




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					:

     '''simple docstring'''





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

         # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_										=					(image_size // patch_size) ** 2
         UpperCAmelCase_										=					num_patches + 1
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

         UpperCAmelCase_										=					self.get_config()

         return config, pixel_values, labels
     def   lowerCamelCase_       (     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   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any]    ):
         UpperCAmelCase_										=					ViTMSNModel(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case    )
         self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Optional[int] ,   __snake_case       :      int ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					self.type_sequence_label_size
         UpperCAmelCase_										=					ViTMSNForImageClassification(__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   labels=__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
         UpperCAmelCase_										=					1
         UpperCAmelCase_										=					ViTMSNForImageClassification(__snake_case    )
         model.to(__snake_case    )
         model.eval()

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




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.prepare_config_and_inputs()
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					config_and_inputs
         UpperCAmelCase_										=					{'''pixel_values''': pixel_values}
         return config, inputs_dict



@require_torch
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





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

     lowerCAmelCase    :	Optional[Any]       		=  False
     lowerCAmelCase    :	int       		=  False
     lowerCAmelCase    :	Optional[int]       		=  False
     lowerCAmelCase    :	Dict       		=  False
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					ViTMSNModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case ,   has_text_modality=__snake_case ,   hidden_size=37    )
     def   lowerCamelCase_       (     self       :      int    ):
         self.config_tester.run_common_tests()
     @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         pass
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCAmelCase_										=					model_class(__snake_case    )
             self.assertIsInstance(model.get_input_embeddings() ,   (nn.Module)    )
             UpperCAmelCase_										=					model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__snake_case ,   nn.Linear    )    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCAmelCase_										=					['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case    )




     @slow
     def   lowerCamelCase_       (     self       :      Any    ):
         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_										=					ViTMSNModel.from_pretrained(__snake_case    )
             self.assertIsNotNone(__snake_case    )




def  SCREAMING_SNAKE_CASE     (	)  ->							Union[str, Any]:
    UpperCAmelCase_										=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
    return image



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

     '''simple docstring'''





     @cached_property
     def   lowerCamelCase_       (     self       :      Any    ):
         return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''    ) if is_vision_available() else None




     @slow
     def   lowerCamelCase_       (     self       :      int    ):
         torch.manual_seed(2    )
         UpperCAmelCase_										=					ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''    ).to(__snake_case    )

         UpperCAmelCase_										=					self.default_image_processor
         UpperCAmelCase_										=					prepare_img()
         UpperCAmelCase_										=					image_processor(images=__snake_case ,   return_tensors='''pt'''    ).to(__snake_case    )

         # forward pass
         with torch.no_grad():
             UpperCAmelCase_										=					model(**__snake_case    )

         # verify the logits
         UpperCAmelCase_										=					torch.Size((1, 10_00)    )
         self.assertEqual(outputs.logits.shape ,   __snake_case    )

         UpperCAmelCase_										=					torch.tensor([-0.0_803, -0.4_454, -0.2_375]    ).to(__snake_case    )

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











from __future__ import annotations

from fractions import Fraction




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							bool:
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							list[str]:
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					11
    UpperCAmelCase_										=					int('''1''' + '''0''' * digit_len				)
    for num in range(__UpperCamelCase	,					__UpperCamelCase				):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__UpperCamelCase	,					__UpperCamelCase				):
                    solutions.append(f'{num}/{den}'				)
            den += 1
        num += 1
        UpperCAmelCase_										=					10
    return solutions




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int = 2				)  ->							int:
    UpperCAmelCase_										=					1.0
    for fraction in fraction_list(__UpperCamelCase				):
        UpperCAmelCase_										=					Fraction(__UpperCamelCase				)
        result *= frac.denominator / frac.numerator
    return int(__UpperCamelCase				)


if __name__ == "__main__":
 print(solution())





