'''simple docstring'''


import copy
from typing import Dict

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


snake_case__	:     Union[str, Any]      =    logging.get_logger(__name__)






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Union[str, Any]		       =						'''semantic'''


  def __init__(					self ,						snake_case_=1_0_0_0_0 ,						snake_case_=True ,						snake_case_=7_6_8 ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False ,						snake_case_=0.7 ,						snake_case_=True ,						snake_case_=1_0_0_4_8 ,						snake_case_=1_2_9_5_9_5 ,						snake_case_=1_2_9_5_9_9 ,						snake_case_=1_0_0_0_0 ,						snake_case_=2_5_6 ,						snake_case_=49.9 ,						**snake_case_ ,						):




       '''simple docstring'''

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

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







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''coarse_acoustics'''


  def __init__(					self ,						snake_case_=True ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False ,						snake_case_=0.7 ,						snake_case_=True ,						snake_case_=1_2_0_4_8 ,						snake_case_=7_5 ,						snake_case_=2 ,						snake_case_=1_2_0_5_0 ,						snake_case_=2_5_6 ,						snake_case_ = 6_3_0 ,						snake_case_ = 6_0 ,						**snake_case_ ,						):




       '''simple docstring'''

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

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







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[str]		       =						'''fine_acoustics'''


  def __init__(					self ,						snake_case_=0.5 ,						snake_case_=5_1_2 ,						snake_case_=1_0_2_4 ,						snake_case_=8 ,						**snake_case_ ,						):




       '''simple docstring'''

       super().__init__(temperature=snake_case_		)

       UpperCAmelCase_				:  str  = max_fine_history_length
       UpperCAmelCase_				:  Optional[Any]  = max_fine_input_length
       UpperCAmelCase_				:  Tuple  = n_fine_codebooks







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[int]		       =						'''bark'''
  lowerCamelCase_						:Dict		       =						True


  def __init__(					self ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_=2_4_0_0_0 ,						snake_case_=1_0_2_4 ,						**snake_case_ ,						):




       '''simple docstring'''

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

       UpperCAmelCase_				:  str  = BarkSemanticGenerationConfig(**snake_case_		)
       UpperCAmelCase_				:  List[str]  = BarkCoarseGenerationConfig(**snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = BarkFineGenerationConfig(**snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = sample_rate
       UpperCAmelCase_				:  List[str]  = codebook_size


  @classmethod
  def _UpperCamelCase	(					cls ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						**snake_case_ ,						):




       '''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() ,						**snake_case_ ,						)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = copy.deepcopy(self.__dict__		)

       UpperCAmelCase_				:  str  = self.semantic_config.to_dict()
       UpperCAmelCase_				:  Any  = self.coarse_acoustics_config.to_dict()
       UpperCAmelCase_				:  str  = self.fine_acoustics_config.to_dict()

       UpperCAmelCase_				:  Tuple  = self.__class__.model_type
       return output







'''simple docstring'''


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = key
       UpperCAmelCase_				:  Tuple  = freq






  def __str__(					self		):




       '''simple docstring'''

       return F'''Node(key={self.key}, freq={self.freq})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     if i > j or i < 0 or j > len(lowerCamelCase_  ) - 1:
          return

     UpperCAmelCase_				:  Optional[int]  = root[i][j]
     if parent == -1:  # root does not have a parent
          print(F'''{key[node]} is the root of the binary search tree.'''  )
     elif is_left:
          print(F'''{key[node]} is the left child of key {parent}.'''  )
     else:
          print(F'''{key[node]} is the right child of key {parent}.'''  )

     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	node - 1      ,	key[node]      ,	lowerCamelCase_  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	node + 1      ,	lowerCamelCase_      ,	key[node]      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     nodes.sort(key=lambda lowerCamelCase_  : node.key  )

     UpperCAmelCase_				:  int  = len(lowerCamelCase_  )

     UpperCAmelCase_				:  Dict  = [nodes[i].key for i in range(lowerCamelCase_  )]
     UpperCAmelCase_				:  Any  = [nodes[i].freq for i in range(lowerCamelCase_  )]

     # This 2D array stores the overall tree cost (which's as minimized as possible);
     # for a single key, cost is equal to frequency of the key.
     UpperCAmelCase_				:  Optional[Any]  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCAmelCase_				:  Any  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # stores tree roots that will be used later for constructing binary search tree
     UpperCAmelCase_				:  Optional[int]  = [[i if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]

     for interval_length in range(2      ,	n + 1  ):
          for i in range(n - interval_length + 1  ):
               UpperCAmelCase_				:  Optional[Any]  = i + interval_length - 1

               UpperCAmelCase_				:  Tuple  = sys.maxsize  # set the value to "infinity"
               UpperCAmelCase_				:  Any  = total[i][j - 1] + freqs[j]

               # Apply Knuth's optimization
               # Loop without optimization: for r in range(i, j + 1):
               for r in range(root[i][j - 1]      ,	root[i + 1][j] + 1  ):  # r is a temporal root
                    UpperCAmelCase_				:  str  = dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    UpperCAmelCase_				:  Optional[int]  = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    UpperCAmelCase_				:  Optional[int]  = left + total[i][j] + right

                    if dp[i][j] > cost:
                         UpperCAmelCase_				:  Dict  = cost
                         UpperCAmelCase_				:  Tuple  = r

     print('Binary search tree nodes:'  )
     for node in nodes:
          print(lowerCamelCase_  )

     print(F'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	0      ,	n - 1      ,	-1      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = [Node(lowerCamelCase_      ,	randint(1      ,	50  )  ) for i in range(10      ,	0      ,	-1  )]
     find_optimal_binary_search_tree(lowerCamelCase_  )


if __name__ == "__main__":
 main()






