




from __future__ import annotations

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int | float | str      ,				lowercase      :			int | float | str	):


      '''simple docstring'''



      if nth_term == "":
            return [""]
      lowerCamelCase_							=       int(__lowerCamelCase	)
      lowerCamelCase_							=       int(__lowerCamelCase	)
      lowerCamelCase_							=       []
      for temp in range(int(__lowerCamelCase	)	):
            series.append(f"""1 / {pow(temp + 1      ,				int(__lowerCamelCase	)	)}""" if series else '1'	)
      return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase							:						Optional[Any]					       =  int(input("Enter the last number (nth term) of the P-Series"))
 lowerCamelCase							:						List[str]					       =  int(input("Enter the power for  P-Series"))
 print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
 print(p_series(nth_term, power))







'''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    __magic_name__				:






       def __init__(     self    ,				snake_case    ,				snake_case=2    ,				snake_case=True    ,				snake_case=False    ,				snake_case=1_0    ,				snake_case=3    ,				snake_case=3_2 * 8    ,				snake_case=3_2 * 8    ,				snake_case=4    ,				snake_case=6_4    ,				)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =parent
          _UpperCAmelCase  :  Optional[int]				       =batch_size
          _UpperCAmelCase  :  List[str]				       =is_training
          _UpperCAmelCase  :  Union[str, Any]				       =use_auxiliary_loss
          _UpperCAmelCase  :  Dict				       =num_queries
          _UpperCAmelCase  :  Tuple				       =num_channels
          _UpperCAmelCase  :  Optional[Any]				       =min_size
          _UpperCAmelCase  :  Any				       =max_size
          _UpperCAmelCase  :  Optional[int]				       =num_labels
          _UpperCAmelCase  :  Optional[int]				       =hidden_dim
          _UpperCAmelCase  :  Dict				       =hidden_dim






       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
              snake_case)

          _UpperCAmelCase  :  List[str]				       =torch.ones([self.batch_size, self.min_size, self.max_size]    ,				device=snake_case)

          _UpperCAmelCase  :  int				       =(
              torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,				device=snake_case) > 0.5
          ).float()
          _UpperCAmelCase  :  Union[str, Any]				       =(torch.rand((self.batch_size, self.num_labels)    ,				device=snake_case) > 0.5).long()

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






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




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =MaskaFormerConfig(
              hidden_size=self.hidden_dim    ,				)
          _UpperCAmelCase  :  int				       =self.num_queries
          _UpperCAmelCase  :  int				       =self.num_labels

          _UpperCAmelCase  :  List[Any]				       =[1, 1, 1, 1]
          _UpperCAmelCase  :  int				       =self.num_channels

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






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =self.prepare_config_and_inputs()
          _UpperCAmelCase  :  Union[str, Any]				       ={'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
          return config, inputs_dict






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =output.encoder_hidden_states
          _UpperCAmelCase  :  Dict				       =output.pixel_decoder_hidden_states
          _UpperCAmelCase  :  List[Any]				       =output.transformer_decoder_hidden_states

          self.parent.assertTrue(len(snake_case)    ,				len(config.backbone_config.depths))
          self.parent.assertTrue(len(snake_case)    ,				len(config.backbone_config.depths))
          self.parent.assertTrue(len(snake_case)    ,				config.decoder_layers)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case=False)		->    Any:




          '''simple docstring'''
          with torch.no_grad():
             _UpperCAmelCase  :  Tuple				       =MaskaFormerModel(config=snake_case)
             model.to(snake_case)
             model.eval()

             _UpperCAmelCase  :  List[Any]				       =model(pixel_values=snake_case    ,				pixel_mask=snake_case)
             _UpperCAmelCase  :  Union[str, Any]				       =model(snake_case    ,				output_hidden_states=snake_case)

          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(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case)		->    Tuple:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =MaskaFormerForUniversalSegmentation(config=snake_case)
          model.to(snake_case)
          model.eval()

          def comm_check_on_output(snake_case):
             # 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  :  Optional[Any]				       =model(pixel_values=snake_case    ,				pixel_mask=snake_case)
             _UpperCAmelCase  :  Optional[Any]				       =model(snake_case)

             comm_check_on_output(snake_case)

             _UpperCAmelCase  :  str				       =model(
                 pixel_values=snake_case    ,				pixel_mask=snake_case    ,				mask_labels=snake_case    ,				class_labels=snake_case)

          comm_check_on_output(snake_case)

          self.parent.assertTrue(result.loss is not None)
          self.parent.assertEqual(result.loss.shape    ,				torch.Size([1]))






@require_torch
class    __magic_name__				(						lowerCAmelCase   ,lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       UpperCAmelCase		 ={"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

       UpperCAmelCase		 =False
       UpperCAmelCase		 =False
       UpperCAmelCase		 =False
       UpperCAmelCase		 =False






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =MaskaFormerModelTester(self)
          _UpperCAmelCase  :  Dict				       =ConfigTester(self    ,				config_class=snake_case    ,				has_text_modality=snake_case)






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




          '''simple docstring'''
          self.config_tester.run_common_tests()






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




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  int				       =self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskaformer_model(snake_case    ,				**snake_case    ,				output_hidden_states=snake_case)






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




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case)






       @unittest.skip(reason='Mask2Former does not use inputs_embeds')
       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          pass






       @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method')
       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          pass






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




          '''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)		->    List[str]:




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =self.model_tester.prepare_config_and_inputs_for_common()

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

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






       @slow
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             _UpperCAmelCase  :  Dict				       =MaskaFormerModel.from_pretrained(snake_case)
             self.assertIsNotNone(snake_case)






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




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =(self.model_tester.min_size,) * 2
          _UpperCAmelCase  :  Optional[Any]				       ={
              'pixel_values': torch.randn((2, 3, *size)    ,				device=snake_case),
              'mask_labels': torch.randn((2, 1_0, *size)    ,				device=snake_case),
              'class_labels': torch.zeros(2    ,				1_0    ,				device=snake_case).long(),
          }
          _UpperCAmelCase  :  List[str]				       =self.model_tester.get_config()

          _UpperCAmelCase  :  Tuple				       =MaskaFormerForUniversalSegmentation(snake_case).to(snake_case)
          _UpperCAmelCase  :  Any				       =model(**snake_case)
          self.assertTrue(outputs.loss is not None)






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




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskaformer_model(snake_case    ,				**snake_case    ,				output_hidden_states=snake_case)






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Any				       =self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _UpperCAmelCase  :  Union[str, Any]				       =model_class(snake_case).to(snake_case)
             _UpperCAmelCase  :  Dict				       =model(**snake_case    ,				output_attentions=snake_case)
             self.assertTrue(outputs.attentions is not None)






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          if not self.model_tester.is_training:
             return

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

          _UpperCAmelCase  :  List[str]				       =model_class(snake_case)
          model.to(snake_case)
          model.train()

          _UpperCAmelCase  :  Tuple				       =model(snake_case    ,				mask_labels=snake_case    ,				class_labels=snake_case).loss
          loss.backward()






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =self.all_model_classes[1]
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  str				       =self.model_tester.prepare_config_and_inputs()
          _UpperCAmelCase  :  Tuple				       =True
          _UpperCAmelCase  :  str				       =True

          _UpperCAmelCase  :  Union[str, Any]				       =model_class(snake_case).to(snake_case)
          model.train()

          _UpperCAmelCase  :  Union[str, Any]				       =model(snake_case    ,				mask_labels=snake_case    ,				class_labels=snake_case)

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

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

          _UpperCAmelCase  :  int				       =outputs.transformer_decoder_hidden_states[0]
          transformer_decoder_hidden_states.retain_grad()

          _UpperCAmelCase  :  List[str]				       =outputs.attentions[0]
          attentions.retain_grad()

          outputs.loss.backward(retain_graph=snake_case)

          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 lowerCamelCase__     (		):







   '''simple docstring'''




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






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






       @cached_property
       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          return "facebook/mask2former-swin-small-coco-instance"






       @cached_property
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




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






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =MaskaFormerModel.from_pretrained(self.model_checkpoints).to(snake_case)
          _UpperCAmelCase  :  List[str]				       =self.default_image_processor
          _UpperCAmelCase  :  int				       =prepare_img()
          _UpperCAmelCase  :  Any				       =image_processor(snake_case    ,				return_tensors='pt').to(snake_case)
          _UpperCAmelCase  :  Dict				       =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(snake_case    ,				(1, 3, 3_8_4, 3_8_4))

          with torch.no_grad():
             _UpperCAmelCase  :  int				       =model(**snake_case)

          _UpperCAmelCase  :  List[Any]				       =torch.tensor(
              [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]).to(snake_case)
          self.assertTrue(
              torch.allclose(
                  outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,				snake_case    ,				atol=snake_case))

          _UpperCAmelCase  :  Dict				       =torch.tensor(
              [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]).to(snake_case)
          self.assertTrue(
              torch.allclose(
                  outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,				snake_case    ,				atol=snake_case))

          _UpperCAmelCase  :  Optional[int]				       =torch.tensor(
              [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]).to(snake_case)
          self.assertTrue(
              torch.allclose(
                  outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,				snake_case    ,				atol=snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(snake_case).eval()
          _UpperCAmelCase  :  str				       =self.default_image_processor
          _UpperCAmelCase  :  Any				       =prepare_img()
          _UpperCAmelCase  :  Dict				       =image_processor(snake_case    ,				return_tensors='pt').to(snake_case)
          _UpperCAmelCase  :  List[Any]				       =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(snake_case    ,				(1, 3, 3_8_4, 3_8_4))

          with torch.no_grad():
             _UpperCAmelCase  :  Dict				       =model(**snake_case)
          # masks_queries_logits
          _UpperCAmelCase  :  Union[str, Any]				       =outputs.masks_queries_logits
          self.assertEqual(
              masks_queries_logits.shape    ,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
          _UpperCAmelCase  :  str				       =[
              [-8.78_39, -9.00_56, -8.81_21],
              [-7.41_04, -7.03_13, -6.54_01],
              [-6.61_05, -6.34_27, -6.46_75],
          ]
          _UpperCAmelCase  :  Union[str, Any]				       =torch.tensor(snake_case).to(snake_case)
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,				snake_case    ,				atol=snake_case))
          # class_queries_logits
          _UpperCAmelCase  :  Any				       =outputs.class_queries_logits
          self.assertEqual(class_queries_logits.shape    ,				(1, model.config.num_queries, model.config.num_labels + 1))
          _UpperCAmelCase  :  Optional[Any]				       =torch.tensor(
              [
                  [1.83_24, -8.08_35, -4.19_22],
                  [0.84_50, -9.00_50, -3.60_53],
                  [0.30_45, -7.72_93, -3.02_75],
              ]).to(snake_case)
          self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,				snake_case    ,				atol=snake_case))






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(snake_case).eval()
          _UpperCAmelCase  :  Optional[Any]				       =self.default_image_processor

          _UpperCAmelCase  :  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'    ,				)

          _UpperCAmelCase  :  int				       =inputs['pixel_values'].to(snake_case)
          _UpperCAmelCase  :  Union[str, Any]				       =[el.to(snake_case) for el in inputs['mask_labels']]
          _UpperCAmelCase  :  Tuple				       =[el.to(snake_case) for el in inputs['class_labels']]

          with torch.no_grad():
             _UpperCAmelCase  :  List[str]				       =model(**snake_case)

          self.assertTrue(outputs.loss is not None)

