


"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()







class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[int]													=						PriorTransformer
   __UpperCAmelCase				: Any													=						'''hidden_states'''







   @property
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[str]         =					4
       snake_case					:   int         =					8
       snake_case					:   str         =					7

       snake_case					:   List[Any]         =					floats_tensor((batch_size, embedding_dim)		).to(UpperCamelCase__		)

       snake_case					:   str         =					floats_tensor((batch_size, embedding_dim)		).to(UpperCamelCase__		)
       snake_case					:   Dict         =					floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(UpperCamelCase__		)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }







   def 				lowerCamelCase	(			self		,			UpperCamelCase__=0		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(UpperCamelCase__		)
       snake_case					:   str         =					4
       snake_case					:   List[Any]         =					8
       snake_case					:   int         =					7

       snake_case					:   Any         =					torch.randn((batch_size, embedding_dim)		).to(UpperCamelCase__		)

       snake_case					:   Optional[int]         =					torch.randn((batch_size, embedding_dim)		).to(UpperCamelCase__		)
       snake_case					:   str         =					torch.randn((batch_size, num_embeddings, embedding_dim)		).to(UpperCamelCase__		)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }







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

       '''simple docstring'''




       return (4, 8)







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return (4, 8)







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

       '''simple docstring'''




       snake_case					:   int         =					{
           "num_attention_heads": 2,
           "attention_head_dim": 4,
           "num_layers": 2,
           "embedding_dim": 8,
           "num_embeddings": 7,
           "additional_embeddings": 4,
       }
       snake_case					:   Union[str, Any]         =					self.dummy_input
       return init_dict, inputs_dict







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

       '''simple docstring'''




       snake_case      ,snake_case					:   List[str]         =					PriorTransformer.from_pretrained(
           "hf-internal-testing/prior-dummy"		,			output_loading_info=UpperCamelCase__		)
       self.assertIsNotNone(UpperCamelCase__		)
       self.assertEqual(len(loading_info["missing_keys"]		)		,			0		)

       model.to(UpperCamelCase__		)
       snake_case					:   int         =					model(**self.dummy_input		)[0]

       assert hidden_states is not None, "Make sure output is not None"







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

       '''simple docstring'''




       snake_case      ,snake_case					:   Optional[Any]         =					self.prepare_init_args_and_inputs_for_common()

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

       snake_case					:   str         =					["hidden_states", "timestep"]
       self.assertListEqual(arg_names[:2]		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"		)
       snake_case					:   Tuple         =					model.to(UpperCamelCase__		)

       if hasattr(UpperCamelCase__		,			"set_default_attn_processor"		):
           model.set_default_attn_processor()

       snake_case					:   Optional[Any]         =					self.get_dummy_seed_input()

       with torch.no_grad():
           snake_case					:   Tuple         =					model(**UpperCamelCase__		)[0]

       snake_case					:   int         =					output[0, :5].flatten().cpu()
       print(UpperCamelCase__		)

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       snake_case					:   Optional[int]         =					torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
       self.assertTrue(torch_all_close(UpperCamelCase__		,			UpperCamelCase__		,			rtol=1e-2		)		)







@slow
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		,			UpperCamelCase__=1		,			UpperCamelCase__=768		,			UpperCamelCase__=77		,			UpperCamelCase__=0		)			->	str:

       '''simple docstring'''




       torch.manual_seed(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					batch_size
       snake_case					:   Union[str, Any]         =					embedding_dim
       snake_case					:   Optional[int]         =					num_embeddings

       snake_case					:   str         =					torch.randn((batch_size, embedding_dim)		).to(UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					torch.randn((batch_size, embedding_dim)		).to(UpperCamelCase__		)
       snake_case					:   str         =					torch.randn((batch_size, num_embeddings, embedding_dim)		).to(UpperCamelCase__		)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }







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

       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
           [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
           # fmt: on
       ]		)
   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   int         =					PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"		,			subfolder="prior"		)
       model.to(UpperCamelCase__		)
       snake_case					:   Any         =					self.get_dummy_seed_input(seed=UpperCamelCase__		)

       with torch.no_grad():
           snake_case					:   Dict         =					model(**UpperCamelCase__		)[0]

       assert list(sample.shape		) == [1, 768]

       snake_case					:   List[str]         =					sample[0, :8].flatten().cpu()
       print(UpperCamelCase__		)
       snake_case					:   Tuple         =					torch.tensor(UpperCamelCase__		)

       assert torch_all_close(UpperCamelCase__		,			UpperCamelCase__		,			atol=1e-3		)









"""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)





