

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        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Dict		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   """laion/clap-htsat-unfused"""
        SCREAMING_SNAKE_CASE :      Optional[int]       =   tempfile.mkdtemp()


  def  _lowercase      (			self    : Optional[int]				,				**UpperCAmelCase__    : Any		)					->str:
        """simple docstring"""







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


  def  _lowercase      (			self    : int				,				**UpperCAmelCase__    : List[Any]		)					->int:
        """simple docstring"""







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


  def  _lowercase      (			self    : int		)					->Optional[int]:
        """simple docstring"""







        shutil.rmtree(self.tmpdirname		)


  def  _lowercase      (			self    : List[Any]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_tokenizer()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_feature_extractor()

        SCREAMING_SNAKE_CASE :      Dict       =   ClapProcessor(tokenizer=UpperCAmelCase__				,				feature_extractor=UpperCAmelCase__		)

        processor.save_pretrained(self.tmpdirname		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   ClapProcessor(tokenizer=self.get_tokenizer()				,				feature_extractor=self.get_feature_extractor()		)
        processor.save_pretrained(self.tmpdirname		)

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

        SCREAMING_SNAKE_CASE :      Tuple       =   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  _lowercase      (			self    : Any		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.get_feature_extractor()
        SCREAMING_SNAKE_CASE :      int       =   self.get_tokenizer()

        SCREAMING_SNAKE_CASE :      Dict       =   ClapProcessor(tokenizer=UpperCAmelCase__				,				feature_extractor=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[Any]       =   floats_list((3, 1_0_0_0)		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   feature_extractor(UpperCAmelCase__				,				return_tensors="""np"""		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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  _lowercase      (			self    : Optional[int]		)					->str:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      List[str]       =   ClapProcessor(tokenizer=UpperCAmelCase__				,				feature_extractor=UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      Optional[int]       =   processor(text=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   tokenizer(UpperCAmelCase__		)

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


  def  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_feature_extractor()
        SCREAMING_SNAKE_CASE :      Dict       =   self.get_tokenizer()

        SCREAMING_SNAKE_CASE :      str       =   ClapProcessor(tokenizer=UpperCAmelCase__				,				feature_extractor=UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      List[Any]       =   processor.batch_decode(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      int       =   tokenizer.batch_decode(UpperCAmelCase__		)

        self.assertListEqual(UpperCAmelCase__				,				UpperCAmelCase__		)




  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      List[Any]       =   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"""				,				)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

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




