






'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase__						:int          =			{
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:str          =			["""Mask2FormerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Union[str, Any]          =			[
       """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Mask2FormerForUniversalSegmentation""",
       """Mask2FormerModel""",
       """Mask2FormerPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:List[str]          =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)











'''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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : str					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]	,    **A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : str	,    **A__      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            shutil.rmtree(self.tmpdirname					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_tokenizer()
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_feature_extractor()

            __lowerCamelCase						:       Dict				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            processor.save_pretrained(self.tmpdirname					)
            __lowerCamelCase						:       Optional[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 					a_					(					self      : str					):


            """simple docstring"""







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

            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""					)
            __lowerCamelCase						:       Any				   =				self.get_feature_extractor(do_normalize=A__	,    padding_value=1.0					)

            __lowerCamelCase						:       List[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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







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

            __lowerCamelCase						:       Optional[int]				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Dict				   =				floats_list((3, 1000)					)

            __lowerCamelCase						:       str				   =				feature_extractor(A__	,    return_tensors="""np"""					)
            __lowerCamelCase						:       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 					a_					(					self      : Any					):


            """simple docstring"""







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

            __lowerCamelCase						:       str				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       int				   =				"""This is a test string"""

            __lowerCamelCase						:       Any				   =				processor(text=A__					)

            __lowerCamelCase						:       Any				   =				tokenizer(A__					)

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[str]				   =				self.get_tokenizer()

            __lowerCamelCase						:       int				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

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

            __lowerCamelCase						:       int				   =				processor.batch_decode(A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.batch_decode(A__					)

            self.assertListEqual(A__	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







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

            __lowerCamelCase						:       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"""	,    )



