

'''simple docstring'''

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{"vocab_file": "tokenizer.model"}

lowerCAmelCase_						   =			{
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
lowerCAmelCase_						   =			{
    "hf-internal-testing/llama-tokenizer": 2_0_4_8,
}
lowerCAmelCase_						   =			"▁"

lowerCAmelCase_   ,	lowerCAmelCase_						   =			"[INST]", "[/INST]"
lowerCAmelCase_   ,	lowerCAmelCase_						   =			"<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase_						   =			"You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   VOCAB_FILES_NAMES
				snake_case_           =   PRETRAINED_VOCAB_FILES_MAP
				snake_case_           =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_           =   ['''input_ids''', '''attention_mask''']

				def __init__(					self,	lowercase_,	lowercase_="<unk>",	lowercase_="<s>",	lowercase_="</s>",	lowercase_=None,	lowercase_ = None,	lowercase_=True,	lowercase_=False,	lowercase_=False,	lowercase_=True,	**lowercase_,	)						->       Optional[int]:
					snake_case							 =				{} if sp_model_kwargs is None else sp_model_kwargs
					snake_case							 =				AddedToken(lowercase_,	lstrip=lowercase_,	rstrip=lowercase_   ) if isinstance(lowercase_,	lowercase_   ) else bos_token
					snake_case							 =				AddedToken(lowercase_,	lstrip=lowercase_,	rstrip=lowercase_   ) if isinstance(lowercase_,	lowercase_   ) else eos_token
					snake_case							 =				AddedToken(lowercase_,	lstrip=lowercase_,	rstrip=lowercase_   ) if isinstance(lowercase_,	lowercase_   ) else unk_token
					snake_case							 =				AddedToken(lowercase_,	lstrip=lowercase_,	rstrip=lowercase_   ) if isinstance(lowercase_,	lowercase_   ) else pad_token
					super().__init__(
					    bos_token=lowercase_,	eos_token=lowercase_,	unk_token=lowercase_,	pad_token=lowercase_,	add_bos_token=lowercase_,	add_eos_token=lowercase_,	sp_model_kwargs=self.sp_model_kwargs,	clean_up_tokenization_spaces=lowercase_,	legacy=lowercase_,	**lowercase_,	)
					if legacy:
						logger.warning_once(
						    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
						    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )
					snake_case							 =				legacy
					snake_case							 =				vocab_file
					snake_case							 =				add_bos_token
					snake_case							 =				add_eos_token
					snake_case							 =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(lowercase_   )

				def __getstate__(					self   )						->       str:
					snake_case							 =				self.__dict__.copy()
					snake_case							 =				None
					snake_case							 =				self.sp_model.serialized_model_proto()
					return state

				def __setstate__(					self,	lowercase_   )						->       str:
					snake_case							 =				d
					snake_case							 =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )

				@property
				def 				_lowerCamelCase						(					self   )						->       List[str]:
					return self.sp_model.get_piece_size()

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					snake_case							 =				{self.convert_ids_to_tokens(lowercase_   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab

				def 				_lowerCamelCase						(					self,	lowercase_,	**lowercase_   )						->       List[str]:
					# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
					# the beginning of the text
					if not self.legacy:
						snake_case							 =				SPIECE_UNDERLINE + text.replace(lowercase_,	' '   )
					return super().tokenize(lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Any:
					if not self.legacy:
						snake_case							 =				text.startswith(lowercase_   )
						if is_first:
							snake_case							 =				text[1:]

					snake_case							 =				self.sp_model.encode(lowercase_,	out_type=lowercase_   )

					if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(lowercase_   ):
						snake_case							 =				([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
					return tokens

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					return self.sp_model.piece_to_id(lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					snake_case							 =				self.sp_model.IdToPiece(lowercase_   )
					return token

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					snake_case							 =				[]
					snake_case							 =				''
					snake_case							 =				False
					for i, token in enumerate(lowercase_   ):
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							if not prev_is_special and i != 0:
								out_string += " "
							out_string += self.sp_model.decode(lowercase_   ) + token
							snake_case							 =				True
							snake_case							 =				[]
						else:
							current_sub_tokens.append(lowercase_   )
							snake_case							 =				False
					out_string += self.sp_model.decode(lowercase_   )
					return out_string

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None   )						->       Tuple[str]:
					if not os.path.isdir(lowercase_   ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
						return
					snake_case							 =				os.path.join(
					    lowercase_,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

					if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase_   ) and os.path.isfile(self.vocab_file   ):
						copyfile(self.vocab_file,	lowercase_   )
					elif not os.path.isfile(self.vocab_file   ):
						with open(lowercase_,	'wb'   ) as fi:
							snake_case							 =				self.sp_model.serialized_model_proto()
							fi.write(lowercase_   )

					return (out_vocab_file,)

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=None   )						->       Tuple:
					snake_case							 =				[self.bos_token_id] if self.add_bos_token else []
					snake_case							 =				[self.eos_token_id] if self.add_eos_token else []

					snake_case							 =				bos_token_id + token_ids_a + eos_token_id

					if token_ids_a is not None:
						snake_case							 =				output + bos_token_id + token_ids_a + eos_token_id

					return output

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None,	lowercase_ = False   )						->       List[int]:
					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=lowercase_,	token_ids_a=lowercase_,	already_has_special_tokens=lowercase_   )

					snake_case							 =				[1] if self.add_bos_token else []
					snake_case							 =				[1] if self.add_eos_token else []

					if token_ids_a is None:
						return bos_token_id + ([0] * len(lowercase_   )) + eos_token_id
					return (
					    bos_token_id
					    + ([0] * len(lowercase_   ))
					    + eos_token_id
					    + bos_token_id
					    + ([0] * len(lowercase_   ))
					    + eos_token_id
					)

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None   )						->       List[int]:
					snake_case							 =				[self.bos_token_id] if self.add_bos_token else []
					snake_case							 =				[self.eos_token_id] if self.add_eos_token else []

					snake_case							 =				[0] * len(bos_token_id + token_ids_a + eos_token_id   )

					if token_ids_a is not None:
						output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

					return output






				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[int]:
					snake_case							 =				list(conversation.iter_texts()   )
					if not all([is_user for is_user, msg in dialogue[::2]]   ) or not all(
					    [not is_user for is_user, msg in dialogue[1::2]]   ):
						raise ValueError(
						    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'   )

					snake_case							 =				[]
					if len(conversation.past_user_inputs   ) > 0:
						if not conversation.past_user_inputs[0].startswith(lowercase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							snake_case							 =				(
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
					elif not dialogue[0][1].startswith(lowercase_   ) or E_SYS not in dialogue[0][1]:
						snake_case							 =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

					dialog_tokens += sum(
					    [
					        [self.bos_token_id]
					        + self.encode(
					            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ''',	add_special_tokens=lowercase_   )
					        + [self.eos_token_id]
					        for prompt, answer in zip(dialogue[::2],	dialogue[1::2]   )
					    ],	[],	)
					if not (dialogue[-1][0]):
						raise ValueError(F'''Last message must be from user, got {dialogue[-1]["role"]}'''   )
					dialog_tokens += [self.bos_token_id] + self.encode(
					    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}''',	add_special_tokens=lowercase_   )
					return dialog_tokens








'''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_						   =			"\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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''bark_module'''
				snake_case_           =   ['''past_key_values''']

				snake_case_           =   {
				    '''num_attention_heads''': '''num_heads''',
				    '''num_hidden_layers''': '''num_layers''',
				    '''vocab_size''': '''input_vocab_size''',
				    '''window_size''': '''block_size''',
				}

				def __init__(					self,	lowercase_=1024,	lowercase_=10048,	lowercase_=10048,	lowercase_=12,	lowercase_=12,	lowercase_=768,	lowercase_=0.0,	lowercase_=True,	lowercase_=0.02,	lowercase_=True,	**lowercase_,	)						->       List[str]:
					snake_case							 =				block_size
					snake_case							 =				input_vocab_size
					snake_case							 =				output_vocab_size
					snake_case							 =				num_layers
					snake_case							 =				num_heads
					snake_case							 =				hidden_size
					snake_case							 =				dropout
					snake_case							 =				bias
					snake_case							 =				use_cache
					snake_case							 =				initializer_range

					super().__init__(**lowercase_   )






				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_ = None,	lowercase_ = False,	lowercase_ = False,	lowercase_ = None,	lowercase_ = "main",	**lowercase_,	)						->       "PretrainedConfig":
					snake_case							 =				cache_dir
					snake_case							 =				force_download
					snake_case							 =				local_files_only
					snake_case							 =				revision

					cls._set_token_in_kwargs(lowercase_,	lowercase_   )

					snake_case , snake_case							 =				cls.get_config_dict(lowercase_,	**lowercase_   )

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







@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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''fine_acoustics'''

				def __init__(					self,	lowercase_=True,	lowercase_=8,	lowercase_=1,	**lowercase_   )						->       Tuple:
					snake_case							 =				n_codes_total
					snake_case							 =				n_codes_given

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







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''bark'''
				snake_case_           =   True

				def __init__(					self,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_=0.02,	**lowercase_,	)						->       List[str]:
					if semantic_config is None:
						snake_case							 =				{}
						logger.info('semantic_config is None. initializing the semantic model with default values.'   )

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

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

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

					snake_case							 =				BarkSemanticConfig(**lowercase_   )
					snake_case							 =				BarkCoarseConfig(**lowercase_   )
					snake_case							 =				BarkFineConfig(**lowercase_   )
					snake_case							 =				AutoConfig.for_model(**lowercase_   )

					snake_case							 =				initializer_range

					super().__init__(**lowercase_   )

				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	**lowercase_,	)						->       Any:
					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(),	**lowercase_,	)






				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				copy.deepcopy(self.__dict__   )

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

					snake_case							 =				self.__class__.model_type
					return output





