




'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

a    :							Optional[int]										=    TypeVar('T')






class 		a							(  Generic[T]	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   Tuple   ):
									snake_case_		     =							data
									snake_case_		     =							self
									snake_case_		     =							0







class 		a							(  Generic[T]	):


				def __init__(						self       :   int   ):
									# map from node name to the node object
									snake_case_		     =							{}


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Union[str, Any]   ):
									# create a new set with x as its member
									snake_case_		     =							DisjointSetTreeNode(lowercase_   )


				def     A_					(						self       :   int			,							lowercase_       :   List[str]   ):
									# find the set x belongs to (with path-compression)
									snake_case_		     =							self.map[data]
									if elem_ref != elem_ref.parent:
														snake_case_		     =							self.find_set(elem_ref.parent.data   )
									return elem_ref.parent


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									# helper function for union operation
									if nodea.rank > nodea.rank:
														snake_case_		     =							nodea
									else:
														snake_case_		     =							nodea
														if nodea.rank == nodea.rank:
																			nodea.rank += 1


				def     A_					(						self       :   Dict			,							lowercase_       :   str			,							lowercase_       :   str   ):
									# merge 2 disjoint sets
									self.link(self.find_set(lowercase_   )			,							self.find_set(lowercase_   )   )







class 		a							(  Generic[T]	):


				def __init__(						self       :   str   ):
									# connections: map from the node to the neighbouring nodes (with weights)
									snake_case_		     =							{}


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									# add a node ONLY if its not present in the graph
									if node not in self.connections:
														snake_case_		     =							{}


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   int			,							lowercase_       :   Optional[int]   ):
									# add an edge with the given weight
									self.add_node(lowercase_   )
									self.add_node(lowercase_   )
									snake_case_		     =							weight
									snake_case_		     =							weight


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							[]
									snake_case_		     =							set()
									for start in self.connections:
														for end in self.connections[start]:
																			if (start, end) not in seen:
																								seen.add((end, start)   )
																								edges.append((start, end, self.connections[start][end])   )
									edges.sort(key=lambda lowercase_   : x[2]   )

									# creating the disjoint set
									snake_case_		     =							DisjointSetTree[T]()
									for node in self.connections:
														disjoint_set.make_set(lowercase_   )

									# MST generation
									snake_case_		     =							0
									snake_case_		     =							0
									snake_case_		     =							GraphUndirectedWeighted[T]()
									while num_edges < len(self.connections   ) - 1:
														snake_case_     ,snake_case_     ,snake_case_		     =							edges[index]
														index += 1
														snake_case_		     =							disjoint_set.find_set(lowercase_   )
														snake_case_		     =							disjoint_set.find_set(lowercase_   )
														if parent_u != parent_v:
																			num_edges += 1
																			graph.add_edge(lowercase_			,							lowercase_			,							lowercase_   )
																			disjoint_set.union(lowercase_			,							lowercase_   )
									return graph



"""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			):
				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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



