


'''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


UpperCAmelCase_ :     List[str]								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Tuple								=      {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						'''yolos'''


 def __init__(      self      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-12      ,      __lowercase=[512, 864]      ,      __lowercase=16      ,      __lowercase=3      ,      __lowercase=True      ,      __lowercase=100      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=1      ,      __lowercase=5      ,      __lowercase=2      ,      __lowercase=5      ,      __lowercase=2      ,      __lowercase=0.1      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__a						)

  __A  :		List[str]       =			hidden_size
  __A  :		Dict       =			num_hidden_layers
  __A  :		int       =			num_attention_heads
  __A  :		Optional[Any]       =			intermediate_size
  __A  :		Any       =			hidden_act
  __A  :		Tuple       =			hidden_dropout_prob
  __A  :		Optional[int]       =			attention_probs_dropout_prob
  __A  :		Optional[int]       =			initializer_range
  __A  :		Optional[Any]       =			layer_norm_eps
  __A  :		int       =			image_size
  __A  :		Tuple       =			patch_size
  __A  :		Any       =			num_channels
  __A  :		str       =			qkv_bias
  __A  :		Union[str, Any]       =			num_detection_tokens
  __A  :		Optional[Any]       =			use_mid_position_embeddings
  __A  :		Optional[int]       =			auxiliary_loss
  # Hungarian matcher
  __A  :		Union[str, Any]       =			class_cost
  __A  :		List[str]       =			bbox_cost
  __A  :		Union[str, Any]       =			giou_cost
  # Loss coefficients
  __A  :		Union[str, Any]       =			bbox_loss_coefficient
  __A  :		List[str]       =			giou_loss_coefficient
  __A  :		List[str]       =			eos_coefficient


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Any    							=						version.parse('''1.11'''				)


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 1E-4







 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 12




'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






