




'''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    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					'ylacombe/bark-small'
     __a			=					tempfile.mkdtemp()
     __a			=					'en_speaker_1'
     __a			=					'This is a test string'
     __a			=					'speaker_embeddings_path.json'
     __a			=					'speaker_embeddings'







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    Union[str, Any]:
     return AutoTokenizer.from_pretrained(self.checkpoint      ,							**__lowerCAmelCase      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     shutil.rmtree(self.tmpdirname      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					self.get_tokenizer()

     __a			=					BarkProcessor(tokenizer=__lowerCAmelCase      )

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

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







   @slow
   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					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      ,							)

     __a			=					self.get_tokenizer(bos_token='(BOS)'      ,							eos_token='(EOS)'      )

     __a			=					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 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					BarkProcessor.from_pretrained(
         pretrained_processor_name_or_path=self.checkpoint      ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,							)

     __a			=					35
     __a			=					2
     __a			=					8

     __a			=					{
         '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
     __a			=					processor(text=self.input_string      ,							voice_preset=__lowerCAmelCase      )

     __a			=					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
     __a			=					os.path.join(self.tmpdirname      ,							'file.npz'      )
     np.savez(__lowerCAmelCase      ,							**__lowerCAmelCase      )
     __a			=					processor(text=self.input_string      ,							voice_preset=__lowerCAmelCase      )
     __a			=					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
     __a			=					processor(text=self.input_string      ,							voice_preset=self.voice_preset      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					self.get_tokenizer()

     __a			=					BarkProcessor(tokenizer=__lowerCAmelCase      )

     __a			=					processor(text=self.input_string      )

     __a			=					tokenizer(
         self.input_string      ,							padding='max_length'      ,							max_length=256      ,							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()      )









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase_ 		=      {"mobilebert-uncased": 5_12}


UpperCAmelCase_ 		=      {}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      MobileBertTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    List[Any]:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         normalizer_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or normalizer_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'      ,							UpperCamelCase      ) != tokenize_chinese_chars
     ):
       __a			=					getattr(UpperCamelCase      ,							normalizer_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					tokenize_chinese_chars
       __a			=					normalizer_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )



