

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						A__						=							None

A__						=							logging.get_logger(__name__)
A__						=							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A__						=							{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

A__						=							{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

A__						=							"▁"

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  VOCAB_FILES_NAMES
	_lowercase  :   str       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   Dict       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase  :   Dict       				=  AlbertTokenizer







	def __init__(   self: List[str]  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: Optional[Any]=None  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: str=False  , __UpperCamelCase: Dict="[CLS]"  , __UpperCamelCase: List[str]="[SEP]"  , __UpperCamelCase: int="<unk>"  , __UpperCamelCase: int="[SEP]"  , __UpperCamelCase: List[str]="<pad>"  , __UpperCamelCase: Optional[int]="[CLS]"  , __UpperCamelCase: Union[str, Any]="[MASK]"  , **__UpperCamelCase: int  , ):

						'''simple docstring'''





						__magic_name__							=    (
						    AddedToken(__UpperCamelCase  , lstrip=__UpperCamelCase  , rstrip=__UpperCamelCase  , normalized=__UpperCamelCase      )
						    if isinstance(__UpperCamelCase  , __UpperCamelCase      )
						    else mask_token
						)

						super().__init__(
						    __UpperCamelCase  , tokenizer_file=__UpperCamelCase  , do_lower_case=__UpperCamelCase  , remove_space=__UpperCamelCase  , keep_accents=__UpperCamelCase  , bos_token=__UpperCamelCase  , eos_token=__UpperCamelCase  , unk_token=__UpperCamelCase  , sep_token=__UpperCamelCase  , pad_token=__UpperCamelCase  , cls_token=__UpperCamelCase  , mask_token=__UpperCamelCase  , **__UpperCamelCase  , )

						__magic_name__							=    do_lower_case
						__magic_name__							=    remove_space
						__magic_name__							=    keep_accents
						__magic_name__							=    vocab_file
						__magic_name__							=    False if not self.vocab_file else True







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None      ):

						'''simple docstring'''





						__magic_name__							=    [self.sep_token_id]
						__magic_name__							=    [self.cls_token_id]
						if token_ids_a is None:
											return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None      ):

						'''simple docstring'''





						__magic_name__							=    [self.sep_token_id]
						__magic_name__							=    [self.cls_token_id]

						if token_ids_a is None:
											return len(cls + token_ids_a + sep      ) * [0]
						return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[str] = None      ):

						'''simple docstring'''





						if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'      )

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

						if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCamelCase      ):
											copyfile(self.vocab_file  , __UpperCamelCase      )

						return (out_vocab_file,)







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 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'laion/clap-htsat-unfused'
						__magic_name__							=    tempfile.mkdtemp()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_tokenizer()
						__magic_name__							=    self.get_feature_extractor()

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

						processor.save_pretrained(self.tmpdirname      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





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

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

						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

						__magic_name__							=    floats_list((3, 10_00)      )

						__magic_name__							=    feature_extractor(__UpperCamelCase  , return_tensors='np'      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

						__magic_name__							=    'This is a test string'

						__magic_name__							=    processor(text=__UpperCamelCase      )

						__magic_name__							=    tokenizer(__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

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

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

						self.assertListEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    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'  , )




