



import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


__snake_case	:				List[str]  					=			logging.get_logger(__name__)


__snake_case	:				Optional[int]  					=			{
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

__snake_case	:				Union[str, Any]  					=			'\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="bark_module"
     _lowerCamelCase					: List[str]				   =["past_key_values"]

     _lowerCamelCase					: Tuple				   ={
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
         "vocab_size": "input_vocab_size",
         "window_size": "block_size",
     }





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							Any=1_0_2_4	,      _lowerCamelCase :							Tuple=1_0_0_4_8	,      _lowerCamelCase :							Union[str, Any]=1_0_0_4_8	,      _lowerCamelCase :							Any=1_2	,      _lowerCamelCase :							Dict=1_2	,      _lowerCamelCase :							List[Any]=7_6_8	,      _lowerCamelCase :							Optional[Any]=0.0	,      _lowerCamelCase :							Tuple=True	,      _lowerCamelCase :							List[Any]=0.02	,      _lowerCamelCase :							int=True	,      **_lowerCamelCase :							Tuple	,      ):
          A__				=   block_size
          A__				=   input_vocab_size
          A__				=   output_vocab_size
          A__				=   num_layers
          A__				=   num_heads
          A__				=   hidden_size
          A__				=   dropout
          A__				=   bias
          A__				=   use_cache
          A__				=   initializer_range

          super().__init__(**_lowerCamelCase )





     @classmethod
     def 						A__  (					cls :							Tuple	,      _lowerCamelCase :							Union[str, os.PathLike]	,      _lowerCamelCase :							Optional[Union[str, os.PathLike]] = None	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[Union[str, bool]] = None	,      _lowerCamelCase :							str = "main"	,      **_lowerCamelCase :							Tuple	,      ):
          A__				=   cache_dir
          A__				=   force_download
          A__				=   local_files_only
          A__				=   revision

          cls._set_token_in_kwargs(_lowerCamelCase	,      _lowerCamelCase )

          A__	,     A__				=   cls.get_config_dict(_lowerCamelCase	,      **_lowerCamelCase )

          # get the config dict if we are loading from Bark
          if config_dict.get('''model_type''' ) == "bark":
               A__				=   config_dict[F'''{cls.model_type}_config''']

          if "model_type" in config_dict and hasattr(cls	,      '''model_type''' ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

          return cls.from_dict(_lowerCamelCase	,      **_lowerCamelCase )


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"      , model="BarkSemanticModel"				)      , "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="semantic"


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"      , model="BarkCoarseModel"				)      , "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: str				   ="coarse_acoustics"


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"      , model="BarkFineModel"				)      , "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Optional[Any]				   ="fine_acoustics"





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							Optional[Any]=True	,      _lowerCamelCase :							List[str]=8	,      _lowerCamelCase :							Union[str, Any]=1	,      **_lowerCamelCase :							List[str] ):
          A__				=   n_codes_total
          A__				=   n_codes_given

          super().__init__(tie_word_embeddings=_lowerCamelCase	,      **_lowerCamelCase )


class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="bark"
     _lowerCamelCase					: Dict				   =True





     def __init__(					self :							Optional[Any]	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Tuple=0.02	,      **_lowerCamelCase :							Dict	,      ):
          if semantic_config is None:
               A__				=   {}
               logger.info('''semantic_config is None. initializing the semantic model with default values.''' )

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

          if fine_acoustics_config is None:
               A__				=   {}
               logger.info('''fine_acoustics_config is None. initializing the fine model with default values.''' )

          if codec_config is None:
               A__				=   {}
               logger.info('''codec_config is None. initializing the codec model with default values.''' )

          A__				=   BarkSemanticConfig(**_lowerCamelCase )
          A__				=   BarkCoarseConfig(**_lowerCamelCase )
          A__				=   BarkFineConfig(**_lowerCamelCase )
          A__				=   AutoConfig.for_model(**_lowerCamelCase )

          A__				=   initializer_range

          super().__init__(**_lowerCamelCase )





     @classmethod
     def 						A__  (					cls :							Tuple	,      _lowerCamelCase :							BarkSemanticConfig	,      _lowerCamelCase :							BarkCoarseConfig	,      _lowerCamelCase :							BarkFineConfig	,      _lowerCamelCase :							AutoConfig	,      **_lowerCamelCase :							List[str]	,      ):
          return cls(
              semantic_config=semantic_config.to_dict()	,      coarse_acoustics_config=coarse_acoustics_config.to_dict()	,      fine_acoustics_config=fine_acoustics_config.to_dict()	,      codec_config=codec_config.to_dict()	,      **_lowerCamelCase	,      )





     def 						A__  (					self :							Any ):
          A__				=   copy.deepcopy(self.__dict__ )

          A__				=   self.semantic_config.to_dict()
          A__				=   self.coarse_acoustics_config.to_dict()
          A__				=   self.fine_acoustics_config.to_dict()
          A__				=   self.codec_config.to_dict()

          A__				=   self.__class__.model_type
          return output








"""simple docstring"""







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =HerbertTokenizer
     _lowerCamelCase					: Optional[int]				   =HerbertTokenizerFast
     _lowerCamelCase					: Optional[Any]				   =True





     def 						A__  (					self :							Dict ):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''	,      encoding='''utf-8''' ) as f_data:
               A__				=   f_data.read().replace('''\n\n'''	,      '''\n''' ).strip()

          A__				=   [
              '''<s>''',
              '''</s>''',
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''w</w>''',
              '''r</w>''',
              '''t</w>''',
              '''lo''',
              '''low''',
              '''er</w>''',
              '''low</w>''',
              '''lowest</w>''',
              '''newer</w>''',
              '''wider</w>''',
              ''',</w>''',
              '''<unk>''',
          ]
          A__				=   dict(zip(_lowerCamelCase	,      range(len(_lowerCamelCase ) ) ) )
          A__				=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

          A__				=   os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file'''] )
          A__				=   os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file	,      '''w''' ) as fp:
               fp.write(json.dumps(_lowerCamelCase ) )
          with open(self.merges_file	,      '''w''' ) as fp:
               fp.write('''\n'''.join(_lowerCamelCase ) )





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          A__				=   '''lower newer'''
          A__				=   '''lower newer'''
          return input_text, output_text





     def 						A__  (					self :							int ):
          A__				=   self.tokenizer_class(vocab_file=self.vocab_file	,      merges_file=self.merges_file )

          A__				=   '''lower'''
          A__				=   ['''low''', '''er</w>''']
          A__				=   tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokens + ['''<unk>''']
          A__				=   [1_6, 1_7, 2_3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )	,      _lowerCamelCase )





     def 						A__  (					self :							Optional[int] ):
          if not self.test_rust_tokenizer:
               return

          A__				=   self.get_tokenizer()
          A__				=   self.get_rust_tokenizer()

          A__				=   '''lower,newer'''

          A__				=   tokenizer.tokenize(_lowerCamelCase )
          A__				=   rust_tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   self.get_rust_tokenizer()
          A__				=   tokenizer.encode(_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )





     @slow
     def 						A__  (					self :							Any ):
          A__				=   self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased''' )

          A__				=   tokenizer.encode('''konstruowanie sekwencji'''	,      add_special_tokens=_lowerCamelCase )
          A__				=   tokenizer.encode('''konstruowanie wielu sekwencji'''	,      add_special_tokens=_lowerCamelCase )

          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,      _lowerCamelCase )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]



