
"""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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor







class     lowercase(unittest.TestCase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=7     ,				__SCREAMING_SNAKE_CASE=3     ,				__SCREAMING_SNAKE_CASE=1_8     ,				__SCREAMING_SNAKE_CASE=3_0     ,				__SCREAMING_SNAKE_CASE=4_0_0     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]     ,				__SCREAMING_SNAKE_CASE=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]     ,				__SCREAMING_SNAKE_CASE=True     ,				)		->     int:







         """simple docstring"""



         a__		=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         a__		=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
         a__		=			parent
         a__		=			batch_size
         a__		=			num_channels
         a__		=			image_size
         a__		=			min_resolution
         a__		=			max_resolution
         a__		=			do_resize
         a__		=			size
         a__		=			do_center_crop
         a__		=			crop_size
         a__		=			do_normalize
         a__		=			image_mean
         a__		=			image_std
         a__		=			do_convert_rgb



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=False   )		->     Any:







         """simple docstring"""



         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
             a__		=			[]
             for i in range(self.batch_size   ):
                 image_inputs.append(
                     np.random.randint(
                         2_5_5     ,				size=(self.num_channels, self.max_resolution, self.max_resolution)     ,				dtype=np.uinta   )   )
         else:
             a__		=			[]
             for i in range(self.batch_size   ):
                 a__	,     a__		=			np.random.choice(np.arange(self.min_resolution     ,				self.max_resolution   )     ,				2   )
                 image_inputs.append(np.random.randint(2_5_5     ,				size=(self.num_channels, width, height)     ,				dtype=np.uinta   )   )

         if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             a__		=			[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE     ,				0     ,				-1   )   ) for x in image_inputs]

         if torchify:
             a__		=			[torch.from_numpy(__SCREAMING_SNAKE_CASE   ) for x in image_inputs]

         return image_inputs







@require_torch
@require_vision
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Optional[Any]						=     ChineseCLIPImageProcessor if is_vision_available() else None



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







         """simple docstring"""



         a__		=			ChineseCLIPImageProcessingTester(self     ,				do_center_crop=__SCREAMING_SNAKE_CASE   )



     @property
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_resize'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'size'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_normalize'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'image_mean'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'image_std'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_convert_rgb'   )   )



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







         """simple docstring"""



         a__		=			self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size     ,				{'height': 2_2_4, 'width': 2_2_4}   )
         self.assertEqual(image_processor.crop_size     ,				{'height': 1_8, 'width': 1_8}   )

         a__		=			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 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         pass



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







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         a__		=			self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				Image.Image   )

         # Test not batched input
         a__		=			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
         a__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         a__		=			self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE     ,				numpify=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				np.ndarray   )

         # Test not batched input
         a__		=			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
         a__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         a__		=			self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE     ,				torchify=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				torch.Tensor   )

         # Test not batched input
         a__		=			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
         a__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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'],
             )     ,				)







@require_torch
@require_vision
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	str						=     ChineseCLIPImageProcessor if is_vision_available() else None



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			ChineseCLIPImageProcessingTester(self     ,				num_channels=4     ,				do_center_crop=__SCREAMING_SNAKE_CASE   )
         a__		=			3



     @property
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_resize'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'size'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_normalize'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'image_mean'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'image_std'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_convert_rgb'   )   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         pass




     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         a__		=			self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				Image.Image   )

         # Test not batched input
         a__		=			image_processing(image_inputs[0]     ,				return_tensors='pt'   ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,				(
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,				)

         # Test batched
         a__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'   ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,				(
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,				)




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


