



"""simple docstring"""
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      =				logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			bool						=     field(default=_lowerCAmelCase    ,						metadata={"""help""": """Whether to use SortishSampler or not."""}				)
					_A		:			bool						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}				)
					_A		:			Optional[int]						=     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."""
					        )
					    }    ,						)
					_A		:			Optional[int]						=     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."""
					        )
					    }    ,						)
					_A		:			Optional[Union[str, Path, GenerationConfig]]						=     field(
					    default=_lowerCAmelCase    ,						metadata={
					        """help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
					    }    ,						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	str:
											snake_case_ :List[str]						=			super().to_dict()
											for k, v in d.items():
																	if isinstance(snake_case						,     snake_case						):
																							snake_case_ :Dict						=			v.to_dict()
											return d





"""simple docstring"""
from __future__ import annotations

from collections import Counter
from random import random
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Tuple						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			{}






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						)   ->	None:
											snake_case_ :str						=			{}






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							str						,     snake_case:							float						)   ->	None:
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											snake_case_ :Dict						=			probability






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	list[str]:
											return list(self.connections						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							str						)   ->	str:
											snake_case_ :Optional[Any]						=			0
											snake_case_ :List[str]						=			random()

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

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			MarkovChainGraphUndirectedUnweighted()

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

						snake_case_ :int						=			Counter(graph.get_nodes()	)
						snake_case_ :Optional[Any]						=			start

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

						return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
