




"""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:
					A   =       None


A   =       logging.get_logger(__name__)

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

A   =       {
    """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""",
    },
}

A   =       {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

A   =       """▁"""

# Segments (not really needed)
A   =       0
A   =       1
A   =       2
A   =       3
A   =       4



class      a__				(	__magic_name__   ):
			lowercase_    =	VOCAB_FILES_NAMES
			lowercase_    =	PRETRAINED_VOCAB_FILES_MAP
			lowercase_    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase_    =	"left"
			lowercase_    =	XLNetTokenizer


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    int=None      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Optional[Any]=True      ,  UpperCamelCase_      :    Any=False      ,  UpperCamelCase_      :    Optional[int]="<s>"      ,  UpperCamelCase_      :    Union[str, Any]="</s>"      ,  UpperCamelCase_      :    Union[str, Any]="<unk>"      ,  UpperCamelCase_      :    Optional[Any]="<sep>"      ,  UpperCamelCase_      :    List[Any]="<pad>"      ,  UpperCamelCase_      :    Union[str, Any]="<cls>"      ,  UpperCamelCase_      :    int="<mask>"      ,  UpperCamelCase_      :    Optional[Any]=["<eop>", "<eod>"]      ,  **UpperCamelCase_      :    List[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					AddedToken(UpperCamelCase_      ,  lstrip=UpperCamelCase_      ,  rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_      ,  UpperCamelCase_) else mask_token

						super().__init__(
						    vocab_file=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_      ,  additional_special_tokens=UpperCamelCase_      ,  **UpperCamelCase_      ,  )

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


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					[self.sep_token_id]
						__UpperCAmelCase	: List[str]		=					[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       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					[self.sep_token_id]
						__UpperCAmelCase	: str		=					[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       a_     (      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
						__UpperCAmelCase	: int		=					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,)












"""simple docstring"""





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      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					"ylacombe/bark-small"
						__UpperCAmelCase	: Any		=					tempfile.mkdtemp()
						__UpperCAmelCase	: List[str]		=					"en_speaker_1"
						__UpperCAmelCase	: List[Any]		=					"This is a test string"
						__UpperCAmelCase	: List[str]		=					"speaker_embeddings_path.json"
						__UpperCAmelCase	: Tuple		=					"speaker_embeddings"


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return AutoTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: 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      ,  )

						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[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       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "semantic_prompt": np.ones(UpperCamelCase_),
						    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
						    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
						}

						# test providing already loaded voice_preset
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: str		=					inputs["history_prompt"]
						for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()      ,  processed_voice_preset.get(UpperCamelCase_      ,  np.array([])).tolist())

						# test loading voice preset from npz file
						__UpperCAmelCase	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					inputs["history_prompt"]

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

						# test loading voice preset from the hub
						__UpperCAmelCase	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					tokenizer(
						    self.input_string      ,  padding="max_length"      ,  max_length=256      ,  add_special_tokens=UpperCamelCase_      ,  return_attention_mask=UpperCamelCase_      ,  return_token_type_ids=UpperCamelCase_      ,  )

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


