

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

			if is_vision_available():
						from transformers import MaskFormerImageProcessor

if is_vision_available():
			from PIL import Image

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=2       ,							_a=True       ,							_a=False       ,							_a=1_0       ,							_a=3       ,							_a=3_2 * 4       ,							_a=3_2 * 6       ,							_a=4       ,							_a=3_2       ,							)    ->    int:
												_a	:    Union[str, Any]         =		parent
												_a	:    Optional[int]         =		batch_size
												_a	:    List[Any]         =		is_training
												_a	:    List[str]         =		use_auxiliary_loss
												_a	:    List[Any]         =		num_queries
												_a	:    int         =		num_channels
												_a	:    Any         =		min_size
												_a	:    List[str]         =		max_size
												_a	:    Any         =		num_labels
												_a	:    Dict         =		mask_feature_size


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
												    _a       )

												_a	:    str         =		torch.ones([self.batch_size, self.min_size, self.max_size]       ,							device=_a       )

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

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


							def 				__lowercase				(			self       )    ->    Any:
												return MaskFormerConfig.from_backbone_and_decoder_configs(
												    backbone_config=SwinConfig(
												        depths=[1, 1, 1, 1]       ,							)       ,							decoder_config=DetrConfig(
												        decoder_ffn_dim=1_2_8       ,							num_queries=self.num_queries       ,							decoder_attention_heads=2       ,							d_model=self.mask_feature_size       ,							)       ,							mask_feature_size=self.mask_feature_size       ,							fpn_feature_size=self.mask_feature_size       ,							num_channels=self.num_channels       ,							num_labels=self.num_labels       ,							)


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a      ,					_a      ,					_a      ,					_a      ,					_a	:    str         =		self.prepare_config_and_inputs()
												_a	:    Union[str, Any]         =		{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
												return config, inputs_dict


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    Optional[int]:
												_a	:    Any         =		output.encoder_hidden_states
												_a	:    Union[str, Any]         =		output.pixel_decoder_hidden_states
												_a	:    Union[str, Any]         =		output.transformer_decoder_hidden_states

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a=False       )    ->    int:
												with torch.no_grad():
																	_a	:    str         =		MaskFormerModel(config=_a       )
																	model.to(_a       )
																	model.eval()

																	_a	:    str         =		model(pixel_values=_a       ,							pixel_mask=_a       )
																	_a	:    Tuple         =		model(_a       ,							output_hidden_states=_a       )
												# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
												# encoder and pixel decoder
												self.parent.assertEqual(
												    output.transformer_decoder_last_hidden_state.shape       ,							(self.batch_size, self.num_queries, self.mask_feature_size)       ,							)
												# 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(_a       ,							_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    str:
												_a	:    str         =		MaskFormerForInstanceSegmentation(config=_a       )
												model.to(_a       )
												model.eval()

												def comm_check_on_output(_a       ):
																	# 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():
																	_a	:    str         =		model(pixel_values=_a       ,							pixel_mask=_a       )
																	_a	:    Dict         =		model(_a       )

																	comm_check_on_output(_a       )

																	_a	:    Any         =		model(
																	    pixel_values=_a       ,							pixel_mask=_a       ,							mask_labels=_a       ,							class_labels=_a       )

												comm_check_on_output(_a       )

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



@require_torch
class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							UpperCAmelCase__       :						Tuple	    =    (
							    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__       :						Dict	    =    False
							UpperCAmelCase__       :						int	    =    False
							UpperCAmelCase__       :						Union[str, Any]	    =    False
							UpperCAmelCase__       :						Dict	    =    False


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Union[str, Any]         =		MaskFormerModelTester(self       )
												_a	:    Union[str, Any]         =		ConfigTester(self       ,							config_class=_a       ,							has_text_modality=_a       )


							def 				__lowercase				(			self       )    ->    int:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    List[str]:
												_a      ,					_a	:    List[str]         =		self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(_a       ,							**_a       ,							output_hidden_states=_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_a       )


							@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''       )
							def 				__lowercase				(			self       )    ->    str:
												pass


							@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''       )
							def 				__lowercase				(			self       )    ->    List[Any]:
												pass


							@unittest.skip(reason='''MaskFormer is not a generative model'''       )
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												pass


							@unittest.skip(reason='''MaskFormer does not use token embeddings'''       )
							def 				__lowercase				(			self       )    ->    Dict:
												pass


							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''       )
							def 				__lowercase				(			self       )    ->    Dict:
												pass


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


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a      ,					_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a	:    Optional[Any]         =		['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,							_a       )


							@slow
							def 				__lowercase				(			self       )    ->    Optional[int]:
												for model_name in ["facebook/maskformer-swin-small-coco"]:
																	_a	:    Union[str, Any]         =		MaskFormerModel.from_pretrained(_a       )
																	self.assertIsNotNone(_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    Tuple         =		(self.model_tester.min_size,) * 2
												_a	:    List[str]         =		{
												    '''pixel_values''': torch.randn((2, 3, *size)       ,							device=_a       ),
												    '''mask_labels''': torch.randn((2, 1_0, *size)       ,							device=_a       ),
												    '''class_labels''': torch.zeros(2       ,							1_0       ,							device=_a       ).long(),
												}

												_a	:    int         =		MaskFormerForInstanceSegmentation(MaskFormerConfig()       ).to(_a       )
												_a	:    Dict         =		model(**_a       )
												self.assertTrue(outputs.loss is not None       )


							def 				__lowercase				(			self       )    ->    List[str]:
												_a      ,					_a	:    Tuple         =		self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(_a       ,							**_a       ,							output_hidden_states=_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a      ,					_a	:    Any         =		self.model_tester.prepare_config_and_inputs_for_common()

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


							def 				__lowercase				(			self       )    ->    List[Any]:
												if not self.model_tester.is_training:
																	return
												# only MaskFormerForInstanceSegmentation has the loss
												_a	:    Union[str, Any]         =		self.all_model_classes[1]
												_a      ,					_a      ,					_a      ,					_a      ,					_a	:    str         =		self.model_tester.prepare_config_and_inputs()

												_a	:    str         =		model_class(_a       )
												model.to(_a       )
												model.train()

												_a	:    List[str]         =		model(_a       ,							mask_labels=_a       ,							class_labels=_a       ).loss
												loss.backward()




							def 				__lowercase				(			self       )    ->    List[str]:
												# only MaskFormerForInstanceSegmentation has the loss
												_a	:    Tuple         =		self.all_model_classes[1]
												_a      ,					_a      ,					_a      ,					_a      ,					_a	:    Optional[int]         =		self.model_tester.prepare_config_and_inputs()
												_a	:    Dict         =		True
												_a	:    Optional[int]         =		True

												_a	:    List[str]         =		model_class(_a       )
												model.to(_a       )
												model.train()

												_a	:    Tuple         =		model(_a       ,							mask_labels=_a       ,							class_labels=_a       )

												_a	:    int         =		outputs.encoder_hidden_states[0]
												encoder_hidden_states.retain_grad()

												_a	:    Dict         =		outputs.pixel_decoder_hidden_states[0]
												pixel_decoder_hidden_states.retain_grad()
												# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
												_a	:    int         =		outputs.transformer_decoder_hidden_states[0]
												transformer_decoder_hidden_states.retain_grad()

												_a	:    Dict         =		outputs.attentions[0]
												attentions.retain_grad()

												outputs.loss.backward(retain_graph=_a       )

												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    __UpperCAmelCase (	)			->     Any:

					"""simple docstring"""

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







@require_vision
@slow
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@cached_property
							def 				__lowercase				(			self       )    ->    Tuple:
												return (
												    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
												    if is_vision_available()
												    else None
												)


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Optional[int]         =		MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''       ).to(_a       )
												_a	:    List[Any]         =		self.default_image_processor
												_a	:    int         =		prepare_img()
												_a	:    List[str]         =		image_processor(_a       ,							return_tensors='''pt'''       ).to(_a       )
												_a	:    str         =		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(_a       ,							(1, 3, 8_0_0, 1_0_8_8)       )

												with torch.no_grad():
																	_a	:    Union[str, Any]         =		model(**_a       )

												_a	:    Tuple         =		torch.tensor(
												    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]       ).to(_a       )
												self.assertTrue(
												    torch.allclose(
												        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,							_a       ,							atol=_a       )       )

												_a	:    Any         =		torch.tensor(
												    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]       ).to(_a       )
												self.assertTrue(
												    torch.allclose(
												        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,							_a       ,							atol=_a       )       )

												_a	:    List[str]         =		torch.tensor(
												    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]       ).to(_a       )
												self.assertTrue(
												    torch.allclose(
												        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,							_a       ,							atol=_a       )       )


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    List[str]         =		(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
												    .to(_a       )
												    .eval()
												)
												_a	:    Optional[int]         =		self.default_image_processor
												_a	:    str         =		prepare_img()
												_a	:    str         =		image_processor(_a       ,							return_tensors='''pt'''       ).to(_a       )
												_a	:    int         =		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(_a       ,							(1, 3, 8_0_0, 1_0_8_8)       )

												with torch.no_grad():
																	_a	:    List[str]         =		model(**_a       )
												# masks_queries_logits
												_a	:    Tuple         =		outputs.masks_queries_logits
												self.assertEqual(
												    masks_queries_logits.shape       ,							(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,							)
												_a	:    Dict         =		[
												    [-1.373_7124, -1.772_4937, -1.936_4233],
												    [-1.597_7281, -1.986_7939, -2.152_3695],
												    [-1.579_5398, -1.926_9832, -2.09_3942],
												]
												_a	:    str         =		torch.tensor(_a       ).to(_a       )
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,							_a       ,							atol=_a       )       )
												# class_queries_logits
												_a	:    List[Any]         =		outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape       ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
												_a	:    List[str]         =		torch.tensor(
												    [
												        [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
												        [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
												        [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
												    ]       ).to(_a       )
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,							_a       ,							atol=_a       )       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    List[Any]         =		(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''       )
												    .to(_a       )
												    .eval()
												)
												_a	:    List[Any]         =		self.default_image_processor
												_a	:    Any         =		prepare_img()
												_a	:    Optional[Any]         =		image_processor(_a       ,							return_tensors='''pt'''       ).to(_a       )
												_a	:    int         =		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(_a       ,							(1, 3, 8_0_0, 1_0_8_8)       )

												with torch.no_grad():
																	_a	:    Any         =		model(**_a       )
												# masks_queries_logits
												_a	:    List[str]         =		outputs.masks_queries_logits
												self.assertEqual(
												    masks_queries_logits.shape       ,							(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,							)
												_a	:    Optional[int]         =		[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
												_a	:    Optional[Any]         =		torch.tensor(_a       ).to(_a       )
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,							_a       ,							atol=_a       )       )
												# class_queries_logits
												_a	:    Optional[int]         =		outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape       ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
												_a	:    Union[str, Any]         =		torch.tensor(
												    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]       ).to(_a       )
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,							_a       ,							atol=_a       )       )




							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    List[str]         =		(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
												    .to(_a       )
												    .eval()
												)
												_a	:    List[str]         =		self.default_image_processor

												_a	:    List[Any]         =		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'''       ,							)

												_a	:    Dict         =		inputs['''pixel_values'''].to(_a       )
												_a	:    str         =		[el.to(_a       ) for el in inputs['''mask_labels''']]
												_a	:    str         =		[el.to(_a       ) for el in inputs['''class_labels''']]

												with torch.no_grad():
																	_a	:    int         =		model(**_a       )

												self.assertTrue(outputs.loss is not None       )






import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



