






import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class        _SCREAMING_SNAKE_CASE			(     _a						,  unittest.TestCase ):
	snake_case__				:	Optional[Any]	      =    RealmTokenizer
	snake_case__				:	List[Any]	      =    RealmTokenizerFast
	snake_case__				:	str	      =    True
	snake_case__				:	Optional[Any]	      =    True
	snake_case__				:	str	      =    filter_non_english





	def 		_A					(						self							:   Tuple      ):
		super().setUp()

		UpperCamelCase	:List[Any]		=      [
		    """[UNK]""",
		    """[CLS]""",
		    """[SEP]""",
		    """[PAD]""",
		    """[MASK]""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		UpperCamelCase	:Optional[Any]		=      os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["""vocab_file"""]      )
		with open(self.vocab_file					,    """w"""					,    encoding="""utf-8"""      ) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Optional[int]      ):
		UpperCamelCase	:int		=      """UNwant\u00E9d,running"""
		UpperCamelCase	:Dict		=      """unwanted, running"""
		return input_text, output_text





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Tuple		=      self.tokenizer_class(self.vocab_file      )

		UpperCamelCase	:int		=      tokenizer.tokenize("""UNwant\u00E9d,running"""      )
		self.assertListEqual(__lowerCamelCase					,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]      )
		self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase      )					,    [9, 6, 7, 12, 10, 11]      )





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

		UpperCamelCase	:List[str]		=      self.get_tokenizer()
		UpperCamelCase	:str		=      self.get_rust_tokenizer()

		UpperCamelCase	:List[str]		=      """UNwant\u00E9d,running"""

		UpperCamelCase	:Dict		=      tokenizer.tokenize(__lowerCamelCase      )
		UpperCamelCase	:Tuple		=      rust_tokenizer.tokenize(__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	:int		=      tokenizer.encode(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase      )
		UpperCamelCase	:int		=      rust_tokenizer.encode(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	:int		=      self.get_rust_tokenizer()
		UpperCamelCase	:Optional[int]		=      tokenizer.encode(__lowerCamelCase      )
		UpperCamelCase	:Optional[int]		=      rust_tokenizer.encode(__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )

		# With lower casing
		UpperCamelCase	:Optional[int]		=      self.get_tokenizer(do_lower_case=__lowerCamelCase      )
		UpperCamelCase	:Any		=      self.get_rust_tokenizer(do_lower_case=__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      """UNwant\u00E9d,running"""

		UpperCamelCase	:Optional[Any]		=      tokenizer.tokenize(__lowerCamelCase      )
		UpperCamelCase	:Tuple		=      rust_tokenizer.tokenize(__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	:Any		=      tokenizer.encode(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase      )
		UpperCamelCase	:str		=      rust_tokenizer.encode(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	:Any		=      self.get_rust_tokenizer()
		UpperCamelCase	:Any		=      tokenizer.encode(__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      rust_tokenizer.encode(__lowerCamelCase      )
		self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:int		=      BasicTokenizer()

		self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""      )					,    ["""ah""", """\u535A""", """\u63A8""", """zz"""]      )





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:List[Any]		=      BasicTokenizer(do_lower_case=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )					,    ["""hello""", """!""", """how""", """are""", """you""", """?"""]      )
		self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )					,    ["""hello"""]      )





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Optional[Any]		=      BasicTokenizer(do_lower_case=__lowerCamelCase					,    strip_accents=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )					,    ["""hällo""", """!""", """how""", """are""", """you""", """?"""]      )
		self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )					,    ["""h\u00E9llo"""]      )





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Dict		=      BasicTokenizer(do_lower_case=__lowerCamelCase					,    strip_accents=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )					,    ["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
		self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )					,    ["""hello"""]      )





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Optional[int]		=      BasicTokenizer(do_lower_case=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )					,    ["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
		self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )					,    ["""hello"""]      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Tuple		=      BasicTokenizer(do_lower_case=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )					,    ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Tuple		=      BasicTokenizer(do_lower_case=__lowerCamelCase					,    strip_accents=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )					,    ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Optional[int]		=      BasicTokenizer(do_lower_case=__lowerCamelCase					,    strip_accents=__lowerCamelCase      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )					,    ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





	def 		_A					(						self							:   Any      ):
		UpperCamelCase	:str		=      BasicTokenizer(do_lower_case=__lowerCamelCase					,    never_split=["""[UNK]"""]      )

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""      )					,    ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]      )





	def 		_A					(						self							:   Any      ):
		UpperCamelCase	:List[str]		=      ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

		UpperCamelCase	:str		=      {}
		for i, token in enumerate(__lowerCamelCase      ):
			UpperCamelCase	:int		=      i
		UpperCamelCase	:Optional[Any]		=      WordpieceTokenizer(vocab=__lowerCamelCase					,    unk_token="""[UNK]"""      )

		self.assertListEqual(tokenizer.tokenize(""""""      )					,    []      )

		self.assertListEqual(tokenizer.tokenize("""unwanted running"""      )					,    ["""un""", """##want""", """##ed""", """runn""", """##ing"""]      )

		self.assertListEqual(tokenizer.tokenize("""unwantedX running"""      )					,    ["""[UNK]""", """runn""", """##ing"""]      )





	def 		_A					(						self							:   Optional[Any]      ):
		self.assertTrue(_is_whitespace(""" """      )      )
		self.assertTrue(_is_whitespace("""\t"""      )      )
		self.assertTrue(_is_whitespace("""\r"""      )      )
		self.assertTrue(_is_whitespace("""\n"""      )      )
		self.assertTrue(_is_whitespace("""\u00A0"""      )      )

		self.assertFalse(_is_whitespace("""A"""      )      )
		self.assertFalse(_is_whitespace("""-"""      )      )





	def 		_A					(						self							:   Optional[int]      ):
		self.assertTrue(_is_control("""\u0005"""      )      )

		self.assertFalse(_is_control("""A"""      )      )
		self.assertFalse(_is_control(""" """      )      )
		self.assertFalse(_is_control("""\t"""      )      )
		self.assertFalse(_is_control("""\r"""      )      )





	def 		_A					(						self							:   str      ):
		self.assertTrue(_is_punctuation("""-"""      )      )
		self.assertTrue(_is_punctuation("""$"""      )      )
		self.assertTrue(_is_punctuation("""`"""      )      )
		self.assertTrue(_is_punctuation("""."""      )      )

		self.assertFalse(_is_punctuation("""A"""      )      )
		self.assertFalse(_is_punctuation(""" """      )      )





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:Union[str, Any]		=      self.get_tokenizer()

		# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
		self.assertListEqual([tokenizer.tokenize(__lowerCamelCase      ) for t in ["""Test""", """\xad""", """test"""]]					,    [["""[UNK]"""], [], ["""[UNK]"""]]      )

		if self.test_rust_tokenizer:
			UpperCamelCase	:List[str]		=      self.get_rust_tokenizer()
			self.assertListEqual(
			    [rust_tokenizer.tokenize(__lowerCamelCase      ) for t in ["""Test""", """\xad""", """test"""]]					,    [["""[UNK]"""], [], ["""[UNK]"""]]      )





	@slow
	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Union[str, Any]		=      self.tokenizer_class.from_pretrained("""bert-base-uncased"""      )

		UpperCamelCase	:Any		=      tokenizer.encode("""sequence builders"""					,    add_special_tokens=__lowerCamelCase      )
		UpperCamelCase	:Dict		=      tokenizer.encode("""multi-sequence build"""					,    add_special_tokens=__lowerCamelCase      )

		UpperCamelCase	:List[str]		=      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase      )
		UpperCamelCase	:List[str]		=      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase					,    __lowerCamelCase      )

		assert encoded_sentence == [101] + text + [102]
		assert encoded_pair == [101] + text + [102] + text_a + [102]





	def 		_A					(						self							:   Tuple      ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
				UpperCamelCase	:List[Any]		=      self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,    **__lowerCamelCase      )

				UpperCamelCase	:str		=      F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
				UpperCamelCase	:Optional[int]		=      tokenizer_r.encode_plus(
				    __lowerCamelCase					,    return_attention_mask=__lowerCamelCase					,    return_token_type_ids=__lowerCamelCase					,    return_offsets_mapping=__lowerCamelCase					,    add_special_tokens=__lowerCamelCase					,    )

				UpperCamelCase	:List[str]		=      tokenizer_r.do_lower_case if hasattr(__lowerCamelCase					,    """do_lower_case"""      ) else False
				UpperCamelCase	:Union[str, Any]		=      (
				    [
				        ((0, 0), tokenizer_r.cls_token),
				        ((0, 1), """A"""),
				        ((1, 2), ""","""),
				        ((3, 5), """na"""),
				        ((5, 6), """##ï"""),
				        ((6, 8), """##ve"""),
				        ((9, 15), tokenizer_r.mask_token),
				        ((16, 21), """Allen"""),
				        ((21, 23), """##NL"""),
				        ((23, 24), """##P"""),
				        ((25, 33), """sentence"""),
				        ((33, 34), """."""),
				        ((0, 0), tokenizer_r.sep_token),
				    ]
				    if not do_lower_case
				    else [
				        ((0, 0), tokenizer_r.cls_token),
				        ((0, 1), """a"""),
				        ((1, 2), ""","""),
				        ((3, 8), """naive"""),
				        ((9, 15), tokenizer_r.mask_token),
				        ((16, 21), """allen"""),
				        ((21, 23), """##nl"""),
				        ((23, 24), """##p"""),
				        ((25, 33), """sentence"""),
				        ((33, 34), """."""),
				        ((0, 0), tokenizer_r.sep_token),
				    ]
				)

				self.assertEqual(
				    [e[1] for e in expected_results]					,    tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]      )      )
				self.assertEqual([e[0] for e in expected_results]					,    tokens["""offset_mapping"""]      )





	@slow
	def 		_A					(						self							:   List[str]      ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
				UpperCamelCase	:str		=      self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,    **__lowerCamelCase      )
				UpperCamelCase	:str		=      self.tokenizer_class.from_pretrained(__lowerCamelCase					,    **__lowerCamelCase      )
				UpperCamelCase	:List[Any]		=      [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

				UpperCamelCase	:Optional[int]		=      tokenizer_r.batch_encode_candidates(__lowerCamelCase					,    max_length=10					,    return_tensors="""np"""      )
				UpperCamelCase	:Any		=      tokenizer_p.batch_encode_candidates(__lowerCamelCase					,    max_length=10					,    return_tensors="""np"""      )

				UpperCamelCase	:Dict		=      (2, 2, 10)

				self.assertEqual(encoded_sentence_r["""input_ids"""].shape					,    __lowerCamelCase      )
				self.assertEqual(encoded_sentence_r["""attention_mask"""].shape					,    __lowerCamelCase      )
				self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape					,    __lowerCamelCase      )

				self.assertEqual(encoded_sentence_p["""input_ids"""].shape					,    __lowerCamelCase      )
				self.assertEqual(encoded_sentence_p["""attention_mask"""].shape					,    __lowerCamelCase      )
				self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape					,    __lowerCamelCase      )








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


UpperCAmelCase_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		Dict           = {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_					:		Any           = '''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	str	      =    """bark_module"""
	snake_case__				:	int	      =    ["""past_key_values"""]

	snake_case__				:	Optional[int]	      =    {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	    """vocab_size""": """input_vocab_size""",
	    """window_size""": """block_size""",
	}





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]=1_024					,    __lowerCamelCase							:   int=10_048					,    __lowerCamelCase							:   List[Any]=10_048					,    __lowerCamelCase							:   str=12					,    __lowerCamelCase							:   Dict=12					,    __lowerCamelCase							:   Tuple=768					,    __lowerCamelCase							:   Dict=0.0					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Tuple=0.02					,    __lowerCamelCase							:   List[str]=True					,    **__lowerCamelCase							:   str					,    ):
		UpperCamelCase	:Optional[int]		=      block_size
		UpperCamelCase	:List[Any]		=      input_vocab_size
		UpperCamelCase	:int		=      output_vocab_size
		UpperCamelCase	:int		=      num_layers
		UpperCamelCase	:Union[str, Any]		=      num_heads
		UpperCamelCase	:Dict		=      hidden_size
		UpperCamelCase	:Any		=      dropout
		UpperCamelCase	:List[str]		=      bias
		UpperCamelCase	:Tuple		=      use_cache
		UpperCamelCase	:Any		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __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							:   List[Any]					,    ):
		UpperCamelCase	:Dict		=      cache_dir
		UpperCamelCase	:List[Any]		=      force_download
		UpperCamelCase	:List[Any]		=      local_files_only
		UpperCamelCase	:int		=      revision

		cls._set_token_in_kwargs(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	, UpperCamelCase	:Dict		=      cls.get_config_dict(__lowerCamelCase					,    **__lowerCamelCase      )

		# get the config dict if we are loading from Bark
		if config_dict.get("""model_type"""      ) == "bark":
			UpperCamelCase	:List[str]		=      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""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Optional[int]	      =    """semantic"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""						,  model="""BarkCoarseModel""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    """coarse_acoustics"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""						,  model="""BarkFineModel""" )						,  """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	List[Any]	      =    """fine_acoustics"""





	def __init__(						self							:   Any					,    __lowerCamelCase							:   str=True					,    __lowerCamelCase							:   Optional[int]=8					,    __lowerCamelCase							:   Dict=1					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:List[str]		=      n_codes_total
		UpperCamelCase	:str		=      n_codes_given

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

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Union[str, Any]	      =    """bark"""
	snake_case__				:	Union[str, Any]	      =    True





	def __init__(						self							:   int					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   str=0.02					,    **__lowerCamelCase							:   List[str]					,    ):
		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	:Any		=      {}
			logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""      )

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

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

		UpperCamelCase	:Tuple		=      BarkSemanticConfig(**__lowerCamelCase      )
		UpperCamelCase	:str		=      BarkCoarseConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      BarkFineConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      AutoConfig.for_model(**__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   BarkSemanticConfig					,    __lowerCamelCase							:   BarkCoarseConfig					,    __lowerCamelCase							:   BarkFineConfig					,    __lowerCamelCase							:   AutoConfig					,    **__lowerCamelCase							:   int					,    ):
		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							:   Dict      ):
		UpperCamelCase	:List[Any]		=      copy.deepcopy(self.__dict__      )

		UpperCamelCase	:Any		=      self.semantic_config.to_dict()
		UpperCamelCase	:Optional[int]		=      self.coarse_acoustics_config.to_dict()
		UpperCamelCase	:Tuple		=      self.fine_acoustics_config.to_dict()
		UpperCamelCase	:List[str]		=      self.codec_config.to_dict()

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