



"""simple docstring"""
import warnings
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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"segformer"






							def __init__(   self			,     a_=3			,     a_=4			,     a_=[2, 2, 2, 2]			,     a_=[8, 4, 2, 1]			,     a_=[3_2, 6_4, 1_6_0, 2_5_6]			,     a_=[7, 3, 3, 3]			,     a_=[4, 2, 2, 2]			,     a_=[1, 2, 5, 8]			,     a_=[4, 4, 4, 4]			,     a_="gelu"			,     a_=0.0			,     a_=0.0			,     a_=0.1			,     a_=0.02			,     a_=0.1			,     a_=1e-6			,     a_=2_5_6			,     a_=2_5_5			,     **a_			,     ):
									super().__init__(**a_      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
											warnings.warn(
											    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
											    " removed, as the behaviour will default to that of reshape_last_stage = True."			,     a_			,     )

									a_     :      Union[str, Any] 						=     num_channels
									a_     :      Any 						=     num_encoder_blocks
									a_     :      Union[str, Any] 						=     depths
									a_     :      Union[str, Any] 						=     sr_ratios
									a_     :      List[str] 						=     hidden_sizes
									a_     :      Union[str, Any] 						=     patch_sizes
									a_     :      Tuple 						=     strides
									a_     :      Optional[Any] 						=     mlp_ratios
									a_     :      str 						=     num_attention_heads
									a_     :      Union[str, Any] 						=     hidden_act
									a_     :      Any 						=     hidden_dropout_prob
									a_     :      Tuple 						=     attention_probs_dropout_prob
									a_     :      List[Any] 						=     classifier_dropout_prob
									a_     :      Optional[Any] 						=     initializer_range
									a_     :      str 						=     drop_path_rate
									a_     :      Union[str, Any] 						=     layer_norm_eps
									a_     :      str 						=     decoder_hidden_size
									a_     :      List[Any] 						=     kwargs.get("reshape_last_stage"			,     a_      )
									a_     :      List[str] 						=     semantic_loss_ignore_index




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					version.parse("1.11"				)






							@property
							def snake_case_  (   self      ):
									return OrderedDict(
									    [
									        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
									    ]      )






							@property
							def snake_case_  (   self      ):
									return 1e-4






							@property
							def snake_case_  (   self      ):
									return 1_2




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()