

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_			:



      """simple docstring"""







      def __init__( self	:				Optional[int]     ,snake_case	:				List[Any]     ,snake_case	:				Union[str, Any]=13     ,snake_case	:				Optional[Any]=32     ,snake_case	:				Dict=2     ,snake_case	:				List[str]=3     ,snake_case	:				Dict=16     ,snake_case	:				str=[1, 2, 1]     ,snake_case	:				List[str]=[2, 2, 4]     ,snake_case	:				List[Any]=2     ,snake_case	:				Dict=2.0     ,snake_case	:				str=True     ,snake_case	:				List[str]=0.0     ,snake_case	:				str=0.0     ,snake_case	:				Tuple=0.1     ,snake_case	:				Tuple="gelu"     ,snake_case	:				List[str]=False     ,snake_case	:				Optional[int]=True     ,snake_case	:				Dict=0.02     ,snake_case	:				Union[str, Any]=1e-5     ,snake_case	:				List[str]=True     ,snake_case	:				Tuple=None     ,snake_case	:				Dict=True     ,snake_case	:				Any=10     ,snake_case	:				str=8     ,):
           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 		=type_sequence_label_size
           SCREAMING_SNAKE_CASE 		=encoder_stride
      def 			_lowerCAmelCase	( self	:				List[str]							):
           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	:				Optional[Any]							):
           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     ,snake_case	:				Union[str, Any]     ,snake_case	:				List[Any]     ,snake_case	:				List[Any]							):
           SCREAMING_SNAKE_CASE 		=TFSwinModel(config=snake_case							)
           SCREAMING_SNAKE_CASE 		=model(snake_case							)

           SCREAMING_SNAKE_CASE 		=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
           SCREAMING_SNAKE_CASE 		=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	:				List[Any]     ,snake_case	:				List[Any]     ,snake_case	:				Optional[Any]     ,snake_case	:				Dict							):
           SCREAMING_SNAKE_CASE 		=TFSwinForMaskedImageModeling(config=snake_case							)
           SCREAMING_SNAKE_CASE 		=model(snake_case							)
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

           # test greyscale images
           SCREAMING_SNAKE_CASE 		=1
           SCREAMING_SNAKE_CASE 		=TFSwinForMaskedImageModeling(snake_case							)

           SCREAMING_SNAKE_CASE 		=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
           SCREAMING_SNAKE_CASE 		=model(snake_case							)
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, 1, self.image_size, self.image_size)							)
      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				Dict     ,snake_case	:				int     ,snake_case	:				Any							):
           SCREAMING_SNAKE_CASE 		=self.type_sequence_label_size
           SCREAMING_SNAKE_CASE 		=TFSwinForImageClassification(snake_case							)
           SCREAMING_SNAKE_CASE 		=model(snake_case     ,labels=snake_case							)
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)							)

           # test greyscale images
           SCREAMING_SNAKE_CASE 		=1
           SCREAMING_SNAKE_CASE 		=TFSwinForImageClassification(snake_case							)
           SCREAMING_SNAKE_CASE 		=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
           SCREAMING_SNAKE_CASE 		=model(snake_case							)
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_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_tf
class 						a_			(				lowerCamelCase_					,     lowerCamelCase_					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= (
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase     						= (
          {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      __UpperCAmelCase     						= False
      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=TFSwinModelTester(self							)
           SCREAMING_SNAKE_CASE 		=ConfigTester(self     ,config_class=snake_case     ,embed_dim=37							)
      def 			_lowerCAmelCase	( self	:				Any							):
           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							):
           return
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case							)
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*snake_case							)
      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case							)
      @unittest.skip(reason='Swin does not use inputs_embeds'							)
      def 			_lowerCAmelCase	( self	:				int							):
           pass
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           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()     ,tf.keras.layers.Layer							)
                SCREAMING_SNAKE_CASE 		=model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case     ,tf.keras.layers.Dense							)							)
      def 			_lowerCAmelCase	( self	:				Optional[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							)
                SCREAMING_SNAKE_CASE 		=inspect.signature(model.call							)
                # 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							)
      def 			_lowerCAmelCase	( self	:				str							):
           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							)
                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							)
                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							)
                SCREAMING_SNAKE_CASE 		=model(**self._prepare_for_class(snake_case     ,snake_case							)							)

                if hasattr(self.model_tester     ,'num_hidden_states_types'							):
                     SCREAMING_SNAKE_CASE 		=self.model_tester.num_hidden_states_types
                else:
                     # also another +1 for reshaped_hidden_states
                     SCREAMING_SNAKE_CASE 		=2
                self.assertEqual(out_len + added_hidden_states     ,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]     ,)
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				List[str]     ,snake_case	:				List[Any]     ,snake_case	:				Any     ,snake_case	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=model_class(snake_case							)
           SCREAMING_SNAKE_CASE 		=model(**self._prepare_for_class(snake_case     ,snake_case							)							)
           SCREAMING_SNAKE_CASE 		=outputs.hidden_states

           SCREAMING_SNAKE_CASE 		=getattr(
               self.model_tester     ,'expected_num_hidden_layers'     ,len(self.model_tester.depths							) + 1							)
           self.assertEqual(len(snake_case							)     ,snake_case							)

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

           SCREAMING_SNAKE_CASE 		=(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]     ,)

           SCREAMING_SNAKE_CASE 		=outputs.reshaped_hidden_states
           self.assertEqual(len(snake_case							)     ,snake_case							)

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

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

           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]							)     ,[num_patches, self.model_tester.embed_dim]     ,)
      def 			_lowerCAmelCase	( self	:				Any							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE 		=to_atuple(self.model_tester.image_size							)

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE 		=True
                self.check_hidden_states_output(snake_case     ,snake_case     ,snake_case     ,snake_case							)

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

                self.check_hidden_states_output(snake_case     ,snake_case     ,snake_case     ,snake_case							)
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE 		=3

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

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

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE 		=True
                self.check_hidden_states_output(snake_case     ,snake_case     ,snake_case     ,(padded_height, padded_width)							)

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


      @slow
      def 			_lowerCAmelCase	( self	:				Dict							):
           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE 		=TFSwinModel.from_pretrained(snake_case							)
                self.assertIsNotNone(snake_case							)







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



      """simple docstring"""







      @cached_property
      def 			_lowerCAmelCase	( self	:				str							):
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
               if is_vision_available()
               else None
           )


      @slow
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
           SCREAMING_SNAKE_CASE 		=self.default_image_processor

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

           # forward pass
           SCREAMING_SNAKE_CASE 		=model(snake_case							)

           # verify the logits
           SCREAMING_SNAKE_CASE 		=tf.TensorShape((1, 1000)							)
           self.assertEqual(outputs.logits.shape     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=tf.constant([-0.0_948, -0.6_454, -0.0_921]							)
           self.assertTrue(np.allclose(outputs.logits[0, :3]     ,snake_case     ,atol=1e-4							)							)




def      snake_case__							(  lowerCAmelCase_ = 100     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[[0 for _ in range(lowerCAmelCase_     )] for _ in range(m + 1     )]
     for i in range(m + 1     ):
          SCREAMING_SNAKE_CASE 		=1

     for n in range(m + 1     ):
          for k in range(1,       lowerCAmelCase_     ):
               memo[n][k] += memo[n][k - 1]
               if n > k:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1] - 1


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

