





'''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						:


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


   def 					snake_case				( self					):
       A       :       List[str]							=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
           lowerCamelCase_					)

       A       :       Any							=						torch.ones([self.batch_size, self.min_size, self.max_size]    ,   device=lowerCamelCase_					)

       A       :       Optional[int]							=						(
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,   device=lowerCamelCase_					) > 0.5
       ).float()
       A       :       int							=						(torch.rand((self.batch_size, self.num_labels)    ,   device=lowerCamelCase_					) > 0.5).long()

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


   def 					snake_case				( self					):
       A       :       str							=						MaskaFormerConfig(
           hidden_size=self.hidden_dim    ,   )
       A       :       List[str]							=						self.num_queries
       A       :       int							=						self.num_labels

       A       :       Union[str, Any]							=						[1, 1, 1, 1]
       A       :       List[Any]							=						self.num_channels

       A       :       Dict							=						64
       A       :       int							=						128
       A       :       Dict							=						self.hidden_dim
       A       :       Optional[int]							=						self.hidden_dim
       A       :       str							=						self.hidden_dim
       return config


   def 					snake_case				( self					):
       A,	A,	A,	A,	A       :       List[str]							=						self.prepare_config_and_inputs()
       A       :       Any							=						{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
       return config, inputs_dict


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       int							=						output.encoder_hidden_states
       A       :       Union[str, Any]							=						output.pixel_decoder_hidden_states
       A       :       Union[str, 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    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=False					):
       with torch.no_grad():
           A       :       Optional[Any]							=						MaskaFormerModel(config=lowerCamelCase_					)
           model.to(lowerCamelCase_					)
           model.eval()

           A       :       Optional[Any]							=						model(pixel_values=lowerCamelCase_    ,   pixel_mask=lowerCamelCase_					)
           A       :       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    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Tuple							=						MaskaFormerForUniversalSegmentation(config=lowerCamelCase_					)
       model.to(lowerCamelCase_					)
       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():
           A       :       str							=						model(pixel_values=lowerCamelCase_    ,   pixel_mask=lowerCamelCase_					)
           A       :       Optional[Any]							=						model(lowerCamelCase_					)

           comm_check_on_output(lowerCamelCase_					)

           A       :       Dict							=						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   _lowercase						(							a__		,					a__		,					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 					snake_case				( self					):
       A       :       str							=						MaskaFormerModelTester(self					)
       A       :       List[str]							=						ConfigTester(self    ,   config_class=lowerCamelCase_    ,   has_text_modality=lowerCamelCase_					)


   def 					snake_case				( self					):
       self.config_tester.run_common_tests()


   def 					snake_case				( self					):
       A,	A       :       Optional[int]							=						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					):
       A       :       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					):
       pass


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


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


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


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


   def 					snake_case				( self					):
       A,	A       :       Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()

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

           A       :       List[Any]							=						['''pixel_values''']
           self.assertListEqual(arg_names[:1]    ,   lowerCamelCase_					)


   @slow
   def 					snake_case				( self					):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
           A       :       Optional[Any]							=						MaskaFormerModel.from_pretrained(lowerCamelCase_					)
           self.assertIsNotNone(lowerCamelCase_					)


   def 					snake_case				( self					):
       A       :       Optional[int]							=						(self.model_tester.min_size,) * 2
       A       :       Tuple							=						{
           '''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(),
       }
       A       :       str							=						self.model_tester.get_config()

       A       :       Tuple							=						MaskaFormerForUniversalSegmentation(lowerCamelCase_					).to(lowerCamelCase_					)
       A       :       int							=						model(**lowerCamelCase_					)
       self.assertTrue(outputs.loss is not None					)


   def 					snake_case				( self					):
       A,	A       :       str							=						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					):
       A,	A       :       Any							=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           A       :       Tuple							=						model_class(lowerCamelCase_					).to(lowerCamelCase_					)
           A       :       Any							=						model(**lowerCamelCase_    ,   output_attentions=lowerCamelCase_					)
           self.assertTrue(outputs.attentions is not None					)


   def 					snake_case				( self					):
       if not self.model_tester.is_training:
           return

       A       :       Union[str, Any]							=						self.all_model_classes[1]
       A,	A,	A,	A,	A       :       Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()

       A       :       Optional[int]							=						model_class(lowerCamelCase_					)
       model.to(lowerCamelCase_					)
       model.train()

       A       :       Union[str, Any]							=						model(lowerCamelCase_    ,   mask_labels=lowerCamelCase_    ,   class_labels=lowerCamelCase_					).loss
       loss.backward()


   def 					snake_case				( self					):
       A       :       Tuple							=						self.all_model_classes[1]
       A,	A,	A,	A,	A       :       Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
       A       :       List[Any]							=						True
       A       :       List[Any]							=						True

       A       :       List[str]							=						model_class(lowerCamelCase_					).to(lowerCamelCase_					)
       model.train()

       A       :       str							=						model(lowerCamelCase_    ,   mask_labels=lowerCamelCase_    ,   class_labels=lowerCamelCase_					)

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

       A       :       Tuple							=						outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

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

       A       :       Optional[Any]							=						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					)







snake_case_      =	1E-4
def 							_lowerCamelCase(  )		->  Dict:
    A       :       Optional[int]							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image





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


   @cached_property
   def 					snake_case				( self					):
       return "facebook/mask2former-swin-small-coco-instance"


   @cached_property
   def 					snake_case				( self					):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints					) if is_vision_available() else None


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(lowerCamelCase_					)
       A       :       str							=						self.default_image_processor
       A       :       Optional[int]							=						prepare_img()
       A       :       Union[str, Any]							=						image_processor(lowerCamelCase_    ,   return_tensors='''pt'''					).to(lowerCamelCase_					)
       A       :       List[Any]							=						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():
           A       :       Tuple							=						model(**lowerCamelCase_					)

       A       :       Any							=						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_					)					)

       A       :       List[str]							=						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_					)					)

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

       A       :       str							=						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       :       Dict							=						inputs['''pixel_values'''].to(lowerCamelCase_					)
       A       :       str							=						[el.to(lowerCamelCase_					) for el in inputs['''mask_labels''']]
       A       :       int							=						[el.to(lowerCamelCase_					) for el in inputs['''class_labels''']]

       with torch.no_grad():
           A       :       int							=						model(**lowerCamelCase_					)

       self.assertTrue(outputs.loss is not None					)







'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[int]:

    if num <= 0:
        A       :       str							=						F'''{num}: Invalid input, please enter a positive integer.'''
        raise ValueError(UpperCamelCase__		)

    A       :       int							=						[True] * (num + 1)
    A       :       Dict							=						[]
    A       :       str							=						2
    A       :       Any							=						int(math.sqrt(UpperCamelCase__		)		)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(UpperCamelCase__		)

            # Set multiples of start be False
            for i in range(start * start    ,       num + 1    ,       UpperCamelCase__		):
                if sieve[i] is True:
                    A       :       Union[str, Any]							=						False

        start += 1

    for j in range(end + 1    ,       num + 1		):
        if sieve[j] is True:
            prime.append(UpperCamelCase__		)

    return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
