

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''semantic'''




 def __init__(			self  ,  _UpperCAmelCase=10_000  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=768  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=0.7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=10_048  ,  _UpperCAmelCase=129_595  ,  _UpperCAmelCase=129_599  ,  _UpperCAmelCase=10_000  ,  _UpperCAmelCase=256  ,  _UpperCAmelCase=4_9.9  ,  **_UpperCAmelCase  ,  ):
   super().__init__(
       temperature=_UpperCAmelCase  ,  do_sample=_UpperCAmelCase  ,  eos_token_id=_UpperCAmelCase  ,  renormalize_logits=_UpperCAmelCase  ,  max_new_tokens=_UpperCAmelCase  ,  output_scores=_UpperCAmelCase  ,  return_dict_in_generate=_UpperCAmelCase  ,  output_hidden_states=_UpperCAmelCase  ,  output_attentions=_UpperCAmelCase  ,  **_UpperCAmelCase  ,  )

   __UpperCAmelCase	:  List[Any]		=     text_encoding_offset
   __UpperCAmelCase	:  str		=     text_pad_token
   __UpperCAmelCase	:  Any		=     eos_token_id
   __UpperCAmelCase	:  Dict		=     semantic_infer_token
   __UpperCAmelCase	:  Tuple		=     semantic_vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     max_input_semantic_length
   __UpperCAmelCase	:  Optional[Any]		=     semantic_rate_hz



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''coarse_acoustics'''




 def __init__(			self  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=0.7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=12_048  ,  _UpperCAmelCase=75  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=12_050  ,  _UpperCAmelCase=256  ,  _UpperCAmelCase = 630  ,  _UpperCAmelCase = 60  ,  **_UpperCAmelCase  ,  ):
   super().__init__(
       temperature=_UpperCAmelCase  ,  do_sample=_UpperCAmelCase  ,  renormalize_logits=_UpperCAmelCase  ,  output_scores=_UpperCAmelCase  ,  return_dict_in_generate=_UpperCAmelCase  ,  output_hidden_states=_UpperCAmelCase  ,  output_attentions=_UpperCAmelCase  ,  **_UpperCAmelCase  ,  )

   __UpperCAmelCase	:  Optional[int]		=     coarse_semantic_pad_token
   __UpperCAmelCase	:  Dict		=     coarse_rate_hz
   __UpperCAmelCase	:  int		=     n_coarse_codebooks
   __UpperCAmelCase	:  str		=     coarse_infer_token
   __UpperCAmelCase	:  Union[str, Any]		=     max_coarse_input_length
   __UpperCAmelCase	:  List[str]		=     max_coarse_history
   __UpperCAmelCase	:  str		=     sliding_window_len



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''fine_acoustics'''




 def __init__(			self  ,  _UpperCAmelCase=0.5  ,  _UpperCAmelCase=512  ,  _UpperCAmelCase=1_024  ,  _UpperCAmelCase=8  ,  **_UpperCAmelCase  ,  ):
   super().__init__(temperature=_UpperCAmelCase							)

   __UpperCAmelCase	:  Union[str, Any]		=     max_fine_history_length
   __UpperCAmelCase	:  Optional[int]		=     max_fine_input_length
   __UpperCAmelCase	:  Tuple		=     n_fine_codebooks



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''bark'''
 _lowerCAmelCase									=	True




 def __init__(			self  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase=24_000  ,  _UpperCAmelCase=1_024  ,  **_UpperCAmelCase  ,  ):
   if semantic_config is None:
     __UpperCAmelCase	:  Union[str, Any]		=     {}
     logger.info('semantic_config is None. initializing the semantic model with default values.'							)

   if coarse_acoustics_config is None:
     __UpperCAmelCase	:  Tuple		=     {}
     logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'							)

   if fine_acoustics_config is None:
     __UpperCAmelCase	:  Union[str, Any]		=     {}
     logger.info('fine_acoustics_config is None. initializing the fine model with default values.'							)

   __UpperCAmelCase	:  List[str]		=     BarkSemanticGenerationConfig(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Tuple		=     BarkCoarseGenerationConfig(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Tuple		=     BarkFineGenerationConfig(**_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     sample_rate
   __UpperCAmelCase	:  int		=     codebook_size




 @classmethod
 def     a       (			cls  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  **_UpperCAmelCase  ,  ):
   return cls(
       semantic_config=semantic_config.to_dict()  ,  coarse_acoustics_config=coarse_acoustics_config.to_dict()  ,  fine_acoustics_config=fine_acoustics_config.to_dict()  ,  **_UpperCAmelCase  ,  )




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     copy.deepcopy(self.__dict__							)

   __UpperCAmelCase	:  int		=     self.semantic_config.to_dict()
   __UpperCAmelCase	:  Union[str, Any]		=     self.coarse_acoustics_config.to_dict()
   __UpperCAmelCase	:  List[Any]		=     self.fine_acoustics_config.to_dict()

   __UpperCAmelCase	:  List[str]		=     self.__class__.model_type
   return output








import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_ (			_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	SpeechaTextaTokenizer
 _lowerCAmelCase									=	False




 def     a       (			self							):
   super().setUp()

   __UpperCAmelCase	:  List[Any]		=     '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '							)
   __UpperCAmelCase	:  Dict		=     ['he re</w> 123', 'here a 1456']
   __UpperCAmelCase	:  Optional[int]		=     dict(zip(_UpperCAmelCase  ,  range(len(_UpperCAmelCase							)							)							)							)

   __UpperCAmelCase	:  List[Any]		=     {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

   __UpperCAmelCase	:  Tuple		=     tempfile.mkdtemp()
   __UpperCAmelCase	:  str		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']							)
   __UpperCAmelCase	:  List[Any]		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['merges_file']							)

   with open(self.vocab_file  ,  'w'  ,  encoding='utf-8'							) as fp:
     fp.write(json.dumps(_UpperCAmelCase							) + '\n'							)

   with open(self.merges_file  ,  'w'							) as fp:
     fp.write('\n'.join(_UpperCAmelCase							)							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,  '<s>'							)
   self.assertEqual(vocab_keys[1]  ,  '<pad>'							)
   self.assertEqual(vocab_keys[-1]  ,  'vocab'							)
   self.assertEqual(len(_UpperCAmelCase							)  ,  14							)




 def     a       (			self							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,  14							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   # make sure @@ is correctly concatenated
   __UpperCAmelCase	:  int		=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
   __UpperCAmelCase	:  List[str]		=     tokenizer.decode(_UpperCAmelCase							)

   self.assertTrue(output_string == 'herecouple words ofthe'							)




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     tokenizer.save_pretrained(_UpperCAmelCase							)
     os.remove(os.path.join(_UpperCAmelCase  ,  'merges.txt'							)							)

     # load tokenizer without merges file should not throw an error
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     # save tokenizer and load again
     tokenizer.save_pretrained(_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   self.assertIsNotNone(_UpperCAmelCase							)




 def     a       (			self							):
   if not self.test_slow_tokenizer:
     return

   __UpperCAmelCase	:  List[Any]		=     inspect.signature(self.tokenizer_class.__init__							)
   __UpperCAmelCase	:  List[Any]		=     self.get_tokenizer()

   for parameter_name, parameter in signature.parameters.items():
     if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
       self.assertIn(_UpperCAmelCase  ,  tokenizer.init_kwargs							)





