

'''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 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[str]:
      UpperCAmelCase	:     int						 =			'laion/clap-htsat-unfused'
      UpperCAmelCase	:     Optional[int]						 =			tempfile.mkdtemp()


     def        UpperCAmelCase_	(		self			:					Dict					,      **lowercase_			:					str	)						->     Dict:
      return RobertaTokenizer.from_pretrained(self.checkpoint					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      **lowercase_			:					Union[str, Any]	)						->     List[Any]:
      return ClapFeatureExtractor.from_pretrained(self.checkpoint					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[str]:
      shutil.rmtree(self.tmpdirname	)


     def        UpperCAmelCase_	(		self			:					int	)						->     List[str]:
      UpperCAmelCase	:     Dict						 =			self.get_tokenizer()
      UpperCAmelCase	:     List[Any]						 =			self.get_feature_extractor()

      UpperCAmelCase	:     List[Any]						 =			ClapProcessor(tokenizer=lowercase_					,      feature_extractor=lowercase_	)

      processor.save_pretrained(self.tmpdirname	)
      UpperCAmelCase	:     Dict						 =			ClapProcessor.from_pretrained(self.tmpdirname	)

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

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


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Dict:
      UpperCAmelCase	:     Tuple						 =			ClapProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()	)
      processor.save_pretrained(self.tmpdirname	)

      UpperCAmelCase	:     str						 =			self.get_tokenizer(bos_token='(BOS)'					,      eos_token='(EOS)'	)
      UpperCAmelCase	:     int						 =			self.get_feature_extractor(do_normalize=lowercase_					,      padding_value=1.0	)

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

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

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


     def        UpperCAmelCase_	(		self			:					Dict	)						->     List[Any]:
      UpperCAmelCase	:     List[str]						 =			self.get_feature_extractor()
      UpperCAmelCase	:     str						 =			self.get_tokenizer()

      UpperCAmelCase	:     List[Any]						 =			ClapProcessor(tokenizer=lowercase_					,      feature_extractor=lowercase_	)

      UpperCAmelCase	:     List[str]						 =			floats_list((3, 1_000)	)

      UpperCAmelCase	:     List[str]						 =			feature_extractor(lowercase_					,      return_tensors='np'	)
      UpperCAmelCase	:     Optional[Any]						 =			processor(audios=lowercase_					,      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        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      UpperCAmelCase	:     Union[str, Any]						 =			self.get_feature_extractor()
      UpperCAmelCase	:     int						 =			self.get_tokenizer()

      UpperCAmelCase	:     Tuple						 =			ClapProcessor(tokenizer=lowercase_					,      feature_extractor=lowercase_	)

      UpperCAmelCase	:     Tuple						 =			'This is a test string'

      UpperCAmelCase	:     List[Any]						 =			processor(text=lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			tokenizer(lowercase_	)

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


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
      UpperCAmelCase	:     Dict						 =			self.get_feature_extractor()
      UpperCAmelCase	:     Dict						 =			self.get_tokenizer()

      UpperCAmelCase	:     Tuple						 =			ClapProcessor(tokenizer=lowercase_					,      feature_extractor=lowercase_	)

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

      UpperCAmelCase	:     Optional[Any]						 =			processor.batch_decode(lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			tokenizer.batch_decode(lowercase_	)

      self.assertListEqual(lowercase_					,      lowercase_	)




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Dict:
      UpperCAmelCase	:     Tuple						 =			self.get_feature_extractor()
      UpperCAmelCase	:     List[Any]						 =			self.get_tokenizer()

      UpperCAmelCase	:     str						 =			ClapProcessor(tokenizer=lowercase_					,      feature_extractor=lowercase_	)

      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_torch_available, is_vision_available


lowercase__         =      {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "VanForImageClassification",
     "VanModel",
     "VanPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 lowercase__         =      _LazyModule(__name__, globals()["__file__"], _import_structure)




