



"""simple docstring"""
import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= BarthezTokenizer
					lowercase__   :       List[str]										= BarthezTokenizerFast
					lowercase__   :       int										= True
					lowercase__   :       List[str]										= True

					def 			snake_case__      (    self   ):
									super().setUp()

									_lowerCamelCase        =				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''   )
									tokenizer.save_pretrained(self.tmpdirname   )
									tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''<pad>'''
									_lowerCamelCase        =				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__   )				,       lowerCamelCase__   )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__   )				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0]				,       '''<s>'''   )
									self.assertEqual(vocab_keys[1]				,       '''<pad>'''   )
									self.assertEqual(vocab_keys[-1]				,       '''<mask>'''   )
									self.assertEqual(len(lowerCamelCase__   )				,       1_0_1_1_2_2   )

					def 			snake_case__      (    self   ):
									self.assertEqual(self.get_tokenizer().vocab_size				,       1_0_1_1_2_2   )

					@require_torch
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									_lowerCamelCase        =				[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

									_lowerCamelCase        =				self.tokenizer(
									    lowerCamelCase__				,       max_length=len(lowerCamelCase__   )				,       padding=lowerCamelCase__				,       truncation=lowerCamelCase__				,       return_tensors='''pt'''   )
									self.assertIsInstance(lowerCamelCase__				,       lowerCamelCase__   )

									self.assertEqual((2, 6)				,       batch.input_ids.shape   )
									self.assertEqual((2, 6)				,       batch.attention_mask.shape   )
									_lowerCamelCase        =				batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									if not self.test_rust_tokenizer:
													return

									_lowerCamelCase        =				self.get_tokenizer()
									_lowerCamelCase        =				self.get_rust_tokenizer()

									_lowerCamelCase        =				'''I was born in 92000, and this is falsé.'''

									_lowerCamelCase        =				tokenizer.tokenize(lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.tokenize(lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				tokenizer.encode(lowerCamelCase__				,       add_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.encode(lowerCamelCase__				,       add_special_tokens=lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				self.get_rust_tokenizer()
									_lowerCamelCase        =				tokenizer.encode(lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.encode(lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )





					@slow
					def 			snake_case__      (    self   ):
									# fmt: off
									_lowerCamelCase        =				{'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_lowerCamelCase        =				[
									    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
									    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
									    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
									    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
									    '''telles que la traduction et la synthèse de texte.''',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCamelCase__				,       model_name='''moussaKam/mbarthez'''				,       revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''				,       sequences=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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=3_0				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=3				,       lowerCamelCase__=0.6				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				mask_ratio
									_lowerCamelCase        =				scope

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

									_lowerCamelCase        =				self.get_config()

									return config, pixel_values, labels

					def 			snake_case__      (    self   ):
									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				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEModel(config=lowerCamelCase__   )
									_lowerCamelCase        =				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				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									# expected sequence length = num_patches
									_lowerCamelCase        =				(self.image_size // self.patch_size) ** 2
									_lowerCamelCase        =				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        =				1
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )

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





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


@require_tf
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[Any]										= (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					lowercase__   :       Dict										= {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

					lowercase__   :       Optional[Any]										= False
					lowercase__   :       Union[str, Any]										= False
					lowercase__   :       str										= False
					lowercase__   :       List[str]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       has_text_modality=lowerCamelCase__				,       hidden_size=3_7   )

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''   )
					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowerCamelCase        =				['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__   )

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

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

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

													return inputs_np_dict

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )

													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				prepare_numpy_arrays(lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )
													self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									# make masks reproducible
									np.random.seed(2   )

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

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

									super().check_pt_tf_models(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				{
									    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__   )
									}

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

									for main_layer_class in tf_main_layer_classes:
													_lowerCamelCase        =				main_layer_class(lowerCamelCase__   )

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

													_lowerCamelCase        =				tf.keras.Model(lowerCamelCase__				,       outputs=main_layer(lowerCamelCase__   )   )
													_lowerCamelCase        =				model(lowerCamelCase__   )

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

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

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

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

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model.get_config()
													# make sure that returned config is jsonifiable, which is required by keras
													json.dumps(lowerCamelCase__   )
													_lowerCamelCase        =				model_class.from_config(model.get_config()   )
													# make sure it also accepts a normal config
													_lowerCamelCase        =				model_class.from_config(model.config   )
													_lowerCamelCase        =				new_model(lowerCamelCase__   )  # Build model
													new_model.set_weights(model.get_weights()   )
													_lowerCamelCase        =				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
    to get deterministic results.'''   )
					def 			snake_case__      (    self   ):
									pass

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





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''   )
									self.assertIsNotNone(lowerCamelCase__   )



def        lowerCAmelCase_(   )	->       List[Any]:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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





					'''simple docstring'''



					@cached_property
					def 			snake_case__      (    self   ):
									return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''   ) if is_vision_available() else None





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

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

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

									# forward pass
									_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )

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

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

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