





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


A 			=  logging.get_logger(__name__)

A 			=  {
    "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 				__lowercase			(			_UpperCAmelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''levit'''







					def __init__(						self	,			_UpperCAmelCase=224	,			_UpperCAmelCase=3	,			_UpperCAmelCase=3	,			_UpperCAmelCase=2	,			_UpperCAmelCase=1	,			_UpperCAmelCase=16	,			_UpperCAmelCase=[128, 256, 384]	,			_UpperCAmelCase=[4, 8, 12]	,			_UpperCAmelCase=[4, 4, 4]	,			_UpperCAmelCase=[16, 16, 16]	,			_UpperCAmelCase=0	,			_UpperCAmelCase=[2, 2, 2]	,			_UpperCAmelCase=[2, 2, 2]	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**lowerCamelCase_     )
									__a :						List[Any]			       =  image_size
									__a :						List[str]			       =  num_channels
									__a :						Any			       =  kernel_size
									__a :						Tuple			       =  stride
									__a :						Dict			       =  padding
									__a :						int			       =  hidden_sizes
									__a :						Optional[int]			       =  num_attention_heads
									__a :						Tuple			       =  depths
									__a :						List[Any]			       =  key_dim
									__a :						Optional[int]			       =  drop_path_rate
									__a :						Tuple			       =  patch_size
									__a :						List[str]			       =  attention_ratio
									__a :						Optional[Any]			       =  mlp_ratio
									__a :						str			       =  initializer_range
									__a :						List[str]			       =  [
									    ['''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 				__lowercase			(			_UpperCAmelCase							):







					'''simple docstring'''




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







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







					@property
					def  _lowerCamelCase		(						self     ):
									return 1e-4






"""simple docstring"""






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  42
					__lowerCAmelCase  =  42






class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									__a :						list[list[Edge]]			       =  [[] for _ in range(_UpperCAmelCase     )]
									__a :						Dict			       =  size







					def __getitem__(						self	,			_UpperCAmelCase     ):
									return iter(self._graph[vertex]     )







					@property
					def  _lowerCamelCase		(						self     ):
									return self._size







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									if weight not in (0, 1):
													raise ValueError('''Edge weight must be either 0 or 1.'''     )

									if to_vertex < 0 or to_vertex >= self.size:
													raise ValueError('''Vertex indexes must be in [0; size).'''     )

									self._graph[from_vertex].append(Edge(_UpperCAmelCase	,			_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  deque([start_vertex]     )
									__a :						list[int | None]			       =  [None] * self.size
									__a :						int			       =  0

									while queue:
													__a :						Any			       =  queue.popleft()
													__a :						Tuple			       =  distances[current_vertex]
													if current_distance is None:
																	continue

													for edge in self[current_vertex]:
																	__a :						List[str]			       =  current_distance + edge.weight
																	__a :						str			       =  distances[edge.destination_vertex]
																	if (
																	    isinstance(_UpperCAmelCase	,			_UpperCAmelCase     )
																	    and new_distance >= dest_vertex_distance
																	):
																					continue
																	__a :						str			       =  new_distance
																	if edge.weight == 0:
																					queue.appendleft(edge.destination_vertex     )
																	else:
																					queue.append(edge.destination_vertex     )

									if distances[finish_vertex] is None:
													raise ValueError('''No path from start_vertex to finish_vertex.'''     )

									return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()