


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 	snake_case_       :
					def __init__(				self       :Optional[Any]      ,__snake_case       :List[str]      ,__snake_case       :Any=13      ,__snake_case       :Dict=30      ,__snake_case       :Dict=2      ,__snake_case       :Dict=3      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Any=True      ,__snake_case       :Dict=32      ,__snake_case       :str=2      ,__snake_case       :List[Any]=4      ,__snake_case       :Tuple=37      ,__snake_case       :Dict="gelu"      ,__snake_case       :Optional[Any]=0.1      ,__snake_case       :List[Any]=0.1      ,__snake_case       :Tuple=10      ,__snake_case       :Dict=0.02      ,__snake_case       :Union[str, Any]=3      ,__snake_case       :List[str]=0.6      ,__snake_case       :Tuple=None      ,) ->   Optional[Any]:
												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 			lowerCamelCase__(				self       :Tuple				) ->   List[str]:
												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 			lowerCamelCase__(				self       :int				) ->   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      ,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=__snake_case      ,initializer_range=self.initializer_range      ,mask_ratio=self.mask_ratio      ,)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :Dict				) ->   List[Any]:
												a__					=					TFViTMAEModel(config=__snake_case				)
												a__					=					model(__snake_case      ,training=__snake_case				)
												self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Dict      ,__snake_case       :Any      ,__snake_case       :str				) ->   Any:
												a__					=					TFViTMAEForPreTraining(__snake_case				)
												a__					=					model(__snake_case      ,training=__snake_case				)
												# expected sequence length = num_patches
												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__					=					TFViTMAEForPreTraining(__snake_case				)

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



					def 			lowerCamelCase__(				self       :List[Any]				) ->   int:
												a__					=					self.prepare_config_and_inputs()
												((a__)				, (a__)				, (a__))					=					config_and_inputs
												a__					=					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_tf
class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   List[str]   				=      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					UpperCAmelCase__ :   str   				=      {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

					UpperCAmelCase__ :   List[str]   				=      False
					UpperCAmelCase__ :   List[Any]   				=      False
					UpperCAmelCase__ :   Optional[Any]   				=      False
					UpperCAmelCase__ :   List[str]   				=      False
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					TFViTMAEModelTester(self				)
												a__					=					ConfigTester(self      ,config_class=__snake_case      ,has_text_modality=__snake_case      ,hidden_size=37				)
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='ViTMAE does not use inputs_embeds'				)
					def 			lowerCamelCase__(				self       :str				) ->   int:
												pass
					def 			lowerCamelCase__(				self       :Tuple				) ->   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(__snake_case				)
																			self.assertIsInstance(model.get_input_embeddings()      ,(tf.keras.layers.Layer)				)
																			a__					=					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case      ,tf.keras.layers.Layer				)				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   str:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																			a__					=					['pixel_values']
																			self.assertListEqual(arg_names[:1]      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												# make the mask reproducible
												np.random.seed(2				)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)
																			a__					=					self._prepare_for_class(__snake_case      ,__snake_case				)

																			a__					=					model(__snake_case      ,noise=__snake_case				)

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

																			self.assertLess(np.sum(np.abs(output_dict - output_keywords				)				)      ,1E-6				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												# make the mask reproducible
												np.random.seed(2				)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

																			return inputs_np_dict

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)

																			a__					=					self._prepare_for_class(__snake_case      ,__snake_case				)
																			a__					=					prepare_numpy_arrays(__snake_case				)

																			a__					=					model(__snake_case      ,noise=__snake_case				)
																			a__					=					model(**__snake_case      ,noise=__snake_case				)
																			self.assert_outputs_same(__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Optional[Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :List[str]				) ->   List[str]:
												# make masks reproducible
												np.random.seed(2				)

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

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

												super().check_pt_tf_models(__snake_case      ,__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   Optional[int]:
												# make mask reproducible
												np.random.seed(2				)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

												a__					=					{
												    module_member
												    for model_class in self.all_model_classes
												    for module in (import_module(model_class.__module__				),)
												    for module_member_name in dir(__snake_case				)
												    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(__snake_case      ,__snake_case				),)
												    if isinstance(__snake_case      ,__snake_case				)
												    and tf.keras.layers.Layer in module_member.__bases__
												    and getattr(__snake_case      ,'_keras_serializable'      ,__snake_case				)
												}

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

												for main_layer_class in tf_main_layer_classes:
																			a__					=					main_layer_class(__snake_case				)

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

																			a__					=					tf.keras.Model(__snake_case      ,outputs=main_layer(__snake_case				)				)
																			a__					=					model(__snake_case				)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										a__					=					os.path.join(__snake_case      ,'keras_model.h5'				)
																										model.save(__snake_case				)
																										a__					=					tf.keras.models.load_model(
																										    __snake_case      ,custom_objects={main_layer_class.__name__: main_layer_class}				)
																										assert isinstance(__snake_case      ,tf.keras.Model				)
																										a__					=					model(__snake_case				)
																										self.assert_outputs_same(__snake_case      ,__snake_case				)
					@slow
					def 			lowerCamelCase__(				self       :str				) ->   int:
												# make mask reproducible
												np.random.seed(2				)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)
																			a__					=					self._prepare_for_class(__snake_case      ,__snake_case				)
																			a__					=					model(__snake_case      ,noise=__snake_case				)

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

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

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

																										a__					=					np.amax(np.abs(out_a - out_a				)				)
																										self.assertLessEqual(__snake_case      ,1E-5				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   List[Any]:
												# make mask reproducible
												np.random.seed(2				)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)
																			a__					=					self._prepare_for_class(__snake_case      ,__snake_case				)

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

																			self.assert_outputs_same(__snake_case      ,__snake_case				)
					@unittest.skip(
					    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Union[str, Any]:
												pass
					@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Dict:
												pass



					@slow
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[str]:
												a__					=					TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'				)
												self.assertIsNotNone(__snake_case				)






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







@require_tf
@require_vision
class 	snake_case_       (unittest.TestCase				):
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'				) if is_vision_available() else None



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

												a__					=					TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'				)

												a__					=					self.default_image_processor
												a__					=					prepare_img()
												a__					=					image_processor(images=__snake_case      ,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)
												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
												a__					=					model(**__snake_case      ,noise=__snake_case				)

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

												a__					=					tf.convert_to_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]]				)

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








from __future__ import annotations







class 	snake_case_       :
					def __init__(				self       :List[str]      ,__snake_case       :int				) ->   None:
												a__					=					data
												a__					=					None
												a__					=					None






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):  # In Order traversal of the tree
							if tree:
														display(tree.left       )
														print(tree.data       )
														display(tree.right       )






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):
							return 1 + max(depth_of_tree(tree.left       ) ,   depth_of_tree(tree.right       )       ) if tree else 0






def 							__lowercase       (			__lowerCAmelCase							:    Node       ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left       ) and is_full_binary_tree(tree.right       )
							else:
														return not tree.left and not tree.right






def 							__lowercase       (			):  # Main function for testing.
							a__					=					Node(1       )
							a__					=					Node(2       )
							a__					=					Node(3       )
							a__					=					Node(4       )
							a__					=					Node(5       )
							a__					=					Node(6       )
							a__					=					Node(7       )
							a__					=					Node(8       )
							a__					=					Node(9       )

							print(is_full_binary_tree(__lowerCAmelCase       )       )
							print(depth_of_tree(__lowerCAmelCase       )       )
							print('Tree is: '       )
							display(__lowerCAmelCase       )


if __name__ == "__main__":
						main()




