




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				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				"laion/clap-htsat-unfused"
          a     =				tempfile.mkdtemp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Optional[Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Any:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a     =				self.get_feature_extractor()

          a     =				ClapProcessor(tokenizer=A  ,		feature_extractor=A			)

          processor.save_pretrained(self.tmpdirname			)
          a     =				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       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				ClapProcessor(tokenizer=self.get_tokenizer()  ,		feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

          a     =				self.get_tokenizer(bos_token="(BOS)"  ,		eos_token="(EOS)"			)
          a     =				self.get_feature_extractor(do_normalize=A  ,		padding_value=1.0			)

          a     =				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       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.get_feature_extractor()
          a     =				self.get_tokenizer()

          a     =				ClapProcessor(tokenizer=A  ,		feature_extractor=A			)

          a     =				floats_list((3, 1000)			)

          a     =				feature_extractor(A  ,		return_tensors="np"			)
          a     =				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       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.get_feature_extractor()
          a     =				self.get_tokenizer()

          a     =				ClapProcessor(tokenizer=A  ,		feature_extractor=A			)

          a     =				"This is a test string"

          a     =				processor(text=A			)

          a     =				tokenizer(A			)

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.get_feature_extractor()
          a     =				self.get_tokenizer()

          a     =				ClapProcessor(tokenizer=A  ,		feature_extractor=A			)

          a     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          a     =				processor.batch_decode(A			)
          a     =				tokenizer.batch_decode(A			)

          self.assertListEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_feature_extractor()
          a     =				self.get_tokenizer()

          a     =				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"  ,		)







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__				:      int	   = logging.get_logger(__name__)


lowercase__				:      Dict	   = "▁"

lowercase__				:      Union[str, Any]	   = {"vocab_file": "spiece.model"}

lowercase__				:      Union[str, Any]	   = {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowercase__				:      Tuple	   = {
    "google/reformer-crime-and-punishment": 524_288,
}




class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     VOCAB_FILES_NAMES
     a		:      List[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      Dict		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="</s>"  ,		A="<unk>"  ,		A=[]  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A  ,		unk_token=A  ,		additional_special_tokens=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**A  ,		)

          a     =				vocab_file
          a     =				spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.sp_model.get_piece_size()







     def       lowerCAmelCase_    (     self			)  ->   Dict[str, int]:



          '''simple docstring'''



          a     =				{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab







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



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          return state







     def __setstate__(     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				d

          # for backward compatibility
          if not hasattr(self  ,		"sp_model_kwargs"			):
               a     =				{}

          a     =				spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(self.vocab_file			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          return self.sp_model.encode(A  ,		out_type=A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.piece_to_id(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          if index < self.sp_model.get_piece_size():
               a     =				self.sp_model.IdToPiece(A			)
          return token







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          a     =				""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(A			) + token
                    a     =				[]
               else:
                    current_sub_tokens.append(A			)
          out_string += self.sp_model.decode(A			)
          return out_string.strip()







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[str]:



          '''simple docstring'''



          if not os.path.isdir(A			):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
          a     =				os.path.join(
              A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

          if os.path.abspath(self.vocab_file			) != os.path.abspath(A			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file  ,		A			)
          elif not os.path.isfile(self.vocab_file			):
               with open(A  ,		"wb"			) as fi:
                    a     =				self.sp_model.serialized_model_proto()
                    fi.write(A			)

          return (out_vocab_file,)

