
"""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   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=13   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=32   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=37   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=0.6   ,       SCREAMING_SNAKE_CASE__=None   ,       )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   parent
											SCREAMING_SNAKE_CASE__						:       Tuple           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   image_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   num_channels
											SCREAMING_SNAKE_CASE__						:       str           =   is_training
											SCREAMING_SNAKE_CASE__						:       List[str]           =   use_labels
											SCREAMING_SNAKE_CASE__						:       Any           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       Dict           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   type_sequence_label_size
											SCREAMING_SNAKE_CASE__						:       List[str]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   mask_ratio
											SCREAMING_SNAKE_CASE__						:       str           =   scope

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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__						:       str           =   ids_tensor([self.batch_size]   ,       self.type_sequence_label_size    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_config()

											return config, pixel_values, labels



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""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=SCREAMING_SNAKE_CASE__   ,       initializer_range=self.initializer_range   ,       mask_ratio=self.mask_ratio   ,       )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   TFViTMAEModel(config=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(SCREAMING_SNAKE_CASE__   ,       training=SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   model(SCREAMING_SNAKE_CASE__   ,       training=SCREAMING_SNAKE_CASE__    )
											# expected sequence length = num_patches
											SCREAMING_SNAKE_CASE__						:       str           =   (self.image_size // self.patch_size) ** 2
											SCREAMING_SNAKE_CASE__						:       Any           =   self.patch_size**2 * self.num_channels
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, num_patches, expected_num_channels)    )

											# test greyscale images
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   1
											SCREAMING_SNAKE_CASE__						:       Any           =   TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
											SCREAMING_SNAKE_CASE__						:       int           =   model(SCREAMING_SNAKE_CASE__   ,       training=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   self.patch_size**2
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, num_patches, expected_num_channels)    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.prepare_config_and_inputs()
											((SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__))						:       List[str]           =   config_and_inputs
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {"""pixel_values""": pixel_values}
											return config, inputs_dict






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


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				__UpperCamelCase      :						List[str]					       =   {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

				__UpperCamelCase      :						Optional[int]					       =   False
				__UpperCamelCase      :						List[Any]					       =   False
				__UpperCamelCase      :						List[str]					       =   False
				__UpperCamelCase      :						List[str]					       =   False



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   TFViTMAEModelTester(self    )
											SCREAMING_SNAKE_CASE__						:       str           =   ConfigTester(self   ,       config_class=SCREAMING_SNAKE_CASE__   ,       has_text_modality=SCREAMING_SNAKE_CASE__   ,       hidden_size=37    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.config_tester.run_common_tests()



				@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""    )
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.model_tester.prepare_config_and_inputs_for_common()

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



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE__						:       Any           =   ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   self.model_tester.prepare_config_and_inputs_for_common()

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

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Any           =   model_class(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       Dict           =   model(SCREAMING_SNAKE_CASE__   ,       noise=SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(**SCREAMING_SNAKE_CASE__   ,       noise=SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   outputs_dict[0].numpy()
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   outputs_keywords[0].numpy()

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



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



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

																		return inputs_np_dict

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model_class(SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prepare_numpy_arrays(SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       int           =   model(SCREAMING_SNAKE_CASE__   ,       noise=SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       str           =   model(**SCREAMING_SNAKE_CASE__   ,       noise=SCREAMING_SNAKE_CASE__    )
																		self.assert_outputs_same(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											np.random.seed(2    )

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

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

											super().check_pt_tf_models(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   self.model_tester.prepare_config_and_inputs_for_common()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {
											    module_member
											    for model_class in self.all_model_classes
											    for module in (import_module(model_class.__module__    ),)
											    for module_member_name in dir(SCREAMING_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(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ),)
											    if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(SCREAMING_SNAKE_CASE__   ,       """_keras_serializable"""   ,       SCREAMING_SNAKE_CASE__    )
											}

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

											for main_layer_class in tf_main_layer_classes:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   main_layer_class(SCREAMING_SNAKE_CASE__    )

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

																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tf.keras.Model(SCREAMING_SNAKE_CASE__   ,       outputs=main_layer(SCREAMING_SNAKE_CASE__    )    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   model(SCREAMING_SNAKE_CASE__    )

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



				@slow
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

																		if model_class.__name__ == "TFViTMAEModel":
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   outputs.last_hidden_state.numpy()
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   0
																		else:
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   outputs.logits.numpy()
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   0

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

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

																									SCREAMING_SNAKE_CASE__						:       Dict           =   np.amax(np.abs(out_a - out_a    )    )
																									self.assertLessEqual(SCREAMING_SNAKE_CASE__   ,       1E-5    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   self.model_tester.prepare_config_and_inputs_for_common()

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

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   model_class(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       str           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

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

																		self.assert_outputs_same(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				@unittest.skip(
				    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""    )
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



				@slow
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )






def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Dict           =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							return image



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


				"""simple docstring"""



				@cached_property
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




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



				@slow
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											np.random.seed(2    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    )

											SCREAMING_SNAKE_CASE__						:       int           =   self.default_image_processor
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   prepare_img()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   image_processor(images=SCREAMING_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)
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ViTMAEConfig()
											SCREAMING_SNAKE_CASE__						:       Dict           =   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
											SCREAMING_SNAKE_CASE__						:       int           =   np.random.uniform(size=(1, num_patches)    )

											# forward pass
											SCREAMING_SNAKE_CASE__						:       Tuple           =   model(**SCREAMING_SNAKE_CASE__   ,       noise=SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )






"""simple docstring"""




UpperCAmelCase__      :    Any    							= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase__      :    Any    							= [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase__      :    Optional[int]    							= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




