






import os
from distutils.util import strtobool




def      __A					(     _lowercase					,			_lowercase ):
					for e in env_keys:
										_A								= int(os.environ.get(_lowerCamelCase					,			-1 ) )
										if val >= 0:
															return val
					return default




def      __A					(     _lowercase					,			_lowercase=False ):
					_A								= os.environ.get(_lowerCamelCase					,			str(_lowerCamelCase ) )
					return strtobool(_lowerCamelCase ) == 1  # As its name indicates `strtobool` actually returns an int...




def      __A					(     _lowercase					,			_lowercase="no" ):
					_A								= os.environ.get(_lowerCamelCase					,			str(_lowerCamelCase ) )
					return value











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 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   int=2		,     __A:   List[str]=True		,     __A:   List[Any]=False		,     __A:   Union[str, Any]=10		,     __A:   Optional[int]=3		,     __A:   List[Any]=32 * 4		,     __A:   Dict=32 * 6		,     __A:   Optional[Any]=4		,     __A:   Any=32		,     )    ->				str:
												_A								= parent
												_A								= batch_size
												_A								= is_training
												_A								= use_auxiliary_loss
												_A								= num_queries
												_A								= num_channels
												_A								= min_size
												_A								= max_size
												_A								= num_labels
												_A								= mask_feature_size

							def       __A (  self:   Dict   )    ->				Optional[int]:
												_A								= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
												    __A   )

												_A								= torch.ones([self.batch_size, self.min_size, self.max_size]		,     device=__A   )

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

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

							def       __A (  self:   Optional[Any]   )    ->				Tuple:
												return MaskFormerConfig.from_backbone_and_decoder_configs(
												    backbone_config=SwinConfig(
												        depths=[1, 1, 1, 1]		,     )		,     decoder_config=DetrConfig(
												        decoder_ffn_dim=1_28		,     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       __A (  self:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A							,_A								= self.prepare_config_and_inputs()
												_A								= {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
												return config, inputs_dict

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   Dict   )    ->				int:
												_A								= output.encoder_hidden_states
												_A								= output.pixel_decoder_hidden_states
												_A								= 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       __A (  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   Dict=False   )    ->				Any:
												with torch.no_grad():
																	_A								= MaskFormerModel(config=__A   )
																	model.to(__A   )
																	model.eval()

																	_A								= model(pixel_values=__A		,     pixel_mask=__A   )
																	_A								= 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       __A (  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Union[str, Any]		,     __A:   List[Any]   )    ->				int:
												_A								= MaskFormerForInstanceSegmentation(config=__A   )
												model.to(__A   )
												model.eval()

												def comm_check_on_output(__A:   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():
																	_A								= model(pixel_values=__A		,     pixel_mask=__A   )
																	_A								= model(__A   )

																	comm_check_on_output(__A   )

																	_A								= 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 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							A_						  =					(
							    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							A_						  =					False
							A_						  =					False
							A_						  =					False
							A_						  =					False

							def       __A (  self:   int   )    ->				Tuple:
												_A								= MaskFormerModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A		,     has_text_modality=__A   )

							def       __A (  self:   List[Any]   )    ->				Dict:
												self.config_tester.run_common_tests()

							def       __A (  self:   Optional[Any]   )    ->				int:
												_A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(__A		,     **__A		,     output_hidden_states=__A   )

							def       __A (  self:   Dict   )    ->				Optional[Any]:
												_A								= 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       __A (  self:   int   )    ->				Tuple:
												pass

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

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

							@unittest.skip(reason='''MaskFormer does not use token embeddings'''   )
							def       __A (  self:   int   )    ->				List[str]:
												pass

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

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

							def       __A (  self:   Dict   )    ->				Optional[Any]:
												_A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()

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

																	_A								= ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]		,     __A   )

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

							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												_A								= (self.model_tester.min_size,) * 2
												_A								= {
												    '''pixel_values''': torch.randn((2, 3, *size)		,     device=__A   ),
												    '''mask_labels''': torch.randn((2, 10, *size)		,     device=__A   ),
												    '''class_labels''': torch.zeros(2		,     10		,     device=__A   ).long(),
												}

												_A								= MaskFormerForInstanceSegmentation(MaskFormerConfig()   ).to(__A   )
												_A								= model(**__A   )
												self.assertTrue(outputs.loss is not None   )

							def       __A (  self:   Optional[Any]   )    ->				List[Any]:
												_A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(__A		,     **__A		,     output_hidden_states=__A   )

							def       __A (  self:   Any   )    ->				Tuple:
												_A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()

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

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

												_A								= model_class(__A   )
												model.to(__A   )
												model.train()

												_A								= model(__A		,     mask_labels=__A		,     class_labels=__A   ).loss
												loss.backward()



							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												# only MaskFormerForInstanceSegmentation has the loss
												_A								= self.all_model_classes[1]
												_A							,_A							,_A							,_A							,_A								= self.model_tester.prepare_config_and_inputs()
												_A								= True
												_A								= True

												_A								= model_class(__A   )
												model.to(__A   )
												model.train()

												_A								= model(__A		,     mask_labels=__A		,     class_labels=__A   )

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

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

												_A								= 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      __A					(     ):

					'''simple docstring'''







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





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





							"""simple docstring"""

							@cached_property
							def       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
												return (
												    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''   )
												    if is_vision_available()
												    else None
												)

							def       __A (  self:   List[Any]   )    ->				Any:
												_A								= MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''   ).to(__A   )
												_A								= self.default_image_processor
												_A								= prepare_img()
												_A								= image_processor(__A		,     return_tensors='''pt'''   ).to(__A   )
												_A								= 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(__A		,     (1, 3, 8_00, 10_88)   )

												with torch.no_grad():
																	_A								= model(**__A   )

												_A								= torch.tensor(
												    [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]   ).to(__A   )
												self.assertTrue(
												    torch.allclose(
												        outputs.encoder_last_hidden_state[0, 0, :3, :3]		,     __A		,     atol=__A   )   )

												_A								= torch.tensor(
												    [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]   ).to(__A   )
												self.assertTrue(
												    torch.allclose(
												        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,     __A		,     atol=__A   )   )

												_A								= torch.tensor(
												    [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]   ).to(__A   )
												self.assertTrue(
												    torch.allclose(
												        outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,     __A		,     atol=__A   )   )

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= (
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''   )
												    .to(__A   )
												    .eval()
												)
												_A								= self.default_image_processor
												_A								= prepare_img()
												_A								= image_processor(__A		,     return_tensors='''pt'''   ).to(__A   )
												_A								= 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(__A		,     (1, 3, 8_00, 10_88)   )

												with torch.no_grad():
																	_A								= model(**__A   )
												# masks_queries_logits
												_A								= 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								= [
												    [-1.3_737_124, -1.7_724_937, -1.9_364_233],
												    [-1.5_977_281, -1.9_867_939, -2.1_523_695],
												    [-1.5_795_398, -1.9_269_832, -2.093_942],
												]
												_A								= torch.tensor(__A   ).to(__A   )
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,     __A		,     atol=__A   )   )
												# class_queries_logits
												_A								= outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape		,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
												_A								= torch.tensor(
												    [
												        [1.65_12e00, -5.25_72e00, -3.35_19e00],
												        [3.61_69e-02, -5.90_25e00, -2.93_13e00],
												        [1.07_66e-04, -7.76_30e00, -5.12_63e00],
												    ]   ).to(__A   )
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,     __A		,     atol=__A   )   )

							def       __A (  self:   List[Any]   )    ->				Dict:
												_A								= (
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''   )
												    .to(__A   )
												    .eval()
												)
												_A								= self.default_image_processor
												_A								= prepare_img()
												_A								= image_processor(__A		,     return_tensors='''pt'''   ).to(__A   )
												_A								= 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(__A		,     (1, 3, 8_00, 10_88)   )

												with torch.no_grad():
																	_A								= model(**__A   )
												# masks_queries_logits
												_A								= 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								= [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
												_A								= torch.tensor(__A   ).to(__A   )
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,     __A		,     atol=__A   )   )
												# class_queries_logits
												_A								= outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape		,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
												_A								= torch.tensor(
												    [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]   ).to(__A   )
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,     __A		,     atol=__A   )   )



							def       __A (  self:   Optional[Any]   )    ->				str:
												_A								= (
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''   )
												    .to(__A   )
												    .eval()
												)
												_A								= self.default_image_processor

												_A								= image_processor(
												    [np.zeros((3, 8_00, 13_33)   ), np.zeros((3, 8_00, 13_33)   )]		,     segmentation_maps=[np.zeros((3_84, 3_84)   ).astype(np.floataa   ), np.zeros((3_84, 3_84)   ).astype(np.floataa   )]		,     return_tensors='''pt'''		,     )

												_A								= inputs['''pixel_values'''].to(__A   )
												_A								= [el.to(__A   ) for el in inputs['''mask_labels''']]
												_A								= [el.to(__A   ) for el in inputs['''class_labels''']]

												with torch.no_grad():
																	_A								= model(**__A   )

												self.assertTrue(outputs.loss is not None   )



