

'''simple docstring'''
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


_lowerCamelCase	 =			logging.get_logger(__name__)


_lowerCamelCase	 =			{
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

_lowerCamelCase	 =			"""
    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  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="bark_module"
			__A   :     List[Any]          =["past_key_values"]

			__A   :     List[Any]          ={
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			    "vocab_size": "input_vocab_size",
			    "window_size": "block_size",
			}






			def __init__(   self       ,_snake_case=10_24       ,_snake_case=1_00_48       ,_snake_case=1_00_48       ,_snake_case=12       ,_snake_case=12       ,_snake_case=7_68       ,_snake_case=0.0       ,_snake_case=True       ,_snake_case=0.02       ,_snake_case=True       ,**_snake_case       ,):
				UpperCAmelCase_			:	Optional[int]      =   block_size
				UpperCAmelCase_			:	int      =   input_vocab_size
				UpperCAmelCase_			:	List[str]      =   output_vocab_size
				UpperCAmelCase_			:	Union[str, Any]      =   num_layers
				UpperCAmelCase_			:	Any      =   num_heads
				UpperCAmelCase_			:	Tuple      =   hidden_size
				UpperCAmelCase_			:	Optional[Any]      =   dropout
				UpperCAmelCase_			:	str      =   bias
				UpperCAmelCase_			:	int      =   use_cache
				UpperCAmelCase_			:	Union[str, Any]      =   initializer_range

				super().__init__(**_snake_case			)






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,_snake_case = None       ,_snake_case = False       ,_snake_case = False       ,_snake_case = None       ,_snake_case = "main"       ,**_snake_case       ,):
				UpperCAmelCase_			:	Tuple      =   cache_dir
				UpperCAmelCase_			:	Dict      =   force_download
				UpperCAmelCase_			:	Optional[int]      =   local_files_only
				UpperCAmelCase_			:	int      =   revision

				cls._set_token_in_kwargs(_snake_case       ,_snake_case			)

				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[Any]      =   cls.get_config_dict(_snake_case       ,**_snake_case			)

				# get the config dict if we are loading from Bark
				if config_dict.get("model_type"			) == "bark":
					UpperCAmelCase_			:	Any      =   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(_snake_case       ,**_snake_case			)




@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  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Union[str, Any]          ="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  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          ="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  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[str]          ="fine_acoustics"






			def __init__(   self       ,_snake_case=True       ,_snake_case=8       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	str      =   n_codes_total
				UpperCAmelCase_			:	Any      =   n_codes_given

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




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[Any]          ="bark"
			__A   :     Any          =True






			def __init__(   self       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,_snake_case=0.02       ,**_snake_case       ,):
				if semantic_config is None:
					UpperCAmelCase_			:	Dict      =   {}
					logger.info("semantic_config is None. initializing the semantic model with default values."			)

				if coarse_acoustics_config is None:
					UpperCAmelCase_			:	Union[str, Any]      =   {}
					logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."			)

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

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

				UpperCAmelCase_			:	Dict      =   BarkSemanticConfig(**_snake_case			)
				UpperCAmelCase_			:	List[str]      =   BarkCoarseConfig(**_snake_case			)
				UpperCAmelCase_			:	Any      =   BarkFineConfig(**_snake_case			)
				UpperCAmelCase_			:	Dict      =   AutoConfig.for_model(**_snake_case			)

				UpperCAmelCase_			:	Tuple      =   initializer_range

				super().__init__(**_snake_case			)






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,**_snake_case       ,):
				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()       ,**_snake_case       ,)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	str      =   copy.deepcopy(self.__dict__			)

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

				UpperCAmelCase_			:	Tuple      =   self.__class__.model_type
				return output








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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =False if not self.vocab_file else True

										try:
														import jieba
										except ModuleNotFoundError as error:
														raise error.__class__(
														    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
														    '''See https://pypi.org/project/jieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[2]

										if token_ids_a is None:
														return len(token_ids_a + sep     ) * [0] + cls_segment_id
										return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




						def 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''     )

										if not os.path.isdir(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

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

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text