



import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTMAEForPreTraining, ViTMAEModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Tuple=13			,				lowerCAmelCase  :			List[Any]=30			,				lowerCAmelCase  :			List[str]=2			,				lowerCAmelCase  :			List[str]=3			,				lowerCAmelCase  :			Union[str, Any]=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=5			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Dict=37			,				lowerCAmelCase  :			str="gelu"			,				lowerCAmelCase  :			str=0.1			,				lowerCAmelCase  :			Any=0.1			,				lowerCAmelCase  :			Union[str, Any]=10			,				lowerCAmelCase  :			Dict=0.02			,				lowerCAmelCase  :			Any=3			,				lowerCAmelCase  :			Union[str, Any]=0.6			,				lowerCAmelCase  :			Optional[int]=None			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  is_training
         lowercase__     =  use_labels
         lowercase__     =  hidden_size
         lowercase__     =  num_hidden_layers
         lowercase__     =  num_attention_heads
         lowercase__     =  intermediate_size
         lowercase__     =  hidden_act
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  attention_probs_dropout_prob
         lowercase__     =  type_sequence_label_size
         lowercase__     =  initializer_range
         lowercase__     =  mask_ratio
         lowercase__     =  scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         lowercase__     =  (image_size // patch_size) ** 2
         lowercase__     =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)))


   def 			UpperCAmelCase		(						self  :			List[Any])	->				int:




         """simple docstring"""

         lowercase__     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__     =  None
         if self.use_labels:
               lowercase__     =  ids_tensor([self.batch_size]			,				self.type_sequence_label_size)

         lowercase__     =  self.get_config()

         return config, pixel_values, labels


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         return ViTMAEConfig(
             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=lowerCAmelCase			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Any)	->				Dict:




         """simple docstring"""

         lowercase__     =  ViTMAEModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  ViTMAEForPreTraining(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase)
         lowercase__     =  (self.image_size // self.patch_size) ** 2
         lowercase__     =  self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels))

         # test greyscale images
         lowercase__     =  1
         lowercase__     =  ViTMAEForPreTraining(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         lowercase__     =  model(lowerCAmelCase)
         lowercase__     =  self.patch_size**2
         self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels))



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




         """simple docstring"""

         lowercase__     =  self.prepare_config_and_inputs()
         lowercase__,	lowercase__,	lowercase__     =  config_and_inputs
         lowercase__     =  {'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Optional[int]       				=    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   A    :  Dict       				=    {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

   A    :  List[Any]       				=    False
   A    :  List[Any]       				=    False
   A    :  Optional[Any]       				=    False
   A    :  Dict       				=    False


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

         lowercase__     =  ViTMAEModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowerCAmelCase			,				has_text_modality=lowerCAmelCase			,				hidden_size=37)


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[Any]:




         """simple docstring"""

         self.config_tester.run_common_tests()


   @unittest.skip(reason='ViTMAE does not use inputs_embeds')
   def 			UpperCAmelCase		(						self  :			int)	->				Union[str, Any]:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()			,				(nn.Module))
               lowercase__     =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase			,				nn.Linear))


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               lowercase__     =  inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__     =  [*signature.parameters.keys()]

               lowercase__     =  ['pixel_values']
               self.assertListEqual(arg_names[:1]			,				lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         np.random.seed(2)

         lowercase__     =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
         lowercase__     =  np.random.uniform(size=(self.model_tester.batch_size, num_patches))
         lowercase__     =  torch.from_numpy(lowerCAmelCase)

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         lowercase__     =  pt_noise

         super().check_pt_tf_models(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				Any:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               model.to(lowerCAmelCase)
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2)
               with torch.no_grad():
                     lowercase__     =  model(**self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase))

               lowercase__     =  outputs[0].cpu().numpy()
               lowercase__     =  0

               with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_pretrained(lowerCAmelCase)
                     lowercase__     =  model_class.from_pretrained(lowerCAmelCase)
                     model.to(lowerCAmelCase)
                     # make random mask reproducible
                     torch.manual_seed(2)
                     with torch.no_grad():
                           lowercase__     =  model(**self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase))

                     # Make sure we don't have nans
                     lowercase__     =  after_outputs[0].cpu().numpy()
                     lowercase__     =  0
                     lowercase__     =  np.amax(np.abs(out_a - out_a))
                     self.assertLessEqual(lowerCAmelCase			,				1E-5)


   @unittest.skip(
       reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Union[str, Any]:




         """simple docstring"""

         pass


   @unittest.skip(
       reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         pass


   @unittest.skip(
       reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
   def 			UpperCAmelCase		(						self  :			List[str])	->				int:




         """simple docstring"""

         pass


   @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         pass


   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[int]:




         """simple docstring"""

         pass



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




         """simple docstring"""

         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__     =  ViTMAEModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)

def   _lowerCAmelCase   (						):
      lowercase__     =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      return image



@require_torch
@require_vision
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   @cached_property
   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None



   @slow
   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         np.random.seed(2)

         lowercase__     =  ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowerCAmelCase)

         lowercase__     =  self.default_image_processor
         lowercase__     =  prepare_img()
         lowercase__     =  image_processor(images=lowerCAmelCase			,				return_tensors='pt').to(lowerCAmelCase)

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         lowercase__     =  ViTMAEConfig()
         lowercase__     =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
         lowercase__     =  np.random.uniform(size=(1, num_patches))

         # forward pass
         with torch.no_grad():
               lowercase__     =  model(**lowerCAmelCase			,				noise=torch.from_numpy(lowerCAmelCase).to(device=lowerCAmelCase))

         # verify the logits
         lowercase__     =  torch.Size((1, 1_96, 7_68))
         self.assertEqual(outputs.logits.shape			,				lowerCAmelCase)

         lowercase__     =  torch.tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,				expected_slice.to(lowerCAmelCase)			,				atol=1E-4))





import heapq
import sys

import numpy as np

a__      :				Dict   	=			tuple[int, int]



class       UpperCAmelCase__:

   '''simple docstring'''




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




         """simple docstring"""

         lowercase__     =  []
         lowercase__     =  set()


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




         """simple docstring"""

         if not self.empty():
               return self.elements[0][0]
         else:
               return float('inf')


   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

         return len(self.elements) == 0


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[str])	->				List[str]:




         """simple docstring"""

         if item not in self.set:
               heapq.heappush(self.elements			,				(priority, item))
               self.set.add(lowerCAmelCase)
         else:
               # update
               # print("update", item)
               lowercase__     =  []
               ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
               while x != item:
                     temp.append((pri, x))
                     ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
               temp.append((priority, item))
               for pro, xxx in temp:
                     heapq.heappush(self.elements			,				(pro, xxx))


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			int)	->				Tuple:




         """simple docstring"""

         if item in self.set:
               self.set.remove(lowerCAmelCase)
               lowercase__     =  []
               ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
               while x != item:
                     temp.append((pro, x))
                     ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
               for prito, yyy in temp:
                     heapq.heappush(self.elements			,				(prito, yyy))


   def 			UpperCAmelCase		(						self  :			Dict)	->				List[Any]:




         """simple docstring"""

         return self.elements[0][1]



   def 			UpperCAmelCase		(						self  :			List[str])	->				str:




         """simple docstring"""

         ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
         self.set.remove(lowerCAmelCase)
         return (priority, item)

def   _lowerCAmelCase   (						A__						,  A__	):
      # euclidean distance
      lowercase__     =  np.array(A__	)
      lowercase__     =  np.array(A__	)
      return np.linalg.norm(a - b	)

def   _lowerCAmelCase   (						A__						,  A__	):
      # integer division by time variable
      return consistent_heuristic(A__						,  A__	) // t

def   _lowerCAmelCase   (						A__						,  A__	):
      # manhattan distance
      return abs(p[0] - goal[0]	) + abs(p[1] - goal[1]	)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__     =  g_function[start] + Wa * heuristics[i](A__						,  A__	)
      return ans

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  np.chararray((n, n)	)
      for i in range(A__	):
            for j in range(A__	):
                  lowercase__     =  '*'

      for i in range(A__	):
            for j in range(A__	):
                  if (j, (n - 1) - i) in blocks:
                        lowercase__     =  '#'

      lowercase__     =  '-'
      lowercase__     =  back_pointer[goal]
      while x != start:
            ((lowercase__),	(lowercase__))     =  x
            # print(x)
            lowercase__     =  '-'
            lowercase__     =  back_pointer[x]
      lowercase__     =  '-'

      for i in range(A__	):
            for j in range(A__	):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]						,  end=' '	)
                        print('<-- End position'						,  end=' '	)
                  else:
                        print(grid[i][j]						,  end=' '	)
            print()
      print('^'	)
      print('Start position'	)
      print()
      print('# is an obstacle'	)
      print('- is the path taken by algorithm'	)
      print('PATH TAKEN BY THE ALGORITHM IS:-'	)
      lowercase__     =  back_pointer[goal]
      while x != start:
            print(A__						,  end=' '	)
            lowercase__     =  back_pointer[x]
      print(A__	)
      sys.exit()

def   _lowerCAmelCase   (						A__	):
      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  ):
      for itera in range(A__	):
            open_list[itera].remove_element(A__	)
      # print("s", s)
      # print("j", j)
      ((lowercase__),	(lowercase__))     =  s
      lowercase__     =  (x - 1, y)
      lowercase__     =  (x + 1, y)
      lowercase__     =  (x, y + 1)
      lowercase__     =  (x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(A__	) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(A__	)
                        lowercase__     =  -1
                        lowercase__     =  float('inf'	)

                  if valid(A__	) and g_function[neighbours] > g_function[s] + 1:
                        lowercase__     =  g_function[s] + 1
                        lowercase__     =  s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(A__						,  key(A__						,  0						,  A__						,  A__	)	)
                              if neighbours not in close_list_inad:
                                    for var in range(1						,  A__	):
                                          if key(A__						,  A__						,  A__						,  A__	) <= Wa * key(
                                              A__						,  0						,  A__						,  A__	):
                                                open_list[j].put(
                                                    A__						,  key(A__						,  A__						,  A__						,  A__	)	)

def   _lowerCAmelCase   (						):
      lowercase__     =  []
      for x in range(1						,  5	):
            for y in range(1						,  6	):
                  some_list.append((x, y)	)

      for x in range(15						,  20	):
            some_list.append((x, 17)	)

      for x in range(10						,  19	):
            for y in range(1						,  15	):
                  some_list.append((x, y)	)

    # L block
      for x in range(1						,  4	):
            for y in range(12						,  19	):
                  some_list.append((x, y)	)
      for x in range(3						,  13	):
            for y in range(16						,  19	):
                  some_list.append((x, y)	)
      return some_list


a__      :				str   	=			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a__      :				Any   	=			[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a__      :				Any   	=			make_common_ground()


a__      :				Union[str, Any]   	=			blocks_blk
# hyper parameters
a__      :				List[Any]   	=			1
a__      :				List[str]   	=			1
a__      :				Optional[int]   	=			20
a__      :				Optional[Any]   	=			3  # one consistent and two other inconsistent

# start and end destination
a__      :				Tuple   	=			(0, 0)
a__      :				str   	=			(n - 1, n - 1)

a__      :				Optional[Any]   	=			1

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  {start: 0, goal: float('inf'	)}
      lowercase__     =  {start: -1, goal: -1}
      lowercase__     =  []
      lowercase__     =  set()

      for i in range(A__	):
            open_list.append(PriorityQueue()	)
            open_list[i].put(A__						,  key(A__						,  A__						,  A__						,  A__	)	)

      lowercase__     =  []
      lowercase__     =  []
      while open_list[0].minkey() < float('inf'	):
            for i in range(1						,  A__	):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float('inf'	):
                                    do_something(A__						,  A__						,  A__	)
                        else:
                              lowercase__,	lowercase__     =  open_list[i].top_show()
                              visited.add(A__	)
                              expand_state(
                                  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  )
                              close_list_inad.append(A__	)
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float('inf'	):
                                    do_something(A__						,  A__						,  A__	)
                        else:
                              lowercase__     =  open_list[0].top_show()
                              visited.add(A__	)
                              expand_state(
                                  A__						,  0						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  )
                              close_list_anchor.append(A__	)
      print('No path found to goal'	)
      print()
      for i in range(n - 1						,  -1						,  -1	):
            for j in range(A__	):
                  if (j, i) in blocks:
                        print('#'						,  end=' '	)
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print('*'						,  end=' '	)
                        else:
                              print('-'						,  end=' '	)
                  else:
                        print('*'						,  end=' '	)
                  if (j, i) == (n - 1, n - 1):
                        print('<-- End position'						,  end=' '	)
            print()
      print('^'	)
      print('Start position'	)
      print()
      print('# is an obstacle'	)
      print('- is the path taken by algorithm'	)


if __name__ == "__main__":
       multi_a_star(start, goal, n_heuristic)
