





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a__:      Any									=		logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__      )
class     SCREAMING_SNAKE_CASE__ (						lowerCamelCase_      ):
 __SCREAMING_SNAKE_CASE							       =	field(default=lowerCamelCase_				,					metadata={'''help''': '''Whether to use SortishSampler or not.'''}      )
 __SCREAMING_SNAKE_CASE							       =	field(
     default=lowerCamelCase_				,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}      )
 __SCREAMING_SNAKE_CASE							       =	field(
     default=lowerCamelCase_				,					metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }				,					)
 __SCREAMING_SNAKE_CASE							       =	field(
     default=lowerCamelCase_				,					metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }				,					)
 __SCREAMING_SNAKE_CASE							       =	field(
     default=lowerCamelCase_				,					metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }				,					)



 def 				UpperCamelCase			(							self ):
        A__											=						super().to_dict()
        for k, v in d.items():
               if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
                      A__											=						v.to_dict()
        return d







'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""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		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

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







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

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

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



