

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class   UpperCamelCase			:

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         sample_size=3_2				,     layers_per_block=1				,     block_out_channels=[3_2, 6_4]				,     down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]				,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,     in_channels=3				,     out_channels=6				,     cross_attention_dim=3_2				,     encoder_hid_dim=3_2				,     attention_head_dim=8				,     addition_embed_type='text'				,     addition_embed_type_num_heads=2				,     cross_attention_norm='group_norm'				,     resnet_time_scale_shift='scale_shift'				,     act_fn='gelu'				,     )
     unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler(
         num_train_timesteps=1_0_0_0				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0_001				,     beta_end=0.02				,     thresholding=__A				,     dynamic_thresholding_ratio=0.95				,     sample_max_value=1.0				,     prediction_type='epsilon'				,     variance_type='learned_range'				,     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         sample_size=3_2				,     layers_per_block=[1, 2]				,     block_out_channels=[3_2, 6_4]				,     down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]				,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,     in_channels=6				,     out_channels=6				,     cross_attention_dim=3_2				,     encoder_hid_dim=3_2				,     attention_head_dim=8				,     addition_embed_type='text'				,     addition_embed_type_num_heads=2				,     cross_attention_norm='group_norm'				,     resnet_time_scale_shift='scale_shift'				,     act_fn='gelu'				,     class_embed_type='timestep'				,     mid_block_scale_factor=1.414				,     time_embedding_act_fn='gelu'				,     time_embedding_dim=3_2				,     )
     unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler(
         num_train_timesteps=1_0_0_0				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0_001				,     beta_end=0.02				,     thresholding=__A				,     dynamic_thresholding_ratio=0.95				,     sample_max_value=1.0				,     prediction_type='epsilon'				,     variance_type='learned_range'				,     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler(
         num_train_timesteps=1_0_0_0				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0_001				,     beta_end=0.02				,     )

     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**__A     )
     pipe.to(__A     )
     pipe.set_progress_bar_config(disable=__A     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(__A     )

     SCREAMING_SNAKE_CASE		    =      inputs['prompt']
     SCREAMING_SNAKE_CASE		    =      inputs['generator']
     SCREAMING_SNAKE_CASE		    =      inputs['num_inference_steps']
     SCREAMING_SNAKE_CASE		    =      inputs['output_type']

     if "image" in inputs:
      SCREAMING_SNAKE_CASE		    =      inputs['image']
     else:
      SCREAMING_SNAKE_CASE		    =      None

     if "mask_image" in inputs:
      SCREAMING_SNAKE_CASE		    =      inputs['mask_image']
     else:
      SCREAMING_SNAKE_CASE		    =      None

     if "original_image" in inputs:
      SCREAMING_SNAKE_CASE		    =      inputs['original_image']
     else:
      SCREAMING_SNAKE_CASE		    =      None

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      pipe.encode_prompt(__A     )

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE		    =      {
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
      SCREAMING_SNAKE_CASE		    =      image

     if mask_image is not None:
      SCREAMING_SNAKE_CASE		    =      mask_image

     if original_image is not None:
      SCREAMING_SNAKE_CASE		    =      original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
      setattr(__A				,     __A				,     __A     )

     SCREAMING_SNAKE_CASE		    =      pipe(**__A     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(__A     )
      SCREAMING_SNAKE_CASE		    =      self.pipeline_class.from_pretrained(__A     )
      pipe_loaded.to(__A     )
      pipe_loaded.set_progress_bar_config(disable=__A     )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     for optional_component in pipe._optional_components:
      self.assertTrue(
          getattr(__A				,     __A     ) is None				,     F"""`{optional_component}` did not stay set to None after loading."""				,     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(__A     )

     SCREAMING_SNAKE_CASE		    =      inputs['generator']
     SCREAMING_SNAKE_CASE		    =      inputs['num_inference_steps']
     SCREAMING_SNAKE_CASE		    =      inputs['output_type']

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE		    =      {
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
      SCREAMING_SNAKE_CASE		    =      image

     if mask_image is not None:
      SCREAMING_SNAKE_CASE		    =      mask_image

     if original_image is not None:
      SCREAMING_SNAKE_CASE		    =      original_image

     SCREAMING_SNAKE_CASE		    =      pipe_loaded(**__A     )[0]

     SCREAMING_SNAKE_CASE		    =      np.abs(to_np(__A     ) - to_np(__A     )     ).max()
     self.assertLess(__A				,     1E-4     )




    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**__A     )
     pipe.to(__A     )
     pipe.set_progress_bar_config(disable=__A     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(__A     )
     SCREAMING_SNAKE_CASE		    =      pipe(**__A     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(__A     )
      SCREAMING_SNAKE_CASE		    =      self.pipeline_class.from_pretrained(__A     )
      pipe_loaded.to(__A     )
      pipe_loaded.set_progress_bar_config(disable=__A     )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(__A     )
     SCREAMING_SNAKE_CASE		    =      pipe_loaded(**__A     )[0]

     SCREAMING_SNAKE_CASE		    =      np.abs(to_np(__A     ) - to_np(__A     )     ).max()
     self.assertLess(__A				,     1E-4     )





def  a   (lowerCAmelCase__ = 1_000_000   ):
   __a					=  1
   __a					=  1
   __a					=  {1: 1}

   for inputa in range(2		,				lowerCAmelCase__   ):
      __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())))



