





'''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 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						Tuple			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Optional[int]=True			,					lowerCAmelCase__  :						List[str]=False			,					lowerCAmelCase__  :						Tuple=10			,					lowerCAmelCase__  :						int=3			,					lowerCAmelCase__  :						Union[str, Any]=32 * 8			,					lowerCAmelCase__  :						Dict=32 * 8			,					lowerCAmelCase__  :						Dict=4			,					lowerCAmelCase__  :						Tuple=64			,					)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_auxiliary_loss
            _UpperCamelCase							       =			num_queries
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			min_size
            _UpperCamelCase							       =			max_size
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			hidden_dim
            _UpperCamelCase							       =			hidden_dim





       def snake_case__		(				self  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
                lowerCAmelCase__	)

            _UpperCamelCase							       =			torch.ones([self.batch_size, self.min_size, self.max_size]			,					device=lowerCAmelCase__	)

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

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





       def snake_case__		(				self  :						str	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerConfig(
                hidden_size=self.hidden_dim			,					)
            _UpperCamelCase							       =			self.num_queries
            _UpperCamelCase							       =			self.num_labels

            _UpperCamelCase							       =			[1, 1, 1, 1]
            _UpperCamelCase							       =			self.num_channels

            _UpperCamelCase							       =			64
            _UpperCamelCase							       =			128
            _UpperCamelCase							       =			self.hidden_dim
            _UpperCamelCase							       =			self.hidden_dim
            _UpperCamelCase							       =			self.hidden_dim
            return config





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			self.prepare_config_and_inputs()
            _UpperCamelCase							       =			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
            return config, inputs_dict





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			output.encoder_hidden_states
            _UpperCamelCase							       =			output.pixel_decoder_hidden_states
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						str=False	)       ->	Tuple:

            '''simple docstring'''




            with torch.no_grad():
                 _UpperCamelCase							       =			MaskaFormerModel(config=lowerCAmelCase__	)
                 model.to(lowerCAmelCase__	)
                 model.eval()

                 _UpperCamelCase							       =			model(pixel_values=lowerCAmelCase__			,					pixel_mask=lowerCAmelCase__	)
                 _UpperCamelCase							       =			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 snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()

            def comm_check_on_output(lowerCAmelCase__  :						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():
                 _UpperCamelCase							       =			model(pixel_values=lowerCAmelCase__			,					pixel_mask=lowerCAmelCase__	)
                 _UpperCamelCase							       =			model(lowerCAmelCase__	)

                 comm_check_on_output(lowerCAmelCase__	)

                 _UpperCamelCase							       =			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 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       _snake_case      :	List[Any]									=       {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

       _snake_case      :	Any									=       False
       _snake_case      :	List[str]									=       False
       _snake_case      :	Optional[int]									=       False
       _snake_case      :	int									=       False





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

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__			,					**lowerCAmelCase__			,					output_hidden_states=lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			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 snake_case__		(				self  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





       @unittest.skip(reason='''Mask2Former is not a generative model'''	)
       def snake_case__		(				self  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''Mask2Former does not use token embeddings'''	)
       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''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 snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						List[Any]	)       ->	str:

            '''simple docstring'''




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





       def snake_case__		(				self  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			(self.model_tester.min_size,) * 2
            _UpperCamelCase							       =			{
                '''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(),
            }
            _UpperCamelCase							       =			self.model_tester.get_config()

            _UpperCamelCase							       =			MaskaFormerForUniversalSegmentation(lowerCAmelCase__	).to(lowerCAmelCase__	)
            _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            self.assertTrue(outputs.loss is not None	)





       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__			,					**lowerCAmelCase__			,					output_hidden_states=lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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





       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            if not self.model_tester.is_training:
                 return

            _UpperCamelCase							       =			self.all_model_classes[1]
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()

            _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.train()

            _UpperCamelCase							       =			model(lowerCAmelCase__			,					mask_labels=lowerCAmelCase__			,					class_labels=lowerCAmelCase__	).loss
            loss.backward()







       def snake_case__		(				self  :						Optional[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.all_model_classes[1]
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			True

            _UpperCamelCase							       =			model_class(lowerCAmelCase__	).to(lowerCAmelCase__	)
            model.train()

            _UpperCamelCase							       =			model(lowerCAmelCase__			,					mask_labels=lowerCAmelCase__			,					class_labels=lowerCAmelCase__	)

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

            _UpperCamelCase							       =			outputs.pixel_decoder_hidden_states[0]
            pixel_decoder_hidden_states.retain_grad()

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

            _UpperCamelCase							       =			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	)

lowercase__		:     Tuple			=							1E-4







def    a__							(							)						->						Any:





     """simple docstring"""

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




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






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						List[str]	)       ->	Any:

            '''simple docstring'''




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





       @cached_property
       def snake_case__		(				self  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)
            _UpperCamelCase							       =			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():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            _UpperCamelCase							       =			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__	)	)

            _UpperCamelCase							       =			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__	)	)

            _UpperCamelCase							       =			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 snake_case__		(				self  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase__	).eval()
            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)
            _UpperCamelCase							       =			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():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            # masks_queries_logits
            _UpperCamelCase							       =			outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape			,					(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
            _UpperCamelCase							       =			[
                [-8.7839, -9.0056, -8.8121],
                [-7.4104, -7.0313, -6.5401],
                [-6.6105, -6.3427, -6.4675],
            ]
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	).to(lowerCAmelCase__	)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,					lowerCAmelCase__			,					atol=lowerCAmelCase__	)	)
            # class_queries_logits
            _UpperCamelCase							       =			outputs.class_queries_logits
            self.assertEqual(class_queries_logits.shape			,					(1, model.config.num_queries, model.config.num_labels + 1)	)
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Tuple	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase__	).eval()
            _UpperCamelCase							       =			self.default_image_processor

            _UpperCamelCase							       =			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'''			,					)

            _UpperCamelCase							       =			inputs['''pixel_values'''].to(lowerCAmelCase__	)
            _UpperCamelCase							       =			[el.to(lowerCAmelCase__	) for el in inputs['''mask_labels''']]
            _UpperCamelCase							       =			[el.to(lowerCAmelCase__	) for el in inputs['''class_labels''']]

            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            self.assertTrue(outputs.loss is not None	)










'''simple docstring'''

def    a__							(							lowercase   :	int,   lowercase   :	int,   lowercase   :	int )						->						float:





     """simple docstring"""

     _UpperCamelCase							       =			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def    a__							(							)						->						Tuple:





     """simple docstring"""

     print(sum_of_series(1,   1,   10 ) )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



