



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 BlipImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Tuple  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				List[Any]=7  ,	UpperCamelCase			:				List[Any]=3  ,	UpperCamelCase			:				int=1_8  ,	UpperCamelCase			:				Dict=3_0  ,	UpperCamelCase			:				Dict=4_0_0  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				List[str]=None  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				Tuple=False  ,	UpperCamelCase			:				str=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,	UpperCamelCase			:				int=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,	UpperCamelCase			:				Union[str, Any]=True  ,	)->Dict:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						size if size is not None else {'height': 2_0, 'width': 2_0}
      __SCREAMING_SNAKE_CASE							:     Any							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     str							=						image_size
      __SCREAMING_SNAKE_CASE							:     Tuple							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						do_resize
      __SCREAMING_SNAKE_CASE							:     Dict							=						size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_mean
      __SCREAMING_SNAKE_CASE							:     List[str]							=						image_std
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						do_pad
      __SCREAMING_SNAKE_CASE							:     Dict							=						do_convert_rgb





     def 						__snake_case     (							self			:				Any					)->Any:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
          "do_pad": self.do_pad,
      }






     def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[str, Any]=False  ,	UpperCamelCase			:				str=False  ,	UpperCamelCase			:				List[Any]=False					)->Any:
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
       __SCREAMING_SNAKE_CASE							:     Dict							=						[]
       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:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
       for i in range(self.batch_size					):
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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
       __SCREAMING_SNAKE_CASE							:     str							=						[Image.fromarray(np.moveaxis(_snake_case  ,	0  ,	-1					)					) for x in image_inputs]

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

      return image_inputs






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(a_      ,     unittest.TestCase			):
     lowerCAmelCase       =      BlipImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				Optional[int]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						BlipImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				str					)->int:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(_snake_case  ,	"do_resize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"size"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"image_mean"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"image_std"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_convert_rgb"					)					)





     def 						__snake_case     (							self			:				Tuple					)->int:
      pass





     def 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      # Initialize image_processor
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_processor(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["height"],
              self.image_processor_tester.size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     str							=						image_processor(_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.size["height"],
              self.image_processor_tester.size["width"],
          )  ,	)





     def 						__snake_case     (							self			:				Tuple					)->int:
      # Initialize image_processor
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case  ,	numpify=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	np.ndarray					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     str							=						image_processor(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["height"],
              self.image_processor_tester.size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     str							=						image_processor(_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.size["height"],
              self.image_processor_tester.size["width"],
          )  ,	)






     def 						__snake_case     (							self			:				Dict					)->Tuple:
      # Initialize image_processor
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case  ,	torchify=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	torch.Tensor					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_processor(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["height"],
              self.image_processor_tester.size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(_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.size["height"],
              self.image_processor_tester.size["width"],
          )  ,	)






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(a_      ,     unittest.TestCase			):
     lowerCAmelCase       =      BlipImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				List[Any]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     int							=						BlipImageProcessingTester(self  ,	num_channels=4					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						3





     @property
     def 						__snake_case     (							self			:				Tuple					)->int:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(_snake_case  ,	"do_resize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"size"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"image_mean"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"image_std"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_convert_rgb"					)					)





     def 						__snake_case     (							self			:				Dict					)->str:
      pass






     def 						__snake_case     (							self			:				int					)->Optional[int]:
      # Initialize image_processor
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     str							=						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(image_inputs[0]  ,	return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor(_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.size["height"],
              self.image_processor_tester.size["width"],
          )  ,	)









'''simple docstring'''


from __future__ import annotations







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:

	# Base Case
	if counter == 0 or capacity == 0:
		return 0

	# If weight of the nth item is more than Knapsack of capacity,
	#   then this item cannot be included in the optimal solution,
	# else return the maximum of two cases:
	#   (1) nth item included
	#   (2) not included
	if weights[counter - 1] > capacity:
		return knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
	else:
		UpperCAmelCase_		:   int													=       capacity - weights[counter - 1]
		UpperCAmelCase_		:   Optional[int]													=       values[counter - 1] + knapsack(
		    UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		UpperCAmelCase_		:   Any													=       knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		return max(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






