




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 		UpperCamelCase__	:

    '''simple docstring'''



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

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







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      str			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

           lowerCamelCase   :      List[str]			 =					None
           if self.use_labels:
                  lowerCamelCase   :      Any			 =					ids_tensor([self.batch_size]     ,  self.type_sequence_label_size     )

           lowerCamelCase   :      List[str]			 =					self.get_config()

           return config, pixel_values, labels







    def     _lowercase     (    self     )     ->      Dict:
           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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					ViTMSNModel(config=_A     )
           model.to(_A     )
           model.eval()
           lowerCamelCase   :      List[Any]			 =					model(_A     )
           self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      str			 =					self.type_sequence_label_size
           lowerCamelCase   :      Optional[Any]			 =					ViTMSNForImageClassification(_A     )
           model.to(_A     )
           model.eval()
           lowerCamelCase   :      Dict			 =					model(_A     ,  labels=_A     )
           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   :      str			 =					1
           lowerCamelCase   :      Any			 =					ViTMSNForImageClassification(_A     )
           model.to(_A     )
           model.eval()

           lowerCamelCase   :      Any			 =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
           lowerCamelCase   :      int			 =					model(_A     )
           self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)     )







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					self.prepare_config_and_inputs()
           lowerCamelCase   :      str			 =					config_and_inputs
           lowerCamelCase   :      List[str]			 =					{'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 		UpperCamelCase__	(snake_case__    ,				snake_case__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    lowerCamelCase_      :	str	     =      (
        {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCamelCase_      :	Union[str, Any]	     =      False
    lowerCamelCase_      :	Tuple	     =      False
    lowerCamelCase_      :	str	     =      False
    lowerCamelCase_      :	List[str]	     =      False







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Dict			 =					ViTMSNModelTester(self     )
           lowerCamelCase   :      Tuple			 =					ConfigTester(self     ,  config_class=_A     ,  has_text_modality=_A     ,  hidden_size=37     )







    def     _lowercase     (    self     )     ->      Tuple:
           self.config_tester.run_common_tests()







    @unittest.skip(reason="ViTMSN does not use inputs_embeds"     )
    def     _lowercase     (    self     )     ->      List[str]:
           pass







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Tuple			 =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowerCamelCase   :      int			 =					model_class(_A     )
                  self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)     )
                  lowerCamelCase   :      Optional[int]			 =					model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(_A     ,  nn.Linear     )     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      int			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCamelCase   :      Union[str, Any]			 =					['pixel_values']
                  self.assertListEqual(arg_names[:1]     ,  _A     )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_A     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Optional[Any]			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_A     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCamelCase   :      str			 =					ViTMSNModel.from_pretrained(_A     )
                  self.assertIsNotNone(_A     )




def      A      (      )	-> Any:
       lowerCamelCase   :      str			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image




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

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      Tuple:
           return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"     ) if is_vision_available() else None







    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           torch.manual_seed(2     )
           lowerCamelCase   :      Union[str, Any]			 =					ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"     ).to(_A     )

           lowerCamelCase   :      Dict			 =					self.default_image_processor
           lowerCamelCase   :      Tuple			 =					prepare_img()
           lowerCamelCase   :      List[Any]			 =					image_processor(images=_A     ,  return_tensors="pt"     ).to(_A     )

           # forward pass
           with torch.no_grad():
                  lowerCamelCase   :      Union[str, Any]			 =					model(**_A     )

           # verify the logits
           lowerCamelCase   :      int			 =					torch.Size((1, 1000)     )
           self.assertEqual(outputs.logits.shape     ,  _A     )

           lowerCamelCase   :      str			 =					torch.tensor([-0.0803, -0.4454, -0.2375]     ).to(_A     )

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













import math







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[str]       =				[]
					__A	:						Any       =				2
					__A	:						Union[str, Any]       =				int(math.sqrt(a  )  )  # Size of every segment
					__A	:						Any       =				[True] * (end + 1)
					__A	:						List[Any]       =				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(a  )
															for i in range(start * start					,						end + 1					,						a  ):
																				__A	:						Optional[int]       =				False
										start += 1
					prime += in_prime

					__A	:						Any       =				end + 1
					__A	:						Any       =				min(2 * end					,						a  )

					while low <= n:
										__A	:						List[Any]       =				[True] * (high - low + 1)
										for each in in_prime:
															__A	:						List[str]       =				math.floor(low / each  ) * each
															if t < low:
																				t += each

															for j in range(a					,						high + 1					,						a  ):
																				__A	:						Optional[int]       =				False

										for j in range(len(a  )  ):
															if temp[j] is True:
																				prime.append(j + low  )

										__A	:						Optional[int]       =				high + 1
										__A	:						Tuple       =				min(high + end					,						a  )

					return prime




print(sieve(10**6))




