_tokenizer()			,						feature_extractor=self.get_feature_extractor()    )
            processor.save_pretrained(self.tmpdirname    )

            __snake_case			:Optional[Any]          =						self.get_tokenizer(bos_token="""(BOS)"""			,						eos_token="""(EOS)"""    )
            __snake_case			:Dict          =						self.get_feature_extractor(do_normalize=a__			,						padding_value=1.0    )

            __snake_case			: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 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Any          =						self.get_feature_extractor()
            __snake_case			:List[Any]          =						self.get_tokenizer()

            __snake_case			:Any          =						ClapProcessor(tokenizer=a__			,						feature_extractor=a__    )

            __snake_case			:int          =						floats_list((3, 10_00)    )

            __snake_case			:Optional[int]          =						feature_extractor(a__			,						return_tensors="""np"""    )
            __snake_case			:List[str]          =						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 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.get_feature_extractor()
            __snake_case			:Optional[int]          =						self.get_tokenizer()

            __snake_case			:List[Any]          =						ClapProcessor(tokenizer=a__			,						feature_extractor=a__    )

            __snake_case			:Dict          =						"""This is a test string"""

            __snake_case			:List[Any]          =						processor(text=a__    )

            __snake_case			:Tuple          =						tokenizer(a__    )

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



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_feature_extractor()
            __snake_case			:Dict          =						self.get_tokenizer()

            __snake_case			:Union[str, Any]          =						ClapProcessor(tokenizer=a__			,						feature_extractor=a__    )

            __snake_case			:Union[str, Any]          =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case			:List[str]          =						processor.batch_decode(a__    )
            __snake_case			:List[str]          =						tokenizer.batch_decode(a__    )

            self.assertListEqual(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_feature_extractor()
            __snake_case			:Union[str, Any]          =						self.get_tokenizer()

            __snake_case			:Optional[Any]          =						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"""			,						)










from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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


