




"""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     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Any=2   ,							_UpperCAmelCase	:	Any=True   ,							_UpperCAmelCase	:	str=False   ,							_UpperCAmelCase	:	Union[str, Any]=10   ,							_UpperCAmelCase	:	Optional[Any]=3   ,							_UpperCAmelCase	:	Dict=32 * 8   ,							_UpperCAmelCase	:	Optional[int]=32 * 8   ,							_UpperCAmelCase	:	List[str]=4   ,							_UpperCAmelCase	:	Any=64   ,							):
       _A        =       parent
       _A        =       batch_size
       _A        =       is_training
       _A        =       use_auxiliary_loss
       _A        =       num_queries
       _A        =       num_channels
       _A        =       min_size
       _A        =       max_size
       _A        =       num_labels
       _A        =       hidden_dim
       _A        =       hidden_dim

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
           _UpperCAmelCase		)

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

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

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

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       MaskaFormerConfig(
           hidden_size=self.hidden_dim   ,							)
       _A        =       self.num_queries
       _A        =       self.num_labels

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

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

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A       ,   _A       ,   _A       ,   _A       ,   _A        =       self.prepare_config_and_inputs()
       _A        =       {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       output.encoder_hidden_states
       _A        =       output.pixel_decoder_hidden_states
       _A        =       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	:	Optional[Any]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Tuple=False		):
       with torch.no_grad():
             _A        =       MaskaFormerModel(config=_UpperCAmelCase		)
             model.to(_UpperCAmelCase		)
             model.eval()

             _A        =       model(pixel_values=_UpperCAmelCase   ,							pixel_mask=_UpperCAmelCase		)
             _A        =       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	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	str		):
       _A        =       MaskaFormerForUniversalSegmentation(config=_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()

       def comm_check_on_output(_UpperCAmelCase	:	List[Any]		):
             # 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        =       model(pixel_values=_UpperCAmelCase   ,							pixel_mask=_UpperCAmelCase		)
             _A        =       model(_UpperCAmelCase		)

             comm_check_on_output(_UpperCAmelCase		)

             _A        =       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     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[str]				  =     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 UpperCAmelCase   :					List[Any]				  =     {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 UpperCAmelCase   :					str				  =     False
 UpperCAmelCase   :					List[str]				  =     False
 UpperCAmelCase   :					List[str]				  =     False
 UpperCAmelCase   :					Optional[Any]				  =     False

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       MaskaFormerModelTester(self		)
       _A        =       ConfigTester(self   ,							config_class=_UpperCAmelCase   ,							has_text_modality=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       self.config_tester.run_common_tests()

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A       ,   _A        =       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	:	Dict		):
       _A        =       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	:	List[str]		):
       pass

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

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

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

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

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

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

             _A        =       ['pixel_values']
             self.assertListEqual(arg_names[:1]   ,							_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       (self.model_tester.min_size,) * 2
       _A        =       {
           '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        =       self.model_tester.get_config()

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A       ,   _A        =       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	:	List[Any]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

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

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       if not self.model_tester.is_training:
             return

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

       _A        =       model_class(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.train()

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



 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self.all_model_classes[1]
       _A       ,   _A       ,   _A       ,   _A       ,   _A        =       self.model_tester.prepare_config_and_inputs()
       _A        =       True
       _A        =       True

       _A        =       model_class(_UpperCAmelCase		).to(_UpperCAmelCase		)
       model.train()

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

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

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

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

       _A        =       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		)



a         =      1e-4






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


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







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



 '''simple docstring'''

 @cached_property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return "facebook/mask2former-swin-small-coco-instance"

 @cached_property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_UpperCAmelCase		)
       _A        =       self.default_image_processor
       _A        =       prepare_img()
       _A        =       image_processor(_UpperCAmelCase   ,							return_tensors='pt'		).to(_UpperCAmelCase		)
       _A        =       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        =       model(**_UpperCAmelCase		)

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

       _A        =       image_processor(
           [np.zeros((3, 800, 1_333)		), np.zeros((3, 800, 1_333)		)]   ,							segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]   ,							return_tensors='pt'   ,							)

       _A        =       inputs['pixel_values'].to(_UpperCAmelCase		)
       _A        =       [el.to(_UpperCAmelCase		) for el in inputs['mask_labels']]
       _A        =       [el.to(_UpperCAmelCase		) for el in inputs['class_labels']]

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

       self.assertTrue(outputs.loss is not None		)







"""simple docstring"""

def 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

