






"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowercase    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:Union[tf.Tensor, np.ndarray]   ):

						'''simple docstring'''






						if isinstance(__lowerCamelCase ,						np.ndarray   ):
												return list(tensor.shape   )

						__magic_name__            =     tf.shape(__lowerCamelCase   )

						if tensor.shape == tf.TensorShape(__lowerCamelCase   ):
												return dynamic

						__magic_name__            =     tensor.shape.as_list()

						return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase   )]






def _lowerCAmelCase	(  __lowerCamelCase:tf.Tensor ,						__lowerCamelCase:Optional[int] = None ,						__lowerCamelCase:Optional[str] = None   ):

						'''simple docstring'''






						return tf.nn.softmax(logits=logits + 1E-9 ,						axis=__lowerCamelCase ,						name=__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Tuple ,						__lowerCamelCase:Optional[int]=1E-5 ,						__lowerCamelCase:Optional[int]=-1   ):

						'''simple docstring'''






						if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
												raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."   )

						# Get mean and variance on the axis to be normalized
						__magic_name__      ,    __magic_name__            =     tf.nn.moments(__lowerCamelCase ,						axes=[axis] ,						keepdims=__lowerCamelCase   )

						if axis != -1:
												# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
												# on every dimension except axis
												__magic_name__            =     [1] * inputs.shape.rank
												__magic_name__            =     shape_list(__lowerCamelCase   )[axis]
												__magic_name__            =     tf.reshape(__lowerCamelCase ,						__lowerCamelCase   )
												__magic_name__            =     tf.reshape(__lowerCamelCase ,						__lowerCamelCase   )

						# Compute layer normalization using the batch_normalization
						# function.
						__magic_name__            =     tf.nn.batch_normalization(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						offset=__lowerCamelCase ,						scale=__lowerCamelCase ,						variance_epsilon=__lowerCamelCase ,						)
						return outputs






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:Any=0 ,						__lowerCamelCase:str=-1   ):

						'''simple docstring'''






						if end_dim < 0:
												end_dim += input.shape.rank
						if start_dim < 0:
												start_dim += input.shape.rank

						if start_dim == end_dim:
												return input

						__magic_name__            =     tf.shape(__lowerCamelCase   )
						__magic_name__            =     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
						__magic_name__            =     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,						axis=0   )
						return tf.reshape(__lowerCamelCase ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:tf.Tensor   ):

						'''simple docstring'''






						if not isinstance(__lowerCamelCase ,						tf.Tensor   ):
												__magic_name__            =     tf.convert_to_tensor(__lowerCamelCase   )  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												__magic_name__            =     encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												__magic_name__            =     encoder_attention_mask[:, None, None, :]
						# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
						# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
						# /transformer/transformer_layers.py#L270
						# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
						# encoder_extended_attention_mask.transpose(-1, -2))
						__magic_name__            =     (
						    tf.cast(1 ,						encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask






def _lowerCAmelCase	(  __lowerCamelCase:tf.Tensor ,						__lowerCamelCase:int ,						__lowerCamelCase:str = "input_ids"   ):

						'''simple docstring'''






						tf.debugging.assert_less(
						    __lowerCamelCase ,						tf.cast(__lowerCamelCase ,						dtype=tensor.dtype   ) ,						message=(
						        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase   )}) must be smaller than the embedding '''
						        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
						    ) ,						)






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     6_4_5_1_2
						# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
						# because in that case even chunking the array would not make the saving
						# possible.
						__magic_name__            =     [x for x in data if len(__lowerCamelCase   ) > HDF5_OBJECT_HEADER_LIMIT]

						# Expecting this to never be true.
						if bad_attributes:
												raise RuntimeError(
												    "The following attributes cannot be saved to HDF5 file because "
												    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
												    f'''bytes: {bad_attributes}'''   )

						__magic_name__            =     np.asarray(__lowerCamelCase   )

						__magic_name__            =     1
						__magic_name__            =     np.array_split(__lowerCamelCase ,						__lowerCamelCase   )

						# This will never loop forever thanks to the test above.
						while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data   ):
												num_chunks += 1
												__magic_name__            =     np.array_split(__lowerCamelCase ,						__lowerCamelCase   )

						if num_chunks > 1:
												for chunk_id, chunk_data in enumerate(__lowerCamelCase   ):
																		__magic_name__            =     chunk_data
						else:
												__magic_name__            =     data






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:Tuple   ):

						'''simple docstring'''






						if name in group.attrs:
												__magic_name__            =     [n.decode("utf8"   ) if hasattr(__lowerCamelCase ,						"decode"   ) else n for n in group.attrs[name]]
						else:
												__magic_name__            =     []
												__magic_name__            =     0
												while "%s%d" % (name, chunk_id) in group.attrs:
																		data.extend(
																		    [n.decode("utf8"   ) if hasattr(__lowerCamelCase ,						"decode"   ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]   )
																		chunk_id += 1
						return data






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






						def _expand_single_ad_tensor(__lowerCamelCase:List[Any]   ):
												if isinstance(__lowerCamelCase ,						tf.Tensor   ) and t.shape.rank == 1:
																		return tf.expand_dims(__lowerCamelCase ,						axis=-1   )
												return t

						return tf.nest.map_structure(_expand_single_ad_tensor ,						__lowerCamelCase   )











"""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 						A_							:







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Dict=1_3						,    __lowerCamelCase							:						int=3_0						,    __lowerCamelCase							:						List[Any]=2						,    __lowerCamelCase							:						List[Any]=3						,    __lowerCamelCase							:						Optional[int]=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						int=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Optional[Any]=4						,    __lowerCamelCase							:						List[str]=3_7						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						Optional[int]=1_0						,    __lowerCamelCase							:						Tuple=0.02						,    __lowerCamelCase							:						Any=3						,    __lowerCamelCase							:						Union[str, Any]=0.6						,    __lowerCamelCase							:						Optional[int]=None						,    )				->					Union[str, Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     image_size
										__magic_name__            =     patch_size
										__magic_name__            =     num_channels
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     mask_ratio
										__magic_name__            =     scope

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







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										__magic_name__            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

										__magic_name__            =     self.get_config()

										return config, pixel_values, labels







				def     _snake_case					(							self							:						Optional[int]	)				->					Dict:
										return ViTMAEConfig(
										    image_size=self.image_size						,    patch_size=self.patch_size						,    num_channels=self.num_channels						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    decoder_hidden_size=self.hidden_size						,    decoder_num_hidden_layers=self.num_hidden_layers						,    decoder_num_attention_heads=self.num_attention_heads						,    decoder_intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    is_decoder=__lowerCamelCase						,    initializer_range=self.initializer_range						,    mask_ratio=self.mask_ratio						,    )







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[int]	)				->					Optional[int]:
										__magic_name__            =     TFViTMAEModel(config=__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase						,    training=__lowerCamelCase	)
										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						str	)				->					Union[str, Any]:
										__magic_name__            =     TFViTMAEForPreTraining(__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase						,    training=__lowerCamelCase	)
										# expected sequence length = num_patches
										__magic_name__            =     (self.image_size // self.patch_size) ** 2
										__magic_name__            =     self.patch_size**2 * self.num_channels
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, num_patches, expected_num_channels)	)

										# test greyscale images
										__magic_name__            =     1
										__magic_name__            =     TFViTMAEForPreTraining(__lowerCamelCase	)

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







				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     self.prepare_config_and_inputs()
										((__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__))            =     config_and_inputs
										__magic_name__            =     {"pixel_values": pixel_values}
										return config, inputs_dict





@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							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     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     TFViTMAEModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase						,    has_text_modality=__lowerCamelCase						,    hidden_size=3_7	)







				def     _snake_case					(							self							:						Dict	)				->					Any:
										self.config_tester.run_common_tests()







				@unittest.skip(reason="ViTMAE does not use inputs_embeds"	)
				def     _snake_case					(							self							:						Any	)				->					Optional[Any]:
										pass







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

																__magic_name__            =     ["pixel_values"]
																self.assertListEqual(arg_names[:1]						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Any:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										# make the mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)

																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)

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

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







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										# make the mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

																return inputs_np_dict

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)

																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																__magic_name__            =     prepare_numpy_arrays(__lowerCamelCase	)

																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)
																__magic_name__            =     model(**__lowerCamelCase						,    noise=__lowerCamelCase	)
																self.assert_outputs_same(__lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Dict	)				->					List[Any]:
										# make masks reproducible
										np.random.seed(2	)

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

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

										super().check_pt_tf_models(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

										for main_layer_class in tf_main_layer_classes:
																__magic_name__            =     main_layer_class(__lowerCamelCase	)

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

																__magic_name__            =     tf.keras.Model(__lowerCamelCase						,    outputs=main_layer(__lowerCamelCase	)	)
																__magic_name__            =     model(__lowerCamelCase	)

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







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)

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

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

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

																						__magic_name__            =     np.amax(np.abs(out_a - out_a	)	)
																						self.assertLessEqual(__lowerCamelCase						,    1e-5	)







				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)

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

																self.assert_outputs_same(__lowerCamelCase						,    __lowerCamelCase	)







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







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







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"	)
										self.assertIsNotNone(__lowerCamelCase	)






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image


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







				@cached_property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"	) if is_vision_available() else None







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

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

										__magic_name__            =     self.default_image_processor
										__magic_name__            =     prepare_img()
										__magic_name__            =     image_processor(images=__lowerCamelCase						,    return_tensors="tf"	)

										# prepare a noise vector that will be also used for testing the TF model
										# (this way we can ensure that the PT and TF models operate on the same inputs)
										__magic_name__            =     ViTMAEConfig()
										__magic_name__            =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(1, num_patches)	)

										# forward pass
										__magic_name__            =     model(**__lowerCamelCase						,    noise=__lowerCamelCase	)

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

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

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



