




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{'vocab_file': 'sentencepiece.model'}

UpperCamelCase								=	{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCamelCase								=	{
    'google/rembert': 256,
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = VOCAB_FILES_NAMES
    snake_case__   = PRETRAINED_VOCAB_FILES_MAP
    snake_case__   = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


    def __init__(			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=False  ,      SCREAMING_SNAKE_CASE__	:	Tuple=True  ,      SCREAMING_SNAKE_CASE__	:	Tuple=True  ,      SCREAMING_SNAKE_CASE__	:	Any="[CLS]"  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]="[SEP]"  ,      SCREAMING_SNAKE_CASE__	:	Dict="[UNK]"  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]="[SEP]"  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]="[PAD]"  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]="[CLS]"  ,      SCREAMING_SNAKE_CASE__	:	List[Any]="[MASK]"  ,      **SCREAMING_SNAKE_CASE__	:	str  ,      ) ->			Dict:
         super().__init__(
             do_lower_case=SCREAMING_SNAKE_CASE__  ,      remove_space=SCREAMING_SNAKE_CASE__  ,      keep_accents=SCREAMING_SNAKE_CASE__  ,      bos_token=SCREAMING_SNAKE_CASE__  ,      eos_token=SCREAMING_SNAKE_CASE__  ,      unk_token=SCREAMING_SNAKE_CASE__  ,      sep_token=SCREAMING_SNAKE_CASE__  ,      pad_token=SCREAMING_SNAKE_CASE__  ,      cls_token=SCREAMING_SNAKE_CASE__  ,      mask_token=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )

         lowerCAmelCase__            =					do_lower_case
         lowerCAmelCase__            =					remove_space
         lowerCAmelCase__            =					keep_accents
         lowerCAmelCase__            =					vocab_file

         lowerCAmelCase__            =					spm.SentencePieceProcessor()
         self.sp_model.Load(SCREAMING_SNAKE_CASE__  )


    @property
    def   a (			self	:	int  ) ->			Union[str, Any]:
         return len(self.sp_model  )


    def   a (			self	:	Any  ) ->			str:
         lowerCAmelCase__            =					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab


    def __getstate__(			self	:	Union[str, Any]  ) ->			List[str]:
         lowerCAmelCase__            =					self.__dict__.copy()
         lowerCAmelCase__            =					None
         return state


    def __setstate__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					d
         lowerCAmelCase__            =					spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file  )


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	int=False  ) ->			Optional[int]:
         lowerCAmelCase__            =					self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__  )
         return pieces


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			List[Any]:
         return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			Dict:
         return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			int:
         lowerCAmelCase__            =					self.sp_model.decode_pieces(SCREAMING_SNAKE_CASE__  )
         return out_string


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ) ->			List[int]:
         lowerCAmelCase__            =					[self.sep_token_id]
         lowerCAmelCase__            =					[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   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = 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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
              return [1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1]


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ) ->			List[int]:
         lowerCAmelCase__            =					[self.sep_token_id]
         lowerCAmelCase__            =					[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   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[str] = None  ) ->			Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
              logger.error("Vocabulary path ({}) should be a directory".format(SCREAMING_SNAKE_CASE__  )  )
              return
         lowerCAmelCase__            =					os.path.join(
             SCREAMING_SNAKE_CASE__  ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

         return (out_vocab_file,)




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				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)
							lowercase__	     =						BarkProcessor.from_pretrained(self.tmpdirname						)

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

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						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 ,   )

							lowercase__	     =						self.get_tokenizer(bos_token="""(BOS)""" ,   eos_token="""(EOS)"""						)

							lowercase__	     =						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     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """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
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

							lowercase__	     =						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
							lowercase__	     =						os.path.join(self.tmpdirname ,   """file.npz"""						)
							np.savez(_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)
							lowercase__	     =						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
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=self.voice_preset						)


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						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()						)

