




'''simple docstring'''
from __future__ import annotations





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     )
			# We need to create solution object to save path.
			A_									= [[0 for _ in range(SCREAMING_SNAKE_CASE_     )] for _ in range(SCREAMING_SNAKE_CASE_     )]
			A_									= run_maze(SCREAMING_SNAKE_CASE_       ,0       ,0       ,SCREAMING_SNAKE_CASE_     )
			if solved:
						print('\n'.join(str(SCREAMING_SNAKE_CASE_     ) for row in solutions     )     )
			else:
						print('No solution exists!'     )
			return solved





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     )
			# Final check point.
			if i == j == (size - 1):
						A_									= 1
						return True

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

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

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

									A_									= 0
									return False
			return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()










'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




