
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_snake_case      = logging.getLogger(__name__)
@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=0.0			,	metadata={"help": "The label smoothing epsilon to apply (if not zero)."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(default=__magic_name__			,	metadata={"help": "Whether to SortishSamler or not."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(
     default=__magic_name__			,	metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(default=__magic_name__			,	metadata={"help": "whether to use adafactor"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=__magic_name__			,	metadata={"help": "Encoder layer dropout probability. Goes into model.config."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=__magic_name__			,	metadata={"help": "Decoder layer dropout probability. Goes into model.config."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(default=__magic_name__			,	metadata={"help": "Dropout probability. Goes into model.config."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=__magic_name__			,	metadata={"help": "Attention dropout probability. Goes into model.config."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default="linear"			,	metadata={"help": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''}			,	)




from __future__ import annotations

from collections import Counter
from random import random
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			float   ):






      """simple docstring"""



      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					probability

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return list(self.connections   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					0
      UpperCamelCase						    =					random()

      for dest in self.connections[node]:
           current_probability += self.connections[node][dest]
           if current_probability > random_value:
                return dest
      return ""




def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      dict[str, int]:
     UpperCamelCase						    =					MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(_lowercase						,     _lowercase						,     _lowercase			)

     UpperCamelCase						    =					Counter(graph.get_nodes()			)
     UpperCamelCase						    =					start

     for _ in range(_lowercase			):
          UpperCamelCase						    =					graph.transition(_lowercase			)
          visited[node] += 1

     return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()


