


'''simple docstring'''



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_xlnet import XLNetTokenizer
else:
	__lowercase     :						Optional[int]	=     None


__lowercase     :						Any	=     logging.get_logger(__name__)

__lowercase     :						str	=     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowercase     :						List[Any]	=     {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

__lowercase     :						List[Any]	=     {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

__lowercase     :						str	=     '▁'

# Segments (not really needed)
__lowercase     :						Dict	=     0
__lowercase     :						Optional[int]	=     1
__lowercase     :						Any	=     2
__lowercase     :						List[str]	=     3
__lowercase     :						int	=     4





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   VOCAB_FILES_NAMES
	A_           =   PRETRAINED_VOCAB_FILES_MAP
	A_           =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_           =   "left"
	A_           =   XLNetTokenizer




	def __init__(       self						,    __a=None						,    __a=None						,    __a=False						,    __a=True						,    __a=False						,    __a="<s>"						,    __a="</s>"						,    __a="<unk>"						,    __a="<sep>"						,    __a="<pad>"						,    __a="<cls>"						,    __a="<mask>"						,    __a=["<eop>", "<eod>"]						,    **__a						,    ):

						'''simple docstring'''





						__a      :	str    =  AddedToken(__a						,    lstrip=__a						,    rstrip=__a							) if isinstance(__a						,    __a							) else mask_token

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

						__a      :	Any    =  3
						__a      :	Optional[int]    =  do_lower_case
						__a      :	Optional[int]    =  remove_space
						__a      :	Optional[Any]    =  keep_accents
						__a      :	Union[str, Any]    =  vocab_file
						__a      :	Tuple    =  False if not self.vocab_file else True




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	Dict    =  [self.sep_token_id]
						__a      :	Optional[int]    =  [self.cls_token_id]
						if token_ids_a is None:
											return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	List[str]    =  [self.sep_token_id]
						__a      :	Optional[Any]    =  [2]

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





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = 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(__a							):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
											return
						__a      :	Union[str, Any]    =  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							):
											copyfile(self.vocab_file						,    __a							)

						return (out_vocab_file,)







'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

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

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	int    =  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'						,    )



