






"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__a :    Any								=				logging.get_logger(__name__)

__a :    Union[str, Any]								=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a :    Optional[Any]								=				{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a :    Optional[int]								=				{
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

__a :    str								=				{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE				  =			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE				  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE				  =			["input_ids", "attention_mask", "token_type_ids"]
     SCREAMING_SNAKE_CASE				  =			DebertaTokenizer




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     Tuple=None	,		UpperCamelCase_    :     Any=None	,		UpperCamelCase_    :     Dict=None	,		UpperCamelCase_    :     str="replace"	,		UpperCamelCase_    :     int="[CLS]"	,		UpperCamelCase_    :     Optional[int]="[SEP]"	,		UpperCamelCase_    :     Optional[Any]="[SEP]"	,		UpperCamelCase_    :     Optional[Any]="[CLS]"	,		UpperCamelCase_    :     int="[UNK]"	,		UpperCamelCase_    :     Tuple="[PAD]"	,		UpperCamelCase_    :     Dict="[MASK]"	,		UpperCamelCase_    :     Dict=False	,		**UpperCamelCase_    :     str	,		):


         """simple docstring"""



         super().__init__(
             UpperCamelCase_	,		UpperCamelCase_	,		tokenizer_file=UpperCamelCase_	,		errors=UpperCamelCase_	,		bos_token=UpperCamelCase_	,		eos_token=UpperCamelCase_	,		unk_token=UpperCamelCase_	,		sep_token=UpperCamelCase_	,		cls_token=UpperCamelCase_	,		pad_token=UpperCamelCase_	,		mask_token=UpperCamelCase_	,		add_prefix_space=UpperCamelCase_	,		**UpperCamelCase_	,		)
         __A             =		kwargs.pop("""add_bos_token"""	,		UpperCamelCase_			)

         __A             =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
         if pre_tok_state.get("""add_prefix_space"""	,		UpperCamelCase_			) != add_prefix_space:
             __A             =		getattr(UpperCamelCase_	,		pre_tok_state.pop("""type"""			)			)
             __A             =		add_prefix_space
             __A             =		pre_tok_class(**UpperCamelCase_			)

         __A             =		add_prefix_space




     @property
     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""			)
             return None
         return str(self._mask_token			)




     @mask_token.setter
     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		AddedToken(UpperCamelCase_	,		lstrip=UpperCamelCase_	,		rstrip=UpperCamelCase_			) if isinstance(UpperCamelCase_	,		UpperCamelCase_			) else value
         __A             =		value




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     List[int]	,		UpperCamelCase_    :     Optional[List[int]] = None			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     List[int]	,		UpperCamelCase_    :     Optional[List[int]] = None			):


         """simple docstring"""



         __A             =		[self.sep_token_id]
         __A             =		[self.cls_token_id]

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




     def 						lowerCAmelCase_       (			self    :     Any	,		*UpperCamelCase_    :     List[str]	,		**UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		kwargs.get("""is_split_into_words"""	,		UpperCamelCase_			)
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		*UpperCamelCase_    :     Any	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         __A             =		kwargs.get("""is_split_into_words"""	,		UpperCamelCase_			)

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Optional[str] = None			):


         """simple docstring"""



         __A             =		self._tokenizer.model.save(UpperCamelCase_	,		name=UpperCamelCase_			)
         return tuple(UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     "Conversation"			):


         """simple docstring"""



         __A             =		[]
         for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			) + [self.eos_token_id]			)

         if len(UpperCamelCase_			) > self.model_max_length:
             __A             =		input_ids[-self.model_max_length :]
         return input_ids












import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		eos_token_id=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		max_new_tokens=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		text_encoding_offset
         __A             =		text_pad_token
         __A             =		eos_token_id
         __A             =		semantic_infer_token
         __A             =		semantic_vocab_size
         __A             =		max_input_semantic_length
         __A             =		semantic_rate_hz


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		coarse_semantic_pad_token
         __A             =		coarse_rate_hz
         __A             =		n_coarse_codebooks
         __A             =		coarse_infer_token
         __A             =		max_coarse_input_length
         __A             =		max_coarse_history
         __A             =		sliding_window_len


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



         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."""			)

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """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()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __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.__class__.model_type
         return output




