


'''simple docstring'''







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
 from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image


class  lowerCAmelCase_						:





  """simple docstring"""



  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=1_3				,							SCREAMING_SNAKE_CASE__     :							List[Any]=6_4				,							SCREAMING_SNAKE_CASE__     :							Tuple=2				,							SCREAMING_SNAKE_CASE__     :							List[str]=3				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE__     :							List[str]=True				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=3_2				,							SCREAMING_SNAKE_CASE__     :							Dict=5				,							SCREAMING_SNAKE_CASE__     :							str=4				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=3_7				,							SCREAMING_SNAKE_CASE__     :							List[Any]="gelu"				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.1				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1_0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0_2				,							SCREAMING_SNAKE_CASE__     :							Any=[1, 1_6, 4, 4]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   batch_size
       __a       =   image_size
       __a       =   patch_size
       __a       =   num_channels
       __a       =   is_training
       __a       =   use_labels
       __a       =   hidden_size
       __a       =   num_hidden_layers
       __a       =   num_attention_heads
       __a       =   intermediate_size
       __a       =   hidden_act
       __a       =   hidden_dropout_prob
       __a       =   attention_probs_dropout_prob
       __a       =   type_sequence_label_size
       __a       =   initializer_range
       __a       =   scope
       __a       =   backbone_featmap_shape

       # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       # the number of patches is based on the feature map of the backbone, which by default uses an output stride
       # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
       __a       =   (self.image_size // 3_2) ** 2
       __a       =   num_patches + 1

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       __a       =   self.get_config()

       return config, pixel_values, labels

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
           """hidden_sizes""": [4, 8, 1_6, 3_2],
           """num_groups""": 2,
       }

       return ViTHybridConfig(
           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				,							is_decoder=SCREAMING_SNAKE_CASE__				,							initializer_range=self.initializer_range				,							backbone_featmap_shape=self.backbone_featmap_shape				,							backbone_config=SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       __a       =   ViTHybridModel(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)						)

  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   self.type_sequence_label_size
       __a       =   ViTHybridForImageClassification(SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size)						)


  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       __a ,						__a ,						__a       =   config_and_inputs
       __a       =   {"""pixel_values""": pixel_values}
       return config, inputs_dict


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





  """simple docstring"""



  a_       :List[Any]												=(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
  a_       :int												=(
      {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
      if is_torch_available()
      else {}
  )
  a_       :List[str]												=False
  a_       :List[Any]												=False
  a_       :Optional[int]												=False

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   ViTHybridModelTester(self						)
       __a       =   ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__				,							has_text_modality=SCREAMING_SNAKE_CASE__				,							hidden_size=3_7						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       self.config_tester.run_common_tests()

  @unittest.skip(reason="""ViT does not use inputs_embeds"""						)
  def __a						(					self     :							int						):





       '''simple docstring'''

       pass

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs_for_common()

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

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs_for_common()

       __a       =   _config_zero_init(SCREAMING_SNAKE_CASE__						)
       for model_class in self.all_model_classes:
            __a       =   model_class(config=SCREAMING_SNAKE_CASE__						)
            # Skip the check for the backbone
            for name, module in model.named_modules():
                 if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                      __a       =   [f'''{name}.{key}''' for key in module.state_dict().keys()]
                      break

            for name, param in model.named_parameters():
                 if param.requires_grad:
                      if name in backbone_params:
                           continue
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()				,							[0.0, 1.0]				,							msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,							)


  @slow
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a       =   ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)




def        __lowercase						(      )				->					Dict:
     """simple docstring"""




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



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





  """simple docstring"""



  @cached_property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return (
           ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
           if is_vision_available()
           else None
       )

  @slow
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
           SCREAMING_SNAKE_CASE__						)

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

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

       # verify the logits
       __a       =   torch.Size((1, 1_0_0_0)						)
       self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE__						)

       __a       =   torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]						).to(SCREAMING_SNAKE_CASE__						)

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


  @slow
  @require_accelerate
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""						)
       __a       =   ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""				,							device_map="""auto"""						)

       __a       =   prepare_img()

       __a       =   image_processor(images=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
       __a       =   model(**SCREAMING_SNAKE_CASE__						)
       __a       =   outputs.logits
       # model predicts one of the 1000 ImageNet classes
       __a       =   logits.argmax(-1						).item()

       self.assertTrue(model.config.idalabel[predicted_class_idx]				,							"""tabby, tabby cat"""						)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE = 100_0000		)				->					int:
     """simple docstring"""




     __a       =   1
     __a       =   1
     __a       =   {1: 1}

     for inputa in range(2				,  __SCREAMING_SNAKE_CASE		):
          __a       =   0
          __a       =   inputa

          while True:
               if number in counters:
                    counter += counters[number]
                    break
               if number % 2 == 0:
                    number //= 2
                    counter += 1
               else:
                    __a       =   (3 * number) + 1
                    counter += 1

          if inputa not in counters:
               __a       =   counter

          if counter > pre_counter:
               __a       =   inputa
               __a       =   counter
     return largest_number


if __name__ == "__main__":
 print(solution(int(input().strip())))






