



from __future__ import annotations






def      lowerCAmelCase__		(   lowerCamelCase_		:		list[list[int]]):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					len(__A)
				# We need to create solution object to save path.
				lowerCAmelCase__					:    Any												=					[[0 for _ in range(__A)] for _ in range(__A)]
				lowerCAmelCase__					:    List[Any]												=					run_maze(__A	,0	,0	,__A)
				if solved:
								print('''\n'''.join(str(__A) for row in solutions))
				else:
								print('''No solution exists!''')
				return solved






def      lowerCAmelCase__		(   lowerCamelCase_		:		list[list[int]]	,lowerCamelCase_		:		int	,lowerCamelCase_		:		int	,lowerCamelCase_		:		list[list[int]]):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					len(__A)
				# Final check point.
				if i == j == (size - 1):
								lowerCAmelCase__					:    List[str]												=					1
								return True

				lowerCAmelCase__					:    Optional[int]												=					(not i < 0) and (not j < 0)  # Check lower bounds
				lowerCAmelCase__					:    Union[str, Any]												=					(i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								lowerCAmelCase__					:    Optional[Any]												=					(not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												lowerCAmelCase__					:    Union[str, Any]												=					1

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

												lowerCAmelCase__					:    Tuple												=					0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

