


"""simple docstring"""



import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class _lowerCAmelCase       (   unittest.TestCase			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=3		,			UpperCamelCase__=32		,			UpperCamelCase__=3		,			UpperCamelCase__=10		,			UpperCamelCase__=[10, 20, 30, 40]		,			UpperCamelCase__=[1, 1, 2, 1]		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__="relu"		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					parent
       snake_case					:   str         =					batch_size
       snake_case					:   Union[str, Any]         =					image_size
       snake_case					:   Any         =					num_channels
       snake_case					:   List[str]         =					embeddings_size
       snake_case					:   Dict         =					hidden_sizes
       snake_case					:   Any         =					depths
       snake_case					:   List[str]         =					is_training
       snake_case					:   Tuple         =					use_labels
       snake_case					:   Optional[Any]         =					hidden_act
       snake_case					:   List[Any]         =					num_labels
       snake_case					:   Optional[Any]         =					scope
       snake_case					:   int         =					len(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       snake_case					:   List[Any]         =					self.get_config()

       return config, pixel_values







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       return RegNetConfig(
           num_channels=self.num_channels		,			embeddings_size=self.embeddings_size		,			hidden_sizes=self.hidden_sizes		,			depths=self.depths		,			hidden_act=self.hidden_act		,			num_labels=self.num_labels		,			image_size=self.image_size		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       snake_case					:   str         =					FlaxRegNetModel(config=UpperCamelCase__		)
       snake_case					:   str         =					model(UpperCamelCase__		)

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape		,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.num_labels
       snake_case					:   str         =					FlaxRegNetForImageClassification(config=UpperCamelCase__		)
       snake_case					:   str         =					model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   int         =					self.prepare_config_and_inputs()
       snake_case					:   Dict         =					config_and_inputs
       snake_case					:   Dict         =					{"pixel_values": pixel_values}
       return config, inputs_dict







@require_flax
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[int]													=						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   __UpperCAmelCase				: Any													=						False
   __UpperCAmelCase				: Union[str, Any]													=						False
   __UpperCAmelCase				: Tuple													=						False







   def 				lowerCamelCase	(			self		)			->	None:

       '''simple docstring'''




       snake_case					:   Any         =					FlaxRegNetModelTester(self		)
       snake_case					:   Tuple         =					ConfigTester(self		,			config_class=UpperCamelCase__		,			has_text_modality=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       return







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   str         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Dict         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)







   @unittest.skip(reason="RegNet does not use inputs_embeds"		)
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="RegNet does not support input and output embeddings"		)
   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       pass







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.model_tester.prepare_config_and_inputs_for_common()

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

           snake_case					:   Any         =					["pixel_values"]
           self.assertListEqual(arg_names[:1]		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       def check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   str         =					model_class(UpperCamelCase__		)

           snake_case					:   Optional[int]         =					model(**self._prepare_for_class(UpperCamelCase__		,			UpperCamelCase__		)		)

           snake_case					:   int         =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           snake_case					:   Optional[Any]         =					self.model_tester.num_stages
           self.assertEqual(len(UpperCamelCase__		)		,			expected_num_stages + 1		)

       snake_case					:   Any         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   Tuple         =					True
           check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           snake_case					:   Union[str, Any]         =					True

           check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
               snake_case					:   str         =					self._prepare_for_class(UpperCamelCase__		,			UpperCamelCase__		)
               snake_case					:   str         =					model_class(UpperCamelCase__		)

               @jax.jit
               def model_jitted(UpperCamelCase__		,			**UpperCamelCase__		):
                   return model(pixel_values=UpperCamelCase__		,			**UpperCamelCase__		)

               with self.subTest("JIT Enabled"		):
                   snake_case					:   Dict         =					model_jitted(**UpperCamelCase__		).to_tuple()

               with self.subTest("JIT Disabled"		):
                   with jax.disable_jit():
                       snake_case					:   Union[str, Any]         =					model_jitted(**UpperCamelCase__		).to_tuple()

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







def 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





    snake_case					:   Dict         =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image







@require_flax
class _lowerCAmelCase       (   unittest.TestCase			):







   @cached_property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"		) if is_vision_available() else None







   @slow
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

       snake_case					:   Optional[Any]         =					self.default_image_processor
       snake_case					:   Tuple         =					prepare_img()
       snake_case					:   Dict         =					image_processor(images=UpperCamelCase__		,			return_tensors="np"		)

       snake_case					:   Tuple         =					model(**UpperCamelCase__		)

       # verify the logits
       snake_case					:   int         =					(1, 1000)
       self.assertEqual(outputs.logits.shape		,			UpperCamelCase__		)

       snake_case					:   Dict         =					jnp.array([-0.4180, -1.5051, -3.4836]		)

       self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,			UpperCamelCase__		,			atol=1e-4		)		)









"""simple docstring"""



from __future__ import annotations

__snake_case         =					list[tuple[int, int]]

__snake_case         =					[
    [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],
]

__snake_case         =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					pos_x
       snake_case					:   List[str]         =					pos_y
       snake_case					:   List[Any]         =					(pos_y, pos_x)
       snake_case					:   Optional[int]         =					goal_x
       snake_case					:   Dict         =					goal_y
       snake_case					:   Any         =					g_cost
       snake_case					:   List[Any]         =					parent
       snake_case					:   Union[str, Any]         =					self.calculate_heuristic()







   def 				lowerCamelCase	(			self		)			->	float:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					abs(self.pos_x - self.goal_x		)
       snake_case					:   Dict         =					abs(self.pos_y - self.goal_y		)
       return dx + dy







   def __lt__(			self		,			UpperCamelCase__		)			->	bool:

       '''simple docstring'''




       return self.f_cost < other.f_cost







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   int         =					Node(start[1]		,			start[0]		,			goal[1]		,			goal[0]		,			0		,			UpperCamelCase__		)
       snake_case					:   List[Any]         =					Node(goal[1]		,			goal[0]		,			goal[1]		,			goal[0]		,			9_9999		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					[self.start]
       snake_case					:   list[Node]         =					[]

       snake_case					:   Dict         =					False







   def 				lowerCamelCase	(			self		)			->	Path | None:

       '''simple docstring'''




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

           if current_node.pos == self.target.pos:
               snake_case					:   Tuple         =					True
               return self.retrace_path(UpperCamelCase__		)

           self.closed_nodes.append(UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					self.get_successors(UpperCamelCase__		)

           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(UpperCamelCase__		)
               else:
                   # retrieve the best current path
                   snake_case					:   Dict         =					self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__		)		)

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

       if not self.reached:
           return [self.start.pos]
       return None







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	list[Node]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       for action in delta:
           snake_case					:   Union[str, Any]         =					parent.pos_x + action[1]
           snake_case					:   str         =					parent.pos_y + action[0]

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

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

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Path:

       '''simple docstring'''




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


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

  print("""------""")

  __snake_case         =					GreedyBestFirst(init, goal)
  __snake_case         =					greedy_bf.search()
  if path:
    for pos_x, pos_y in path:
      __snake_case         =					2

    for elem in grid:
      print(elem)





