

"""simple docstring"""


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class     snake_case	(				_lowercase,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  PhobertTokenizer
				SCREAMING_SNAKE_CASE_   :      Dict         =  False

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



						'''simple docstring'''


						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__lowerCAmelCase:       List[Any]          =			["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						__lowerCAmelCase:       str          =			dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__))))
						__lowerCAmelCase:       Optional[int]          =			["""#version: 0.2""", """l à</w>"""]
						__lowerCAmelCase:       Tuple          =			{"""unk_token""": """<unk>"""}

						__lowerCAmelCase:       str          =			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["merges_file"])

						with open(self.vocab_file     ,  "w"     ,  encoding="utf-8") as fp:
								for token in vocab_tokens:
										fp.write(f"{token} {vocab_tokens[token]}\n")
						with open(self.merges_file     ,  "w"     ,  encoding="utf-8") as fp:
								fp.write("\n".join(UpperCamelCase__))

				def 							lowercase_     (	self							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   str:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return PhobertTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				int)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			"""Tôi là VinAI Research"""
						__lowerCAmelCase:       Tuple          =			"""T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text




				def 							lowercase_     (	self							:				Any)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			PhobertTokenizer(self.vocab_file     ,  self.merges_file     ,  **self.special_tokens_map)
						__lowerCAmelCase:       Optional[Any]          =			"""Tôi là VinAI Research"""
						__lowerCAmelCase:       List[str]          =			"""T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						__lowerCAmelCase:       str          =			tokenizer.tokenize(UpperCamelCase__)
						print(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       Any          =			tokens + [tokenizer.unk_token]

						__lowerCAmelCase:       Any          =			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__)     ,  UpperCamelCase__)










'''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     )

