



"""simple docstring"""





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


__lowerCAmelCase       :					str      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					List[Any]      					=					{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





  """simple docstring"""






  _lowerCamelCase			  =       '''levit'''





  def __init__(					self							,    _lowercase=2_2_4							,    _lowercase=3							,    _lowercase=3							,    _lowercase=2							,    _lowercase=1							,    _lowercase=1_6							,    _lowercase=[1_2_8, 2_5_6, 3_8_4]							,    _lowercase=[4, 8, 1_2]							,    _lowercase=[4, 4, 4]							,    _lowercase=[1_6, 1_6, 1_6]							,    _lowercase=0							,    _lowercase=[2, 2, 2]							,    _lowercase=[2, 2, 2]							,    _lowercase=0.02							,    **_lowercase							,    ) ->						Union[str, Any]:







        '''simple docstring'''

        super().__init__(**_lowercase   )
        snake_case_   : Union[str, Any]						=			image_size
        snake_case_   : List[str]						=			num_channels
        snake_case_   : Optional[Any]						=			kernel_size
        snake_case_   : str						=			stride
        snake_case_   : Any						=			padding
        snake_case_   : Dict						=			hidden_sizes
        snake_case_   : Optional[Any]						=			num_attention_heads
        snake_case_   : List[str]						=			depths
        snake_case_   : List[Any]						=			key_dim
        snake_case_   : List[Any]						=			drop_path_rate
        snake_case_   : List[Any]						=			patch_size
        snake_case_   : int						=			attention_ratio
        snake_case_   : int						=			mlp_ratio
        snake_case_   : int						=			initializer_range
        snake_case_   : Tuple						=			[
            ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





  """simple docstring"""






  _lowerCamelCase			  =       version.parse('''1.11'''		)





  @property
  def    UpperCAmelCase__							(					self   ) ->						Mapping[str, Mapping[int, str]]:







        '''simple docstring'''

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





  @property
  def    UpperCAmelCase__							(					self   ) ->						float:







        '''simple docstring'''

        return 1E-4






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			[0] * len(__UpperCamelCase    )
						snake_case_   : List[str]						=			[]
						snake_case_   : Any						=			[1] * len(__UpperCamelCase    )

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCamelCase    )    ):
												if indegree[i] == 0:
																		queue.append(__UpperCamelCase    )

						while queue:
												snake_case_   : Optional[int]						=			queue.pop(0    )
												for x in graph[vertex]:
																		indegree[x] -= 1

																		if long_dist[vertex] + 1 > long_dist[x]:
																								snake_case_   : Union[str, Any]						=			long_dist[vertex] + 1

																		if indegree[x] == 0:
																								queue.append(__UpperCamelCase    )

						print(max(__UpperCamelCase    )    )


# Adjacency list of Graph
__lowerCAmelCase       :					str      					=					{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)

