






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


A        =			logging.get_logger(__name__)

A        =			{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class 							__a							(  __A						):





						'''simple docstring'''



						UpperCAmelCase__      :				Any				    =						"""detr"""
						UpperCAmelCase__      :				List[Any]				    =						["""past_key_values"""]
						UpperCAmelCase__      :				Union[str, Any]				    =						{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}


						def __init__(						self ,    UpperCamelCase__=True ,    UpperCamelCase__=None ,    UpperCamelCase__=3 ,    UpperCamelCase__=100 ,    UpperCamelCase__=6 ,    UpperCamelCase__=2048 ,    UpperCamelCase__=8 ,    UpperCamelCase__=6 ,    UpperCamelCase__=2048 ,    UpperCamelCase__=8 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=True ,    UpperCamelCase__="relu" ,    UpperCamelCase__=256 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=1.0 ,    UpperCamelCase__=False ,    UpperCamelCase__="sine" ,    UpperCamelCase__="resnet50" ,    UpperCamelCase__=True ,    UpperCamelCase__=False ,    UpperCamelCase__=1 ,    UpperCamelCase__=5 ,    UpperCamelCase__=2 ,    UpperCamelCase__=1 ,    UpperCamelCase__=1 ,    UpperCamelCase__=5 ,    UpperCamelCase__=2 ,    UpperCamelCase__=0.1 ,    **UpperCamelCase__ ,    ):







										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.'       )
																		SCREAMING_SNAKE_CASE_   :	int													=   CONFIG_MAPPING['resnet'](out_features=['stage4']       )
														elif isinstance(lowercase_ ,    lowercase_       ):
																		SCREAMING_SNAKE_CASE_   :	Dict													=   backbone_config.get('model_type'       )
																		SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   CONFIG_MAPPING[backbone_model_type]
																		SCREAMING_SNAKE_CASE_   :	int													=   config_class.from_dict(lowercase_       )
														# set timm attributes to None
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   None, None, None

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   use_timm_backbone
										SCREAMING_SNAKE_CASE_   :	int													=   backbone_config
										SCREAMING_SNAKE_CASE_   :	Tuple													=   num_channels
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   num_queries
										SCREAMING_SNAKE_CASE_   :	str													=   d_model
										SCREAMING_SNAKE_CASE_   :	Any													=   encoder_ffn_dim
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   encoder_layers
										SCREAMING_SNAKE_CASE_   :	Dict													=   encoder_attention_heads
										SCREAMING_SNAKE_CASE_   :	List[str]													=   decoder_ffn_dim
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   decoder_layers
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   decoder_attention_heads
										SCREAMING_SNAKE_CASE_   :	Tuple													=   dropout
										SCREAMING_SNAKE_CASE_   :	Tuple													=   attention_dropout
										SCREAMING_SNAKE_CASE_   :	str													=   activation_dropout
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   activation_function
										SCREAMING_SNAKE_CASE_   :	str													=   init_std
										SCREAMING_SNAKE_CASE_   :	List[str]													=   init_xavier_std
										SCREAMING_SNAKE_CASE_   :	int													=   encoder_layerdrop
										SCREAMING_SNAKE_CASE_   :	Any													=   decoder_layerdrop
										SCREAMING_SNAKE_CASE_   :	int													=   encoder_layers
										SCREAMING_SNAKE_CASE_   :	str													=   auxiliary_loss
										SCREAMING_SNAKE_CASE_   :	List[str]													=   position_embedding_type
										SCREAMING_SNAKE_CASE_   :	Tuple													=   backbone
										SCREAMING_SNAKE_CASE_   :	int													=   use_pretrained_backbone
										SCREAMING_SNAKE_CASE_   :	Tuple													=   dilation
										# Hungarian matcher
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   class_cost
										SCREAMING_SNAKE_CASE_   :	List[str]													=   bbox_cost
										SCREAMING_SNAKE_CASE_   :	Tuple													=   giou_cost
										# Loss coefficients
										SCREAMING_SNAKE_CASE_   :	Any													=   mask_loss_coefficient
										SCREAMING_SNAKE_CASE_   :	Tuple													=   dice_loss_coefficient
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   bbox_loss_coefficient
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   giou_loss_coefficient
										SCREAMING_SNAKE_CASE_   :	List[str]													=   eos_coefficient
										super().__init__(is_encoder_decoder=lowercase_ ,    **lowercase_       )


						@property
						def   __snake_case			(						self       ):
										return self.encoder_attention_heads


						@property
						def   __snake_case			(						self       ):
										return self.d_model


						@classmethod
						def   __snake_case			(						cls ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
										return cls(backbone_config=lowercase_ ,    **lowercase_       )






						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	Dict													=   copy.deepcopy(self.__dict__       )
										if output["backbone_config"] is not None:
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.backbone_config.to_dict()
										SCREAMING_SNAKE_CASE_   :	str													=   self.__class__.model_type
										return output






class 							__a							(  __A						):





						'''simple docstring'''



						UpperCAmelCase__      :				Any				    =						version.parse("""1.11"""						)


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


						@property
						def   __snake_case			(						self       ):
										return 1E-5






						@property
						def   __snake_case			(						self       ):
										return 12







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()