



"""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 SCREAMING_SNAKE_CASE__					:



      def __init__(self						,     _lowercase						,     _lowercase=2						,     _lowercase=True						,     _lowercase=False						,     _lowercase=10						,     _lowercase=3						,     _lowercase=32 * 8						,     _lowercase=32 * 8						,     _lowercase=4						,     _lowercase=64						,     ):





             '''simple docstring'''

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



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		str									=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
                 UpperCAmelCase__							)

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

             __a		:		List[str]									=       (
                 torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,     device=UpperCAmelCase__							) > 0.5
             ).float()
             __a		:		Any									=       (torch.rand((self.batch_size, self.num_labels)						,     device=UpperCAmelCase__							) > 0.5).long()

             __a		:		Tuple									=       self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		str									=       MaskaFormerConfig(
                 hidden_size=self.hidden_dim						,     )
             __a		:		List[str]									=       self.num_queries
             __a		:		Tuple									=       self.num_labels

             __a		:		Tuple									=       [1, 1, 1, 1]
             __a		:		Tuple									=       self.num_channels

             __a		:		Any									=       64
             __a		:		List[Any]									=       128
             __a		:		Union[str, Any]									=       self.hidden_dim
             __a		:		str									=       self.hidden_dim
             __a		:		Dict									=       self.hidden_dim
             return config



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       self.prepare_config_and_inputs()
             __a		:		List[str]									=       {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
             return config, inputs_dict



      def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             __a		:		str									=       output.encoder_hidden_states
             __a		:		Optional[Any]									=       output.pixel_decoder_hidden_states
             __a		:		List[Any]									=       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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=False							):





             '''simple docstring'''

             with torch.no_grad():
                    __a		:		int									=       MaskaFormerModel(config=UpperCAmelCase__							)
                    model.to(UpperCAmelCase__							)
                    model.eval()

                    __a		:		List[Any]									=       model(pixel_values=UpperCAmelCase__						,     pixel_mask=UpperCAmelCase__							)
                    __a		:		Dict									=       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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             __a		:		int									=       MaskaFormerForUniversalSegmentation(config=UpperCAmelCase__							)
             model.to(UpperCAmelCase__							)
             model.eval()

             def comm_check_on_output(_lowercase							):
                    # 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		:		Optional[Any]									=       model(pixel_values=UpperCAmelCase__						,     pixel_mask=UpperCAmelCase__							)
                    __a		:		List[Any]									=       model(UpperCAmelCase__							)

                    comm_check_on_output(UpperCAmelCase__							)

                    __a		:		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 SCREAMING_SNAKE_CASE__					(		lowercase__     ,					lowercase__     ,					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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       MaskaFormerModelTester(self							)
             __a		:		Optional[Any]									=       ConfigTester(self						,     config_class=UpperCAmelCase__						,     has_text_modality=UpperCAmelCase__							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             self.config_tester.run_common_tests()



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             pass



      @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""							)
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             pass



      @unittest.skip(reason="""Mask2Former is not a generative model"""							)
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             pass



      @unittest.skip(reason="""Mask2Former does not use token embeddings"""							)
      def     lowerCAmelCase__(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     lowerCAmelCase__(self							):





             '''simple docstring'''

             pass



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





             '''simple docstring'''

             pass



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Any									=       self.model_tester.prepare_config_and_inputs_for_common()

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

                    __a		:		Tuple									=       ['''pixel_values''']
                    self.assertListEqual(arg_names[:1]						,     UpperCAmelCase__							)



      @slow
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

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



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       (self.model_tester.min_size,) * 2
             __a		:		int									=       {
                 '''pixel_values''': torch.randn((2, 3, *size)						,     device=UpperCAmelCase__							),
                 '''mask_labels''': torch.randn((2, 10, *size)						,     device=UpperCAmelCase__							),
                 '''class_labels''': torch.zeros(2						,     10						,     device=UpperCAmelCase__							).long(),
             }
             __a		:		Tuple									=       self.model_tester.get_config()

             __a		:		List[Any]									=       MaskaFormerForUniversalSegmentation(UpperCAmelCase__							).to(UpperCAmelCase__							)
             __a		:		List[str]									=       model(**UpperCAmelCase__							)
             self.assertTrue(outputs.loss is not None							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __a		:		Dict									=       model_class(UpperCAmelCase__							).to(UpperCAmelCase__							)
                    __a		:		Any									=       model(**UpperCAmelCase__						,     output_attentions=UpperCAmelCase__							)
                    self.assertTrue(outputs.attentions is not None							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             if not self.model_tester.is_training:
                    return

             __a		:		Optional[Any]									=       self.all_model_classes[1]
             __a		:		Dict									=       self.model_tester.prepare_config_and_inputs()

             __a		:		str									=       model_class(UpperCAmelCase__							)
             model.to(UpperCAmelCase__							)
             model.train()

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





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Union[str, Any]									=       self.all_model_classes[1]
             __a		:		Dict									=       self.model_tester.prepare_config_and_inputs()
             __a		:		str									=       True
             __a		:		Tuple									=       True

             __a		:		Union[str, Any]									=       model_class(UpperCAmelCase__							).to(UpperCAmelCase__							)
             model.train()

             __a		:		int									=       model(UpperCAmelCase__						,     mask_labels=UpperCAmelCase__						,     class_labels=UpperCAmelCase__							)

             __a		:		Optional[Any]									=       outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

             __a		:		int									=       outputs.pixel_decoder_hidden_states[0]
             pixel_decoder_hidden_states.retain_grad()

             __a		:		Optional[Any]									=       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=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							)







lowercase__			   =  1e-4





def       __magic_name__    (   ):
       __a		:		List[Any]									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
       return image







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



      @cached_property
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

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



      @cached_property
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

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



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(UpperCAmelCase__							)
             __a		:		Dict									=       self.default_image_processor
             __a		:		Optional[int]									=       prepare_img()
             __a		:		Any									=       image_processor(UpperCAmelCase__						,     return_tensors="""pt"""							).to(UpperCAmelCase__							)
             __a		:		Optional[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(UpperCAmelCase__						,     (1, 3, 384, 384)							)

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

             __a		:		List[Any]									=       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__							)							)

             __a		:		Optional[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__							)							)

             __a		:		int									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[Any]									=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(UpperCAmelCase__							).eval()
             __a		:		str									=       self.default_image_processor
             __a		:		str									=       prepare_img()
             __a		:		Tuple									=       image_processor(UpperCAmelCase__						,     return_tensors="""pt"""							).to(UpperCAmelCase__							)
             __a		:		List[str]									=       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(UpperCAmelCase__						,     (1, 3, 384, 384)							)

             with torch.no_grad():
                    __a		:		Optional[Any]									=       model(**UpperCAmelCase__							)
             # masks_queries_logits
             __a		:		List[Any]									=       outputs.masks_queries_logits
             self.assertEqual(
                 masks_queries_logits.shape						,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
             __a		:		List[Any]									=       [
                 [-8.7839, -9.0056, -8.8121],
                 [-7.4104, -7.0313, -6.5401],
                 [-6.6105, -6.3427, -6.4675],
             ]
             __a		:		Union[str, Any]									=       torch.tensor(UpperCAmelCase__							).to(UpperCAmelCase__							)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,     UpperCAmelCase__						,     atol=UpperCAmelCase__							)							)
             # class_queries_logits
             __a		:		Dict									=       outputs.class_queries_logits
             self.assertEqual(class_queries_logits.shape						,     (1, model.config.num_queries, model.config.num_labels + 1)							)
             __a		:		Optional[Any]									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(UpperCAmelCase__							).eval()
             __a		:		int									=       self.default_image_processor

             __a		:		Tuple									=       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"""						,     )

             __a		:		Tuple									=       inputs['''pixel_values'''].to(UpperCAmelCase__							)
             __a		:		Dict									=       [el.to(UpperCAmelCase__							) for el in inputs['''mask_labels''']]
             __a		:		Union[str, Any]									=       [el.to(UpperCAmelCase__							) for el in inputs['''class_labels''']]

             with torch.no_grad():
                    __a		:		List[str]									=       model(**UpperCAmelCase__							)

             self.assertTrue(outputs.loss is not None							)






"""simple docstring"""

import unittest

from knapsack import knapsack as k




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       0
													__a		:		Optional[Any]									=       [0]
													__a		:		int									=       [0]
													__a		:		str									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)

													__a		:		int									=       [60]
													__a		:		Union[str, Any]									=       [10]
													__a		:		Tuple									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       3
													__a		:		str									=       [1, 2, 3]
													__a		:		Optional[Any]									=       [3, 2, 1]
													__a		:		int									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     5							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       50
													__a		:		Tuple									=       [60, 100, 120]
													__a		:		List[str]									=       [10, 20, 30]
													__a		:		Union[str, Any]									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     220							)


if __name__ == "__main__":
	unittest.main()

