






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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A        =			logging.get_logger(__name__)

A        =			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}


class 							__a							(  __snake_case ,	__snake_case						):





						'''simple docstring'''



						UpperCAmelCase__      :				Optional[int]				    =						'resnet'
						UpperCAmelCase__      :				Optional[Any]				    =						['basic', 'bottleneck']


						def __init__(						self ,    UpperCamelCase__=3 ,    UpperCamelCase__=64 ,    UpperCamelCase__=[256, 512, 1024, 2048] ,    UpperCamelCase__=[3, 4, 6, 3] ,    UpperCamelCase__="bottleneck" ,    UpperCamelCase__="relu" ,    UpperCamelCase__=False ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__ ,    ):
										super().__init__(**A_       )
										if layer_type not in self.layer_types:
														raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
										SCREAMING_SNAKE_CASE_   :	str													=   num_channels
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   embedding_size
										SCREAMING_SNAKE_CASE_   :	Dict													=   hidden_sizes
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   depths
										SCREAMING_SNAKE_CASE_   :	int													=   layer_type
										SCREAMING_SNAKE_CASE_   :	str													=   hidden_act
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   downsample_in_first_stage
										SCREAMING_SNAKE_CASE_   :	List[str]													=   ["stem"] + [F'''stage{idx}''' for idx in range(1 ,    len(A_       ) + 1       )]
										SCREAMING_SNAKE_CASE_   :	int													=   get_aligned_output_features_output_indices(
										    out_features=A_ ,    out_indices=A_ ,    stage_names=self.stage_names       )






class 							__a							(  __snake_case						):





						'''simple docstring'''



						UpperCAmelCase__      :				List[str]				    =						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-3







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()