
'''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,    A,    A=2,    A=True,    A=False,    A=10,    A=3,    A=32 * 8,    A=32 * 8,    A=4,    A=64,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= parent
									lowerCamelCase					:			Optional[int]									= batch_size
									lowerCamelCase					:			str									= is_training
									lowerCamelCase					:			List[str]									= use_auxiliary_loss
									lowerCamelCase					:			Dict									= num_queries
									lowerCamelCase					:			int									= num_channels
									lowerCamelCase					:			int									= min_size
									lowerCamelCase					:			Optional[Any]									= max_size
									lowerCamelCase					:			str									= num_labels
									lowerCamelCase					:			List[str]									= hidden_dim
									lowerCamelCase					:			Optional[int]									= hidden_dim
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
									    lowerCAmelCase__      )

									lowerCamelCase					:			List[Any]									= torch.ones([self.batch_size, self.min_size, self.max_size],    device=lowerCAmelCase__      )

									lowerCamelCase					:			Any									= (
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],    device=lowerCAmelCase__      ) > 0.5
									).float()
									lowerCamelCase					:			Optional[Any]									= (torch.rand((self.batch_size, self.num_labels),    device=lowerCAmelCase__      ) > 0.5).long()

									lowerCamelCase					:			Dict									= self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= MaskaFormerConfig(
									    hidden_size=self.hidden_dim,    )
									lowerCamelCase					:			Any									= self.num_queries
									lowerCamelCase					:			str									= self.num_labels

									lowerCamelCase					:			Union[str, Any]									= [1, 1, 1, 1]
									lowerCamelCase					:			str									= self.num_channels

									lowerCamelCase					:			List[Any]									= 64
									lowerCamelCase					:			Any									= 128
									lowerCamelCase					:			Optional[int]									= self.hidden_dim
									lowerCamelCase					:			List[Any]									= self.hidden_dim
									lowerCamelCase					:			Optional[Any]									= self.hidden_dim
									return config
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.prepare_config_and_inputs()
									lowerCamelCase					:			int									= {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
									return config, inputs_dict
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= output.encoder_hidden_states
									lowerCamelCase					:			Optional[int]									= output.pixel_decoder_hidden_states
									lowerCamelCase					:			Optional[Any]									= output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(lowerCAmelCase__      ),    len(config.backbone_config.depths      )      )
									self.parent.assertTrue(len(lowerCAmelCase__      ),    len(config.backbone_config.depths      )      )
									self.parent.assertTrue(len(lowerCAmelCase__      ),    config.decoder_layers      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A=False      ):

									"""simple docstring"""


									with torch.no_grad():
													lowerCamelCase					:			List[Any]									= MaskaFormerModel(config=lowerCAmelCase__      )
													model.to(lowerCAmelCase__      )
													model.eval()

													lowerCamelCase					:			Optional[int]									= model(pixel_values=lowerCAmelCase__,    pixel_mask=lowerCAmelCase__      )
													lowerCamelCase					:			Union[str, Any]									= model(lowerCAmelCase__,    output_hidden_states=lowerCAmelCase__      )

									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(lowerCAmelCase__,    lowerCAmelCase__      )




					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__      )
									model.to(lowerCAmelCase__      )
									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():
													lowerCamelCase					:			List[str]									= model(pixel_values=lowerCAmelCase__,    pixel_mask=lowerCAmelCase__      )
													lowerCamelCase					:			Union[str, Any]									= model(lowerCAmelCase__      )

													comm_check_on_output(lowerCAmelCase__      )

													lowerCamelCase					:			Tuple									= model(
													    pixel_values=lowerCAmelCase__,    pixel_mask=lowerCAmelCase__,    mask_labels=lowerCAmelCase__,    class_labels=lowerCAmelCase__      )

									comm_check_on_output(lowerCAmelCase__      )

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





@require_torch
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					_lowerCAmelCase			     =	{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= MaskaFormerModelTester(self      )
									lowerCamelCase					:			Optional[Any]									= ConfigTester(self,    config_class=lowerCAmelCase__,    has_text_modality=lowerCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__,    **lowerCAmelCase__,    output_hidden_states=lowerCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__      )
					@unittest.skip(reason='Mask2Former does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='Mask2Former is not a generative model'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='Mask2Former does not use token embeddings'      )
					def     UpperCAmelCase_							(     self      ):

									"""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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

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

													lowerCamelCase					:			Tuple									= ["pixel_values"]
													self.assertListEqual(arg_names[:1],    lowerCAmelCase__      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
													lowerCamelCase					:			Optional[Any]									= MaskaFormerModel.from_pretrained(lowerCAmelCase__      )
													self.assertIsNotNone(lowerCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= (self.model_tester.min_size,) * 2
									lowerCamelCase					:			List[str]									= {
									    "pixel_values": torch.randn((2, 3, *size),    device=lowerCAmelCase__      ),
									    "mask_labels": torch.randn((2, 10, *size),    device=lowerCAmelCase__      ),
									    "class_labels": torch.zeros(2,    10,    device=lowerCAmelCase__      ).long(),
									}
									lowerCamelCase					:			List[Any]									= self.model_tester.get_config()

									lowerCamelCase					:			Union[str, Any]									= MaskaFormerForUniversalSegmentation(lowerCAmelCase__      ).to(lowerCAmelCase__      )
									lowerCamelCase					:			List[Any]									= model(**lowerCAmelCase__      )
									self.assertTrue(outputs.loss is not None      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__,    **lowerCAmelCase__,    output_hidden_states=lowerCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			Union[str, Any]									= model_class(lowerCAmelCase__      ).to(lowerCAmelCase__      )
													lowerCamelCase					:			Union[str, Any]									= model(**lowerCAmelCase__,    output_attentions=lowerCAmelCase__      )
													self.assertTrue(outputs.attentions is not None      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.model_tester.is_training:
													return

									lowerCamelCase					:			Union[str, Any]									= self.all_model_classes[1]
									lowerCamelCase					:			Optional[int]									= self.model_tester.prepare_config_and_inputs()

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

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.all_model_classes[1]
									lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs()
									lowerCamelCase					:			Tuple									= True
									lowerCamelCase					:			int									= True

									lowerCamelCase					:			Optional[int]									= model_class(lowerCAmelCase__      ).to(lowerCAmelCase__      )
									model.train()

									lowerCamelCase					:			int									= model(lowerCAmelCase__,    mask_labels=lowerCAmelCase__,    class_labels=lowerCAmelCase__      )

									lowerCamelCase					:			List[Any]									= outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									lowerCamelCase					:			List[str]									= outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									lowerCamelCase					:			Union[str, Any]									= outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									lowerCamelCase					:			Tuple									= outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=lowerCAmelCase__      )

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




@require_vision
@slow
class     __snake_case		(   unittest.TestCase):
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return "facebook/mask2former-swin-small-coco-instance"
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			str									= MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(lowerCAmelCase__      )
									lowerCamelCase					:			int									= self.default_image_processor
									lowerCamelCase					:			Tuple									= prepare_img()
									lowerCamelCase					:			Optional[int]									= image_processor(lowerCAmelCase__,    return_tensors='pt'      ).to(lowerCAmelCase__      )
									lowerCamelCase					:			Any									= 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(lowerCAmelCase__,    (1, 3, 384, 384)      )

									with torch.no_grad():
													lowerCamelCase					:			int									= model(**lowerCAmelCase__      )

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

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

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

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowerCAmelCase__      ).eval()
									lowerCamelCase					:			List[str]									= self.default_image_processor
									lowerCamelCase					:			Dict									= prepare_img()
									lowerCamelCase					:			Optional[int]									= image_processor(lowerCAmelCase__,    return_tensors='pt'      ).to(lowerCAmelCase__      )
									lowerCamelCase					:			int									= 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(lowerCAmelCase__,    (1, 3, 384, 384)      )

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowerCAmelCase__      ).eval()
									lowerCamelCase					:			List[Any]									= self.default_image_processor

									lowerCamelCase					:			Any									= image_processor(
									    [np.zeros((3, 800, 1333)      ), np.zeros((3, 800, 1333)      )],    segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )],    return_tensors='pt',    )

									lowerCamelCase					:			int									= inputs["pixel_values"].to(lowerCAmelCase__      )
									lowerCamelCase					:			Union[str, Any]									= [el.to(lowerCAmelCase__      ) for el in inputs["mask_labels"]]
									lowerCamelCase					:			str									= [el.to(lowerCAmelCase__      ) for el in inputs["class_labels"]]

									with torch.no_grad():
													lowerCamelCase					:			Dict									= model(**lowerCAmelCase__      )

									self.assertTrue(outputs.loss is not None      )





'''simple docstring'''






import numpy as np





def    UpperCAmelCase							(  UpperCAmelCase__							:							np.array):
				return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
