




"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}




class 			A__	(     _SCREAMING_SNAKE_CASE):
		A_						:	Any           =     'camembert'

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=3_05_22	,		_SCREAMING_SNAKE_CASE=7_68	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=30_72	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=5_12	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=0.02	,		_SCREAMING_SNAKE_CASE=1E-12	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=0	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE="absolute"	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE	,		bos_token_id=_SCREAMING_SNAKE_CASE	,		eos_token_id=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

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




class 			A__	(     _SCREAMING_SNAKE_CASE):

		@property
		def 							__lowerCamelCase							(				self			):
			if self.task == "multiple-choice":
				__lowerCAmelCase      :						int						      =				{0: """batch""", 1: """choice""", 2: """sequence"""}
			else:
				__lowerCAmelCase      :						str						      =				{0: """batch""", 1: """sequence"""}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ]			)

"""simple docstring"""







import os
def    __lowerCAmelCase    (_UpperCamelCase = "input.txt"		):

	with open(os.path.join(os.path.dirname(_UpperCamelCase		)	,  _UpperCamelCase		)		) as input_file:
		__lowerCAmelCase      :						Optional[Any]						      =				[
		    [int(_UpperCamelCase		) for element in line.split(','		)]
		    for line in input_file.readlines()
		]

	__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				len(matrix[0]		)

	__lowerCAmelCase      :						int						      =				[[-1 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				matrix[i][0]

	for j in range(1	,  _UpperCamelCase		):
		for i in range(_UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i - 1][j] + matrix[i][j]		)

		for i in range(rows - 2	,  -1	,  -1		):
			__lowerCAmelCase      :						List[str]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i + 1][j] + matrix[i][j]		)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
							print(f'{solution() = }')