
'''simple docstring'''
lowercase__						=			[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       bool:


		'''simple docstring'''


		return 0 <= row < rows and 0 <= col < cols




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					Union[str, Any]     =	len(SCREAMING_SNAKE_CASE__ )
		snake_case     :					str     =	len(mat[0] )
		if is_safe(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) and (row, col) not in seen and mat[row][col] == 1:
				seen.add((row, col) )
				return (
				    1
				    + depth_first_search(row + 1   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
				    + depth_first_search(row - 1   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
				    + depth_first_search(SCREAMING_SNAKE_CASE__   ,					col + 1   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
				    + depth_first_search(SCREAMING_SNAKE_CASE__   ,					col - 1   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
				)
		else:
				return 0




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					set     =	set()

		snake_case     :					Optional[int]     =	0
		for row, line in enumerate(SCREAMING_SNAKE_CASE__ ):
				for col, item in enumerate(SCREAMING_SNAKE_CASE__ ):
						if item == 1 and (row, col) not in seen:
								# Maximizing the area
								snake_case     :					List[str]     =	max(SCREAMING_SNAKE_CASE__   ,					depth_first_search(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) )
		return max_area


if __name__ == "__main__":
						import doctest

						print(find_max_area(matrix))  # Output -> 6
						doctest.testmod()




'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
		snake_case     :					Any     =	GPTNeoConfig(
		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
		print(F'Building PyTorch model from configuration: {config}' )
		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )

		# Load weights from tf checkpoint
		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )

		# Save pytorch-model
		print(F'Save PyTorch model to {pytorch_dump_path}' )
		model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
						lowercase__						=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						lowercase__						=			parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


