






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     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=2		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=10		,		_UpperCAmelCase=3		,		_UpperCAmelCase=32 * 8		,		_UpperCAmelCase=32 * 8		,		_UpperCAmelCase=4		,		_UpperCAmelCase=64		,		)      ->			List[Any]:
     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  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
         _UpperCAmelCase			)

     UpperCamelCase_						=  torch.ones([self.batch_size, self.min_size, self.max_size]		,		device=_UpperCAmelCase			)

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

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




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     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  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.prepare_config_and_inputs()
     UpperCamelCase_						=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
     return config, inputs_dict




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  output.encoder_hidden_states
     UpperCamelCase_						=  output.pixel_decoder_hidden_states
     UpperCamelCase_						=  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False			)      ->			Any:
     with torch.no_grad():
        UpperCamelCase_						=  MaskaFormerModel(config=_UpperCAmelCase			)
        model.to(_UpperCAmelCase			)
        model.eval()

        UpperCamelCase_						=  model(pixel_values=_UpperCAmelCase		,		pixel_mask=_UpperCAmelCase			)
        UpperCamelCase_						=  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation(config=_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()

     def comm_check_on_output(_UpperCAmelCase			):
        # 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=_UpperCAmelCase		,		pixel_mask=_UpperCAmelCase			)
        UpperCamelCase_						=  model(_UpperCAmelCase			)

        comm_check_on_output(_UpperCAmelCase			)

        UpperCamelCase_						=  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     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  A_						       = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

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




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  MaskaFormerModelTester(self			)
     UpperCamelCase_						=  ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     self.config_tester.run_common_tests()




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase		,		**_UpperCAmelCase		,		output_hidden_states=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  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  _UpperCAmelCase  (  self			)      ->			Any:
     pass




  @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'			)
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     pass




  @unittest.skip(reason='Mask2Former is not a generative model'			)
  def  _UpperCAmelCase  (  self			)      ->			Any:
     pass




  @unittest.skip(reason='Mask2Former does not use token embeddings'			)
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     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			)      ->			int:
     pass




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




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        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]		,		_UpperCAmelCase			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
        UpperCamelCase_						=  MaskaFormerModel.from_pretrained(_UpperCAmelCase			)
        self.assertIsNotNone(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  (self.model_tester.min_size,) * 2
     UpperCamelCase_						=  {
         '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(),
     }
     UpperCamelCase_						=  self.model_tester.get_config()

     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation(_UpperCAmelCase			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  model(**_UpperCAmelCase			)
     self.assertTrue(outputs.loss is not None			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase		,		**_UpperCAmelCase		,		output_hidden_states=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

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




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.train()

     UpperCamelCase_						=  model(_UpperCAmelCase		,		mask_labels=_UpperCAmelCase		,		class_labels=_UpperCAmelCase			).loss
     loss.backward()




  def  _UpperCAmelCase  (  self			)      ->			int:
     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(_UpperCAmelCase			).to(_UpperCAmelCase			)
     model.train()

     UpperCamelCase_						=  model(_UpperCAmelCase		,		mask_labels=_UpperCAmelCase		,		class_labels=_UpperCAmelCase			)

     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=_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			)




snake_case__   :						List[Any]													=   1E-4






def      _snake_case     ():
   UpperCamelCase_						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


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






  """simple docstring"""





  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return "facebook/mask2former-swin-small-coco-instance"




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)
     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(_UpperCAmelCase		,		(1, 3, 384, 384)			)

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

     UpperCamelCase_						=  torch.tensor(
         [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)

     UpperCamelCase_						=  torch.tensor(
         [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)

     UpperCamelCase_						=  torch.tensor(
         [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			).eval()
     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)
     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(_UpperCAmelCase		,		(1, 3, 384, 384)			)

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)
     # 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_						=  [
         [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
         [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
         [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
     ]
     UpperCamelCase_						=  torch.tensor(_UpperCAmelCase			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)
     # 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(
         [
             [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
             [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
             [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
         ]			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			).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(_UpperCAmelCase			)
     UpperCamelCase_						=  [el.to(_UpperCAmelCase			) for el in inputs['mask_labels']]
     UpperCamelCase_						=  [el.to(_UpperCAmelCase			) for el in inputs['class_labels']]

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

     self.assertTrue(outputs.loss is not None			)










'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


if __name__ == "__main__":
							print(f"""{solution() = }""")




