




"""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       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	Any    ,   lowercase__					:	List[Any]    ,   lowercase__					:	str=1_3    ,   lowercase__					:	Dict=3_0    ,   lowercase__					:	List[str]=2    ,   lowercase__					:	Optional[int]=3    ,   lowercase__					:	Optional[int]=True    ,   lowercase__					:	List[str]=True    ,   lowercase__					:	int=3_2    ,   lowercase__					:	Dict=2    ,   lowercase__					:	Tuple=4    ,   lowercase__					:	str=3_7    ,   lowercase__					:	Any="gelu"    ,   lowercase__					:	Optional[Any]=0.1    ,   lowercase__					:	Tuple=0.1    ,   lowercase__					:	Dict=1_0    ,   lowercase__					:	Optional[Any]=0.0_2    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Union[str, Any]=0.6    ,   lowercase__					:	str=None    ,   ):
						__lowercase				:		Union[str, Any]												=				parent
						__lowercase				:		int												=				batch_size
						__lowercase				:		Dict												=				image_size
						__lowercase				:		List[str]												=				patch_size
						__lowercase				:		Optional[int]												=				num_channels
						__lowercase				:		str												=				is_training
						__lowercase				:		Dict												=				use_labels
						__lowercase				:		Any												=				hidden_size
						__lowercase				:		str												=				num_hidden_layers
						__lowercase				:		Tuple												=				num_attention_heads
						__lowercase				:		Optional[int]												=				intermediate_size
						__lowercase				:		Dict												=				hidden_act
						__lowercase				:		int												=				hidden_dropout_prob
						__lowercase				:		Any												=				attention_probs_dropout_prob
						__lowercase				:		Optional[int]												=				type_sequence_label_size
						__lowercase				:		int												=				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				:		Union[str, Any]												=				(image_size // patch_size) ** 2
						__lowercase				:		Tuple												=				int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		Dict												=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						__lowercase				:		List[str]												=				self.get_config()

						return config, pixel_values, labels



				def       snake_case    (     self					:	int						):
						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=lowercase__    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	int    ,   lowercase__					:	Tuple    ,   lowercase__					:	str						):
						__lowercase				:		Dict												=				TFViTMAEModel(config=lowercase__						)
						__lowercase				:		List[Any]												=				model(lowercase__    ,   training=lowercase__						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Any    ,   lowercase__					:	Union[str, Any]						):
						__lowercase				:		List[Any]												=				TFViTMAEForPreTraining(lowercase__						)
						__lowercase				:		Dict												=				model(lowercase__    ,   training=lowercase__						)
						# expected sequence length = num_patches
						__lowercase				:		List[Any]												=				(self.image_size // self.patch_size) ** 2
						__lowercase				:		str												=				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				:		Any												=				1
						__lowercase				:		Union[str, Any]												=				TFViTMAEForPreTraining(lowercase__						)

						__lowercase				:		str												=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
						__lowercase				:		Optional[int]												=				model(lowercase__    ,   training=lowercase__						)
						__lowercase				:		List[Any]												=				self.patch_size**2
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)						)



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		int												=				self.prepare_config_and_inputs()
						(__lowercase)				:		Dict												=				config_and_inputs
						__lowercase				:		str												=				{"pixel_values": pixel_values}
						return config, inputs_dict





@require_tf
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				__UpperCAmelCase :     Union[str, Any]       =      {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

				__UpperCAmelCase :     int       =      False
				__UpperCAmelCase :     str       =      False
				__UpperCAmelCase :     Any       =      False
				__UpperCAmelCase :     Dict       =      False



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		List[str]												=				TFViTMAEModelTester(self						)
						__lowercase				:		int												=				ConfigTester(self    ,   config_class=lowercase__    ,   has_text_modality=lowercase__    ,   hidden_size=3_7						)



				def       snake_case    (     self					:	str						):
						self.config_tester.run_common_tests()



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



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		List[Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

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



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Tuple												=				self.model_tester.prepare_config_and_inputs_for_common()

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

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



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		List[str]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase__						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		str												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						# make the mask reproducible
						np.random.seed(2						)

						__lowercase				:		Optional[Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

						__lowercase				:		List[Any]												=				int((config.image_size // config.patch_size) ** 2						)
						__lowercase				:		List[str]												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

						for model_class in self.all_model_classes:
								__lowercase				:		int												=				model_class(lowercase__						)
								__lowercase				:		Optional[int]												=				self._prepare_for_class(lowercase__    ,   lowercase__						)

								__lowercase				:		Optional[int]												=				model(lowercase__    ,   noise=lowercase__						)

								__lowercase				:		Optional[Any]												=				copy.deepcopy(self._prepare_for_class(lowercase__    ,   lowercase__						)						)
								__lowercase				:		int												=				model(**lowercase__    ,   noise=lowercase__						)
								__lowercase				:		Dict												=				outputs_dict[0].numpy()
								__lowercase				:		int												=				outputs_keywords[0].numpy()

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



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

						__lowercase				:		int												=				self.model_tester.prepare_config_and_inputs_for_common()

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

						def prepare_numpy_arrays(lowercase__					:	List[Any]						):
								__lowercase				:		List[str]												=				{}
								for k, v in inputs_dict.items():
										if tf.is_tensor(lowercase__						):
												__lowercase				:		List[str]												=				v.numpy()
										else:
												__lowercase				:		int												=				np.array(lowercase__						)

								return inputs_np_dict

						for model_class in self.all_model_classes:
								__lowercase				:		int												=				model_class(lowercase__						)

								__lowercase				:		str												=				self._prepare_for_class(lowercase__    ,   lowercase__						)
								__lowercase				:		str												=				prepare_numpy_arrays(lowercase__						)

								__lowercase				:		Union[str, Any]												=				model(lowercase__    ,   noise=lowercase__						)
								__lowercase				:		Optional[Any]												=				model(**lowercase__    ,   noise=lowercase__						)
								self.assert_outputs_same(lowercase__    ,   lowercase__						)



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

						__lowercase				:		Any												=				int((tf_model.config.image_size // tf_model.config.patch_size) ** 2						)
						__lowercase				:		List[Any]												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
						__lowercase				:		Optional[Any]												=				tf.constant(lowercase__						)

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

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



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

						__lowercase				:		str												=				self.model_tester.prepare_config_and_inputs_for_common()

						__lowercase				:		Optional[int]												=				{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__						),)
						    for module_member_name in dir(lowercase__						)
						    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(lowercase__    ,   lowercase__						),)
						    if isinstance(lowercase__    ,   lowercase__						)
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(lowercase__    ,   "_keras_serializable"    ,   lowercase__						)
						}

						__lowercase				:		str												=				int((config.image_size // config.patch_size) ** 2						)
						__lowercase				:		Optional[Any]												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
						__lowercase				:		Optional[int]												=				tf.convert_to_tensor(lowercase__						)
						inputs_dict.update({"noise": noise}						)

						for main_layer_class in tf_main_layer_classes:
								__lowercase				:		Any												=				main_layer_class(lowercase__						)

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

								__lowercase				:		Optional[Any]												=				tf.keras.Model(lowercase__    ,   outputs=main_layer(lowercase__						)						)
								__lowercase				:		Tuple												=				model(lowercase__						)

								with tempfile.TemporaryDirectory() as tmpdirname:
										__lowercase				:		List[Any]												=				os.path.join(lowercase__    ,   "keras_model.h5"						)
										model.save(lowercase__						)
										__lowercase				:		Optional[int]												=				tf.keras.models.load_model(
										    lowercase__    ,   custom_objects={main_layer_class.__name__: main_layer_class}						)
										assert isinstance(lowercase__    ,   tf.keras.Model						)
										__lowercase				:		Optional[int]												=				model(lowercase__						)
										self.assert_outputs_same(lowercase__    ,   lowercase__						)



				@slow
				def       snake_case    (     self					:	List[str]						):
						# make mask reproducible
						np.random.seed(2						)

						__lowercase				:		int												=				self.model_tester.prepare_config_and_inputs_for_common()

						__lowercase				:		Optional[Any]												=				int((config.image_size // config.patch_size) ** 2						)
						__lowercase				:		List[Any]												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

						for model_class in self.all_model_classes:
								__lowercase				:		int												=				model_class(lowercase__						)
								__lowercase				:		Optional[int]												=				self._prepare_for_class(lowercase__    ,   lowercase__						)
								__lowercase				:		List[Any]												=				model(lowercase__    ,   noise=lowercase__						)

								if model_class.__name__ == "TFViTMAEModel":
										__lowercase				:		int												=				outputs.last_hidden_state.numpy()
										__lowercase				:		Tuple												=				0
								else:
										__lowercase				:		Any												=				outputs.logits.numpy()
										__lowercase				:		Any												=				0

								with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(lowercase__    ,   saved_model=lowercase__						)
										__lowercase				:		Optional[Any]												=				model_class.from_pretrained(lowercase__						)
										__lowercase				:		Tuple												=				model(lowercase__    ,   noise=lowercase__						)

										if model_class.__name__ == "TFViTMAEModel":
												__lowercase				:		List[str]												=				after_outputs["last_hidden_state"].numpy()
												__lowercase				:		Any												=				0
										else:
												__lowercase				:		Any												=				after_outputs["logits"].numpy()
												__lowercase				:		Any												=				0

										__lowercase				:		Dict												=				np.amax(np.abs(out_a - out_a						)						)
										self.assertLessEqual(lowercase__    ,   1e-5						)



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

						__lowercase				:		Dict												=				self.model_tester.prepare_config_and_inputs_for_common()

						__lowercase				:		List[str]												=				int((config.image_size // config.patch_size) ** 2						)
						__lowercase				:		Optional[int]												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

						for model_class in self.all_model_classes:
								__lowercase				:		str												=				model_class(lowercase__						)
								__lowercase				:		Optional[int]												=				self._prepare_for_class(lowercase__    ,   lowercase__						)

								__lowercase				:		int												=				model(lowercase__    ,   noise=lowercase__						)
								__lowercase				:		str												=				model.get_config()
								# make sure that returned config is jsonifiable, which is required by keras
								json.dumps(lowercase__						)
								__lowercase				:		Any												=				model_class.from_config(model.get_config()						)
								# make sure it also accepts a normal config
								__lowercase				:		Union[str, Any]												=				model_class.from_config(model.config						)
								__lowercase				:		Union[str, Any]												=				new_model(lowercase__						)  # Build model
								new_model.set_weights(model.get_weights()						)
								__lowercase				:		Optional[Any]												=				new_model(lowercase__    ,   noise=lowercase__						)

								self.assert_outputs_same(lowercase__    ,   lowercase__						)



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



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



				@slow
				def       snake_case    (     self					:	Any						):
						__lowercase				:		str												=				TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"						)
						self.assertIsNotNone(lowercase__						)





def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image




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


				"""simple docstring"""





				@cached_property
				def       snake_case    (     self					:	List[Any]						):
						return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"						) if is_vision_available() else None



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

						__lowercase				:		str												=				TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"						)

						__lowercase				:		List[str]												=				self.default_image_processor
						__lowercase				:		Tuple												=				prepare_img()
						__lowercase				:		Tuple												=				image_processor(images=lowercase__    ,   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)
						__lowercase				:		Any												=				ViTMAEConfig()
						__lowercase				:		Any												=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
						__lowercase				:		Union[str, Any]												=				np.random.uniform(size=(1, num_patches)						)

						# forward pass
						__lowercase				:		str												=				model(**lowercase__    ,   noise=lowercase__						)

						# verify the logits
						__lowercase				:		Dict												=				tf.convert_to_tensor([1, 1_9_6, 7_6_8]						)
						self.assertEqual(outputs.logits.shape    ,   lowercase__						)

						__lowercase				:		Any												=				tf.convert_to_tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]						)

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






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->list:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				[0] * len(_lowerCamelCase				)

		for i in range(1,  len(_lowerCamelCase				)				):
				# use last results for better performance - dynamic programming
				__lowercase				:		Optional[Any]												=				prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						__lowercase				:		List[str]												=				prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				__lowercase				:		Any												=				j

		return prefix_result

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		return max(prefix_function(_lowerCamelCase				)				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
