

"""simple docstring"""


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     snake_case	:

				def __init__(	self							:				Any     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Any=2     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Dict=False     ,  UpperCamelCase__							:				str=1_0     ,  UpperCamelCase__							:				Any=3     ,  UpperCamelCase__							:				List[Any]=3_2 * 8     ,  UpperCamelCase__							:				List[Any]=3_2 * 8     ,  UpperCamelCase__							:				str=4     ,  UpperCamelCase__							:				List[str]=6_4     ,  )->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			parent
						__lowerCAmelCase:       Any          =			batch_size
						__lowerCAmelCase:       Tuple          =			is_training
						__lowerCAmelCase:       Dict          =			use_auxiliary_loss
						__lowerCAmelCase:       List[Any]          =			num_queries
						__lowerCAmelCase:       str          =			num_channels
						__lowerCAmelCase:       Union[str, Any]          =			min_size
						__lowerCAmelCase:       List[Any]          =			max_size
						__lowerCAmelCase:       Dict          =			num_labels
						__lowerCAmelCase:       int          =			hidden_dim
						__lowerCAmelCase:       Union[str, Any]          =			hidden_dim

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
						    UpperCamelCase__)

						__lowerCAmelCase:       int          =			torch.ones([self.batch_size, self.min_size, self.max_size]     ,  device=UpperCamelCase__)

						__lowerCAmelCase:       Dict          =			(
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,  device=UpperCamelCase__) > 0.5
						).float()
						__lowerCAmelCase:       Union[str, Any]          =			(torch.rand((self.batch_size, self.num_labels)     ,  device=UpperCamelCase__) > 0.5).long()

						__lowerCAmelCase:       Optional[Any]          =			self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			MaskaFormerConfig(
						    hidden_size=self.hidden_dim     ,  )
						__lowerCAmelCase:       Union[str, Any]          =			self.num_queries
						__lowerCAmelCase:       Optional[int]          =			self.num_labels

						__lowerCAmelCase:       Optional[int]          =			[1, 1, 1, 1]
						__lowerCAmelCase:       str          =			self.num_channels

						__lowerCAmelCase:       List[Any]          =			6_4
						__lowerCAmelCase:       str          =			1_2_8
						__lowerCAmelCase:       Any          =			self.hidden_dim
						__lowerCAmelCase:       List[str]          =			self.hidden_dim
						__lowerCAmelCase:       int          =			self.hidden_dim
						return config

				def 							lowercase_     (	self							:				Tuple)->   Dict:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			output.encoder_hidden_states
						__lowerCAmelCase:       Optional[Any]          =			output.pixel_decoder_hidden_states
						__lowerCAmelCase:       str          =			output.transformer_decoder_hidden_states

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

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				List[str]=False)->   Optional[Any]:



						'''simple docstring'''


						with torch.no_grad():
								__lowerCAmelCase:       List[str]          =			MaskaFormerModel(config=UpperCamelCase__)
								model.to(UpperCamelCase__)
								model.eval()

								__lowerCAmelCase:       int          =			model(pixel_values=UpperCamelCase__     ,  pixel_mask=UpperCamelCase__)
								__lowerCAmelCase:       Union[str, Any]          =			model(UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)

						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(UpperCamelCase__     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				List[str])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			MaskaFormerForUniversalSegmentation(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()

						def comm_check_on_output(UpperCamelCase__							:				Tuple):
								# 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():
								__lowerCAmelCase:       Tuple          =			model(pixel_values=UpperCamelCase__     ,  pixel_mask=UpperCamelCase__)
								__lowerCAmelCase:       int          =			model(UpperCamelCase__)

								comm_check_on_output(UpperCamelCase__)

								__lowerCAmelCase:       List[str]          =			model(
								    pixel_values=UpperCamelCase__     ,  pixel_mask=UpperCamelCase__     ,  mask_labels=UpperCamelCase__     ,  class_labels=UpperCamelCase__)

						comm_check_on_output(UpperCamelCase__)

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





@require_torch
class     snake_case	(				__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

				SCREAMING_SNAKE_CASE_   :      Any         =  False
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  False
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  False

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



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			MaskaFormerModelTester(self)
						__lowerCAmelCase:       Optional[int]          =			ConfigTester(self     ,  config_class=UpperCamelCase__     ,  has_text_modality=UpperCamelCase__)

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



						'''simple docstring'''


						self.config_tester.run_common_tests()

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



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__     ,  **UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase__)

				@unittest.skip(reason="Mask2Former does not use inputs_embeds")
				def 							lowercase_     (	self							:				Any)->   Dict:



						'''simple docstring'''


						pass

				@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method")
				def 							lowercase_     (	self							:				Union[str, Any])->   str:



						'''simple docstring'''


						pass

				@unittest.skip(reason="Mask2Former is not a generative model")
				def 							lowercase_     (	self							:				str)->   List[Any]:



						'''simple docstring'''


						pass

				@unittest.skip(reason="Mask2Former does not use token embeddings")
				def 							lowercase_     (	self							:				Tuple)->   Optional[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 							lowercase_     (	self							:				str)->   str:



						'''simple docstring'''


						pass

				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
				def 							lowercase_     (	self							:				List[Any])->   List[Any]:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowerCAmelCase:       str          =			["pixel_values"]
								self.assertListEqual(arg_names[:1]     ,  UpperCamelCase__)

				@slow
				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
								__lowerCAmelCase:       Union[str, Any]          =			MaskaFormerModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			(self.model_tester.min_size,) * 2
						__lowerCAmelCase:       Dict          =			{
						    "pixel_values": torch.randn((2, 3, *size)     ,  device=UpperCamelCase__),
						    "mask_labels": torch.randn((2, 1_0, *size)     ,  device=UpperCamelCase__),
						    "class_labels": torch.zeros(2     ,  1_0     ,  device=UpperCamelCase__).long(),
						}
						__lowerCAmelCase:       Dict          =			self.model_tester.get_config()

						__lowerCAmelCase:       int          =			MaskaFormerForUniversalSegmentation(UpperCamelCase__).to(UpperCamelCase__)
						__lowerCAmelCase:       str          =			model(**UpperCamelCase__)
						self.assertTrue(outputs.loss is not None)

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



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       Tuple          =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__     ,  **UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       Tuple          =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								__lowerCAmelCase:       Dict          =			model_class(UpperCamelCase__).to(UpperCamelCase__)
								__lowerCAmelCase:       List[str]          =			model(**UpperCamelCase__     ,  output_attentions=UpperCamelCase__)
								self.assertTrue(outputs.attentions is not None)

				def 							lowercase_     (	self							:				Dict)->   Optional[int]:



						'''simple docstring'''


						if not self.model_tester.is_training:
								return

						__lowerCAmelCase:       Dict          =			self.all_model_classes[1]
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       str          =			self.model_tester.prepare_config_and_inputs()

						__lowerCAmelCase:       Tuple          =			model_class(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.train()

						__lowerCAmelCase:       Union[str, Any]          =			model(UpperCamelCase__     ,  mask_labels=UpperCamelCase__     ,  class_labels=UpperCamelCase__).loss
						loss.backward()




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



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			self.all_model_classes[1]
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       str          =			self.model_tester.prepare_config_and_inputs()
						__lowerCAmelCase:       Dict          =			True
						__lowerCAmelCase:       List[Any]          =			True

						__lowerCAmelCase:       int          =			model_class(UpperCamelCase__).to(UpperCamelCase__)
						model.train()

						__lowerCAmelCase:       Tuple          =			model(UpperCamelCase__     ,  mask_labels=UpperCamelCase__     ,  class_labels=UpperCamelCase__)

						__lowerCAmelCase:       str          =			outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						__lowerCAmelCase:       Dict          =			outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()

						__lowerCAmelCase:       Any          =			outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						__lowerCAmelCase:       Optional[int]          =			outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=UpperCamelCase__)

						self.assertIsNotNone(encoder_hidden_states.grad)
						self.assertIsNotNone(pixel_decoder_hidden_states.grad)
						self.assertIsNotNone(transformer_decoder_hidden_states.grad)
						self.assertIsNotNone(attentions.grad)


__A						   =			1E-4






def     a__    (    )	->	Optional[int]:
		__lowerCAmelCase:       Tuple          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image





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

				@cached_property
				def 							lowercase_     (	self							:				Any)->   Tuple:



						'''simple docstring'''


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

				@cached_property
				def 							lowercase_     (	self							:				Tuple)->   int:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			MaskaFormerModel.from_pretrained(self.model_checkpoints).to(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			self.default_image_processor
						__lowerCAmelCase:       List[str]          =			prepare_img()
						__lowerCAmelCase:       List[str]          =			image_processor(UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)
						__lowerCAmelCase:       Any          =			inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
						# check size
						self.assertEqual(UpperCamelCase__     ,  (1, 3, 3_8_4, 3_8_4))

						with torch.no_grad():
								__lowerCAmelCase:       List[str]          =			model(**UpperCamelCase__)

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

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

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

				def 							lowercase_     (	self							:				int)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(UpperCamelCase__).eval()
						__lowerCAmelCase:       int          =			self.default_image_processor
						__lowerCAmelCase:       Tuple          =			prepare_img()
						__lowerCAmelCase:       List[str]          =			image_processor(UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
						# check size
						self.assertEqual(UpperCamelCase__     ,  (1, 3, 3_8_4, 3_8_4))

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




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



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(UpperCamelCase__).eval()
						__lowerCAmelCase:       Any          =			self.default_image_processor

						__lowerCAmelCase:       List[str]          =			image_processor(
						    [np.zeros((3, 8_0_0, 1_3_3_3)), np.zeros((3, 8_0_0, 1_3_3_3))]     ,  segmentation_maps=[np.zeros((3_8_4, 3_8_4)).astype(np.floataa), np.zeros((3_8_4, 3_8_4)).astype(np.floataa)]     ,  return_tensors="pt"     ,  )

						__lowerCAmelCase:       Tuple          =			inputs["pixel_values"].to(UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			[el.to(UpperCamelCase__) for el in inputs["mask_labels"]]
						__lowerCAmelCase:       Dict          =			[el.to(UpperCamelCase__) for el in inputs["class_labels"]]

						with torch.no_grad():
								__lowerCAmelCase:       int          =			model(**UpperCamelCase__)

						self.assertTrue(outputs.loss is not None)





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  """deformable_detr"""
				SCREAMING_SNAKE_CASE_   :      int         =  {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}

				def __init__(	self							:				int     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				int=3     ,  UpperCamelCase__							:				Dict=3_0_0     ,  UpperCamelCase__							:				Optional[int]=1_0_2_4     ,  UpperCamelCase__							:				int=6     ,  UpperCamelCase__							:				List[Any]=1_0_2_4     ,  UpperCamelCase__							:				List[Any]=8     ,  UpperCamelCase__							:				str=6     ,  UpperCamelCase__							:				str=1_0_2_4     ,  UpperCamelCase__							:				Union[str, Any]=8     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Tuple=2_5_6     ,  UpperCamelCase__							:				Tuple=0.1     ,  UpperCamelCase__							:				Optional[int]=0.0     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Dict=1.0     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				List[str]="sine"     ,  UpperCamelCase__							:				Any="resnet50"     ,  UpperCamelCase__							:				List[Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=4     ,  UpperCamelCase__							:				Tuple=4     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				Optional[int]=3_0_0     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[Any]=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.25     ,  UpperCamelCase__							:				List[Any]=False     ,  **UpperCamelCase__							:				Dict     ,  )->   Optional[int]:



						'''simple docstring'''


						if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

						if not use_timm_backbone:
								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
										__lowerCAmelCase:       List[Any]          =			CONFIG_MAPPING["resnet"](out_features=["stage4"])
								elif isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										__lowerCAmelCase:       int          =			backbone_config.get("model_type")
										__lowerCAmelCase:       List[str]          =			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase:       Any          =			config_class.from_dict(UpperCamelCase__)
						__lowerCAmelCase:       int          =			use_timm_backbone
						__lowerCAmelCase:       Any          =			backbone_config
						__lowerCAmelCase:       Tuple          =			num_channels
						__lowerCAmelCase:       str          =			num_queries
						__lowerCAmelCase:       List[str]          =			max_position_embeddings
						__lowerCAmelCase:       List[Any]          =			d_model
						__lowerCAmelCase:       Union[str, Any]          =			encoder_ffn_dim
						__lowerCAmelCase:       Tuple          =			encoder_layers
						__lowerCAmelCase:       List[str]          =			encoder_attention_heads
						__lowerCAmelCase:       Any          =			decoder_ffn_dim
						__lowerCAmelCase:       Union[str, Any]          =			decoder_layers
						__lowerCAmelCase:       List[Any]          =			decoder_attention_heads
						__lowerCAmelCase:       List[Any]          =			dropout
						__lowerCAmelCase:       Optional[Any]          =			attention_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_function
						__lowerCAmelCase:       Dict          =			init_std
						__lowerCAmelCase:       int          =			init_xavier_std
						__lowerCAmelCase:       str          =			encoder_layerdrop
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss
						__lowerCAmelCase:       List[Any]          =			position_embedding_type
						__lowerCAmelCase:       str          =			backbone
						__lowerCAmelCase:       Tuple          =			use_pretrained_backbone
						__lowerCAmelCase:       int          =			dilation
						# deformable attributes
						__lowerCAmelCase:       Union[str, Any]          =			num_feature_levels
						__lowerCAmelCase:       Optional[Any]          =			encoder_n_points
						__lowerCAmelCase:       Dict          =			decoder_n_points
						__lowerCAmelCase:       Optional[Any]          =			two_stage
						__lowerCAmelCase:       Tuple          =			two_stage_num_proposals
						__lowerCAmelCase:       int          =			with_box_refine
						if two_stage is True and with_box_refine is False:
								raise ValueError("If two_stage is True, with_box_refine must be True.")
						# Hungarian matcher
						__lowerCAmelCase:       str          =			class_cost
						__lowerCAmelCase:       List[str]          =			bbox_cost
						__lowerCAmelCase:       List[str]          =			giou_cost
						# Loss coefficients
						__lowerCAmelCase:       Tuple          =			mask_loss_coefficient
						__lowerCAmelCase:       int          =			dice_loss_coefficient
						__lowerCAmelCase:       Any          =			bbox_loss_coefficient
						__lowerCAmelCase:       str          =			giou_loss_coefficient
						__lowerCAmelCase:       int          =			eos_coefficient
						__lowerCAmelCase:       Tuple          =			focal_alpha
						__lowerCAmelCase:       Optional[Any]          =			disable_custom_kernels
						super().__init__(is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__)

				@property
				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						return self.encoder_attention_heads

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



						'''simple docstring'''


						return self.d_model




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



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
								__lowerCAmelCase:       str          =			self.backbone_config.to_dict()
						__lowerCAmelCase:       Tuple          =			self.__class__.model_type
						return output


