


def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						0
      for i in range(1						,	1_001      ):
            total += i**i
      return str(UpperCAmelCase      )[-10:]


if __name__ == "__main__":
    print(solution())









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 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : int       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Tuple=2       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : Union[str, Any]=False       ,  _lowerCamelCase  : Dict=1_0       ,  _lowerCamelCase  : Union[str, Any]=3       ,  _lowerCamelCase  : Tuple=3_2 * 8       ,  _lowerCamelCase  : int=3_2 * 8       ,  _lowerCamelCase  : str=4       ,  _lowerCamelCase  : List[Any]=6_4       ,  ):






         '''simple docstring'''



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

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
             _lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						torch.ones([self.batch_size, self.min_size, self.max_size]       ,  device=_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,  device=_lowerCamelCase							) > 0.5
         ).float()
         __lowerCamelCase     :      List[Any]       						=						(torch.rand((self.batch_size, self.num_labels)       ,  device=_lowerCamelCase							) > 0.5).long()

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

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						MaskaFormerConfig(
             hidden_size=self.hidden_dim       ,  )
         __lowerCamelCase     :      List[Any]       						=						self.num_queries
         __lowerCamelCase     :      List[str]       						=						self.num_labels

         __lowerCamelCase     :      List[str]       						=						[1, 1, 1, 1]
         __lowerCamelCase     :      Optional[int]       						=						self.num_channels

         __lowerCamelCase     :      Optional[int]       						=						6_4
         __lowerCamelCase     :      int       						=						1_2_8
         __lowerCamelCase     :      Any       						=						self.hidden_dim
         __lowerCamelCase     :      List[str]       						=						self.hidden_dim
         __lowerCamelCase     :      List[Any]       						=						self.hidden_dim
         return config

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      int       						=						self.prepare_config_and_inputs()
         __lowerCamelCase     :      Optional[int]       						=						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
         return config, inputs_dict

   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						output.encoder_hidden_states
         __lowerCamelCase     :      Any       						=						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   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : List[str]=False							):






         '''simple docstring'''



         with torch.no_grad():
               __lowerCamelCase     :      Any       						=						MaskaFormerModel(config=_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.eval()

               __lowerCamelCase     :      Dict       						=						model(pixel_values=_lowerCamelCase       ,  pixel_mask=_lowerCamelCase							)
               __lowerCamelCase     :      Optional[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   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						MaskaFormerForUniversalSegmentation(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()

         def comm_check_on_output(_lowerCamelCase  : Optional[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():
               __lowerCamelCase     :      Optional[int]       						=						model(pixel_values=_lowerCamelCase       ,  pixel_mask=_lowerCamelCase							)
               __lowerCamelCase     :      Tuple       						=						model(_lowerCamelCase							)

               comm_check_on_output(_lowerCamelCase							)

               __lowerCamelCase     :      str       						=						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 							_UpperCamelCase (						A,A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Union[str, Any]    = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   a_				:							Union[str, Any]    = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

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

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						MaskaFormerModelTester(self							)
         __lowerCamelCase     :      int       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  has_text_modality=_lowerCamelCase							)

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         self.config_tester.run_common_tests()

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Optional[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   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						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  : Dict							):






         '''simple docstring'''



         pass

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






         '''simple docstring'''



         pass

   @unittest.skip(reason="""Mask2Former is not a generative model"""							)
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         pass

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






         '''simple docstring'''



         pass

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






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						self.model_tester.prepare_config_and_inputs_for_common()

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

               __lowerCamelCase     :      List[Any]       						=						["""pixel_values"""]
               self.assertListEqual(arg_names[:1]       ,  _lowerCamelCase							)

   @slow
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						(self.model_tester.min_size,) * 2
         __lowerCamelCase     :      Optional[Any]       						=						{
             """pixel_values""": torch.randn((2, 3, *size)       ,  device=_lowerCamelCase							),
             """mask_labels""": torch.randn((2, 1_0, *size)       ,  device=_lowerCamelCase							),
             """class_labels""": torch.zeros(2       ,  1_0       ,  device=_lowerCamelCase							).long(),
         }
         __lowerCamelCase     :      Any       						=						self.model_tester.get_config()

         __lowerCamelCase     :      Tuple       						=						MaskaFormerForUniversalSegmentation(_lowerCamelCase							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						model(**_lowerCamelCase							)
         self.assertTrue(outputs.loss is not None							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      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   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



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

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

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         if not self.model_tester.is_training:
               return

         __lowerCamelCase     :      str       						=						self.all_model_classes[1]
         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						self.model_tester.prepare_config_and_inputs()

         __lowerCamelCase     :      List[Any]       						=						model_class(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.train()

         __lowerCamelCase     :      Tuple       						=						model(_lowerCamelCase       ,  mask_labels=_lowerCamelCase       ,  class_labels=_lowerCamelCase							).loss
         loss.backward()


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.all_model_classes[1]
         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs()
         __lowerCamelCase     :      Optional[int]       						=						True
         __lowerCamelCase     :      Any       						=						True

         __lowerCamelCase     :      Any       						=						model_class(_lowerCamelCase							).to(_lowerCamelCase							)
         model.train()

         __lowerCamelCase     :      Optional[Any]       						=						model(_lowerCamelCase       ,  mask_labels=_lowerCamelCase       ,  class_labels=_lowerCamelCase							)

         __lowerCamelCase     :      Union[str, Any]       						=						outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         __lowerCamelCase     :      Any       						=						outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         __lowerCamelCase     :      Optional[Any]       						=						outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         __lowerCamelCase     :      List[str]       						=						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							)

__UpperCamelCase    :						str     =							1E-4





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      return image




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

   '''simple docstring'''







   @cached_property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



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

   @cached_property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						self.default_image_processor
         __lowerCamelCase     :      Dict       						=						prepare_img()
         __lowerCamelCase     :      List[Any]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[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(_lowerCamelCase       ,  (1, 3, 3_8_4, 3_8_4)							)

         with torch.no_grad():
               __lowerCamelCase     :      str       						=						model(**_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

         __lowerCamelCase     :      Union[str, Any]       						=						torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

         __lowerCamelCase     :      Tuple       						=						torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							).eval()
         __lowerCamelCase     :      Any       						=						self.default_image_processor
         __lowerCamelCase     :      Union[str, Any]       						=						prepare_img()
         __lowerCamelCase     :      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] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
         # check size
         self.assertEqual(_lowerCamelCase       ,  (1, 3, 3_8_4, 3_8_4)							)

         with torch.no_grad():
               __lowerCamelCase     :      Dict       						=						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       						=						[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         __lowerCamelCase     :      List[str]       						=						torch.tensor(_lowerCamelCase							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)
         # class_queries_logits
         __lowerCamelCase     :      Union[str, Any]       						=						outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape       ,  (1, model.config.num_queries, model.config.num_labels + 1)							)
         __lowerCamelCase     :      List[Any]       						=						torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							).eval()
         __lowerCamelCase     :      Dict       						=						self.default_image_processor

         __lowerCamelCase     :      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"""       ,  )

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

         with torch.no_grad():
               __lowerCamelCase     :      Dict       						=						model(**_lowerCamelCase							)

         self.assertTrue(outputs.loss is not None							)



