



import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    import PIL

    from transformers import FlavaImageProcessor
    from transformers.image_utils import PILImageResampling
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )
else:
    __magic_name__        =  __magic_name__        =  __magic_name__        =  __magic_name__        =  None

class 					lowercase						(	unittest.TestCase ):




       '''simple docstring'''







       def __init__(						self		,       _snake_case		,       _snake_case=7		,       _snake_case=3		,       _snake_case=30		,       _snake_case=400		,       _snake_case=True		,       _snake_case=None		,       _snake_case=True		,       _snake_case=None		,       _snake_case=None		,       _snake_case=True		,       _snake_case=1 / 255		,       _snake_case=True		,       _snake_case=FLAVA_IMAGE_MEAN		,       _snake_case=FLAVA_IMAGE_STD		,       _snake_case=14		,       _snake_case=75		,       _snake_case=None		,       _snake_case=16		,       _snake_case=0.3		,       _snake_case=None		,       _snake_case=True		,       _snake_case=None		,       _snake_case=None		,       _snake_case=True		,       _snake_case=None		,       _snake_case=True		,       _snake_case=True		,       _snake_case=FLAVA_CODEBOOK_MEAN		,       _snake_case=FLAVA_CODEBOOK_STD		,       )					->		Dict:

           """simple docstring"""
           UpperCAmelCase     =    size if size is not None else {'height': 224, 'width': 224}
           UpperCAmelCase     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
           UpperCAmelCase     =    codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
           UpperCAmelCase     =    codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

           UpperCAmelCase     =    parent
           UpperCAmelCase     =    batch_size
           UpperCAmelCase     =    num_channels
           UpperCAmelCase     =    do_resize
           UpperCAmelCase     =    do_rescale
           UpperCAmelCase     =    rescale_factor
           UpperCAmelCase     =    min_resolution
           UpperCAmelCase     =    max_resolution
           UpperCAmelCase     =    size
           UpperCAmelCase     =    resample if resample is not None else PILImageResampling.BICUBIC
           UpperCAmelCase     =    do_normalize
           UpperCAmelCase     =    image_mean
           UpperCAmelCase     =    image_std
           UpperCAmelCase     =    do_center_crop
           UpperCAmelCase     =    crop_size

           UpperCAmelCase     =    input_size_patches
           UpperCAmelCase     =    total_mask_patches
           UpperCAmelCase     =    mask_group_max_patches
           UpperCAmelCase     =    mask_group_min_patches
           UpperCAmelCase     =    mask_group_min_aspect_ratio
           UpperCAmelCase     =    mask_group_max_aspect_ratio

           UpperCAmelCase     =    codebook_do_resize
           UpperCAmelCase     =    codebook_size
           UpperCAmelCase     =    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
           UpperCAmelCase     =    codebook_do_center_crop
           UpperCAmelCase     =    codebook_crop_size
           UpperCAmelCase     =    codebook_do_map_pixels
           UpperCAmelCase     =    codebook_do_normalize
           UpperCAmelCase     =    codebook_image_mean
           UpperCAmelCase     =    codebook_image_std






       def 				snake_case_ (						self   )					->		Optional[Any]:

           """simple docstring"""
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "resample": self.resample,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "input_size_patches": self.input_size_patches,
               "total_mask_patches": self.total_mask_patches,
               "mask_group_max_patches": self.mask_group_max_patches,
               "mask_group_min_patches": self.mask_group_min_patches,
               "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
               "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
               "codebook_do_resize": self.codebook_do_resize,
               "codebook_size": self.codebook_size,
               "codebook_resample": self.codebook_resample,
               "codebook_do_center_crop": self.codebook_do_center_crop,
               "codebook_crop_size": self.codebook_crop_size,
               "codebook_do_map_pixels": self.codebook_do_map_pixels,
               "codebook_do_normalize": self.codebook_do_normalize,
               "codebook_image_mean": self.codebook_image_mean,
               "codebook_image_std": self.codebook_image_std,
           }






       def 				snake_case_ (						self   )					->		List[str]:

           """simple docstring"""
           return (self.size["height"], self.size["width"])






       def 				snake_case_ (						self   )					->		Any:

           """simple docstring"""
           return (
               (self.input_size_patches, self.input_size_patches)
               if not isinstance(self.input_size_patches		,       _snake_case   )
               else self.input_size_patches
           )






       def 				snake_case_ (						self   )					->		Dict:

           """simple docstring"""
           return (self.codebook_size["height"], self.codebook_size["width"])

@require_torch
@require_vision
class 					lowercase						(	__UpperCamelCase				, unittest.TestCase ):




       '''simple docstring'''







       __SCREAMING_SNAKE_CASE										=					FlavaImageProcessor if is_vision_available() else None
       __SCREAMING_SNAKE_CASE										=					None






       def 				snake_case_ (						self   )					->		List[Any]:

           """simple docstring"""
           UpperCAmelCase     =    FlavaImageProcessingTester(self   )






       @property
       def 				snake_case_ (						self   )					->		int:

           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()






       def 				snake_case_ (						self   )					->		Union[str, Any]:

           """simple docstring"""
           UpperCAmelCase     =    self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(_snake_case		,       '''image_mean'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''image_std'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''do_normalize'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''do_resize'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''resample'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''crop_size'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''do_center_crop'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''do_rescale'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''rescale_factor'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''masking_generator'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_do_resize'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_size'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_resample'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_do_center_crop'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_crop_size'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_do_map_pixels'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_do_normalize'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_image_mean'''   )   )
           self.assertTrue(hasattr(_snake_case		,       '''codebook_image_std'''   )   )






       def 				snake_case_ (						self   )					->		Dict:

           """simple docstring"""
           UpperCAmelCase     =    self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size		,       {'''height''': 224, '''width''': 224}   )
           self.assertEqual(image_processor.crop_size		,       {'''height''': 224, '''width''': 224}   )
           self.assertEqual(image_processor.codebook_size		,       {'''height''': 112, '''width''': 112}   )
           self.assertEqual(image_processor.codebook_crop_size		,       {'''height''': 112, '''width''': 112}   )

           UpperCAmelCase     =    self.image_processing_class.from_dict(
               self.image_processor_dict		,       size=42		,       crop_size=84		,       codebook_size=33		,       codebook_crop_size=66   )
           self.assertEqual(image_processor.size		,       {'''height''': 42, '''width''': 42}   )
           self.assertEqual(image_processor.crop_size		,       {'''height''': 84, '''width''': 84}   )
           self.assertEqual(image_processor.codebook_size		,       {'''height''': 33, '''width''': 33}   )
           self.assertEqual(image_processor.codebook_crop_size		,       {'''height''': 66, '''width''': 66}   )






       def 				snake_case_ (						self   )					->		str:

           """simple docstring"""
           pass






       def 				snake_case_ (						self   )					->		Any:

           """simple docstring"""
           # Initialize image_processing
           UpperCAmelCase     =    self.image_processing_class(**self.image_processor_dict   )
           # create random PIL images
           UpperCAmelCase     =    prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_snake_case   )
           for image in image_inputs:
               self.assertIsInstance(_snake_case		,       PIL.Image.Image   )

           # Test not batched input
           UpperCAmelCase     =    image_processing(image_inputs[0]		,       return_tensors='''pt'''   )

           # Test no bool masked pos
           self.assertFalse('''bool_masked_pos''' in encoded_images   )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()

           self.assertEqual(
               encoded_images.pixel_values.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

           # Test batched
           UpperCAmelCase     =    image_processing(_snake_case		,       return_tensors='''pt'''   )
           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()

           # Test no bool masked pos
           self.assertFalse('''bool_masked_pos''' in encoded_images   )

           self.assertEqual(
               encoded_images.pixel_values.shape		,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,       )






       def 				snake_case_ (						self		,       _snake_case		,       _snake_case   )					->		str:

           """simple docstring"""
           # Initialize image_processing
           UpperCAmelCase     =    self.image_processing_class(**self.image_processor_dict   )
           # create random tensors
           UpperCAmelCase     =    prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_snake_case		,       **_snake_case   )
           for image in image_inputs:
               self.assertIsInstance(_snake_case		,       _snake_case   )

           # Test not batched input
           UpperCAmelCase     =    image_processing(image_inputs[0]		,       return_tensors='''pt'''   )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

           UpperCAmelCase     =    image_processing(_snake_case		,       return_image_mask=_snake_case		,       return_tensors='''pt'''   )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape		,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,       )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape		,       (
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )		,       )

           # Test batched
           UpperCAmelCase     =    image_processing(_snake_case		,       return_tensors='''pt'''   ).pixel_values

           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.shape		,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,       )

           # Test masking
           UpperCAmelCase     =    image_processing(_snake_case		,       return_image_mask=_snake_case		,       return_tensors='''pt'''   )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape		,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,       )

           UpperCAmelCase     =    self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape		,       (
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )		,       )






       def 				snake_case_ (						self   )					->		Optional[Any]:

           """simple docstring"""
           self._test_call_framework(np.ndarray		,       prepare_kwargs={'''numpify''': True}   )






       def 				snake_case_ (						self   )					->		Dict:

           """simple docstring"""
           self._test_call_framework(torch.Tensor		,       prepare_kwargs={'''torchify''': True}   )






       def 				snake_case_ (						self   )					->		Any:

           """simple docstring"""
           # Initialize image_processing
           random.seed(1234   )
           UpperCAmelCase     =    self.image_processing_class(**self.image_processor_dict   )
           UpperCAmelCase     =    prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_snake_case		,       torchify=_snake_case   )

           # Test not batched input
           UpperCAmelCase     =    image_processing(image_inputs[0]		,       return_image_mask=_snake_case		,       return_tensors='''pt'''   )
           self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,       75   )






       def 				snake_case_ (						self   )					->		List[str]:

           """simple docstring"""
           # Initialize image_processing
           UpperCAmelCase     =    self.image_processing_class(**self.image_processor_dict   )
           # create random PIL images
           UpperCAmelCase     =    prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_snake_case   )
           for image in image_inputs:
               self.assertIsInstance(_snake_case		,       PIL.Image.Image   )

           # Test not batched input
           UpperCAmelCase     =    image_processing(image_inputs[0]		,       return_codebook_pixels=_snake_case		,       return_tensors='''pt'''   )
           UpperCAmelCase     =    self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

           # Test batched
           UpperCAmelCase     =    image_processing(_snake_case		,       return_codebook_pixels=_snake_case		,       return_tensors='''pt'''   )
           UpperCAmelCase     =    self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape		,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,       )








def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__ = 100						):
 __lowerCamelCase		:							List[Any]							=						[[0 for _ in range(SCREAMING_SNAKE_CASE__						)] for _ in range(m + 1						)]
 for i in range(m + 1						):
  __lowerCamelCase		:							Any							=						1

 for n in range(m + 1						):
  for k in range(1			,       SCREAMING_SNAKE_CASE__						):
   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())))

