





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       A_			:
	def __init__(       self				,   _A				,   _A=1_3				,   _A=3_0				,   _A=2				,   _A=3				,   _A=True				,   _A=True				,   _A=3_2				,   _A=2				,   _A=4				,   _A=3_7				,   _A="gelu"				,   _A=0.1				,   _A=0.1				,   _A=1_0				,   _A=0.02				,   _A=3				,   _A=0.6				,   _A=None				,   ):






				'''simple docstring'''







				UpperCAmelCase  						=							parent
				UpperCAmelCase  						=							batch_size
				UpperCAmelCase  						=							image_size
				UpperCAmelCase  						=							patch_size
				UpperCAmelCase  						=							num_channels
				UpperCAmelCase  						=							is_training
				UpperCAmelCase  						=							use_labels
				UpperCAmelCase  						=							hidden_size
				UpperCAmelCase  						=							num_hidden_layers
				UpperCAmelCase  						=							num_attention_heads
				UpperCAmelCase  						=							intermediate_size
				UpperCAmelCase  						=							hidden_act
				UpperCAmelCase  						=							hidden_dropout_prob
				UpperCAmelCase  						=							attention_probs_dropout_prob
				UpperCAmelCase  						=							type_sequence_label_size
				UpperCAmelCase  						=							initializer_range
				UpperCAmelCase  						=							mask_ratio
				UpperCAmelCase  						=							scope

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






				'''simple docstring'''







				UpperCAmelCase  						=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

				UpperCAmelCase  						=							self.get_config()

				return config, pixel_values, labels
	def 	_lowercase							(       self						):






				'''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				,   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=UpperCamelCase_				,   initializer_range=self.initializer_range				,   mask_ratio=self.mask_ratio				,   )
	def 	_lowercase							(       self				,   _A				,   _A				,   _A						):






				'''simple docstring'''







				UpperCAmelCase  						=							TFViTMAEModel(config=UpperCamelCase_						)
				UpperCAmelCase  						=							model(UpperCamelCase_				,   training=UpperCamelCase_						)
				self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)						)
	def 	_lowercase							(       self				,   _A				,   _A				,   _A						):






				'''simple docstring'''







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

				# test greyscale images
				UpperCAmelCase  						=							1
				UpperCAmelCase  						=							TFViTMAEForPreTraining(UpperCamelCase_						)

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







	def 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							self.prepare_config_and_inputs()
				((UpperCAmelCase)						,     (UpperCAmelCase)						,     (UpperCAmelCase))  						=							config_and_inputs
				UpperCAmelCase  						=							{'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_tf
class       A_			(a_	,				a_	,				unittest.TestCase     ):
	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 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							TFViTMAEModelTester(self						)
				UpperCAmelCase  						=							ConfigTester(self				,   config_class=UpperCamelCase_				,   has_text_modality=UpperCamelCase_				,   hidden_size=3_7						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				pass
	def 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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






				'''simple docstring'''







				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase  						=							['''pixel_values''']
							self.assertListEqual(arg_names[:1]				,   UpperCamelCase_						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase_						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				np.random.seed(2						)

				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
							UpperCAmelCase  						=							model_class(UpperCamelCase_						)
							UpperCAmelCase  						=							self._prepare_for_class(UpperCamelCase_				,   UpperCamelCase_						)

							UpperCAmelCase  						=							model(UpperCamelCase_				,   noise=UpperCamelCase_						)

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

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






				'''simple docstring'''







				np.random.seed(2						)

				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

							return inputs_np_dict

				for model_class in self.all_model_classes:
							UpperCAmelCase  						=							model_class(UpperCamelCase_						)

							UpperCAmelCase  						=							self._prepare_for_class(UpperCamelCase_				,   UpperCamelCase_						)
							UpperCAmelCase  						=							prepare_numpy_arrays(UpperCamelCase_						)

							UpperCAmelCase  						=							model(UpperCamelCase_				,   noise=UpperCamelCase_						)
							UpperCAmelCase  						=							model(**UpperCamelCase_				,   noise=UpperCamelCase_						)
							self.assert_outputs_same(UpperCamelCase_				,   UpperCamelCase_						)
	def 	_lowercase							(       self				,   _A				,   _A				,   _A						):






				'''simple docstring'''







				np.random.seed(2						)

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

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

				super().check_pt_tf_models(UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				np.random.seed(2						)

				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

				for main_layer_class in tf_main_layer_classes:
							UpperCAmelCase  						=							main_layer_class(UpperCamelCase_						)

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

							UpperCAmelCase  						=							tf.keras.Model(UpperCamelCase_				,   outputs=main_layer(UpperCamelCase_						)						)
							UpperCAmelCase  						=							model(UpperCamelCase_						)

							with tempfile.TemporaryDirectory() as tmpdirname:
										UpperCAmelCase  						=							os.path.join(UpperCamelCase_				,   '''keras_model.h5'''						)
										model.save(UpperCamelCase_						)
										UpperCAmelCase  						=							tf.keras.models.load_model(
										    UpperCamelCase_				,   custom_objects={main_layer_class.__name__: main_layer_class}						)
										assert isinstance(UpperCamelCase_				,   tf.keras.Model						)
										UpperCAmelCase  						=							model(UpperCamelCase_						)
										self.assert_outputs_same(UpperCamelCase_				,   UpperCamelCase_						)
	@slow
	def 	_lowercase							(       self						):






				'''simple docstring'''







				np.random.seed(2						)

				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
							UpperCAmelCase  						=							model_class(UpperCamelCase_						)
							UpperCAmelCase  						=							self._prepare_for_class(UpperCamelCase_				,   UpperCamelCase_						)
							UpperCAmelCase  						=							model(UpperCamelCase_				,   noise=UpperCamelCase_						)

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

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

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

										UpperCAmelCase  						=							np.amax(np.abs(out_a - out_a						)						)
										self.assertLessEqual(UpperCamelCase_				,   1E-5						)
	def 	_lowercase							(       self						):






				'''simple docstring'''







				np.random.seed(2						)

				UpperCAmelCase						,     UpperCAmelCase  						=							self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
							UpperCAmelCase  						=							model_class(UpperCamelCase_						)
							UpperCAmelCase  						=							self._prepare_for_class(UpperCamelCase_				,   UpperCamelCase_						)

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

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






				'''simple docstring'''







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






				'''simple docstring'''







				pass







	@slow
	def 	_lowercase							(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''						)
				self.assertIsNotNone(UpperCamelCase_						)





def 		__SCREAMING_SNAKE_CASE    (							)    ->							Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase  						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			return image






@require_tf
@require_vision
class       A_			(unittest.TestCase     ):
	@cached_property
	def 	_lowercase							(       self						):






				'''simple docstring'''







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







	@slow
	def 	_lowercase							(       self						):






				'''simple docstring'''







				np.random.seed(2						)

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

				UpperCAmelCase  						=							self.default_image_processor
				UpperCAmelCase  						=							prepare_img()
				UpperCAmelCase  						=							image_processor(images=UpperCamelCase_				,   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)
				UpperCAmelCase  						=							ViTMAEConfig()
				UpperCAmelCase  						=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
				UpperCAmelCase  						=							np.random.uniform(size=(1, num_patches)						)

				# forward pass
				UpperCAmelCase  						=							model(**UpperCamelCase_				,   noise=UpperCamelCase_						)

				# verify the logits
				UpperCAmelCase  						=							tf.convert_to_tensor([1, 1_9_6, 7_6_8]						)
				self.assertEqual(outputs.logits.shape				,   UpperCamelCase_						)

				UpperCAmelCase  						=							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]				,   UpperCamelCase_				,   atol=1E-4						)










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



