






'''simple docstring'''







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_A	:			Optional[Any]         =logging.get_logger(__name__)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase=None				,			UpperCamelCase=None      )      ->       Tuple:
							return field(default_factory=lambda: default				,			metadata=UpperCamelCase      )



@dataclass
class _lowercase	:
							a				      =       list_field(
							    default=[]				,      metadata={
							        """help""": (
							            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
							            """ of all available models"""
							        )
							    }				,      )

							a				      =       list_field(
							    default=[8]				,      metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}		)

							a				      =       list_field(
							    default=[8, 32, 128, 512]				,      metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}				,      )

							a				      =       field(
							    default=_lowercase				,      metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}				,      )
							a				      =       field(
							    default=_lowercase				,      metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}				,      )
							a				      =       field(
							    default=_lowercase				,      metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Use FP16 to accelerate inference."""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Benchmark training of model"""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Verbose memory tracing"""}		)
							a				      =       field(
							    default=_lowercase				,      metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}				,      )
							a				      =       field(
							    default=_lowercase				,      metadata={
							        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
							    }				,      )
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Trace memory line by line"""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Save result to a CSV file"""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Save all print statements in a log file"""}		)
							a				      =       field(default=_lowercase				,      metadata={"""help""": """Whether to print environment information"""}		)
							a				      =       field(
							    default=_lowercase				,      metadata={
							        """help""": (
							            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
							            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
							            """ for debugging / testing and on TPU."""
							        )
							    }				,      )
							a				      =       field(
							    default=F"""inference_time_{round(time()		)}.csv"""				,      metadata={"""help""": """CSV filename used if saving time results to csv."""}				,      )
							a				      =       field(
							    default=F"""inference_memory_{round(time()		)}.csv"""				,      metadata={"""help""": """CSV filename used if saving memory results to csv."""}				,      )
							a				      =       field(
							    default=F"""train_time_{round(time()		)}.csv"""				,      metadata={"""help""": """CSV filename used if saving time results to csv for training."""}				,      )
							a				      =       field(
							    default=F"""train_memory_{round(time()		)}.csv"""				,      metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}				,      )
							a				      =       field(
							    default=F"""env_info_{round(time()		)}.csv"""				,      metadata={"""help""": """CSV filename used if saving environment information."""}				,      )
							a				      =       field(
							    default=F"""log_{round(time()		)}.csv"""				,      metadata={"""help""": """Log filename used if print statements are saved in log."""}				,      )
							a				      =       field(default=3				,      metadata={"""help""": """Times an experiment will be run."""}		)
							a				      =       field(
							    default=_lowercase				,      metadata={
							        """help""": (
							            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
							            """ model weights."""
							        )
							    }				,      )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														warnings.warn(
														    F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
														    """ are deprecated in general and it is advised to use external Benchmarking libraries """
														    """ to benchmark Transformer models."""			,							UpperCamelCase__			,							)




							def 		lowerCamelCase_				(							self:				List[str]     ):
														return json.dumps(dataclasses.asdict(self     )			,							indent=2     )




							@property
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														if len(self.models     ) <= 0:
																					raise ValueError(
																					    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
																					    """ bert-base-cased` or `args.models = ['bert-base-cased']."""     )
														return self.models




							@property
							def 		lowerCamelCase_				(							self:				int     ):
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info("""Multiprocessing is currently not possible on TPU."""     )
																					return False
														else:
																					return True









'''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 _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Dict=13			,							UpperCamelCase__:				int=30			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				Tuple=3			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				Tuple=4			,							UpperCamelCase__:				Optional[Any]=37			,							UpperCamelCase__:				List[Any]="gelu"			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Tuple=10			,							UpperCamelCase__:				Optional[Any]=0.02			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				str=0.6			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	List[Any]         =       parent
														lowerCamelCase__					:	Optional[Any]         =       batch_size
														lowerCamelCase__					:	Union[str, Any]         =       image_size
														lowerCamelCase__					:	Any         =       patch_size
														lowerCamelCase__					:	Union[str, Any]         =       num_channels
														lowerCamelCase__					:	Optional[Any]         =       is_training
														lowerCamelCase__					:	int         =       use_labels
														lowerCamelCase__					:	List[str]         =       hidden_size
														lowerCamelCase__					:	Optional[Any]         =       num_hidden_layers
														lowerCamelCase__					:	Any         =       num_attention_heads
														lowerCamelCase__					:	Optional[Any]         =       intermediate_size
														lowerCamelCase__					:	Optional[Any]         =       hidden_act
														lowerCamelCase__					:	Any         =       hidden_dropout_prob
														lowerCamelCase__					:	Tuple         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Dict         =       type_sequence_label_size
														lowerCamelCase__					:	Optional[int]         =       initializer_range
														lowerCamelCase__					:	List[str]         =       mask_ratio
														lowerCamelCase__					:	Optional[int]         =       scope

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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	str         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	List[Any]         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Union[str, Any]         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

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

														return config, pixel_values, labels




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




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       TFViTMAEForPreTraining(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														# expected sequence length = num_patches
														lowerCamelCase__					:	List[str]         =       (self.image_size // self.patch_size) ** 2
														lowerCamelCase__					:	Union[str, 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
														lowerCamelCase__					:	List[Any]         =       1
														lowerCamelCase__					:	Union[str, Any]         =       TFViTMAEForPreTraining(UpperCamelCase__     )

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




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



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							a				      =       {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	int         =       TFViTMAEModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

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




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__					:	Tuple         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__     )




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

														lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

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

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														# make the mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       prepare_numpy_arrays(UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )
																					self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				str     ):
														# make masks reproducible
														np.random.seed(2     )

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

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

														super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# make mask reproducible
														np.random.seed(2     )

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

														lowerCamelCase__					:	List[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(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__     )
														}

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

														for main_layer_class in tf_main_layer_classes:
																					lowerCamelCase__					:	List[str]         =       main_layer_class(UpperCamelCase__     )

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

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

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




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(UpperCamelCase__			,							saved_model=UpperCamelCase__     )
																												lowerCamelCase__					:	Dict         =       model_class.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

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

																												lowerCamelCase__					:	int         =       np.amax(np.abs(out_a - out_a     )     )
																												self.assertLessEqual(UpperCamelCase__			,							1e-5     )




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

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(UpperCamelCase__     )
																					lowerCamelCase__					:	int         =       model_class.from_config(model.get_config()     )
																					# make sure it also accepts a normal config
																					lowerCamelCase__					:	int         =       model_class.from_config(model.config     )
																					lowerCamelCase__					:	List[Any]         =       new_model(UpperCamelCase__     )  # Build model
																					new_model.set_weights(model.get_weights()     )
																					lowerCamelCase__					:	List[Any]         =       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
    to get deterministic results."""     )
							def 		lowerCamelCase_				(							self:				List[str]     ):
														pass




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




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Tuple         =       TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""     )
														self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Any         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




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




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

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

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

														# forward pass
														lowerCamelCase__					:	str         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Any         =       tf.convert_to_tensor([1, 196, 768]     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	str         =       tf.convert_to_tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]     )

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

