

"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTMSNForImageClassification, ViTMSNModel
     from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13    ,						SCREAMING_SNAKE_CASE__=30    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=37    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					List[str]:
         lowerCamelCase__						=			parent
         lowerCamelCase__						=			batch_size
         lowerCamelCase__						=			image_size
         lowerCamelCase__						=			patch_size
         lowerCamelCase__						=			num_channels
         lowerCamelCase__						=			is_training
         lowerCamelCase__						=			use_labels
         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			hidden_dropout_prob
         lowerCamelCase__						=			attention_probs_dropout_prob
         lowerCamelCase__						=			type_sequence_label_size
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			scope

         # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         lowerCamelCase__						=			(image_size // patch_size) ** 2
         lowerCamelCase__						=			num_patches + 1





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         lowerCamelCase__						=			None
         if self.use_labels:
            lowerCamelCase__						=			ids_tensor([self.batch_size]    ,						self.type_sequence_label_size		)

         lowerCamelCase__						=			self.get_config()

         return config, pixel_values, labels





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return ViTMSNConfig(
             image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						initializer_range=self.initializer_range    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			ViTMSNModel(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			self.type_sequence_label_size
         lowerCamelCase__						=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
         print("Labels: {labels}"		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)		)

         # test greyscale images
         lowerCamelCase__						=			1
         lowerCamelCase__						=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

         lowerCamelCase__						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.prepare_config_and_inputs()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			config_and_inputs
         lowerCamelCase__						=			{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
      __a								=	(
          {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
          if is_torch_available()
          else {}
      )

      __a								=	False
      __a								=	False
      __a								=	False
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			ViTMSNModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						has_text_modality=SCREAMING_SNAKE_CASE__    ,						hidden_size=37		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         pass





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)		)
            lowerCamelCase__						=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__    ,						nn.Linear		)		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

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

            lowerCamelCase__						=			["pixel_values"]
            self.assertListEqual(arg_names[:1]    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)





      @slow
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCamelCase__						=			ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)






def 	UpperCAmelCase__   (				)      ->      int:




   """simple docstring"""
   lowerCamelCase__						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image


@require_torch
@require_vision
class 		_A       (			unittest.TestCase					):





      @cached_property
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         torch.manual_seed(2		)
         lowerCamelCase__						=			ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			self.default_image_processor
         lowerCamelCase__						=			prepare_img()
         lowerCamelCase__						=			image_processor(images=SCREAMING_SNAKE_CASE__    ,						return_tensors="pt"		).to(SCREAMING_SNAKE_CASE__		)

         # forward pass
         with torch.no_grad():
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__		)

         # verify the logits
         lowerCamelCase__						=			torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			torch.tensor([-0.08_03, -0.44_54, -0.23_75]		).to(SCREAMING_SNAKE_CASE__		)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-4		)		)







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if num <= 0:
      raise ValueError("Input must be a positive integer"   )

   lowerCamelCase__						=			[True] * (num + 1)

   lowerCamelCase__						=			2
   while p * p <= num:
      if primes[p]:
         for i in range(p * p	,    num + 1	,    A__   ):
            lowerCamelCase__						=			False
      p += 1

   return [prime for prime in range(2	,    num + 1   ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a positive integer: ''').strip())
     print(prime_sieve_eratosthenes(user_num))




