





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      	=		{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):
						__magic_name__:			Dict       =       '''yolos'''






						def __init__(      self      :      List[str]		,   _A      :      Optional[Any]=768		,   _A      :      List[str]=12		,   _A      :      str=12		,   _A      :      int=3072		,   _A      :      Optional[int]="gelu"		,   _A      :      Optional[Any]=0.0		,   _A      :      str=0.0		,   _A      :      List[Any]=0.0_2		,   _A      :      Union[str, Any]=1E-12		,   _A      :      Optional[Any]=[512, 864]		,   _A      :      List[Any]=16		,   _A      :      List[Any]=3		,   _A      :      Dict=True		,   _A      :      Union[str, Any]=100		,   _A      :      Dict=True		,   _A      :      Any=False		,   _A      :      Union[str, Any]=1		,   _A      :      Tuple=5		,   _A      :      int=2		,   _A      :      Optional[int]=5		,   _A      :      Any=2		,   _A      :      Optional[Any]=0.1		,   **_A      :      Optional[int]		,   )							->    Tuple:







							"""simple docstring"""


							super().__init__(**a_	)

							snake_case_		:			Optional[Any]											=	hidden_size
							snake_case_		:			Optional[int]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Any											=	intermediate_size
							snake_case_		:			int											=	hidden_act
							snake_case_		:			Dict											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	initializer_range
							snake_case_		:			Optional[Any]											=	layer_norm_eps
							snake_case_		:			Optional[Any]											=	image_size
							snake_case_		:			Dict											=	patch_size
							snake_case_		:			List[Any]											=	num_channels
							snake_case_		:			Any											=	qkv_bias
							snake_case_		:			str											=	num_detection_tokens
							snake_case_		:			int											=	use_mid_position_embeddings
							snake_case_		:			int											=	auxiliary_loss
							# Hungarian matcher
							snake_case_		:			Optional[int]											=	class_cost
							snake_case_		:			Optional[Any]											=	bbox_cost
							snake_case_		:			Union[str, Any]											=	giou_cost
							# Loss coefficients
							snake_case_		:			Optional[int]											=	bbox_loss_coefficient
							snake_case_		:			Tuple											=	giou_loss_coefficient
							snake_case_		:			Optional[int]											=	eos_coefficient
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):
						__magic_name__:			Dict       =       version.parse("1.11"					)






						@property
						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]	)






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    str:







							"""simple docstring"""


							return 1E-4






						@property
						def UpperCAmelCase_				(      self      :      Dict	)							->    Optional[Any]:







							"""simple docstring"""


							return 12








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

