
from __future__ import annotations

__lowerCAmelCase     :	Any       			=  list[tuple[int, int]]

__lowerCAmelCase     :	int       			=  [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__lowerCAmelCase     :	List[str]       			=  ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					List[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					float     ,				UpperCamelCase__     :					Node | None     ,				)     ->      int:







    """simple docstring"""






    __magic_name__								=			pos_x
    __magic_name__								=			pos_y
    __magic_name__								=			(pos_y, pos_x)
    __magic_name__								=			goal_x
    __magic_name__								=			goal_y
    __magic_name__								=			g_cost
    __magic_name__								=			parent
    __magic_name__								=			self.calculate_heuristic()
  def 				_lowercase				(					self     :					Tuple       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			abs(self.pos_x - self.goal_x       )
    __magic_name__								=			abs(self.pos_y - self.goal_y       )
    return dx + dy





  def __lt__(					self     :					int     ,				UpperCamelCase__     :					List[Any]       )     ->      Tuple:







    """simple docstring"""






    return self.f_cost < other.f_cost







class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					tuple[int, int]     ,				UpperCamelCase__     :					tuple[int, int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			Node(start[1]     ,				start[0]     ,				goal[1]     ,				goal[0]     ,				0     ,				a__       )
    __magic_name__								=			Node(goal[1]     ,				goal[0]     ,				goal[1]     ,				goal[0]     ,				9_9999     ,				a__       )

    __magic_name__								=			[self.start]
    __magic_name__								=			[]

    __magic_name__								=			False
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      int:







    """simple docstring"""






    while self.open_nodes:
      # Open Nodes are sorted using __lt__
      self.open_nodes.sort()
      __magic_name__								=			self.open_nodes.pop(0       )

      if current_node.pos == self.target.pos:
        __magic_name__								=			True
        return self.retrace_path(a__       )

      self.closed_nodes.append(a__       )
      __magic_name__								=			self.get_successors(a__       )

      for child_node in successors:
        if child_node in self.closed_nodes:
          continue

        if child_node not in self.open_nodes:
          self.open_nodes.append(a__       )
        else:
          # retrieve the best current path
          __magic_name__								=			self.open_nodes.pop(self.open_nodes.index(a__       )       )

          if child_node.g_cost < better_node.g_cost:
            self.open_nodes.append(a__       )
          else:
            self.open_nodes.append(a__       )

    if not self.reached:
      return [self.start.pos]
    return None
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					Node       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]
    for action in delta:
      __magic_name__								=			parent.pos_x + action[1]
      __magic_name__								=			parent.pos_y + action[0]

      if not (0 <= pos_x <= len(grid[0]       ) - 1 and 0 <= pos_y <= len(a__       ) - 1):
        continue

      if grid[pos_y][pos_x] != 0:
        continue

      successors.append(
          Node(
              a__     ,				a__     ,				self.target.pos_y     ,				self.target.pos_x     ,				parent.g_cost + 1     ,				a__     ,				)       )
    return successors





  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Node | None       )     ->      int:







    """simple docstring"""






    __magic_name__								=			node
    __magic_name__								=			[]
    while current_node is not None:
      path.append((current_node.pos_y, current_node.pos_x)       )
      __magic_name__								=			current_node.parent
    path.reverse()
    return path


if __name__ == "__main__":
    __lowerCAmelCase     :	Any       			=  (0, 0)
    __lowerCAmelCase     :	int       			=  (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __lowerCAmelCase     :	str       			=  GreedyBestFirst(init, goal)
    __lowerCAmelCase     :	Optional[Any]       			=  greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __lowerCAmelCase     :	Union[str, Any]       			=  2

        for elem in grid:
            print(elem)



'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : Optional[Any]			,							a__  : List[Any]			,							a__  : Any=13			,							a__  : Optional[int]=30			,							a__  : List[Any]=2			,							a__  : Union[str, Any]=3			,							a__  : Tuple=True			,							a__  : Tuple=True			,							a__  : List[str]=32			,							a__  : Tuple=5			,							a__  : List[str]=4			,							a__  : str=37			,							a__  : Dict="gelu"			,							a__  : Union[str, Any]=0.1			,							a__  : Union[str, Any]=0.1			,							a__  : List[Any]=10			,							a__  : List[Any]=0.02			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	parent
  lowerCAmelCase__     :   Union[str, Any]     		=	batch_size
  lowerCAmelCase__     :   List[Any]     		=	image_size
  lowerCAmelCase__     :   int     		=	patch_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[str]     		=	is_training
  lowerCAmelCase__     :   str     		=	use_labels
  lowerCAmelCase__     :   Dict     		=	hidden_size
  lowerCAmelCase__     :   Any     		=	num_hidden_layers
  lowerCAmelCase__     :   Any     		=	num_attention_heads
  lowerCAmelCase__     :   Any     		=	intermediate_size
  lowerCAmelCase__     :   Union[str, Any]     		=	hidden_act
  lowerCAmelCase__     :   Optional[int]     		=	hidden_dropout_prob
  lowerCAmelCase__     :   str     		=	attention_probs_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	type_sequence_label_size
  lowerCAmelCase__     :   List[str]     		=	initializer_range

  # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   Tuple     		=	(image_size // patch_size) ** 2
  lowerCAmelCase__     :   Dict     		=	num_patches + 1


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   Any     		=	ViTConfig(
      image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=a__			,							initializer_range=self.initializer_range			,							)

  return config, pixel_values


 def    _A	(    self  : Optional[Any]			,							a__  : Optional[int]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	FlaxViTModel(config=a__    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   List[str]     		=	(self.image_size, self.image_size)
  lowerCAmelCase__     :   List[str]     		=	(self.patch_size, self.patch_size)
  lowerCAmelCase__     :   int     		=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )


 def    _A	(    self  : str			,							a__  : Optional[int]			,							a__  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Any     		=	FlaxViTForImageClassification(config=a__    )
  lowerCAmelCase__     :   Dict     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Tuple     		=	1
  lowerCAmelCase__     :   List[Any]     		=	FlaxViTForImageClassification(a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   int     		=	model(a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     
  )     :   Tuple     		=	config_and_inputs
  lowerCAmelCase__     :   Optional[Any]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_flax
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Union[str, Any]	=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	FlaxViTModelTester(self    )
  lowerCAmelCase__     :   Union[str, Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Dict     		=	model_class(a__    )
   lowerCAmelCase__     :   str     		=	inspect.signature(model.__call__    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCAmelCase__     :   List[Any]     		=	[*signature.parameters.keys()]

   lowerCAmelCase__     :   List[str]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__    ):
    lowerCAmelCase__     :   List[Any]     		=	self._prepare_for_class(a__			,							a__    )
    lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )

    @jax.jit
    def model_jitted(a__  : Optional[int]			,							**a__  : Optional[Any]    ):
     return model(pixel_values=a__			,							**a__    )

    with self.subTest("JIT Enabled"    ):
     lowerCAmelCase__     :   Any     		=	model_jitted(**a__    ).to_tuple()

    with self.subTest("JIT Disabled"    ):
     with jax.disable_jit():
      lowerCAmelCase__     :   str     		=	model_jitted(**a__    ).to_tuple()

    self.assertEqual(len(a__    )			,							len(a__    )    )
    for jitted_output, output in zip(a__			,							a__    ):
     self.assertEqual(jitted_output.shape			,							output.shape    )


 @slow
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  for model_class_name in self.all_model_classes:
   lowerCAmelCase__     :   Optional[Any]     		=	model_class_name.from_pretrained("google/vit-base-patch16-224"    )
   lowerCAmelCase__     :   Tuple     		=	model(np.ones((1, 3, 224, 224)    )    )
   self.assertIsNotNone(a__    )





