




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""ernie"""

					def __init__(							self					,			__lowercase=30_522					,			__lowercase=768					,			__lowercase=12					,			__lowercase=12					,			__lowercase=3_072					,			__lowercase="gelu"					,			__lowercase=0.1					,			__lowercase=0.1					,			__lowercase=512					,			__lowercase=2					,			__lowercase=3					,			__lowercase=False					,			__lowercase=0.02					,			__lowercase=1E-12					,			__lowercase=0					,			__lowercase="absolute"					,			__lowercase=True					,			__lowercase=None					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(pad_token_id=__lowercase					,			**__lowercase   )

						lowerCamelCase_				:						Optional[int]				  =				vocab_size
						lowerCamelCase_				:						Union[str, Any]				  =				hidden_size
						lowerCamelCase_				:						List[Any]				  =				num_hidden_layers
						lowerCamelCase_				:						str				  =				num_attention_heads
						lowerCamelCase_				:						Tuple				  =				hidden_act
						lowerCamelCase_				:						Optional[Any]				  =				intermediate_size
						lowerCamelCase_				:						Union[str, Any]				  =				hidden_dropout_prob
						lowerCamelCase_				:						int				  =				attention_probs_dropout_prob
						lowerCamelCase_				:						Optional[int]				  =				max_position_embeddings
						lowerCamelCase_				:						Optional[int]				  =				type_vocab_size
						lowerCamelCase_				:						Any				  =				task_type_vocab_size
						lowerCamelCase_				:						Dict				  =				use_task_id
						lowerCamelCase_				:						Union[str, Any]				  =				initializer_range
						lowerCamelCase_				:						Tuple				  =				layer_norm_eps
						lowerCamelCase_				:						Optional[Any]				  =				position_embedding_type
						lowerCamelCase_				:						str				  =				use_cache
						lowerCamelCase_				:						Optional[Any]				  =				classifier_dropout


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_				:						str				  =				{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							lowerCamelCase_				:						int				  =				{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )






UpperCamelCase__						=		[
    [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    snake_case   (				A		,  A		,  A		,  A     )				->				bool:




	'''simple docstring'''


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






def    snake_case   (				A		,  A		,  A		,  A     )				->				int:




	'''simple docstring'''


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






def    snake_case   (				A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						set				  =				set()

	lowerCamelCase_				:						Optional[Any]				  =				0
	for row, line in enumerate(A     ):
		for col, item in enumerate(A     ):
			if item == 1 and (row, col) not in seen:
				# Maximizing the area
				lowerCamelCase_				:						Optional[Any]				  =				max(A		,  depth_first_search(A		,  A		,  A		,  A     )     )
	return max_area


if __name__ == "__main__":
							import doctest

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