
"""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  lowerCAmelCase_ :



						'''simple docstring'''


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

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      int:
							A        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

							A        =      self.get_config()

							return config, pixel_values, labels


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      List[str]:
							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=A_     ,initializer_range=self.initializer_range     ,mask_ratio=self.mask_ratio     ,)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					Any     ,A_   :					Dict     ,A_   :					int					)   ->      str:
							A        =      ViTMAEModel(config=A_					)
							model.to(A_					)
							model.eval()
							A        =      model(A_					)
							self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					Optional[int]     ,A_   :					Dict     ,A_   :					Dict					)   ->      int:
							A        =      ViTMAEForPreTraining(A_					)
							model.to(A_					)
							model.eval()
							A        =      model(A_					)
							A        =      (self.image_size // self.patch_size) ** 2
							A        =      self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape     ,(self.batch_size, num_patches, expected_num_channels)					)

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




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







@require_torch
class  lowerCAmelCase_ (					snake_case_						,    snake_case_						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			str					=			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						_lowerCamelCase:			Tuple					=			{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

						_lowerCamelCase:			Optional[int]					=			False
						_lowerCamelCase:			Any					=			False
						_lowerCamelCase:			Union[str, Any]					=			False
						_lowerCamelCase:			List[Any]					=			False


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Optional[Any]:
							A        =      ViTMAEModelTester(self					)
							A        =      ConfigTester(self     ,config_class=A_     ,has_text_modality=A_     ,hidden_size=37					)


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


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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Optional[Any]:
							A      ,					A        =      self.model_tester.prepare_config_and_inputs_for_common()

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Union[str, Any]:
							A      ,					A        =      self.model_tester.prepare_config_and_inputs_for_common()

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

								A        =      ['pixel_values']
								self.assertListEqual(arg_names[:1]     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Tuple:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Tuple:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Optional[int]     ,A_   :					Tuple     ,A_   :					Union[str, Any]					)   ->      Optional[Any]:
							# make masks reproducible
							np.random.seed(2					)

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

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

							super().check_pt_tf_models(A_     ,A_     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Optional[Any]:
							A      ,					A        =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      List[Any]:
							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A        =      ViTMAEModel.from_pretrained(A_					)
								self.assertIsNotNone(A_					)






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







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



						'''simple docstring'''


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Optional[int]:
							return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'					) if is_vision_available() else None




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

							A        =      ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'					).to(A_					)

							A        =      self.default_image_processor
							A        =      prepare_img()
							A        =      image_processor(images=A_     ,return_tensors='pt'					).to(A_					)

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

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

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

							A        =      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(A_					)     ,atol=1e-4					)					)

from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


