




'''simple docstring'''





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
		from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image




class  _snake_case		:




			def __init__(					self				,							a__				,							a__=3				,							a__=32				,							a__=3				,							a__=10				,							a__=[8, 16, 32, 64]				,							a__=[1, 1, 2, 1]				,							a__=True				,							a__=True				,							a__="relu"				,							a__=3				,							a__=None				,							a__=["stage2", "stage3", "stage4"]				,							a__=[2, 3, 4]				,							a__=1				,							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      image_size
										snake_case_						=      num_channels
										snake_case_						=      embeddings_size
										snake_case_						=      hidden_sizes
										snake_case_						=      depths
										snake_case_						=      is_training
										snake_case_						=      use_labels
										snake_case_						=      hidden_act
										snake_case_						=      num_labels
										snake_case_						=      scope
										snake_case_						=      len(a__							)
										snake_case_						=      out_features
										snake_case_						=      out_indices
										snake_case_						=      num_groups




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

										'''simple docstring'''




										snake_case_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										snake_case_						=      None
										if self.use_labels:
																	snake_case_						=      ids_tensor([self.batch_size]				,							self.num_labels							)

										snake_case_						=      self.get_config()

										return config, pixel_values, labels




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

										'''simple docstring'''




										return BitConfig(
										    num_channels=self.num_channels				,							embeddings_size=self.embeddings_size				,							hidden_sizes=self.hidden_sizes				,							depths=self.depths				,							hidden_act=self.hidden_act				,							num_labels=self.num_labels				,							out_features=self.out_features				,							out_indices=self.out_indices				,							num_groups=self.num_groups				,							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      BitModel(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__							)
										self.parent.assertEqual(
										    result.last_hidden_state.shape				,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      self.num_labels
										snake_case_						=      BitForImageClassification(a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__				,							labels=a__							)
										self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      BitBackbone(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__							)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps							)				,							len(config.out_features							)							)
										self.parent.assertListEqual(list(result.feature_maps[0].shape							)				,							[self.batch_size, self.hidden_sizes[1], 4, 4]							)

										# verify channels
										self.parent.assertEqual(len(model.channels							)				,							len(config.out_features							)							)
										self.parent.assertListEqual(model.channels				,							config.hidden_sizes[1:]							)

										# verify backbone works with out_features=None
										snake_case_						=      None
										snake_case_						=      BitBackbone(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__							)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps							)				,							1							)
										self.parent.assertListEqual(list(result.feature_maps[0].shape							)				,							[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

										# verify channels
										self.parent.assertEqual(len(model.channels							)				,							1							)
										self.parent.assertListEqual(model.channels				,							[config.hidden_sizes[-1]]							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.prepare_config_and_inputs()
										snake_case_					,      snake_case_					,      snake_case_						=      config_and_inputs
										snake_case_						=      {"pixel_values": pixel_values}
										return config, inputs_dict




@require_torch
class  _snake_case		(	lowercase_						,						lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      str						       =    (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			lowerCAmelCase_     :      int						       =    (
			    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCAmelCase_     :      Union[str, Any]						       =    False
			lowerCAmelCase_     :      Optional[int]						       =    False
			lowerCAmelCase_     :      Any						       =    False
			lowerCAmelCase_     :      Optional[Any]						       =    False
			lowerCAmelCase_     :      Optional[Any]						       =    False




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      BitModelTester(self							)
										snake_case_						=      ConfigTester(self				,							config_class=a__				,							has_text_modality=a__							)




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

										'''simple docstring'''




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




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

										'''simple docstring'''




										return




			@unittest.skip(reason="Bit does not output attentions"							)
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										pass




			@unittest.skip(reason="Bit does not use inputs_embeds"							)
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										pass




			@unittest.skip(reason="Bit does not support input and output embeddings"							)
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										pass




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

										'''simple docstring'''




										snake_case_					,      snake_case_						=      self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_						=      ["pixel_values"]
																	self.assertListEqual(arg_names[:1]				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a__							)




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

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*a__							)




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

										'''simple docstring'''




										snake_case_					,      snake_case_						=      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	snake_case_						=      model_class(config=a__							)
																	for name, module in model.named_modules():
																								if isinstance(a__				,							(nn.BatchNormad, nn.GroupNorm)							):
																															self.assertTrue(
																															    torch.all(module.weight == 1							)				,							msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,							)
																															self.assertTrue(
																															    torch.all(module.bias == 0							)				,							msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										def check_hidden_states_output(a__				,							a__				,							a__							):
																	snake_case_						=      model_class(a__							)
																	model.to(a__							)
																	model.eval()

																	with torch.no_grad():
																								snake_case_						=      model(**self._prepare_for_class(a__				,							a__							)							)

																	snake_case_						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	snake_case_						=      self.model_tester.num_stages
																	self.assertEqual(len(a__							)				,							expected_num_stages + 1							)

																	# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)				,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,							)

										snake_case_					,      snake_case_						=      self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_						=      ["preactivation", "bottleneck"]
										for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																								snake_case_						=      layer_type
																								snake_case_						=      True
																								check_hidden_states_output(a__				,							a__				,							a__							)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								snake_case_						=      True

																								check_hidden_states_output(a__				,							a__				,							a__							)




			@unittest.skip(reason="Bit does not use feedforward chunking"							)
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										pass




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

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*a__							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_						=      BitModel.from_pretrained(a__							)
																	self.assertIsNotNone(a__							)


def      UpperCamelCase_(	):

							'''simple docstring'''






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




@require_torch
@require_vision
class  _snake_case		(	unittest.TestCase							):




			@cached_property
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
										)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(a__							)

										snake_case_						=      self.default_image_processor
										snake_case_						=      prepare_img()
										snake_case_						=      image_processor(images=a__				,							return_tensors="pt"							).to(a__							)

										# forward pass
										with torch.no_grad():
																	snake_case_						=      model(**a__							)

										# verify the logits
										snake_case_						=      torch.Size((1, 1_000)							)
										self.assertEqual(outputs.logits.shape				,							a__							)

										snake_case_						=      torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(a__							)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]				,							a__				,							atol=1e-4							)							)




@require_torch
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      int						       =    (BitBackbone,) if is_torch_available() else ()
			lowerCAmelCase_     :      Optional[Any]						       =    BitConfig

			lowerCAmelCase_     :      Optional[Any]						       =    False




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      BitModelTester(self							)









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

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



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Optional[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						snake_case__	:      Any     		=	None
						if self.use_labels:
										snake_case__	:      Tuple     		=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size  )

						snake_case__	:      List[Any]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :List[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=__lowercase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :Union[str, Any]   ,__lowercase     :Dict   ,__lowercase     :List[str]  ):
						snake_case__	:      Optional[int]     		=	TFViTMAEModel(config=__lowercase  )
						snake_case__	:      int     		=	model(__lowercase   ,training=__lowercase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						snake_case__	:      Dict     		=	TFViTMAEForPreTraining(__lowercase  )
						snake_case__	:      Optional[int]     		=	model(__lowercase   ,training=__lowercase  )
						# expected sequence length = num_patches
						snake_case__	:      Optional[Any]     		=	(self.image_size // self.patch_size) ** 2
						snake_case__	:      int     		=	self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						snake_case__	:      Tuple     		=	1
						snake_case__	:      List[Any]     		=	TFViTMAEForPreTraining(__lowercase  )

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






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	self.prepare_config_and_inputs()
						((snake_case__)		, (snake_case__)		, (snake_case__))	:      Optional[Any]     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_tf
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowerCAmelCase	:    Union[str, Any]											=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    List[Any]											=					False
		__lowerCAmelCase	:    Dict											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	TFViTMAEModelTester(self  )
						snake_case__	:      Dict     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						self.config_tester.run_common_tests()



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



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__		, snake_case__	:      int     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Union[str, Any]     		=	model_class(__lowercase  )
										self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)  )
										snake_case__	:      List[Any]     		=	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowercase   ,tf.keras.layers.Layer  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__		, snake_case__	:      str     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case__	:      Dict     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase  )



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

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

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

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowercase  )
										snake_case__	:      Union[str, Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

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

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



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						# make the mask reproducible
						np.random.seed(2  )

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

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

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

										return inputs_np_dict

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )

										snake_case__	:      List[Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      Dict     		=	prepare_numpy_arrays(__lowercase  )

										snake_case__	:      Tuple     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      Dict     		=	model(**__lowercase   ,noise=__lowercase  )
										self.assert_outputs_same(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[Any]   ,__lowercase     :List[str]  ):
						# make masks reproducible
						np.random.seed(2  )

						snake_case__	:      Union[str, Any]     		=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.constant(__lowercase  )

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

						super().check_pt_tf_models(__lowercase   ,__lowercase   ,__lowercase  )



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

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

						snake_case__	:      List[str]     		=	{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__  ),)
						    for module_member_name in dir(__lowercase  )
						    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(__lowercase   ,__lowercase  ),)
						    if isinstance(__lowercase   ,__lowercase  )
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowercase   ,'''_keras_serializable'''   ,__lowercase  )
						}

						snake_case__	:      Optional[Any]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.convert_to_tensor(__lowercase  )
						inputs_dict.update({'''noise''': noise}  )

						for main_layer_class in tf_main_layer_classes:
										snake_case__	:      List[Any]     		=	main_layer_class(__lowercase  )

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

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

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



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

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

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

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

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

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

														if model_class.__name__ == "TFViTMAEModel":
																		snake_case__	:      Dict     		=	after_outputs['''last_hidden_state'''].numpy()
																		snake_case__	:      List[Any]     		=	0
														else:
																		snake_case__	:      Any     		=	after_outputs['''logits'''].numpy()
																		snake_case__	:      Optional[Any]     		=	0

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



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

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

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

						for model_class in self.all_model_classes:
										snake_case__	:      int     		=	model_class(__lowercase  )
										snake_case__	:      int     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

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

										self.assert_outputs_same(__lowercase   ,__lowercase  )



		@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[Any]  ):
						pass



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






		@slow
		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      str     		=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





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



		@cached_property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






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

						snake_case__	:      Tuple     		=	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

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

						# forward pass
						snake_case__	:      List[str]     		=	model(**__lowercase   ,noise=__lowercase  )

						# verify the logits
						snake_case__	:      Optional[Any]     		=	tf.convert_to_tensor([1, 1_9_6, 7_6_8]  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Tuple     		=	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]   ,__lowercase   ,atol=1e-4  )

