






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 		_snake_case  :






   def __init__(       self		,					a		,					a=13		,					a=30		,					a=2		,					a=3		,					a=True		,					a=True		,					a=32		,					a=5		,					a=4		,					a=37		,					a="gelu"		,					a=0.1		,					a=0.1		,					a=10		,					a=0.02		,					a=3		,					a=0.6		,					a=None		,					)      ->       List[str]:
      SCREAMING_SNAKE_CASE						= parent
      SCREAMING_SNAKE_CASE						= batch_size
      SCREAMING_SNAKE_CASE						= image_size
      SCREAMING_SNAKE_CASE						= patch_size
      SCREAMING_SNAKE_CASE						= num_channels
      SCREAMING_SNAKE_CASE						= is_training
      SCREAMING_SNAKE_CASE						= use_labels
      SCREAMING_SNAKE_CASE						= hidden_size
      SCREAMING_SNAKE_CASE						= num_hidden_layers
      SCREAMING_SNAKE_CASE						= num_attention_heads
      SCREAMING_SNAKE_CASE						= intermediate_size
      SCREAMING_SNAKE_CASE						= hidden_act
      SCREAMING_SNAKE_CASE						= hidden_dropout_prob
      SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE						= type_sequence_label_size
      SCREAMING_SNAKE_CASE						= initializer_range
      SCREAMING_SNAKE_CASE						= mask_ratio
      SCREAMING_SNAKE_CASE						= scope

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

      SCREAMING_SNAKE_CASE						= self.get_config()

      return config, pixel_values, labels






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
      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=UpperCamelCase__		,					initializer_range=self.initializer_range		,					mask_ratio=self.mask_ratio		,					)






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Optional[Any]:
      SCREAMING_SNAKE_CASE						= ViTMAEModel(config=UpperCamelCase__)
      model.to(UpperCamelCase__)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(UpperCamelCase__)
      self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size))






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= ViTMAEForPreTraining(UpperCamelCase__)
      model.to(UpperCamelCase__)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(UpperCamelCase__)
      SCREAMING_SNAKE_CASE						= (self.image_size // self.patch_size) ** 2
      SCREAMING_SNAKE_CASE						= self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, num_patches, expected_num_channels))

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







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE						= config_and_inputs
      SCREAMING_SNAKE_CASE						= {'pixel_values': pixel_values}
      return config, inputs_dict



@require_torch
class 		_snake_case  (  __A   ,    __A   ,    unittest.TestCase					):
   _lowercase		:    Optional[Any]			     =							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   _lowercase		:    Optional[Any]			     =							{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

   _lowercase		:    str			     =							False
   _lowercase		:    Dict			     =							False
   _lowercase		:    Tuple			     =							False
   _lowercase		:    List[Any]			     =							False






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      SCREAMING_SNAKE_CASE						= ViTMAEModelTester(self)
      SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=UpperCamelCase__		,					has_text_modality=UpperCamelCase__		,					hidden_size=37)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      self.config_tester.run_common_tests()






   @unittest.skip(reason='ViTMAE does not use inputs_embeds')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      pass






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE						= ['pixel_values']
         self.assertListEqual(arg_names[:1]		,					UpperCamelCase__)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase__)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__)






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       str:
      # make masks reproducible
      np.random.seed(2)

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

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

      super().check_pt_tf_models(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

            # Make sure we don't have nans
            SCREAMING_SNAKE_CASE						= after_outputs[0].cpu().numpy()
            SCREAMING_SNAKE_CASE						= 0
            SCREAMING_SNAKE_CASE						= np.amax(np.abs(out_a - out_a))
            self.assertLessEqual(UpperCamelCase__		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      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    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      pass






   @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      pass






   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      pass







   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE						= ViTMAEModel.from_pretrained(UpperCamelCase__)
         self.assertIsNotNone(UpperCamelCase__)






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







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






   @cached_property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None







   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2)

      SCREAMING_SNAKE_CASE						= ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCamelCase__)

      SCREAMING_SNAKE_CASE						= self.default_image_processor
      SCREAMING_SNAKE_CASE						= prepare_img()
      SCREAMING_SNAKE_CASE						= image_processor(images=UpperCamelCase__		,					return_tensors='pt').to(UpperCamelCase__)

      # 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)
      SCREAMING_SNAKE_CASE						= ViTMAEConfig()
      SCREAMING_SNAKE_CASE						= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
      SCREAMING_SNAKE_CASE						= np.random.uniform(size=(1, num_patches))

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

      # verify the logits
      SCREAMING_SNAKE_CASE						= torch.Size((1, 196, 768))
      self.assertEqual(outputs.logits.shape		,					UpperCamelCase__)

      SCREAMING_SNAKE_CASE						= 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(UpperCamelCase__)		,					atol=1E-4))












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[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],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

									for node in successors:
												self.node_queue.append(a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= 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		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
												for node in successors[bfs]:
															bfs.node_queue.append(a)

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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

				print('Unidirectional BFS computation time : ', bfs_time)

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

				print('Bidirectional BFS computation time : ', bd_bfs_time)




