



from __future__ import annotations

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase=None  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Dict													=  data
									a__       :						Any													=  None

						def __repr__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]													=  []
									a__       :						Dict													=  self
									while temp:
												string_rep.append(F'''{temp.data}'''  )
												a__       :						List[str]													=  temp.next
									return "->".join(__lowercase  )




def      lowerCAmelCase_					(  _lowercase    :       list)							->	Dict:







			"""simple docstring"""
			if not elements_list:
						raise Exception("""The Elements List is empty""")

			a__       :						Any													=  Node(elements_list[0])
			for i in range(1						,       len(_lowercase)):
						a__       :						int													=  Node(elements_list[i])
						a__       :						Tuple													=  current.next
			return head

def      lowerCAmelCase_					(  _lowercase    :       Node)							->	None:







			"""simple docstring"""
			if head_node is not None and isinstance(_lowercase						,       _lowercase):
						print_reverse(head_node.next)
						print(head_node.data)

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			from doctest import testmod

			testmod()

			a__       :						Tuple													=  make_linked_list([14, 52, 14, 12, 43])
			print("""Linked List:""")
			print(_lowercase)
			print("""Elements in Reverse:""")
			print_reverse(_lowercase)


if __name__ == "__main__":
		main()









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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




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




