






'''simple docstring'''







from __future__ import annotations
def 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			int						):
						lowerCamelCase__         =  set(__lowerCAmelCase						), [start]

						while stack:
												lowerCamelCase__         =  stack.pop()
												explored.add(__lowerCAmelCase						)
												# Differences from BFS:
												# 1) pop last element instead of first one
												# 2) add adjacent elements to stack without exploring them
												for adj in reversed(graph[v]						):
																		if adj not in explored:
																								stack.append(__lowerCAmelCase						)
						return explored


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

if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(depth_first_search(G, 'A'))









'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=30   ,_lowerCAmelCase=2   ,_lowerCAmelCase=3   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=32   ,_lowerCAmelCase=2   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=10   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=3   ,_lowerCAmelCase=0.6   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  patch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_labels
								lowerCamelCase__         =  hidden_size
								lowerCamelCase__         =  num_hidden_layers
								lowerCamelCase__         =  num_attention_heads
								lowerCamelCase__         =  intermediate_size
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  hidden_dropout_prob
								lowerCamelCase__         =  attention_probs_dropout_prob
								lowerCamelCase__         =  type_sequence_label_size
								lowerCamelCase__         =  initializer_range
								lowerCamelCase__         =  mask_ratio
								lowerCamelCase__         =  scope

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

								lowerCamelCase__         =  self.get_config()

								return config, pixel_values, labels






		def 			UpperCamelCase_					(      self       ):
								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   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_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   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  TFViTMAEModel(config=_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  TFViTMAEForPreTraining(_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								# expected sequence length = num_patches
								lowerCamelCase__         =  (self.image_size // self.patch_size) ** 2
								lowerCamelCase__         =  self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)       )

								# test greyscale images
								lowerCamelCase__         =  1
								lowerCamelCase__         =  TFViTMAEForPreTraining(_lowerCAmelCase       )

								lowerCamelCase__         =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								lowerCamelCase__         =  self.patch_size**2
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()
								((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  config_and_inputs
								lowerCamelCase__         =  {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCamelCase__    (a   ,a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		_UpperCamelCase				  =					{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFViTMAEModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=_lowerCAmelCase   ,has_text_modality=_lowerCAmelCase   ,hidden_size=37       )






		def 			UpperCamelCase_					(      self       ):
								self.config_tester.run_common_tests()






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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)       )
														lowerCamelCase__         =  model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_lowerCAmelCase   ,tf.keras.layers.Layer       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__         =  ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								# make the mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														lowerCamelCase__         =  copy.deepcopy(self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )       )
														lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  outputs_dict[0].numpy()
														lowerCamelCase__         =  outputs_keywords[0].numpy()

														self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )   ,1E-6       )






		def 			UpperCamelCase_					(      self       ):
								# make the mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								def prepare_numpy_arrays(_lowerCAmelCase       ):
														lowerCamelCase__         =  {}
														for k, v in inputs_dict.items():
																				if tf.is_tensor(_lowerCAmelCase       ):
																										lowerCamelCase__         =  v.numpy()
																				else:
																										lowerCamelCase__         =  np.array(_lowerCAmelCase       )

														return inputs_np_dict

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  prepare_numpy_arrays(_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								# make masks reproducible
								np.random.seed(2       )

								lowerCamelCase__         =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								lowerCamelCase__         =  tf.constant(_lowerCAmelCase       )

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

								super().check_pt_tf_models(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  {
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__       ),)
								    for module_member_name in dir(_lowerCAmelCase       )
								    if module_member_name.endswith("""MainLayer"""       )
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len("""MainLayer"""       )] == model_class.__name__[: -len("""Model"""       )]
								    for module_member in (getattr(_lowerCAmelCase   ,_lowerCAmelCase       ),)
								    if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       )
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(_lowerCAmelCase   ,"""_keras_serializable"""   ,_lowerCAmelCase       )
								}

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								lowerCamelCase__         =  tf.convert_to_tensor(_lowerCAmelCase       )
								inputs_dict.update({"""noise""": noise}       )

								for main_layer_class in tf_main_layer_classes:
														lowerCamelCase__         =  main_layer_class(_lowerCAmelCase       )

														lowerCamelCase__         =  {
														    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
														}

														lowerCamelCase__         =  tf.keras.Model(_lowerCAmelCase   ,outputs=main_layer(_lowerCAmelCase       )       )
														lowerCamelCase__         =  model(_lowerCAmelCase       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																				lowerCamelCase__         =  os.path.join(_lowerCAmelCase   ,"""keras_model.h5"""       )
																				model.save(_lowerCAmelCase       )
																				lowerCamelCase__         =  tf.keras.models.load_model(
																				    _lowerCAmelCase   ,custom_objects={main_layer_class.__name__: main_layer_class}       )
																				assert isinstance(_lowerCAmelCase   ,tf.keras.Model       )
																				lowerCamelCase__         =  model(_lowerCAmelCase       )
																				self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														if model_class.__name__ == "TFViTMAEModel":
																				lowerCamelCase__         =  outputs.last_hidden_state.numpy()
																				lowerCamelCase__         =  0
														else:
																				lowerCamelCase__         =  outputs.logits.numpy()
																				lowerCamelCase__         =  0

														with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_pretrained(_lowerCAmelCase   ,saved_model=_lowerCAmelCase       )
																				lowerCamelCase__         =  model_class.from_pretrained(_lowerCAmelCase       )
																				lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

																				if model_class.__name__ == "TFViTMAEModel":
																										lowerCamelCase__         =  after_outputs["""last_hidden_state"""].numpy()
																										lowerCamelCase__         =  0
																				else:
																										lowerCamelCase__         =  after_outputs["""logits"""].numpy()
																										lowerCamelCase__         =  0

																				lowerCamelCase__         =  np.amax(np.abs(out_a - out_a       )       )
																				self.assertLessEqual(_lowerCAmelCase   ,1E-5       )






		def 			UpperCamelCase_					(      self       ):
								# make mask reproducible
								np.random.seed(2       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__         =  int((config.image_size // config.patch_size) ** 2       )
								lowerCamelCase__         =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )
														lowerCamelCase__         =  model.get_config()
														# make sure that returned config is jsonifiable, which is required by keras
														json.dumps(_lowerCAmelCase       )
														lowerCamelCase__         =  model_class.from_config(model.get_config()       )
														# make sure it also accepts a normal config
														lowerCamelCase__         =  model_class.from_config(model.config       )
														lowerCamelCase__         =  new_model(_lowerCAmelCase       )  # Build model
														new_model.set_weights(model.get_weights()       )
														lowerCamelCase__         =  new_model(_lowerCAmelCase   ,noise=_lowerCAmelCase       )

														self.assert_outputs_same(_lowerCAmelCase   ,_lowerCAmelCase       )






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






		@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""       )
								self.assertIsNotNone(_lowerCAmelCase       )



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





@require_tf
@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





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






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

								lowerCamelCase__         =  TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""       )

								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(images=_lowerCAmelCase   ,return_tensors="""tf"""       )

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

								# forward pass
								lowerCamelCase__         =  model(**_lowerCAmelCase   ,noise=_lowerCAmelCase       )

								# verify the logits
								lowerCamelCase__         =  tf.convert_to_tensor([1, 1_96, 7_68]       )
								self.assertEqual(outputs.logits.shape   ,_lowerCAmelCase       )

								lowerCamelCase__         =  tf.convert_to_tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

								tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,_lowerCAmelCase   ,atol=1E-4       )

