

'''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








'''simple docstring'''

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


lowerCAmelCase_						   =			logging.get_logger(__name__)

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

lowerCAmelCase_						   =			{
    "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",
    },
}

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

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

def     __magic_name__       ( A							)			->		int:
	snake_case							 =				set()
	snake_case							 =				word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		snake_case							 =				char
	return pairs







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_,	lowercase_="replace",	lowercase_="<|endoftext|>",	lowercase_="<|endoftext|>",	lowercase_="<|endoftext|>",	lowercase_=None,	lowercase_=False,	lowercase_=False,	**lowercase_,	)						->       int:
					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__(
					    errors=lowercase_,	unk_token=lowercase_,	bos_token=lowercase_,	eos_token=lowercase_,	pad_token=lowercase_,	add_prefix_space=lowercase_,	add_bos_token=lowercase_,	**lowercase_,	)
					snake_case							 =				add_bos_token

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

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

				@property
				def 				_lowerCamelCase						(					self   )						->       Tuple:
					return len(self.encoder   )

				def 				_lowerCamelCase						(					self   )						->       str:
					return dict(self.encoder,	**self.added_tokens_encoder   )

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					if token in self.cache:
						return self.cache[token]
					snake_case							 =				tuple(lowercase_   )
					snake_case							 =				get_pairs(lowercase_   )

					if not pairs:
						return token

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

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

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

					snake_case							 =				bos_token_ids + token_ids_a

					if token_ids_a is None:
						return output

					return output + bos_token_ids + token_ids_a

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Union[str, Any]:
					snake_case							 =				[]
					for token in re.findall(self.pat,	lowercase_   ):
						snake_case							 =				''.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(lowercase_   ).split(' '   )   )
					return bpe_tokens

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       int:
					return self.encoder.get(lowercase_,	self.encoder.get(self.unk_token   )   )

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

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					snake_case							 =				''.join(lowercase_   )
					snake_case							 =				bytearray([self.byte_decoder[c] for c in text]   ).decode('utf-8',	errors=self.errors   )
					return text

				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']   )
					snake_case							 =				os.path.join(
					    lowercase_,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']   )

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

					snake_case							 =				0
					with open(lowercase_,	'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 lowercase_   : 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!'   )
								snake_case							 =				token_index
							writer.write(' '.join(lowercase_   ) + '\n'   )
							index += 1

					return vocab_file, merge_file

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=False,	**lowercase_   )						->       List[str]:
					snake_case							 =				kwargs.pop('add_prefix_space',	self.add_prefix_space   )
					if is_split_into_words or add_prefix_space:
						snake_case							 =				' ' + text
					return (text, kwargs)

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = False,	lowercase_ = None,	lowercase_ = None,	**lowercase_,	)						->       str:
					snake_case							 =				super()._decode(
					    token_ids=lowercase_,	skip_special_tokens=lowercase_,	clean_up_tokenization_spaces=lowercase_,	**lowercase_,	)

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

					return decoded_text






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_   )						->       Optional[int]:
					def find_re(lowercase_,	lowercase_,	lowercase_   ):
						snake_case							 =				pattern.search(lowercase_,	lowercase_   )
						return m.start() if m else -1

					snake_case							 =				[re.compile(lowercase_,	re.MULTILINE   ) for pattern in truncate_before_pattern]

					snake_case							 =				list(re.finditer('^print',	lowercase_,	re.MULTILINE   )   )

					if len(lowercase_   ) > 1:
						snake_case							 =				completion[: prints[1].start()]

					snake_case							 =				list(re.finditer('^def',	lowercase_,	re.MULTILINE   )   )

					if len(lowercase_   ) > 1:
						snake_case							 =				completion[: defs[1].start()]

					snake_case							 =				0

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

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





