






'''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 PoolFormerImageProcessor




class        _UpperCAmelCase						(		unittest.TestCase			):




    def __init__(			self  :       Tuple  ,     a  :       List[str]  ,     a  :       Tuple=7  ,     a  :       Tuple=3  ,     a  :       Optional[Any]=3_0  ,     a  :       Any=4_0_0  ,     a  :       Optional[int]=True  ,     a  :       Tuple=None  ,     a  :       Dict=0.9  ,     a  :       Dict=None  ,     a  :       Union[str, Any]=True  ,     a  :       List[str]=[0.5, 0.5, 0.5]  ,     a  :       str=[0.5, 0.5, 0.5]  ,     ):






       '''simple docstring'''






       lowercase_	:    List[str]           =     size if size is not None else {"shortest_edge": 3_0}
       lowercase_	:    str           =     crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
       lowercase_	:    List[str]           =     parent
       lowercase_	:    List[Any]           =     batch_size
       lowercase_	:    Tuple           =     num_channels
       lowercase_	:    Dict           =     min_resolution
       lowercase_	:    List[Any]           =     max_resolution
       lowercase_	:    Dict           =     do_resize_and_center_crop
       lowercase_	:    Optional[Any]           =     size
       lowercase_	:    Dict           =     crop_pct
       lowercase_	:    Tuple           =     crop_size
       lowercase_	:    Tuple           =     do_normalize
       lowercase_	:    Optional[int]           =     image_mean
       lowercase_	:    Tuple           =     image_std





    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }




@require_torch
@require_vision
class        _UpperCAmelCase						(		__UpperCAmelCase				,					unittest.TestCase			):
    __lowerCamelCase:   int		       =    PoolFormerImageProcessor if is_vision_available() else None




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     PoolFormerImageProcessingTester(self			)




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "do_resize_and_center_crop"			)			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "size"			)			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "crop_pct"			)			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "do_normalize"			)			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "image_mean"			)			)
       self.assertTrue(hasattr(_lowerCamelCase  ,     "image_std"			)			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size  ,     {"shortest_edge": 3_0}			)
       self.assertEqual(image_processor.crop_size  ,     {"height": 3_0, "width": 3_0}			)

       lowercase_	:    str           =     self.image_processing_class.from_dict(self.image_processor_dict  ,     size=4_2  ,     crop_size=8_4			)
       self.assertEqual(image_processor.size  ,     {"shortest_edge": 4_2}			)
       self.assertEqual(image_processor.crop_size  ,     {"height": 8_4, "width": 8_4}			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       pass




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       lowercase_	:    Optional[Any]           =     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_lowerCamelCase			)
       for image in image_inputs:
          self.assertIsInstance(_lowerCamelCase  ,     Image.Image			)

       # Test not batched input
       lowercase_	:    Any           =     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    Optional[Any]           =     image_processing(_lowerCamelCase  ,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       lowercase_	:    Optional[int]           =     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_lowerCamelCase  ,     numpify=_lowerCamelCase			)
       for image in image_inputs:
          self.assertIsInstance(_lowerCamelCase  ,     np.ndarray			)

       # Test not batched input
       lowercase_	:    Any           =     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    str           =     image_processing(_lowerCamelCase  ,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )





    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






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

       # Test not batched input
       lowercase_	:    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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    Optional[int]           =     image_processing(_lowerCamelCase  ,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase = 200							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [1, 2, 5, 10, 20, 50, 100, 200]
   lowercase_	:    str           =     [0] * (pence + 1)
   lowercase_	:    Dict           =     1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(_UpperCamelCase						,		pence + 1						,		1							):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(200) == 73682




