



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.swin.modeling_tf_swin import (
     TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFSwinForImageClassification,
     TFSwinForMaskedImageModeling,
     TFSwinModel,
 )


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class        _A			:



       def __init__(						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Dict=13	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=32	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=2	,						__SCREAMING_SNAKE_CASE				:	Any=3	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=16	,						__SCREAMING_SNAKE_CASE				:	Dict=[1, 2, 1]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=[2, 2, 4]	,						__SCREAMING_SNAKE_CASE				:	Tuple=2	,						__SCREAMING_SNAKE_CASE				:	Dict=2.0	,						__SCREAMING_SNAKE_CASE				:	List[str]=True	,						__SCREAMING_SNAKE_CASE				:	Dict=0.0	,						__SCREAMING_SNAKE_CASE				:	List[str]=0.0	,						__SCREAMING_SNAKE_CASE				:	int=0.1	,						__SCREAMING_SNAKE_CASE				:	int="gelu"	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=False	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=True	,						__SCREAMING_SNAKE_CASE				:	Dict=0.02	,						__SCREAMING_SNAKE_CASE				:	str=1E-5	,						__SCREAMING_SNAKE_CASE				:	Any=True	,						__SCREAMING_SNAKE_CASE				:	List[Any]=None	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=True	,						__SCREAMING_SNAKE_CASE				:	Dict=10	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=8	,						):


           '''simple docstring'''

           __a					=							parent
           __a					=							batch_size
           __a					=							image_size
           __a					=							patch_size
           __a					=							num_channels
           __a					=							embed_dim
           __a					=							depths
           __a					=							num_heads
           __a					=							window_size
           __a					=							mlp_ratio
           __a					=							qkv_bias
           __a					=							hidden_dropout_prob
           __a					=							attention_probs_dropout_prob
           __a					=							drop_path_rate
           __a					=							hidden_act
           __a					=							use_absolute_embeddings
           __a					=							patch_norm
           __a					=							layer_norm_eps
           __a					=							initializer_range
           __a					=							is_training
           __a					=							scope
           __a					=							use_labels
           __a					=							type_sequence_label_size
           __a					=							encoder_stride



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''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 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           return SwinConfig(
               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	,						encoder_stride=self.encoder_stride	,						)



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	Any	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Dict):


           '''simple docstring'''

           __a					=							TFSwinModel(config=__SCREAMING_SNAKE_CASE)
           __a					=							model(__SCREAMING_SNAKE_CASE)

           __a					=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           __a					=							int(config.embed_dim * 2 ** (len(config.depths) - 1))

           self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, expected_seq_len, expected_dim))



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           __a					=							TFSwinForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
           __a					=							model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.logits.shape	,						(self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           __a					=							1
           __a					=							TFSwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE)

           __a					=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a					=							model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, 1, self.image_size, self.image_size))



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	List[Any]):


           '''simple docstring'''

           __a					=							self.type_sequence_label_size
           __a					=							TFSwinForImageClassification(__SCREAMING_SNAKE_CASE)
           __a					=							model(__SCREAMING_SNAKE_CASE	,						labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           __a					=							1
           __a					=							TFSwinForImageClassification(__SCREAMING_SNAKE_CASE)
           __a					=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a					=							model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size))





       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

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


@require_tf
class        _A			(      __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase       ):
       UpperCamelCase__    :       Dict		   =		(
           (
               TFSwinModel,
               TFSwinForImageClassification,
               TFSwinForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase__    :       Any		   =		(
           {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
           if is_tf_available()
           else {}
       )

       UpperCamelCase__    :       Dict		   =		False
       UpperCamelCase__    :       Optional[int]		   =		False
       UpperCamelCase__    :       Optional[Any]		   =		False
       UpperCamelCase__    :       str		   =		False



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							TFSwinModelTester(self)
           __a					=							ConfigTester(self	,						config_class=__SCREAMING_SNAKE_CASE	,						embed_dim=37)



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           self.create_and_test_config_common_properties()
           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				:	str):


           '''simple docstring'''

           return



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	List[Any]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

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



       @unittest.skip(reason='''Swin does not use inputs_embeds''')
       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           pass



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''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()	,						tf.keras.layers.Layer)
               __a					=							model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE	,						tf.keras.layers.Dense))



       def 						_lowerCamelCase     (						self				:	str):


           '''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.call)
               # 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 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs_for_common()
           __a					=							True

           for model_class in self.all_model_classes:
               __a					=							True
               __a					=							False
               __a					=							True
               __a					=							model_class(__SCREAMING_SNAKE_CASE)
               __a					=							model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE))
               __a					=							outputs.attentions
               __a					=							len(self.model_tester.depths)
               self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,						__SCREAMING_SNAKE_CASE)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __a					=							True
               __a					=							config.window_size**2
               __a					=							model_class(__SCREAMING_SNAKE_CASE)
               __a					=							model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE))
               __a					=							outputs.attentions
               self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,						__SCREAMING_SNAKE_CASE)

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

               # Check attention is always last and order is fine
               __a					=							True
               __a					=							True
               __a					=							model_class(__SCREAMING_SNAKE_CASE)
               __a					=							model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE))

               if hasattr(self.model_tester	,						'''num_hidden_states_types'''):
                   __a					=							self.model_tester.num_hidden_states_types
               else:
                   # also another +1 for reshaped_hidden_states
                   __a					=							2
               self.assertEqual(out_len + added_hidden_states	,						len(__SCREAMING_SNAKE_CASE))

               __a					=							outputs.attentions

               self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,						__SCREAMING_SNAKE_CASE)

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



       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Dict):


           '''simple docstring'''

           __a					=							model_class(__SCREAMING_SNAKE_CASE)
           __a					=							model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE))
           __a					=							outputs.hidden_states

           __a					=							getattr(
               self.model_tester	,						'''expected_num_hidden_layers'''	,						len(self.model_tester.depths) + 1)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,						__SCREAMING_SNAKE_CASE)

           # Swin has a different seq_length
           __a					=							to_atuple(config.patch_size)

           __a					=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:])	,						[num_patches, self.model_tester.embed_dim]	,						)

           __a					=							outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,						__SCREAMING_SNAKE_CASE)

           __a			,  __a			,  __a			,  __a					=							reshaped_hidden_states[0].shape

           __a					=							tf.reshape(reshaped_hidden_states[0]	,						(batch_size, num_channels, height * width))
           __a					=							tf.transpose(__SCREAMING_SNAKE_CASE	,						(0, 2, 1))

           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])	,						[num_patches, self.model_tester.embed_dim]	,						)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs_for_common()

           __a					=							to_atuple(self.model_tester.image_size)

           for model_class in self.all_model_classes:
               __a					=							True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a					=							True

               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs_for_common()
           __a					=							3

           __a					=							to_atuple(self.model_tester.image_size)
           __a					=							to_atuple(config.patch_size)

           __a					=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __a					=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
               __a					=							True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						(padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a					=							True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						(padded_height, padded_width))





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


           '''simple docstring'''

           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a					=							TFSwinModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


@require_vision
@require_tf
class        _A			(      unittest.TestCase       ):



       @cached_property
       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''')
               if is_vision_available()
               else None
           )





       @slow
       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''')
           __a					=							self.default_image_processor

           __a					=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           __a					=							image_processor(images=__SCREAMING_SNAKE_CASE	,						return_tensors='''tf''')

           # forward pass
           __a					=							model(__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a					=							tf.TensorShape((1, 1_000))
           self.assertEqual(outputs.logits.shape	,						__SCREAMING_SNAKE_CASE)
           __a					=							tf.constant([-0.09_48, -0.64_54, -0.09_21])
           self.assertTrue(np.allclose(outputs.logits[0, :3]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-4))










from __future__ import annotations

__snake_case		:Union[str, Any] 						=	{1: 1}





def 	__snake_case      (     _UpperCAmelCase							):
    if n in COLLATZ_SEQUENCE_LENGTHS:
        return COLLATZ_SEQUENCE_LENGTHS[n]
    __a					=							n // 2 if n % 2 == 0 else 3 * n + 1
    __a					=							collatz_sequence_length(_UpperCAmelCase							) + 1
    __a					=							sequence_length
    return sequence_length





def 	__snake_case      (     _UpperCAmelCase = 1000000							):
    __a					=							max((collatz_sequence_length(_UpperCAmelCase							), i) for i in range(1  ,					_UpperCAmelCase							)							)
    return result[1]


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





