






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


lowerCamelCase      :       Dict    =						logging.get_logger(__name__)

lowerCamelCase      :       List[Any]    =						{'''vocab_file''': '''vocab.txt'''}

lowerCamelCase      :       Optional[int]    =						{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

lowerCamelCase      :       List[str]    =						{
    '''facebook/esm2_t6_8M_UR50D''': 1024,
    '''facebook/esm2_t12_35M_UR50D''': 1024,
}





def    __lowerCAmelCase   (    __snake_case							):
     with open(__snake_case       ,			"r"							) as f:
          __lowerCAmelCase             =       f.read().splitlines()
          return [l.strip() for l in lines]



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				["""input_ids""", """attention_mask"""]


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase="<unk>"       ,    __UpperCamelCase="<cls>"       ,    __UpperCamelCase="<pad>"       ,    __UpperCamelCase="<mask>"       ,    __UpperCamelCase="<eos>"       ,    **__UpperCamelCase       ,    )->						Tuple:
           super().__init__(**__UpperCamelCase						)
           __lowerCAmelCase             =       load_vocab_file(__UpperCamelCase						)
           __lowerCAmelCase             =       dict(enumerate(self.all_tokens						)						)
           __lowerCAmelCase             =       {tok: ind for ind, tok in enumerate(self.all_tokens						)}
           __lowerCAmelCase             =       unk_token
           __lowerCAmelCase             =       cls_token
           __lowerCAmelCase             =       pad_token
           __lowerCAmelCase             =       mask_token
           __lowerCAmelCase             =       eos_token
           __lowerCAmelCase             =       self.all_tokens
           self._create_trie(self.unique_no_split_tokens						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						str:
           return self._id_to_token.get(__UpperCamelCase       ,    self.unk_token						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						int:
           return self._token_to_id.get(__UpperCamelCase       ,    self._token_to_id.get(self.unk_token						)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Tuple:
           return text.split()


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=False						)->						Dict:
           return len(self._id_to_token						)


      def 			__UpperCAmelCase		(						self						)->						int:
           return {token: i for i, token in enumerate(self.all_tokens						)}


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						int:
           return self._token_to_id.get(__UpperCamelCase       ,    self._token_to_id.get(self.unk_token						)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						str:
           return self._id_to_token.get(__UpperCamelCase       ,    self.unk_token						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None						)->						List[int]:
           __lowerCAmelCase             =       [self.cls_token_id]
           __lowerCAmelCase             =       [self.eos_token_id]  # No sep token in ESM vocabulary
           if token_ids_a is None:
                if self.eos_token_id is None:
                     return cls + token_ids_a
                else:
                     return cls + token_ids_a + sep
           elif self.eos_token_id is None:
                raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"						)
           return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False						)->						List[int]:
           if already_has_special_tokens:
                if token_ids_a is not None:
                     raise ValueError(
                         "You should not supply a second sequence if the provided sequence of "
                         "ids is already formatted with special tokens for the model."						)

                return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
           __lowerCAmelCase             =       [1] + ([0] * len(__UpperCamelCase						)) + [1]
           if token_ids_a is not None:
                mask += [0] * len(__UpperCamelCase						) + [1]
           return mask


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Union[str, Any]:
           __lowerCAmelCase             =       os.path.join(__UpperCamelCase       ,    (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"						)
           with open(__UpperCamelCase       ,    "w"						) as f:
                f.write("\n".join(self.all_tokens						)						)
           return (vocab_file,)


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return self.get_vocab_size(with_added_tokens=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = False						)->						int:
           return super()._add_tokens(__UpperCamelCase       ,    special_tokens=__UpperCamelCase						)














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 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

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

           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

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

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       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"       ,    )






