'''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


__UpperCAmelCase  							=logging.get_logger(__name__)


__UpperCAmelCase  							={
    "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  							="\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 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  Any							="bark_module"
     lowerCamelCase						:  List[Any]							=["past_key_values"]

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






     def __init__(				self	:	Optional[int]						,       a	:	List[Any]=10_24						,       a	:	Dict=1_00_48						,       a	:	Dict=1_00_48						,       a	:	List[Any]=12						,       a	:	List[Any]=12						,       a	:	Tuple=7_68						,       a	:	Optional[int]=0.0						,       a	:	Optional[int]=True						,       a	:	Optional[Any]=0.02						,       a	:	int=True						,       **a	:	str						,       ):





         """simple docstring"""



         __lowerCamelCase						=						block_size
         __lowerCamelCase						=						input_vocab_size
         __lowerCamelCase						=						output_vocab_size
         __lowerCamelCase						=						num_layers
         __lowerCamelCase						=						num_heads
         __lowerCamelCase						=						hidden_size
         __lowerCamelCase						=						dropout
         __lowerCamelCase						=						bias
         __lowerCamelCase						=						use_cache
         __lowerCamelCase						=						initializer_range

         super().__init__(**a					)






     @classmethod
     def      SCREAMING_SNAKE_CASE__     (				cls	:	Union[str, Any]						,       a	:	Union[str, os.PathLike]						,       a	:	Optional[Union[str, os.PathLike]] = None						,       a	:	bool = False						,       a	:	bool = False						,       a	:	Optional[Union[str, bool]] = None						,       a	:	str = "main"						,       **a	:	Optional[Any]						,       ):





         """simple docstring"""



         __lowerCamelCase						=						cache_dir
         __lowerCamelCase						=						force_download
         __lowerCamelCase						=						local_files_only
         __lowerCamelCase						=						revision

         cls._set_token_in_kwargs(a						,       a					)

         __lowerCamelCase			,						__lowerCamelCase						=						cls.get_config_dict(a						,       **a					)

         # get the config dict if we are loading from Bark
         if config_dict.get('''model_type'''					) == "bark":
             __lowerCamelCase						=						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(a						,       **a					)




@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 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  List[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 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  Tuple							="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 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  Optional[Any]							="fine_acoustics"






     def __init__(				self	:	Optional[int]						,       a	:	str=True						,       a	:	Union[str, Any]=8						,       a	:	Dict=1						,       **a	:	Optional[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						n_codes_total
         __lowerCamelCase						=						n_codes_given

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




class 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  Any							="bark"
     lowerCamelCase						:  Optional[int]							=True






     def __init__(				self	:	Optional[int]						,       a	:	Dict = None						,       a	:	Dict = None						,       a	:	Dict = None						,       a	:	Dict = None						,       a	:	List[Any]=0.02						,       **a	:	Union[str, Any]						,       ):





         """simple docstring"""



         if semantic_config is None:
             __lowerCamelCase						=						{}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

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

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

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

         __lowerCamelCase						=						BarkSemanticConfig(**a					)
         __lowerCamelCase						=						BarkCoarseConfig(**a					)
         __lowerCamelCase						=						BarkFineConfig(**a					)
         __lowerCamelCase						=						AutoConfig.for_model(**a					)

         __lowerCamelCase						=						initializer_range

         super().__init__(**a					)






     @classmethod
     def      SCREAMING_SNAKE_CASE__     (				cls	:	Union[str, Any]						,       a	:	BarkSemanticConfig						,       a	:	BarkCoarseConfig						,       a	:	BarkFineConfig						,       a	:	AutoConfig						,       **a	:	List[Any]						,       ):





         """simple docstring"""



         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()						,       **a						,       )






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[int]					):





         """simple docstring"""



         __lowerCamelCase						=						copy.deepcopy(self.__dict__					)

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

         __lowerCamelCase						=						self.__class__.model_type
         return output

'''simple docstring'''




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase  							=logging.get_logger(__name__)

__UpperCAmelCase  							={
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

__UpperCAmelCase  							={
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

__UpperCAmelCase  							={"Helsinki-NLP/opus-mt-en-de": 5_1_2}
__UpperCAmelCase  							={}




class 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  str							=VOCAB_FILES_NAMES
     lowerCamelCase						:  Union[str, Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase						:  Optional[int]							=PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase						:  str							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase						:  Union[str, Any]							=["input_ids", "attention_mask"]
     lowerCamelCase						:  List[Any]							=re.compile(">>.+<<"				)  # type: re.Pattern






     def __init__(				self	:	Dict						,       a	:	Tuple						,       a	:	List[Any]						,       a	:	int						,       a	:	Any=None						,       a	:	str=None						,       a	:	int=None						,       a	:	Optional[int]="<unk>"						,       a	:	Optional[int]="</s>"						,       a	:	Optional[int]="<pad>"						,       a	:	Dict=5_12						,       a	:	Optional[Dict[str, Any]] = None						,       a	:	Optional[int]=False						,       **a	:	List[Any]						,       ):





         """simple docstring"""



         __lowerCamelCase						=						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=a						,       target_lang=a						,       unk_token=a						,       eos_token=a						,       pad_token=a						,       model_max_length=a						,       sp_model_kwargs=self.sp_model_kwargs						,       target_vocab_file=a						,       separate_vocabs=a						,       **a						,       )
         assert Path(a					).exists(), f"""cannot find spm source {source_spm}"""

         __lowerCamelCase						=						separate_vocabs
         __lowerCamelCase						=						load_json(a					)
         if self.unk_token not in self.encoder:
             raise KeyError('''<unk> token must be in vocab'''					)
         assert self.pad_token in self.encoder

         if separate_vocabs:
             __lowerCamelCase						=						load_json(a					)
             __lowerCamelCase						=						{v: k for k, v in self.target_encoder.items()}
             __lowerCamelCase						=						[]
         else:
             __lowerCamelCase						=						{v: k for k, v in self.encoder.items()}
             __lowerCamelCase						=						[k for k in self.encoder if k.startswith('''>>'''					) and k.endswith('''<<'''					)]

         __lowerCamelCase						=						source_lang
         __lowerCamelCase						=						target_lang
         __lowerCamelCase						=						[source_spm, target_spm]

         # load SentencePiece model for pre-processing
         __lowerCamelCase						=						load_spm(a						,       self.sp_model_kwargs					)
         __lowerCamelCase						=						load_spm(a						,       self.sp_model_kwargs					)
         __lowerCamelCase						=						self.spm_source
         __lowerCamelCase						=						self.encoder

         # Multilingual target side: default to using first supported language code.

         self._setup_normalizer()






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         try:
             from sacremoses import MosesPunctNormalizer

             __lowerCamelCase						=						MosesPunctNormalizer(self.source_lang					).normalize
         except (ImportError, FileNotFoundError):
             warnings.warn('''Recommended: pip install sacremoses.'''					)
             __lowerCamelCase						=						lambda a					: x






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	str					):





         """simple docstring"""



         return self.punc_normalizer(a					) if x else ""






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Any					):





         """simple docstring"""



         return self.current_encoder.get(a						,       self.current_encoder[self.unk_token]					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						self.language_code_re.match(a					)
         __lowerCamelCase						=						[match.group(0					)] if match else []
         return code, self.language_code_re.sub(''''''						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase			,						__lowerCamelCase						=						self.remove_language_code(a					)
         __lowerCamelCase						=						self.current_spm.encode(a						,       out_type=a					)
         return code + pieces






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	int					):





         """simple docstring"""



         return self.decoder.get(a						,       self.unk_token					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       a	:	Optional[Any]						,       **a	:	Dict					):





         """simple docstring"""



         return super().batch_decode(a						,       **a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]						,       a	:	int						,       **a	:	int					):





         """simple docstring"""



         return super().decode(a						,       **a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	List[str]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         __lowerCamelCase						=						[]
         __lowerCamelCase						=						''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += sp_model.decode_pieces(a					) + token + " "
                 __lowerCamelCase						=						[]
             else:
                 current_sub_tokens.append(a					)
         out_string += sp_model.decode_pieces(a					)
         return out_string.strip()






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	Union[str, Any]						,       a	:	Tuple=None					):





         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_source
         __lowerCamelCase						=						self.encoder






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_target
         if self.separate_vocabs:
             __lowerCamelCase						=						self.target_encoder






     @property
     def      SCREAMING_SNAKE_CASE__     (				self	:	int					):





         """simple docstring"""



         return len(self.encoder					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	str						,       a	:	Optional[str] = None					):





         """simple docstring"""



         if not os.path.isdir(a					):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
             return
         __lowerCamelCase						=						[]

         if self.separate_vocabs:
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']						,       )
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']						,       )
             save_json(self.encoder						,       a					)
             save_json(self.target_encoder						,       a					)
             saved_files.append(a					)
             saved_files.append(a					)
         else:
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']					)
             save_json(self.encoder						,       a					)
             saved_files.append(a					)

         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]						,       self.spm_files						,       [self.spm_source, self.spm_target]						,       ):
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename					)
             if os.path.abspath(a					) != os.path.abspath(a					) and os.path.isfile(a					):
                 copyfile(a						,       a					)
                 saved_files.append(a					)
             elif not os.path.isfile(a					):
                 with open(a						,       '''wb'''					) as fi:
                     __lowerCamelCase						=						spm_model.serialized_model_proto()
                     fi.write(a					)
                 saved_files.append(a					)

         return tuple(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         return self.get_src_vocab()






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         return dict(self.encoder						,       **self.added_tokens_encoder					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         return dict(self.target_encoder						,       **self.added_tokens_decoder					)






     def __getstate__(				self	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						self.__dict__.copy()
         state.update(
             {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}					)
         return state






     def __setstate__(				self	:	Tuple						,       a	:	Dict					):





         """simple docstring"""



         __lowerCamelCase						=						d

         # for backward compatibility
         if not hasattr(self						,       '''sp_model_kwargs'''					):
             __lowerCamelCase						=						{}

         __lowerCamelCase			,						__lowerCamelCase						=						(load_spm(a						,       self.sp_model_kwargs					) for f in self.spm_files)
         __lowerCamelCase						=						self.spm_source
         self._setup_normalizer()






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       *a	:	Tuple						,       **a	:	int					):





         """simple docstring"""



         return 1






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Optional[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]						,       a	:	List						,       a	:	Optional[List] = None						,       a	:	bool = False					):





         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(a					)
         elif token_ids_a is None:
             return self._special_token_mask(a					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					sentencepiece.SentencePieceProcessor:
    __lowerCamelCase						=						sentencepiece.SentencePieceProcessor(**UpperCamelCase__       )
    spm.Load(UpperCamelCase__       )
    return spm






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					None:
    with open(UpperCamelCase__       ,						'''w'''       ) as f:
        json.dump(UpperCamelCase__       ,						UpperCamelCase__       ,						indent=2       )






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Union[Dict, List]:
    with open(UpperCamelCase__       ,						'''r'''       ) as f:
        return json.load(UpperCamelCase__       )
