



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__								=						logging.get_logger(__name__)


class       a							(			lowerCAmelCase_  ,							lowerCAmelCase_				):
		__lowerCAmelCase	:    Union[str, Any]											=					'maskformer-swin'

		__lowerCAmelCase	:    int											=					{
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		}



		def __init__(     self     :Dict   ,__lowercase     :Tuple=2_2_4   ,__lowercase     :Any=4   ,__lowercase     :List[str]=3   ,__lowercase     :str=9_6   ,__lowercase     :List[str]=[2, 2, 6, 2]   ,__lowercase     :Tuple=[3, 6, 1_2, 2_4]   ,__lowercase     :Any=7   ,__lowercase     :Optional[Any]=4.0   ,__lowercase     :Tuple=True   ,__lowercase     :Optional[int]=0.0   ,__lowercase     :Any=0.0   ,__lowercase     :Union[str, Any]=0.1   ,__lowercase     :Optional[Any]="gelu"   ,__lowercase     :Optional[int]=False   ,__lowercase     :Dict=0.02   ,__lowercase     :Tuple=1e-5   ,__lowercase     :Tuple=None   ,__lowercase     :Union[str, Any]=None   ,**__lowercase     :Optional[int]   ,):
						super().__init__(**__lowerCAmelCase  )

						snake_case__	:      Optional[Any]     		=	image_size
						snake_case__	:      Optional[int]     		=	patch_size
						snake_case__	:      str     		=	num_channels
						snake_case__	:      int     		=	embed_dim
						snake_case__	:      Any     		=	depths
						snake_case__	:      List[Any]     		=	len(__lowerCAmelCase  )
						snake_case__	:      int     		=	num_heads
						snake_case__	:      Optional[int]     		=	window_size
						snake_case__	:      List[str]     		=	mlp_ratio
						snake_case__	:      List[str]     		=	qkv_bias
						snake_case__	:      List[Any]     		=	hidden_dropout_prob
						snake_case__	:      int     		=	attention_probs_dropout_prob
						snake_case__	:      Union[str, Any]     		=	drop_path_rate
						snake_case__	:      Any     		=	hidden_act
						snake_case__	:      Any     		=	use_absolute_embeddings
						snake_case__	:      Dict     		=	layer_norm_eps
						snake_case__	:      Union[str, Any]     		=	initializer_range
						# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						snake_case__	:      int     		=	int(embed_dim * 2 ** (len(__lowerCAmelCase  ) - 1)  )
						snake_case__	:      int     		=	['''stem'''] + [F"""stage{idx}""" for idx in range(1   ,len(__lowerCAmelCase  ) + 1  )]
						snake_case__		, snake_case__	:      List[str]     		=	get_aligned_output_features_output_indices(
						    out_features=__lowerCAmelCase   ,out_indices=__lowerCAmelCase   ,stage_names=self.stage_names  )


"""simple docstring"""


from __future__ import annotations

from collections import Counter
from random import random


class 				a :


    def __init__(    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     float    ):
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      _UpperCAmelCase         =					probability


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return list(self.connections    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					0
      _UpperCAmelCase         =					random()

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






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					MarkovChainGraphUndirectedUnweighted()

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

  _UpperCAmelCase         =					Counter(graph.get_nodes()						)
  _UpperCAmelCase         =					start

  for _ in range(lowercase						):
    _UpperCAmelCase         =					graph.transition(lowercase						)
    visited[node] += 1

  return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()
