


def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       if not head:
              return True
       # split the list to two parts
       UpperCAmelCase__						,			UpperCAmelCase__										=     head.next, head
       while fast and fast.next:
              UpperCAmelCase__										=     fast.next.next
              UpperCAmelCase__										=     slow.next
       UpperCAmelCase__										=     slow.next
       UpperCAmelCase__										=     None  # Don't forget here! But forget still works!
       # reverse the second part
       UpperCAmelCase__										=     None
       while second:
              UpperCAmelCase__										=     second.next
              UpperCAmelCase__										=     node
              UpperCAmelCase__										=     second
              UpperCAmelCase__										=     nxt
       # compare two parts
       # second part has the same or one less node
       while node:
              if node.val != head.val:
                     return False
              UpperCAmelCase__										=     node.next
              UpperCAmelCase__										=     head.next
       return True





def __UpperCAmelCase       (		__A			)						->					Tuple:



       '''simple docstring'''





       if not head or not head.next:
              return True

       # 1. Get the midpoint (slow)
       UpperCAmelCase__										=     UpperCAmelCase__										=     UpperCAmelCase__										=     head
       while fast and fast.next:
              UpperCAmelCase__						,			UpperCAmelCase__										=     fast.next.next, slow.next

       # 2. Push the second half into the stack
       UpperCAmelCase__										=     [slow.val]
       while slow.next:
              UpperCAmelCase__										=     slow.next
              stack.append(slow.val			)

       # 3. Comparison
       while stack:
              if stack.pop() != cur.val:
                     return False
              UpperCAmelCase__										=     cur.next

       return True





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       if not head or not head.next:
              return True
       UpperCAmelCase__										=     {}
       UpperCAmelCase__										=     0
       while head:
              if head.val in d:
                     d[head.val].append(__A			)
              else:
                     UpperCAmelCase__										=     [pos]
              UpperCAmelCase__										=     head.next
              pos += 1
       UpperCAmelCase__										=     pos - 1
       UpperCAmelCase__										=     0
       for v in d.values():
              if len(__A			) % 2 != 0:
                     middle += 1
              else:
                     UpperCAmelCase__										=     0
                     for i in range(0 ,						len(__A			)			):
                            if v[i] + v[len(__A			) - 1 - step] != checksum:
                                   return False
                            step += 1
              if middle > 1:
                     return False
       return True









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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


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





