


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image


class 	_a							:

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Optional[int]=2   ,						UpperCamelCase_:     str=True   ,						UpperCamelCase_:     int=False   ,						UpperCamelCase_:     Dict=10   ,						UpperCamelCase_:     List[Any]=3   ,						UpperCamelCase_:     Tuple=32 * 8   ,						UpperCamelCase_:     str=32 * 8   ,						UpperCamelCase_:     Tuple=4   ,						UpperCamelCase_:     Union[str, Any]=64   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				is_training
									lowercase__						=				use_auxiliary_loss
									lowercase__						=				num_queries
									lowercase__						=				num_channels
									lowercase__						=				min_size
									lowercase__						=				max_size
									lowercase__						=				num_labels
									lowercase__						=				hidden_dim
									lowercase__						=				hidden_dim

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
									    SCREAMING_SNAKE_CASE_							)

									lowercase__						=				torch.ones([self.batch_size, self.min_size, self.max_size]   ,						device=SCREAMING_SNAKE_CASE_							)

									lowercase__						=				(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,						device=SCREAMING_SNAKE_CASE_							) > 0.5
									).float()
									lowercase__						=				(torch.rand((self.batch_size, self.num_labels)   ,						device=SCREAMING_SNAKE_CASE_							) > 0.5).long()

									lowercase__						=				self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				MaskaFormerConfig(
									    hidden_size=self.hidden_dim   ,						)
									lowercase__						=				self.num_queries
									lowercase__						=				self.num_labels

									lowercase__						=				[1, 1, 1, 1]
									lowercase__						=				self.num_channels

									lowercase__						=				64
									lowercase__						=				128
									lowercase__						=				self.hidden_dim
									lowercase__						=				self.hidden_dim
									lowercase__						=				self.hidden_dim
									return config

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





									"""simple docstring"""







									lowercase__						=				self.prepare_config_and_inputs()
									lowercase__						=				{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
									return config, inputs_dict

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     int							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				output.encoder_hidden_states
									lowercase__						=				output.pixel_decoder_hidden_states
									lowercase__						=				output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_							)   ,						len(config.backbone_config.depths							)							)
									self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_							)   ,						len(config.backbone_config.depths							)							)
									self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_							)   ,						config.decoder_layers							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Dict=False							)      ->       Optional[Any]:





									"""simple docstring"""







									with torch.no_grad():
											lowercase__						=				MaskaFormerModel(config=SCREAMING_SNAKE_CASE_							)
											model.to(SCREAMING_SNAKE_CASE_							)
											model.eval()

											lowercase__						=				model(pixel_values=SCREAMING_SNAKE_CASE_   ,						pixel_mask=SCREAMING_SNAKE_CASE_							)
											lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						output_hidden_states=SCREAMING_SNAKE_CASE_							)

									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape   ,						(self.batch_size, self.num_queries, self.hidden_dim)   ,						)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None							)
									self.parent.assertTrue(output.encoder_last_hidden_state is not None							)

									if output_hidden_states:
											self.check_output_hidden_state(SCREAMING_SNAKE_CASE_   ,						SCREAMING_SNAKE_CASE_							)


							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Tuple							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()

									def comm_check_on_output(UpperCamelCase_:     int							):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None							)
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None							)
											self.parent.assertTrue(result.encoder_last_hidden_state is not None							)
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,						)
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.num_labels + 1)							)

									with torch.no_grad():
											lowercase__						=				model(pixel_values=SCREAMING_SNAKE_CASE_   ,						pixel_mask=SCREAMING_SNAKE_CASE_							)
											lowercase__						=				model(SCREAMING_SNAKE_CASE_							)

											comm_check_on_output(SCREAMING_SNAKE_CASE_							)

											lowercase__						=				model(
											    pixel_values=SCREAMING_SNAKE_CASE_   ,						pixel_mask=SCREAMING_SNAKE_CASE_   ,						mask_labels=SCREAMING_SNAKE_CASE_   ,						class_labels=SCREAMING_SNAKE_CASE_							)

									comm_check_on_output(SCREAMING_SNAKE_CASE_							)

									self.parent.assertTrue(result.loss is not None							)
									self.parent.assertEqual(result.loss.shape   ,						torch.Size([1]							)							)


@require_torch
class 	_a							(      _lowerCAmelCase					,     _lowerCAmelCase					,     unittest.TestCase      ):
							_lowercase     :      Tuple  				=			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_lowercase     :      int  				=			{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

							_lowercase     :      Dict  				=			False
							_lowercase     :      Optional[Any]  				=			False
							_lowercase     :      str  				=			False
							_lowercase     :      List[str]  				=			False

							def 				lowerCamelCase_			(							self:     Any							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				MaskaFormerModelTester(self							)
									lowercase__						=				ConfigTester(self   ,						config_class=SCREAMING_SNAKE_CASE_   ,						has_text_modality=SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       List[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

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





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_   ,						**SCREAMING_SNAKE_CASE_   ,						output_hidden_states=SCREAMING_SNAKE_CASE_							)

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





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_							)

							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''							)
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Dict:





									"""simple docstring"""







									pass

							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''							)
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[int]:





									"""simple docstring"""







									pass

							@unittest.skip(reason='''Mask2Former is not a generative model'''							)
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[str]:





									"""simple docstring"""







									pass

							@unittest.skip(reason='''Mask2Former does not use token embeddings'''							)
							def 				lowerCamelCase_			(							self:     Any							)      ->       Union[str, Any]:





									"""simple docstring"""







									pass

							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       List[str]:





									"""simple docstring"""







									pass

							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Dict:





									"""simple docstring"""







									pass

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

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

											lowercase__						=				["""pixel_values"""]
											self.assertListEqual(arg_names[:1]   ,						SCREAMING_SNAKE_CASE_							)

							@slow
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											lowercase__						=				MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				(self.model_tester.min_size,) * 2
									lowercase__						=				{
									    """pixel_values""": torch.randn((2, 3, *size)   ,						device=SCREAMING_SNAKE_CASE_							),
									    """mask_labels""": torch.randn((2, 10, *size)   ,						device=SCREAMING_SNAKE_CASE_							),
									    """class_labels""": torch.zeros(2   ,						10   ,						device=SCREAMING_SNAKE_CASE_							).long(),
									}
									lowercase__						=				self.model_tester.get_config()

									lowercase__						=				MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)
									lowercase__						=				model(**SCREAMING_SNAKE_CASE_							)
									self.assertTrue(outputs.loss is not None							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_   ,						**SCREAMING_SNAKE_CASE_   ,						output_hidden_states=SCREAMING_SNAKE_CASE_							)

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





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__						=				model_class(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)
											lowercase__						=				model(**SCREAMING_SNAKE_CASE_   ,						output_attentions=SCREAMING_SNAKE_CASE_							)
											self.assertTrue(outputs.attentions is not None							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Optional[int]:





									"""simple docstring"""







									if not self.model_tester.is_training:
											return

									lowercase__						=				self.all_model_classes[1]
									lowercase__						=				self.model_tester.prepare_config_and_inputs()

									lowercase__						=				model_class(SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.train()

									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						mask_labels=SCREAMING_SNAKE_CASE_   ,						class_labels=SCREAMING_SNAKE_CASE_							).loss
									loss.backward()


							def 				lowerCamelCase_			(							self:     Dict							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.all_model_classes[1]
									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									lowercase__						=				True
									lowercase__						=				True

									lowercase__						=				model_class(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)
									model.train()

									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						mask_labels=SCREAMING_SNAKE_CASE_   ,						class_labels=SCREAMING_SNAKE_CASE_							)

									lowercase__						=				outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									lowercase__						=				outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									lowercase__						=				outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									lowercase__						=				outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_							)

									self.assertIsNotNone(encoder_hidden_states.grad							)
									self.assertIsNotNone(pixel_decoder_hidden_states.grad							)
									self.assertIsNotNone(transformer_decoder_hidden_states.grad							)
									self.assertIsNotNone(attentions.grad							)
lowerCAmelCase	=							1e-4
def       _a			(				):


		"""simple docstring"""




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


@require_vision
@slow
class 	_a							(      unittest.TestCase      ):

							@cached_property
							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[int]:





									"""simple docstring"""







									return "facebook/mask2former-swin-small-coco-instance"

							@cached_property
							def 				lowerCamelCase_			(							self:     Any							)      ->       Dict:





									"""simple docstring"""







									return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None

							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE_							)
									lowercase__						=				self.default_image_processor
									lowercase__						=				prepare_img()
									lowercase__						=				image_processor(SCREAMING_SNAKE_CASE_   ,						return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE_							)
									lowercase__						=				inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
									# check size
									self.assertEqual(SCREAMING_SNAKE_CASE_   ,						(1, 3, 384, 384)							)

									with torch.no_grad():
											lowercase__						=				model(**SCREAMING_SNAKE_CASE_							)

									lowercase__						=				torch.tensor(
									    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]							).to(SCREAMING_SNAKE_CASE_							)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=SCREAMING_SNAKE_CASE_							)							)

									lowercase__						=				torch.tensor(
									    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]							).to(SCREAMING_SNAKE_CASE_							)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=SCREAMING_SNAKE_CASE_							)							)

									lowercase__						=				torch.tensor(
									    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]							).to(SCREAMING_SNAKE_CASE_							)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=SCREAMING_SNAKE_CASE_							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE_							).eval()
									lowercase__						=				self.default_image_processor
									lowercase__						=				prepare_img()
									lowercase__						=				image_processor(SCREAMING_SNAKE_CASE_   ,						return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE_							)
									lowercase__						=				inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
									# check size
									self.assertEqual(SCREAMING_SNAKE_CASE_   ,						(1, 3, 384, 384)							)

									with torch.no_grad():
											lowercase__						=				model(**SCREAMING_SNAKE_CASE_							)
									# masks_queries_logits
									lowercase__						=				outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape   ,						(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
									lowercase__						=				[
									    [-8.7839, -9.0056, -8.8121],
									    [-7.4104, -7.0313, -6.5401],
									    [-6.6105, -6.3427, -6.4675],
									]
									lowercase__						=				torch.tensor(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=SCREAMING_SNAKE_CASE_							)							)
									# class_queries_logits
									lowercase__						=				outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape   ,						(1, model.config.num_queries, model.config.num_labels + 1)							)
									lowercase__						=				torch.tensor(
									    [
									        [1.8324, -8.0835, -4.1922],
									        [0.8450, -9.0050, -3.6053],
									        [0.3045, -7.7293, -3.0275],
									    ]							).to(SCREAMING_SNAKE_CASE_							)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=SCREAMING_SNAKE_CASE_							)							)


							def 				lowerCamelCase_			(							self:     str							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE_							).eval()
									lowercase__						=				self.default_image_processor

									lowercase__						=				image_processor(
									    [np.zeros((3, 800, 1_333)							), np.zeros((3, 800, 1_333)							)]   ,						segmentation_maps=[np.zeros((384, 384)							).astype(np.floataa							), np.zeros((384, 384)							).astype(np.floataa							)]   ,						return_tensors='''pt'''   ,						)

									lowercase__						=				inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE_							)
									lowercase__						=				[el.to(SCREAMING_SNAKE_CASE_							) for el in inputs["""mask_labels"""]]
									lowercase__						=				[el.to(SCREAMING_SNAKE_CASE_							) for el in inputs["""class_labels"""]]

									with torch.no_grad():
											lowercase__						=				model(**SCREAMING_SNAKE_CASE_							)

									self.assertTrue(outputs.loss is not None							)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''lxmert'''
							_lowercase     :      Any  				=			{}

							def __init__(							self:     Any   ,						UpperCamelCase_:     List[Any]=30_522   ,						UpperCamelCase_:     int=768   ,						UpperCamelCase_:     Optional[Any]=12   ,						UpperCamelCase_:     Dict=9_500   ,						UpperCamelCase_:     List[Any]=1_600   ,						UpperCamelCase_:     List[Any]=400   ,						UpperCamelCase_:     List[str]=3_072   ,						UpperCamelCase_:     int="gelu"   ,						UpperCamelCase_:     Any=0.1   ,						UpperCamelCase_:     int=0.1   ,						UpperCamelCase_:     List[str]=512   ,						UpperCamelCase_:     Optional[int]=2   ,						UpperCamelCase_:     Union[str, Any]=0.02   ,						UpperCamelCase_:     Dict=1E-1_2   ,						UpperCamelCase_:     List[Any]=9   ,						UpperCamelCase_:     List[Any]=5   ,						UpperCamelCase_:     Optional[Any]=5   ,						UpperCamelCase_:     str=2_048   ,						UpperCamelCase_:     Dict=4   ,						UpperCamelCase_:     Any=6.67   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     Any=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     Any=True   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				num_qa_labels
									lowercase__						=				num_object_labels
									lowercase__						=				num_attr_labels
									lowercase__						=				l_layers
									lowercase__						=				x_layers
									lowercase__						=				r_layers
									lowercase__						=				visual_feat_dim
									lowercase__						=				visual_pos_dim
									lowercase__						=				visual_loss_normalizer
									lowercase__						=				task_matched
									lowercase__						=				task_mask_lm
									lowercase__						=				task_obj_predict
									lowercase__						=				task_qa
									lowercase__						=				visual_obj_loss
									lowercase__						=				visual_attr_loss
									lowercase__						=				visual_feat_loss
									lowercase__						=				{'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
									super().__init__(**UpperCamelCase_							)


