






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__(    self			)							->				str:
							__magic_name__      :       List[str]       						= """ylacombe/bark-small"""
							__magic_name__      :       int       						= tempfile.mkdtemp()
							__magic_name__      :       Dict       						= """en_speaker_1"""
							__magic_name__      :       int       						= """This is a test string"""
							__magic_name__      :       int       						= """speaker_embeddings_path.json"""
							__magic_name__      :       Optional[int]       						= """speaker_embeddings"""
	def       __magic_name__(    self      ,       **lowerCAmelCase__			)							->				Optional[Any]:
							return AutoTokenizer.from_pretrained(self.checkpoint      ,       **lowerCAmelCase__			)
	def       __magic_name__(    self			)							->				Optional[int]:
							shutil.rmtree(self.tmpdirname			)
	def       __magic_name__(    self			)							->				Optional[int]:
							__magic_name__      :       Dict       						= self.get_tokenizer()

							__magic_name__      :       int       						= BarkProcessor(tokenizer=lowerCAmelCase__			)

							processor.save_pretrained(self.tmpdirname			)
							__magic_name__      :       int       						= BarkProcessor.from_pretrained(self.tmpdirname			)

							self.assertEqual(processor.tokenizer.get_vocab()      ,       tokenizer.get_vocab()			)
	@slow
	def       __magic_name__(    self			)							->				str:
							__magic_name__      :       str       						= BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint      ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,       )
							processor.save_pretrained(
							    self.tmpdirname      ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,       speaker_embeddings_directory=self.speaker_embeddings_directory      ,       )

							__magic_name__      :       str       						= self.get_tokenizer(bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""			)

							__magic_name__      :       Union[str, Any]       						= BarkProcessor.from_pretrained(
							    self.tmpdirname      ,       self.speaker_embeddings_dict_path      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       )

							self.assertEqual(processor.tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab()			)
	def       __magic_name__(    self			)							->				int:
							__magic_name__      :       List[Any]       						= BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint      ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,       )

							__magic_name__      :       Optional[Any]       						= 35
							__magic_name__      :       Dict       						= 2
							__magic_name__      :       Tuple       						= 8

							__magic_name__      :       List[Any]       						= {
							    """semantic_prompt""": np.ones(lowerCAmelCase__			),
							    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)			),
							    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)			),
							}

							# test providing already loaded voice_preset
							__magic_name__      :       str       						= processor(text=self.input_string      ,       voice_preset=lowerCAmelCase__			)

							__magic_name__      :       Any       						= inputs["""history_prompt"""]
							for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()      ,       processed_voice_preset.get(lowerCAmelCase__      ,       np.array([]			)			).tolist()			)

							# test loading voice preset from npz file
							__magic_name__      :       Tuple       						= os.path.join(self.tmpdirname      ,       """file.npz"""			)
							np.savez(lowerCAmelCase__      ,       **lowerCAmelCase__			)
							__magic_name__      :       str       						= processor(text=self.input_string      ,       voice_preset=lowerCAmelCase__			)
							__magic_name__      :       Dict       						= inputs["""history_prompt"""]

							for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()      ,       processed_voice_preset.get(lowerCAmelCase__      ,       np.array([]			)			).tolist()			)

							# test loading voice preset from the hub
							__magic_name__      :       Optional[int]       						= processor(text=self.input_string      ,       voice_preset=self.voice_preset			)







	def       __magic_name__(    self			)							->				List[str]:
							__magic_name__      :       Optional[int]       						= self.get_tokenizer()

							__magic_name__      :       Union[str, Any]       						= BarkProcessor(tokenizer=lowerCAmelCase__			)

							__magic_name__      :       Any       						= processor(text=self.input_string			)

							__magic_name__      :       Dict       						= tokenizer(
							    self.input_string      ,       padding="""max_length"""      ,       max_length=2_56      ,       add_special_tokens=lowerCAmelCase__      ,       return_attention_mask=lowerCAmelCase__      ,       return_token_type_ids=lowerCAmelCase__      ,       )

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








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:
		__magic_name__:					Any							=     None


__magic_name__:					Dict							=     logging.get_logger(__name__)

__magic_name__:					Any							=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__magic_name__:					str							=     {
    "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",
    },
}

__magic_name__:					Optional[Any]							=     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

__magic_name__:					Optional[Any]							=     "▁"

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





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Dict											=   VOCAB_FILES_NAMES
	lowercase__						:					Union[str, Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Tuple											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					List[str]											=   '''left'''
	lowercase__						:					List[str]											=   XLNetTokenizer
	def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<sep>"      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__="<cls>"      ,       lowerCAmelCase__="<mask>"      ,       lowerCAmelCase__=["<eop>", "<eod>"]      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							# Mask token behave like a normal word, i.e. include the space before it
							__magic_name__      :       Optional[int]       						= AddedToken(lowerCAmelCase__      ,       lstrip=lowerCAmelCase__      ,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			) else mask_token

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

							__magic_name__      :       List[str]       						= 3
							__magic_name__      :       str       						= do_lower_case
							__magic_name__      :       Union[str, Any]       						= remove_space
							__magic_name__      :       str       						= keep_accents
							__magic_name__      :       Tuple       						= vocab_file
							__magic_name__      :       List[Any]       						= False if not self.vocab_file else True
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       Any       						= [self.sep_token_id]
							__magic_name__      :       Any       						= [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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       List[str]       						= [self.sep_token_id]
							__magic_name__      :       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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							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(lowerCAmelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							__magic_name__      :       List[str]       						= os.path.join(
							    lowerCAmelCase__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

							return (out_vocab_file,)
