
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


__a							  =						logging.get_logger(__name__)


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

__a							  =						"""
    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        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"bark_module"
	_A  =		["past_key_values"]

	_A  =		{
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	    "vocab_size": "input_vocab_size",
	    "window_size": "block_size",
	}







	def __init__(      self				:			str							,			snake_case__				:			int=10_24							,			snake_case__				:			List[Any]=1_00_48							,			snake_case__				:			List[str]=1_00_48							,			snake_case__				:			List[Any]=12							,			snake_case__				:			List[Any]=12							,			snake_case__				:			Union[str, Any]=7_68							,			snake_case__				:			List[Any]=0.0							,			snake_case__				:			Any=True							,			snake_case__				:			Optional[int]=0.02							,			snake_case__				:			int=True							,			**snake_case__				:			Any							,			):
			"""simple docstring"""





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

			super().__init__(**snake_case__ )







	@classmethod
	def        _a					(      cls				:			Dict							,			snake_case__				:			Union[str, os.PathLike]							,			snake_case__				:			Optional[Union[str, os.PathLike]] = None							,			snake_case__				:			bool = False							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[Union[str, bool]] = None							,			snake_case__				:			str = "main"							,			**snake_case__				:			Any							,			):
			"""simple docstring"""





			A				=cache_dir
			A				=force_download
			A				=local_files_only
			A				=revision

			cls._set_token_in_kwargs(snake_case__							,			snake_case__ )

			A			,    A				=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":
					A				=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        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"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        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"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        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"fine_acoustics"







	def __init__(      self				:			Optional[Any]							,			snake_case__				:			Optional[Any]=True							,			snake_case__				:			Tuple=8							,			snake_case__				:			Tuple=1							,			**snake_case__				:			Any ):
			"""simple docstring"""





			A				=n_codes_total
			A				=n_codes_given

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


class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"bark"
	_A  =		True







	def __init__(      self				:			Optional[Any]							,			snake_case__				:			Dict = None							,			snake_case__				:			Dict = None							,			snake_case__				:			Dict = None							,			snake_case__				:			Dict = None							,			snake_case__				:			Any=0.02							,			**snake_case__				:			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." )

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

			A				=BarkSemanticConfig(**snake_case__ )
			A				=BarkCoarseConfig(**snake_case__ )
			A				=BarkFineConfig(**snake_case__ )
			A				=AutoConfig.for_model(**snake_case__ )

			A				=initializer_range

			super().__init__(**snake_case__ )







	@classmethod
	def        _a					(      cls				:			List[str]							,			snake_case__				:			BarkSemanticConfig							,			snake_case__				:			BarkCoarseConfig							,			snake_case__				:			BarkFineConfig							,			snake_case__				:			AutoConfig							,			**snake_case__				:			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()							,			**snake_case__							,			)







	def        _a					(      self				:			Union[str, Any] ):
			"""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.codec_config.to_dict()

			A				=self.__class__.model_type
			return output






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__a							  =						logging.get_logger(__name__)

__a							  =						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

__a							  =						{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

__a							  =						{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

@lru_cache()
def    UpperCamelCase_ (	)     ->List[Any]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Union[str, Any]:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask"]







	def __init__(      self				:			Optional[int]							,			snake_case__				:			List[Any]							,			snake_case__				:			int							,			snake_case__				:			Tuple="replace"							,			snake_case__				:			List[str]="<|endoftext|>"							,			snake_case__				:			Tuple="<|endoftext|>"							,			snake_case__				:			Union[str, Any]="<|endoftext|>"							,			snake_case__				:			int=None							,			snake_case__				:			Optional[int]=False							,			snake_case__				:			Tuple=False							,			**snake_case__				:			Optional[Any]							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token
			super().__init__(
			    errors=snake_case__							,			unk_token=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			pad_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





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







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			Any							,			snake_case__				:			Optional[Any]=None ):
			"""simple docstring"""





			if self.add_bos_token:
					A				=[self.bos_token_id]
			else:
					A				=[]

			A				=bos_token_ids + token_ids_a

			if token_ids_a is None:
					return output

			return output + bos_token_ids + token_ids_a







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".join(
					    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			str							,			snake_case__				:			Dict ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			List[str]							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





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

			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as f:
					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__ ) + "\n" )

			A				=0
			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as writer:
					writer.write("#version: 0.2\n" )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda snake_case__ : kv[1] ):
							if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]=False							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if is_split_into_words or add_prefix_space:
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			str							,			snake_case__				:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,			snake_case__				:			bool = False							,			snake_case__				:			bool = None							,			snake_case__				:			Optional[List[str]] = None							,			**snake_case__				:			Union[str, Any]							,			):
			"""simple docstring"""





			A				=super()._decode(
			    token_ids=snake_case__							,			skip_special_tokens=snake_case__							,			clean_up_tokenization_spaces=snake_case__							,			**snake_case__							,			)

			if truncate_before_pattern is not None and len(snake_case__ ) > 0:
					A				=self.truncate(snake_case__							,			snake_case__ )

			return decoded_text







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any]							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			def find_re(snake_case__				:			Dict							,			snake_case__				:			Tuple							,			snake_case__				:			int ):
					A				=pattern.search(snake_case__							,			snake_case__ )
					return m.start() if m else -1

			A				=[re.compile(snake_case__							,			re.MULTILINE ) for pattern in truncate_before_pattern]

			A				=list(re.finditer("^print"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: prints[1].start()]

			A				=list(re.finditer("^def"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: defs[1].start()]

			A				=0

			A				=[
			    pos for pos in [find_re(snake_case__							,			snake_case__							,			snake_case__ ) for terminal in terminals] if pos != -1
			]

			if len(snake_case__ ) > 0:
					return completion[: min(snake_case__ )]
			else:
					return completion




