


'''simple docstring'''

from __future__ import annotations





def __snake_case					(      UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  len(_UpperCamelCase		)
 # We need to create solution object to save path.
 lowerCamelCase_      =  [[0 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
 lowerCamelCase_      =  run_maze(_UpperCamelCase					,				0					,				0					,				_UpperCamelCase		)
 if solved:
  print("\n".join(str(_UpperCamelCase		) for row in solutions		)		)
 else:
  print("No solution exists!"		)
 return solved





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  len(_UpperCamelCase		)
 # Final check point.
 if i == j == (size - 1):
  lowerCamelCase_      =  1
  return True

 lowerCamelCase_      =  (not i < 0) and (not j < 0)  # Check lower bounds
 lowerCamelCase_      =  (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  lowerCamelCase_      =  (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   lowerCamelCase_      =  1

   # check for directions
   if (
       run_maze(_UpperCamelCase					,				i + 1					,				_UpperCamelCase					,				_UpperCamelCase		)
       or run_maze(_UpperCamelCase					,				_UpperCamelCase					,				j + 1					,				_UpperCamelCase		)
       or run_maze(_UpperCamelCase					,				i - 1					,				_UpperCamelCase					,				_UpperCamelCase		)
       or run_maze(_UpperCamelCase					,				_UpperCamelCase					,				j - 1					,				_UpperCamelCase		)
   ):
    return True

   lowerCamelCase_      =  0
   return False
 return False


if __name__ == "__main__":
     import doctest

     doctest.testmod()







"""simple docstring"""


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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







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




