
'''simple docstring'''
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():
  from PIL import Image

  from transformers import ConvNextImageProcessor






class       UpperCAmelCase_ (		unittest.TestCase    ):





 def __init__(			self       ,				lowercase_       ,				lowercase_=7       ,				lowercase_=3       ,				lowercase_=18       ,				lowercase_=30       ,				lowercase_=4_00       ,				lowercase_=True       ,				lowercase_=None       ,				lowercase_=0.875       ,				lowercase_=True       ,				lowercase_=[0.5, 0.5, 0.5]       ,				lowercase_=[0.5, 0.5, 0.5]       ,				):
     snake_case_       :			Dict   	=  size if size is not None else {"shortest_edge": 20}
     snake_case_       :			int   	=  parent
     snake_case_       :			List[str]   	=  batch_size
     snake_case_       :			Dict   	=  num_channels
     snake_case_       :			str   	=  image_size
     snake_case_       :			int   	=  min_resolution
     snake_case_       :			List[str]   	=  max_resolution
     snake_case_       :			List[Any]   	=  do_resize
     snake_case_       :			Optional[Any]   	=  size
     snake_case_       :			int   	=  crop_pct
     snake_case_       :			Tuple   	=  do_normalize
     snake_case_       :			str   	=  image_mean
     snake_case_       :			Union[str, Any]   	=  image_std





 def 				snake_case__						(			self):
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "crop_pct": self.crop_pct,
     }






@require_torch
@require_vision
class       UpperCAmelCase_ (		__lowerCamelCase			,  unittest.TestCase    ):
 UpperCAmelCase_									=  ConvNextImageProcessor if is_vision_available() else None





 def 				snake_case__						(			self):
     snake_case_       :			Tuple   	=  ConvNextImageProcessingTester(self)





 @property
 def 				snake_case__						(			self):
     return self.image_processor_tester.prepare_image_processor_dict()





 def 				snake_case__						(			self):
     snake_case_       :			Union[str, Any]   	=  self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(_UpperCamelCase       ,				"do_resize"))
     self.assertTrue(hasattr(_UpperCamelCase       ,				"size"))
     self.assertTrue(hasattr(_UpperCamelCase       ,				"crop_pct"))
     self.assertTrue(hasattr(_UpperCamelCase       ,				"do_normalize"))
     self.assertTrue(hasattr(_UpperCamelCase       ,				"image_mean"))
     self.assertTrue(hasattr(_UpperCamelCase       ,				"image_std"))





 def 				snake_case__						(			self):
     snake_case_       :			List[Any]   	=  self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size       ,				{"shortest_edge": 20})

     snake_case_       :			List[Any]   	=  self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42)
     self.assertEqual(image_processor.size       ,				{"shortest_edge": 42})





 def 				snake_case__						(			self):
     pass





 def 				snake_case__						(			self):
     snake_case_       :			List[Any]   	=  self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     snake_case_       :			Dict   	=  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_UpperCamelCase)
     for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase       ,				Image.Image)

     # Test not batched input
     snake_case_       :			List[str]   	=  image_processing(image_inputs[0]       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)

     # Test batched
     snake_case_       :			Union[str, Any]   	=  image_processing(_UpperCamelCase       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)





 def 				snake_case__						(			self):
     snake_case_       :			int   	=  self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     snake_case_       :			Tuple   	=  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_UpperCamelCase       ,				numpify=_UpperCamelCase)
     for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase       ,				np.ndarray)

     # Test not batched input
     snake_case_       :			str   	=  image_processing(image_inputs[0]       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)

     # Test batched
     snake_case_       :			List[Any]   	=  image_processing(_UpperCamelCase       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)





 def 				snake_case__						(			self):
     snake_case_       :			Optional[int]   	=  self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     snake_case_       :			Optional[Any]   	=  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_UpperCamelCase       ,				torchify=_UpperCamelCase)
     for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase       ,				torch.Tensor)

     # Test not batched input
     snake_case_       :			Dict   	=  image_processing(image_inputs[0]       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)

     # Test batched
     snake_case_       :			Any   	=  image_processing(_UpperCamelCase       ,				return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape       ,				(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["shortest_edge"],
             self.image_processor_tester.size["shortest_edge"],
         )       ,				)










def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=1.0
    for i in range(1,				r + 1	):
        ret *= (n + 1 - i) / i
    return round(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return choose(8 + n,				__UpperCamelCase	) + choose(9 + n,				__UpperCamelCase	) - 10



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return sum(non_bouncy_exact(__UpperCamelCase	) for i in range(1,				n + 1	)	)



def 	UpperCAmelCase_    (	__UpperCamelCase = 100	):
    return non_bouncy_upto(__UpperCamelCase	)


if __name__ == "__main__":
    print(f"""{solution() = }""")






