


"""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 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				'''laion/clap-htsat-unfused'''
              lowercase		:Optional[int]       							=				tempfile.mkdtemp()






       def  __snake_case						(   self  :    Any  ,     **snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Union[str, Any]  ,     **snake_case__  :    int				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              shutil.rmtree(self.tmpdirname				)






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				self.get_tokenizer()
              lowercase		:Dict       							=				self.get_feature_extractor()

              lowercase		:Optional[int]       							=				ClapProcessor(tokenizer=snake_case__  ,     feature_extractor=snake_case__				)

              processor.save_pretrained(self.tmpdirname				)
              lowercase		:List[Any]       							=				ClapProcessor.from_pretrained(self.tmpdirname				)

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

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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Any       							=				ClapProcessor(tokenizer=self.get_tokenizer()  ,     feature_extractor=self.get_feature_extractor()				)
              processor.save_pretrained(self.tmpdirname				)

              lowercase		:Tuple       							=				self.get_tokenizer(bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''				)
              lowercase		:List[Any]       							=				self.get_feature_extractor(do_normalize=snake_case__  ,     padding_value=1.0				)

              lowercase		:Dict       							=				ClapProcessor.from_pretrained(
                  self.tmpdirname  ,     bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''  ,     do_normalize=snake_case__  ,     padding_value=1.0				)

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

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






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






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

              lowercase		:List[str]       							=				ClapProcessor(tokenizer=snake_case__  ,     feature_extractor=snake_case__				)

              lowercase		:Dict       							=				floats_list((3, 1_0_0_0)				)

              lowercase		:List[Any]       							=				feature_extractor(snake_case__  ,     return_tensors='''np'''				)
              lowercase		:str       							=				processor(audios=snake_case__  ,     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  :    List[str]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.get_feature_extractor()
              lowercase		:Any       							=				self.get_tokenizer()

              lowercase		:List[Any]       							=				ClapProcessor(tokenizer=snake_case__  ,     feature_extractor=snake_case__				)

              lowercase		:List[str]       							=				'''This is a test string'''

              lowercase		:List[Any]       							=				processor(text=snake_case__				)

              lowercase		:List[str]       							=				tokenizer(snake_case__				)

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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.get_feature_extractor()
              lowercase		:Tuple       							=				self.get_tokenizer()

              lowercase		:Union[str, Any]       							=				ClapProcessor(tokenizer=snake_case__  ,     feature_extractor=snake_case__				)

              lowercase		:Any       							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              lowercase		:Any       							=				processor.batch_decode(snake_case__				)
              lowercase		:Union[str, Any]       							=				tokenizer.batch_decode(snake_case__				)

              self.assertListEqual(snake_case__  ,     snake_case__				)







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.get_feature_extractor()
              lowercase		:Optional[Any]       							=				self.get_tokenizer()

              lowercase		:Any       							=				ClapProcessor(tokenizer=snake_case__  ,     feature_extractor=snake_case__				)

              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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase      		=			{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LayoutLMv3ForQuestionAnswering''',
     '''LayoutLMv3ForSequenceClassification''',
     '''LayoutLMv3ForTokenClassification''',
     '''LayoutLMv3Model''',
     '''LayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFLayoutLMv3ForQuestionAnswering''',
     '''TFLayoutLMv3ForSequenceClassification''',
     '''TFLayoutLMv3ForTokenClassification''',
     '''TFLayoutLMv3Model''',
     '''TFLayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3FeatureExtractor''']
 UpperCAmelCase      		=			['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

