


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowercase       :      Optional[Any]	=	logging.get_logger(__name__)

_lowercase       :      Dict	=	{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'conditional_detr'
				lowerCAmelCase 		= ['past_key_values']
				lowerCAmelCase 		= {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}

				def __init__(						self				,   a__=True				,   a__=None				,   a__=3				,   a__=300				,   a__=6				,   a__=2048				,   a__=8				,   a__=6				,   a__=2048				,   a__=8				,   a__=0.0				,   a__=0.0				,   a__=True				,   a__="relu"				,   a__=256				,   a__=0.1				,   a__=0.0				,   a__=0.0				,   a__=0.02				,   a__=1.0				,   a__=False				,   a__="sine"				,   a__="resnet50"				,   a__=True				,   a__=False				,   a__=2				,   a__=5				,   a__=2				,   a__=1				,   a__=1				,   a__=2				,   a__=5				,   a__=2				,   a__=0.25				,   **a__				,   )	->				int:
					if backbone_config is not None and use_timm_backbone:
						raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""   )

					if not use_timm_backbone:
						if backbone_config is None:
							logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
							A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
						elif isinstance(a__				,   a__   ):
							A								=				backbone_config.get("""model_type"""   )
							A								=				CONFIG_MAPPING[backbone_model_type]
							A								=				config_class.from_dict(a__   )

					A								=				use_timm_backbone
					A								=				backbone_config
					A								=				num_channels
					A								=				num_queries
					A								=				d_model
					A								=				encoder_ffn_dim
					A								=				encoder_layers
					A								=				encoder_attention_heads
					A								=				decoder_ffn_dim
					A								=				decoder_layers
					A								=				decoder_attention_heads
					A								=				dropout
					A								=				attention_dropout
					A								=				activation_dropout
					A								=				activation_function
					A								=				init_std
					A								=				init_xavier_std
					A								=				encoder_layerdrop
					A								=				decoder_layerdrop
					A								=				encoder_layers
					A								=				auxiliary_loss
					A								=				position_embedding_type
					A								=				backbone
					A								=				use_pretrained_backbone
					A								=				dilation
					# Hungarian matcher
					A								=				class_cost
					A								=				bbox_cost
					A								=				giou_cost
					# Loss coefficients
					A								=				mask_loss_coefficient
					A								=				dice_loss_coefficient
					A								=				cls_loss_coefficient
					A								=				bbox_loss_coefficient
					A								=				giou_loss_coefficient
					A								=				focal_alpha
					super().__init__(is_encoder_decoder=a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return self.encoder_attention_heads

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return self.d_model

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				copy.deepcopy(self.__dict__   )
					if self.backbone_config is not None:
						A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= version.parse('1.11'				)

				@property
				def      _UpperCAmelCase					(						self   )	->				Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
					        ("""pixel_mask""", {0: """batch"""}),
					    ]   )

				@property
				def      _UpperCAmelCase					(						self   )	->				float:
					return 1e-5

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return 12







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


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



