

import copy
from typing import Dict

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


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''semantic'''




 def __init__(			self  ,  _UpperCAmelCase=10_000  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=768  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=0.7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=10_048  ,  _UpperCAmelCase=129_595  ,  _UpperCAmelCase=129_599  ,  _UpperCAmelCase=10_000  ,  _UpperCAmelCase=256  ,  _UpperCAmelCase=4_9.9  ,  **_UpperCAmelCase  ,  ):
   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  ,  )

   __UpperCAmelCase	:  List[Any]		=     text_encoding_offset
   __UpperCAmelCase	:  str		=     text_pad_token
   __UpperCAmelCase	:  Any		=     eos_token_id
   __UpperCAmelCase	:  Dict		=     semantic_infer_token
   __UpperCAmelCase	:  Tuple		=     semantic_vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     max_input_semantic_length
   __UpperCAmelCase	:  Optional[Any]		=     semantic_rate_hz



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''coarse_acoustics'''




 def __init__(			self  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=0.7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=12_048  ,  _UpperCAmelCase=75  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=12_050  ,  _UpperCAmelCase=256  ,  _UpperCAmelCase = 630  ,  _UpperCAmelCase = 60  ,  **_UpperCAmelCase  ,  ):
   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  ,  )

   __UpperCAmelCase	:  Optional[int]		=     coarse_semantic_pad_token
   __UpperCAmelCase	:  Dict		=     coarse_rate_hz
   __UpperCAmelCase	:  int		=     n_coarse_codebooks
   __UpperCAmelCase	:  str		=     coarse_infer_token
   __UpperCAmelCase	:  Union[str, Any]		=     max_coarse_input_length
   __UpperCAmelCase	:  List[str]		=     max_coarse_history
   __UpperCAmelCase	:  str		=     sliding_window_len



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''fine_acoustics'''




 def __init__(			self  ,  _UpperCAmelCase=0.5  ,  _UpperCAmelCase=512  ,  _UpperCAmelCase=1_024  ,  _UpperCAmelCase=8  ,  **_UpperCAmelCase  ,  ):
   super().__init__(temperature=_UpperCAmelCase							)

   __UpperCAmelCase	:  Union[str, Any]		=     max_fine_history_length
   __UpperCAmelCase	:  Optional[int]		=     max_fine_input_length
   __UpperCAmelCase	:  Tuple		=     n_fine_codebooks



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''bark'''
 _lowerCAmelCase									=	True




 def __init__(			self  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase=24_000  ,  _UpperCAmelCase=1_024  ,  **_UpperCAmelCase  ,  ):
   if semantic_config is None:
     __UpperCAmelCase	:  Union[str, Any]		=     {}
     logger.info('semantic_config is None. initializing the semantic model with default values.'							)

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

   if fine_acoustics_config is None:
     __UpperCAmelCase	:  Union[str, Any]		=     {}
     logger.info('fine_acoustics_config is None. initializing the fine model with default values.'							)

   __UpperCAmelCase	:  List[str]		=     BarkSemanticGenerationConfig(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Tuple		=     BarkCoarseGenerationConfig(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Tuple		=     BarkFineGenerationConfig(**_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     sample_rate
   __UpperCAmelCase	:  int		=     codebook_size




 @classmethod
 def     a       (			cls  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  **_UpperCAmelCase  ,  ):
   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     a       (			self							):
   __UpperCAmelCase	:  str		=     copy.deepcopy(self.__dict__							)

   __UpperCAmelCase	:  int		=     self.semantic_config.to_dict()
   __UpperCAmelCase	:  Union[str, Any]		=     self.coarse_acoustics_config.to_dict()
   __UpperCAmelCase	:  List[Any]		=     self.fine_acoustics_config.to_dict()

   __UpperCAmelCase	:  List[str]		=     self.__class__.model_type
   return output












def  A_(      A = 1000 ):
	UpperCAmelCase_		    =  0
	for i in range(A ):
		if i % 3 == 0 or i % 5 == 0:
			result += i
	return result


if __name__ == "__main__":
				print(f"{solution() = }")


