'''simple docstring'''




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     __magic_name__						:







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

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Optional[int]										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										lowercase_					:			List[str]										=       None
										if self.use_labels:
																	lowercase_					:			List[Any]										=       ids_tensor([self.batch_size]				,			self.type_sequence_label_size    )

										lowercase_					:			Optional[int]										=       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=lowercase__				,			initializer_range=self.initializer_range				,			mask_ratio=self.mask_ratio				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[str]										=       ViTMAEModel(config=lowercase__    )
										model.to(lowercase__    )
										model.eval()
										lowercase_					:			Any										=       model(lowercase__    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[Any]				,			lowercase_				:  int				,			lowercase_				:  Optional[Any]    ):
										lowercase_					:			Optional[Any]										=       ViTMAEForPreTraining(lowercase__    )
										model.to(lowercase__    )
										model.eval()
										lowercase_					:			Any										=       model(lowercase__    )
										lowercase_					:			str										=       (self.image_size // self.patch_size) ** 2
										lowercase_					:			List[Any]										=       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_					:			str										=       1
										lowercase_					:			Tuple										=       ViTMAEForPreTraining(lowercase__    )
										model.to(lowercase__    )
										model.eval()
										lowercase_					:			Any										=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										lowercase_					:			Union[str, Any]										=       model(lowercase__    )
										lowercase_					:			Tuple										=       self.patch_size**2
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, num_patches, expected_num_channels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Dict										=       self.prepare_config_and_inputs()
										lowercase_						,						lowercase_						,						lowercase_					:			Tuple										=       config_and_inputs
										lowercase_					:			List[str]										=       {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class     __magic_name__						(    lowerCamelCase__,     lowerCamelCase__,     unittest.TestCase):
			UpperCamelCase__				=       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			UpperCamelCase__				=       {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

			UpperCamelCase__				=       False
			UpperCamelCase__				=       False
			UpperCamelCase__				=       False
			UpperCamelCase__				=       False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			List[Any]										=       ViTMAEModelTester(self    )
										lowercase_					:			Optional[int]										=       ConfigTester(self				,			config_class=lowercase__				,			has_text_modality=lowercase__				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										self.config_tester.run_common_tests()







			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										pass







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_						,						lowercase_					:			Optional[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_						,						lowercase_					:			str										=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_					:			Optional[int]										=       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]				,			lowercase__    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			List[str]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			List[Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*lowercase__    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[Any]    ):
										# make masks reproducible
										np.random.seed(2    )

										lowercase_					:			str										=       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
										lowercase_					:			Dict										=       np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
										lowercase_					:			Optional[Any]										=       torch.from_numpy(lowercase__    )

										# Add `noise` argument.
										# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
										lowercase_					:			Optional[Any]										=       pt_noise

										super().check_pt_tf_models(lowercase__				,			lowercase__				,			lowercase__    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_						,						lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_					:			Optional[int]										=       model_class(lowercase__    )
																	model.to(lowercase__    )
																	model.eval()
																	# make random mask reproducible
																	torch.manual_seed(2    )
																	with torch.no_grad():
																								lowercase_					:			Union[str, Any]										=       model(**self._prepare_for_class(lowercase__				,			lowercase__    )    )

																	lowercase_					:			List[str]										=       outputs[0].cpu().numpy()
																	lowercase_					:			Any										=       0

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

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







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    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
    to get deterministic results."""    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										pass







			@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										pass







			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										pass







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_					:			str										=       ViTMAEModel.from_pretrained(lowercase__    )
																	self.assertIsNotNone(lowercase__    )




def 							lowerCamelCase	(      )			->      int:
							lowercase_					:			Optional[int]										=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
							return image





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







			@cached_property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""    ) if is_vision_available() else None







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										# make random mask reproducible across the PT and TF model
										np.random.seed(2    )

										lowercase_					:			Any										=       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    ).to(lowercase__    )

										lowercase_					:			Dict										=       self.default_image_processor
										lowercase_					:			str										=       prepare_img()
										lowercase_					:			Optional[int]										=       image_processor(images=lowercase__				,			return_tensors="""pt"""    ).to(lowercase__    )

										# 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_					:			Tuple										=       ViTMAEConfig()
										lowercase_					:			List[Any]										=       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
										lowercase_					:			List[Any]										=       np.random.uniform(size=(1, num_patches)    )

										# forward pass
										with torch.no_grad():
																	lowercase_					:			Union[str, Any]										=       model(**lowercase__				,			noise=torch.from_numpy(lowercase__    ).to(device=lowercase__    )    )

										# verify the logits
										lowercase_					:			str										=       torch.Size((1, 196, 768)    )
										self.assertEqual(outputs.logits.shape				,			lowercase__    )

										lowercase_					:			List[str]										=       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(lowercase__    )				,			atol=1E-4    )    )








'''simple docstring'''






from __future__ import annotations


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # We need to create solution object to save path.
 __lowercase							  = [[0 for _ in range(A__      )] for _ in range(A__      )]
 __lowercase							  = run_maze(A__     ,				0     ,				0     ,				A__      )
 if solved:
  print('''\n'''.join(str(A__      ) for row in solutions      )      )
 else:
  print('''No solution exists!'''      )
 return solved


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # Final check point.
 if i == j == (size - 1):
  __lowercase							  = 1
  return True

 __lowercase							  = (not i < 0) and (not j < 0)  # Check lower bounds
 __lowercase							  = (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowercase							  = (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowercase							  = 1

   # check for directions
   if (
       run_maze(A__     ,				i + 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j + 1     ,				A__      )
       or run_maze(A__     ,				i - 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j - 1     ,				A__      )
   ):
    return True

   __lowercase							  = 0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()

