
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 CLIPImageProcessor





class 							A__	(			unittest.TestCase):
      def __init__(   self					,						__magic_name__					,						__magic_name__=7					,						__magic_name__=3					,						__magic_name__=1_8					,						__magic_name__=3_0					,						__magic_name__=4_0_0					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,						__magic_name__=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,						__magic_name__=True					,						):
       lowerCamelCase	:			Union[str, Any]           =   size if size is not None else {"""shortest_edge""": 2_0}
       lowerCamelCase	:			Tuple           =   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
       lowerCamelCase	:			Union[str, Any]           =   parent
       lowerCamelCase	:			Any           =   batch_size
       lowerCamelCase	:			Optional[Any]           =   num_channels
       lowerCamelCase	:			Optional[int]           =   image_size
       lowerCamelCase	:			Dict           =   min_resolution
       lowerCamelCase	:			List[str]           =   max_resolution
       lowerCamelCase	:			Any           =   do_resize
       lowerCamelCase	:			Dict           =   size
       lowerCamelCase	:			Tuple           =   do_center_crop
       lowerCamelCase	:			int           =   crop_size
       lowerCamelCase	:			int           =   do_normalize
       lowerCamelCase	:			List[str]           =   image_mean
       lowerCamelCase	:			int           =   image_std
       lowerCamelCase	:			List[Any]           =   do_convert_rgb
      def 		UpperCamelCase__							(   self   ):
       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 		UpperCamelCase__							(   self					,						__magic_name__=False					,						__magic_name__=False					,						__magic_name__=False   ):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
        lowerCamelCase	:			Optional[Any]           =   []
        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:
        lowerCamelCase	:			List[str]           =   []
        for i in range(self.batch_size   ):
         lowerCamelCase    ,					lowerCamelCase	:			Tuple           =   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
        lowerCamelCase	:			List[str]           =   [Image.fromarray(np.moveaxis(__magic_name__					,						0					,						-1   )   ) for x in image_inputs]

       if torchify:
        lowerCamelCase	:			Any           =   [torch.from_numpy(__magic_name__   ) for x in image_inputs]

       return image_inputs





@require_torch
@require_vision
class 							A__	(			__SCREAMING_SNAKE_CASE						,     unittest.TestCase):
      _UpperCAmelCase       :					List[Any]					 =    CLIPImageProcessor if is_vision_available() else None
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Optional[Any]           =   CLIPImageProcessingTester(self   )
      @property
      def 		UpperCamelCase__							(   self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Tuple           =   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_resize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_normalize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_mean"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_std"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_convert_rgb"""   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size					,						{"""shortest_edge""": 2_0}   )
       self.assertEqual(image_processor.crop_size					,						{"""height""": 1_8, """width""": 1_8}   )

       lowerCamelCase	:			Tuple           =   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 		UpperCamelCase__							(   self   ):
       pass
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			str           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       lowerCamelCase	:			Optional[Any]           =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						Image.Image   )

       # Test not batched input
       lowerCamelCase	:			Tuple           =   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
       lowerCamelCase	:			List[Any]           =   image_processing(__magic_name__					,						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 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			List[Any]           =   self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       lowerCamelCase	:			str           =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__					,						numpify=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						np.ndarray   )

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

       # Test batched
       lowerCamelCase	:			int           =   image_processing(__magic_name__					,						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 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			str           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       lowerCamelCase	:			Any           =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__					,						torchify=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						torch.Tensor   )

       # Test not batched input
       lowerCamelCase	:			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
       lowerCamelCase	:			Dict           =   image_processing(__magic_name__					,						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 							A__	(			__SCREAMING_SNAKE_CASE						,     unittest.TestCase):
      _UpperCAmelCase       :					List[Any]					 =    CLIPImageProcessor if is_vision_available() else None
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			str           =   CLIPImageProcessingTester(self					,						num_channels=4   )
       lowerCamelCase	:			Optional[int]           =   3
      @property
      def 		UpperCamelCase__							(   self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Optional[int]           =   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_resize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_normalize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_mean"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_std"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_convert_rgb"""   )   )
      def 		UpperCamelCase__							(   self   ):
       pass






      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			int           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       lowerCamelCase	:			Optional[int]           =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						Image.Image   )

       # Test not batched input
       lowerCamelCase	:			Optional[Any]           =   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
       lowerCamelCase	:			int           =   image_processing(__magic_name__					,						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"""],
           )					,						)






def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if n < 0:
  return 0
 # table[i] represents the number of ways to get to amount i
 lowerCamelCase	:			int           =   [0] * (n + 1)

 # There is exactly 1 way to get to zero(You pick no coins).
 lowerCamelCase	:			Any           =   1

 # Pick all coins one by one and update table[] values
 # after the index greater than or equal to the value of the
 # picked coin
 for coin_val in s:
  for j in range(lowerCamelCase,			n + 1							):
   table[j] += table[j - coin_val]

 return table[n]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




