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 		a__						:

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_size
							SCREAMING_SNAKE_CASE_  :		int				      =    patch_size
							SCREAMING_SNAKE_CASE_  :		int				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    is_training
							SCREAMING_SNAKE_CASE_  :		Any				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		int				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Any				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    mask_ratio
							SCREAMING_SNAKE_CASE_  :		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)
							SCREAMING_SNAKE_CASE_  :		int				      =    (image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE_  :		str				      =    int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    ids_tensor([self.batch_size],self.type_sequence_label_size    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_config()

							return config, pixel_values, labels

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""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=_UpperCAmelCase,initializer_range=self.initializer_range,mask_ratio=self.mask_ratio,)

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       Tuple,_A      :       str,_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    ViTMAEModel(config=_UpperCAmelCase    )
							model.to(_UpperCAmelCase    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model(_UpperCAmelCase    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[Any],_A      :       int,_A      :       Tuple    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_  :		str				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 		a__						(					_UpperCamelCase    ,		_UpperCamelCase    ,		unittest.TestCase					):
					A	       =							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					A	       =							{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

					A	       =							False
					A	       =							False
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    ViTMAEModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    ConfigTester(self,config_class=_UpperCAmelCase,has_text_modality=_UpperCAmelCase,hidden_size=37    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					@unittest.skip(reason="ViTMAE does not use inputs_embeds"    )
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_class(_UpperCAmelCase    )
									self.assertIsInstance(model.get_input_embeddings(),(nn.Module)    )
									SCREAMING_SNAKE_CASE_  :		int				      =    model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_UpperCAmelCase,nn.Linear    )    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE_  :		str				      =    ['pixel_values']
									self.assertListEqual(arg_names[:1],_UpperCAmelCase    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Dict,_A      :       List[str],_A      :       Tuple    ):







							"""simple docstring"""






							np.random.seed(2    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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_  :		Optional[int]				      =    pt_noise

							super().check_pt_tf_models(_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_class(_UpperCAmelCase    )
									model.to(_UpperCAmelCase    )
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2    )
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_  :		Any				      =    model(**self._prepare_for_class(_UpperCAmelCase,_UpperCAmelCase    )    )

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

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

											# Make sure we don't have nans
											SCREAMING_SNAKE_CASE_  :		Any				      =    after_outputs[0].cpu().numpy()
											SCREAMING_SNAKE_CASE_  :		int				      =    0
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 				__UpperCamelCase   (    self      :       str    ):







							"""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      :       Dict    ):







							"""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      :       Optional[Any]    ):







							"""simple docstring"""






							pass

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







							"""simple docstring"""






							pass

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







							"""simple docstring"""






							pass






					@slow
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ViTMAEModel.from_pretrained(_UpperCAmelCase    )
									self.assertIsNotNone(_UpperCAmelCase    )







def       _snake_case	(   ):
		"""simple docstring"""







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







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

					@cached_property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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






					@slow
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							np.random.seed(2    )

							SCREAMING_SNAKE_CASE_  :		int				      =    ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"    ).to(_UpperCAmelCase    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.default_image_processor
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    prepare_img()
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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_  :		Dict				      =    ViTMAEConfig()
							SCREAMING_SNAKE_CASE_  :		Any				      =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.uniform(size=(1, num_patches)    )

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model(**_UpperCAmelCase,noise=torch.from_numpy(_UpperCAmelCase    ).to(device=_UpperCAmelCase    )    )

							# verify the logits
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.Size((1, 196, 768)    )
							self.assertEqual(outputs.logits.shape,_UpperCAmelCase    )

							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor(
							    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]    )

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





'''simple docstring'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							set()
		# keep track of all the paths to be checked
		_a     :      Any        =							[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				_a     :      Tuple        =							queue.pop(0  )
				# get the last node from the path
				_a     :      Tuple        =							path[-1]
				if node not in explored:
						_a     :      Optional[Any]        =							graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								_a     :      Any        =							list(lowerCAmelCase_  )
								new_path.append(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(lowerCAmelCase_  )

    # in case there's no path between the 2 nodes
		return []



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		_a     :      Optional[int]        =							[start]
		_a     :      Dict        =							set(lowerCAmelCase_  )
		# Keep tab on distances from `start` node.
		_a     :      Dict        =							{start: 0, target: -1}
		while queue:
				_a     :      List[str]        =							queue.pop(0  )
				if node == target:
						_a     :      Any        =							(
						    dist[node] if dist[target] == -1 else min(dist[target]						,			dist[node]  )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								_a     :      Any        =							dist[node] + 1
		return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




