


'''simple docstring'''






import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					:    str	       =    logging.get_logger(__name__)

a_					:    List[Any]	       =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""encodec"""
 def __init__( self      ,				__magic_name__=[1.5, 3.0, 6.0, 12.0, 24.0]      ,				__magic_name__=2_40_00      ,				__magic_name__=1      ,				__magic_name__=False      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=1_28      ,				__magic_name__=32      ,				__magic_name__=1      ,				__magic_name__=[8, 5, 4, 2]      ,				__magic_name__="weight_norm"      ,				__magic_name__=7      ,				__magic_name__=7      ,				__magic_name__=3      ,				__magic_name__=2      ,				__magic_name__=True      ,				__magic_name__="reflect"      ,				__magic_name__=2      ,				__magic_name__=2      ,				__magic_name__=1.0      ,				__magic_name__=10_24      ,				__magic_name__=None      ,				__magic_name__=True      ,				**__magic_name__      ,				)					->							Optional[Any]:
      _a			      =							target_bandwidths
      _a			      =							sampling_rate
      _a			      =							audio_channels
      _a			      =							normalize
      _a			      =							chunk_length_s
      _a			      =							overlap
      _a			      =							hidden_size
      _a			      =							num_filters
      _a			      =							num_residual_layers
      _a			      =							upsampling_ratios
      _a			      =							norm_type
      _a			      =							kernel_size
      _a			      =							last_kernel_size
      _a			      =							residual_kernel_size
      _a			      =							dilation_growth_rate
      _a			      =							use_causal_conv
      _a			      =							pad_mode
      _a			      =							compress
      _a			      =							num_lstm_layers
      _a			      =							trim_right_ratio
      _a			      =							codebook_size
      _a			      =							codebook_dim if codebook_dim is not None else hidden_size
      _a			      =							use_conv_shortcut

      if self.norm_type not in ["weight_norm", "time_group_norm"]:
           raise ValueError(
               f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'						)

      super().__init__(**__magic_name__						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      if self.chunk_length_s is None:
           return None
      else:
           return int(self.chunk_length_s * self.sampling_rate						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      if self.chunk_length_s is None or self.overlap is None:
           return None
      else:
           return max(1      ,				int((1.0 - self.overlap) * self.chunk_length						)						)
 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							np.prod(self.upsampling_ratios						)
      return math.ceil(self.sampling_rate / hop_length						)




 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Optional[int]:




     '''simple docstring'''



     print('\nThe shortest path matrix using Floyd Warshall algorithm\n'		)
     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               if dist[i][j] != float('inf'		):
                    print(int(dist[i][j]		)				,						end='\t'		)
               else:
                    print('INF'				,						end='\t'		)
          print()






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[[float('inf'		) for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]

     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               _a			      =							graph[i][j]

               # check vertex k against all other vertices (i, j)
     for k in range(lowerCAmelCase__		):
          # looping through rows of graph array
          for i in range(lowerCAmelCase__		):
               # looping through columns of graph array
               for j in range(lowerCAmelCase__		):
                    if (
                        dist[i][k] != float('inf'		)
                        and dist[k][j] != float('inf'		)
                        and dist[i][k] + dist[k][j] < dist[i][j]
                    ):
                         _a			      =							dist[i][k] + dist[k][j]

     _print_dist(lowerCAmelCase__				,						lowerCAmelCase__		)
     return dist, v


if __name__ == "__main__":
 a_					:    Optional[Any]          =    int(input("Enter number of vertices: "))
 a_					:    List[Any]          =    int(input("Enter number of edges: "))

 a_					:    Dict          =    [[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  a_					:    Union[str, Any]          =    0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  a_					:    Union[str, Any]          =    int(input("Enter source:"))
  a_					:    int          =    int(input("Enter destination:"))
  a_					:    Tuple          =    float(input("Enter weight:"))
  a_					:    Dict          =    weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0

