




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=__a       )
class 		lowercase_   (			__a       ):


   """simple docstring"""

   lowerCamelCase_      =						field(default='''automatic-speech-recognition'''					,  metadata={'''include_in_asdict_even_if_is_default''': True}       )
   lowerCamelCase_      =						Features({'''audio''': Audio()}       )
   lowerCamelCase_      =						Features({'''transcription''': Value('''string'''       )}       )
   lowerCamelCase_      =						'''audio'''
   lowerCamelCase_      =						'''transcription'''



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""



      if self.audio_column not in features:
         raise ValueError(F"""Column {self.audio_column} is not present in features."""   )
      if not isinstance(features[self.audio_column]  ,				UpperCamelCase__   ):
         raise ValueError(F"""Column {self.audio_column} is not an Audio type."""   )
      _SCREAMING_SNAKE_CASE	=				copy.deepcopy(self   )
      _SCREAMING_SNAKE_CASE	=				self.input_schema.copy()
      _SCREAMING_SNAKE_CASE	=				features[self.audio_column]
      _SCREAMING_SNAKE_CASE	=				input_schema
      return task_template



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      return {self.audio_column: "audio", self.transcription_column: "transcription"}







'''simple docstring'''



import random
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			float	,					__A    :			bool = False  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				{i: [] for i in range(__A  )}

   # if probability is greater or equal than 1, then generate a complete graph
   if probability >= 1:
      return complete_graph(__A  )
   # if probability is lower or equal than 0, then return a graph without edges
   if probability <= 0:
      return graph

   # for each couple of nodes, add an edge from u to v
   # if the number randomly generated is greater than probability probability
   for i in range(__A  ):
      for j in range(i + 1	,					__A  ):
         if random.random() < probability:
            graph[i].append(__A  )
            if not directed:
               # if the graph is undirected, add an edge in from j to i, either
               graph[j].append(__A  )
   return graph
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			dict:
   return {
       i: [j for j in range(__A  ) if i != j] for i in range(__A  )
   }


if __name__ == "__main__":
 import doctest

 doctest.testmod()

