

import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
 from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor



class 						a_			:



      """simple docstring"""







      def __init__( self	:				List[str]     ,snake_case	:				int     ,snake_case	:				List[str]=13     ,snake_case	:				Tuple=32     ,snake_case	:				Any=1     ,snake_case	:				str=3     ,snake_case	:				List[str]=16     ,snake_case	:				List[str]=[1, 2, 1]     ,snake_case	:				Tuple=[2, 2, 4]     ,snake_case	:				Tuple=2     ,snake_case	:				Union[str, Any]=2.0     ,snake_case	:				Tuple=True     ,snake_case	:				List[Any]=0.0     ,snake_case	:				Any=0.0     ,snake_case	:				Optional[Any]=0.1     ,snake_case	:				int="gelu"     ,snake_case	:				Dict=False     ,snake_case	:				Optional[int]=True     ,snake_case	:				List[str]=0.02     ,snake_case	:				Any=1e-5     ,snake_case	:				Tuple=True     ,snake_case	:				Union[str, Any]=None     ,snake_case	:				Dict=False     ,snake_case	:				List[Any]=2     ,):
           SCREAMING_SNAKE_CASE 		=parent
           SCREAMING_SNAKE_CASE 		=batch_size
           SCREAMING_SNAKE_CASE 		=image_size
           SCREAMING_SNAKE_CASE 		=patch_size
           SCREAMING_SNAKE_CASE 		=num_channels
           SCREAMING_SNAKE_CASE 		=embed_dim
           SCREAMING_SNAKE_CASE 		=depths
           SCREAMING_SNAKE_CASE 		=num_heads
           SCREAMING_SNAKE_CASE 		=window_size
           SCREAMING_SNAKE_CASE 		=mlp_ratio
           SCREAMING_SNAKE_CASE 		=qkv_bias
           SCREAMING_SNAKE_CASE 		=hidden_dropout_prob
           SCREAMING_SNAKE_CASE 		=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE 		=drop_path_rate
           SCREAMING_SNAKE_CASE 		=hidden_act
           SCREAMING_SNAKE_CASE 		=use_absolute_embeddings
           SCREAMING_SNAKE_CASE 		=patch_norm
           SCREAMING_SNAKE_CASE 		=layer_norm_eps
           SCREAMING_SNAKE_CASE 		=initializer_range
           SCREAMING_SNAKE_CASE 		=is_training
           SCREAMING_SNAKE_CASE 		=scope
           SCREAMING_SNAKE_CASE 		=use_labels
           SCREAMING_SNAKE_CASE 		=upscale

           # here we set some attributes to make tests pass
           SCREAMING_SNAKE_CASE 		=len(snake_case							)
           SCREAMING_SNAKE_CASE 		=embed_dim
           SCREAMING_SNAKE_CASE 		=(image_size // patch_size) ** 2
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

           SCREAMING_SNAKE_CASE 		=self.get_config()

           return config, pixel_values, labels
      def 			_lowerCAmelCase	( self	:				str							):
           return SwinaSRConfig(
               image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,embed_dim=self.embed_dim     ,depths=self.depths     ,num_heads=self.num_heads     ,window_size=self.window_size     ,mlp_ratio=self.mlp_ratio     ,qkv_bias=self.qkv_bias     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,drop_path_rate=self.drop_path_rate     ,hidden_act=self.hidden_act     ,use_absolute_embeddings=self.use_absolute_embeddings     ,path_norm=self.patch_norm     ,layer_norm_eps=self.layer_norm_eps     ,initializer_range=self.initializer_range     ,upscale=self.upscale     ,)
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				Dict     ,snake_case	:				Dict     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=SwinaSRModel(config=snake_case							)
           model.to(snake_case							)
           model.eval()
           SCREAMING_SNAKE_CASE 		=model(snake_case							)

           self.parent.assertEqual(
               result.last_hidden_state.shape     ,(self.batch_size, self.embed_dim, self.image_size, self.image_size)							)
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				Dict     ,snake_case	:				int     ,snake_case	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=SwinaSRForImageSuperResolution(snake_case							)
           model.to(snake_case							)
           model.eval()
           SCREAMING_SNAKE_CASE 		=model(snake_case							)

           SCREAMING_SNAKE_CASE 		=self.image_size * self.upscale
           self.parent.assertEqual(
               result.reconstruction.shape     ,(self.batch_size, self.num_channels, expected_image_size, expected_image_size)							)


      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=config_and_inputs
           SCREAMING_SNAKE_CASE 		={'pixel_values': pixel_values}
           return config, inputs_dict







@require_torch
class 						a_			(				lowerCamelCase_					,     lowerCamelCase_					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      __UpperCAmelCase     						= {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=SwinaSRModelTester(self							)
           SCREAMING_SNAKE_CASE 		=ConfigTester(self     ,config_class=snake_case     ,embed_dim=37							)
      def 			_lowerCAmelCase	( self	:				Tuple							):
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case							)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_super_resolution(*snake_case							)
      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'							)
      def 			_lowerCAmelCase	( self	:				int							):
           pass
      @unittest.skip(reason='Swin2SR does not use inputs_embeds'							)
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           pass
      @unittest.skip(reason='Swin2SR does not support training yet'							)
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           pass
      @unittest.skip(reason='Swin2SR does not support training yet'							)
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           pass
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE 		=model_class(snake_case							)
                self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)							)
                SCREAMING_SNAKE_CASE 		=model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case     ,nn.Linear							)							)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()

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

                SCREAMING_SNAKE_CASE 		=['pixel_values']
                self.assertListEqual(arg_names[:1]     ,snake_case							)
      @slow
      def 			_lowerCAmelCase	( self	:				Any							):
           for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE 		=SwinaSRModel.from_pretrained(snake_case							)
                self.assertIsNotNone(snake_case							)
      def 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE 		=_config_zero_init(snake_case							)
           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE 		=model_class(config=snake_case							)
                for name, param in model.named_parameters():
                     if "logit_scale" in name:
                          continue
                     if param.requires_grad:
                          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'     ,)


      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE 		=True

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE 		=True
                SCREAMING_SNAKE_CASE 		=False
                SCREAMING_SNAKE_CASE 		=True
                SCREAMING_SNAKE_CASE 		=model_class(snake_case							)
                model.to(snake_case							)
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE 		=model(**self._prepare_for_class(snake_case     ,snake_case							)							)
                SCREAMING_SNAKE_CASE 		=outputs.attentions
                SCREAMING_SNAKE_CASE 		=len(self.model_tester.depths							)
                self.assertEqual(len(snake_case							)     ,snake_case							)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                SCREAMING_SNAKE_CASE 		=True
                SCREAMING_SNAKE_CASE 		=config.window_size**2
                SCREAMING_SNAKE_CASE 		=model_class(snake_case							)
                model.to(snake_case							)
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE 		=model(**self._prepare_for_class(snake_case     ,snake_case							)							)
                SCREAMING_SNAKE_CASE 		=outputs.attentions
                self.assertEqual(len(snake_case							)     ,snake_case							)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]							)     ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,)
                SCREAMING_SNAKE_CASE 		=len(snake_case							)

                # Check attention is always last and order is fine
                SCREAMING_SNAKE_CASE 		=True
                SCREAMING_SNAKE_CASE 		=True
                SCREAMING_SNAKE_CASE 		=model_class(snake_case							)
                model.to(snake_case							)
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE 		=model(**self._prepare_for_class(snake_case     ,snake_case							)							)

                self.assertEqual(out_len + 1     ,len(snake_case							)							)

                SCREAMING_SNAKE_CASE 		=outputs.attentions

                self.assertEqual(len(snake_case							)     ,snake_case							)

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]							)     ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,)







@require_vision
@require_torch
@slow
class 						a_			(				unittest.TestCase	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=SwinaSRImageProcessor()
           SCREAMING_SNAKE_CASE 		=SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'							).to(snake_case							)

           SCREAMING_SNAKE_CASE 		=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
           SCREAMING_SNAKE_CASE 		=processor(images=snake_case     ,return_tensors='pt'							).to(snake_case							)

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE 		=model(**snake_case							)

           # verify the logits
           SCREAMING_SNAKE_CASE 		=torch.Size([1, 3, 976, 1296]							)
           self.assertEqual(outputs.reconstruction.shape     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=torch.tensor(
               [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]							).to(snake_case							)
           self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]     ,snake_case     ,atol=1e-4							)							)




from __future__ import annotations

_lowerCamelCase							={1: 1}







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     if n in COLLATZ_SEQUENCE_LENGTHS:
          return COLLATZ_SEQUENCE_LENGTHS[n]
     SCREAMING_SNAKE_CASE 		=n // 2 if n % 2 == 0 else 3 * n + 1
     SCREAMING_SNAKE_CASE 		=collatz_sequence_length(lowerCAmelCase_     ) + 1
     SCREAMING_SNAKE_CASE 		=sequence_length
     return sequence_length







def      snake_case__							(  lowerCAmelCase_ = 1000000     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=max((collatz_sequence_length(lowerCAmelCase_     ), i) for i in range(1,       lowerCAmelCase_     )     )
     return result[1]


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

