

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 EfficientNetImageProcessor






class     __magic_name__     (							unittest.TestCase     ):


 def __init__(    self		:			Dict      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Optional[int]=1_3      ,    lowerCAmelCase__		:			List[str]=3      ,    lowerCAmelCase__		:			Optional[int]=1_8      ,    lowerCAmelCase__		:			Any=3_0      ,    lowerCAmelCase__		:			Union[str, Any]=4_0_0      ,    lowerCAmelCase__		:			Any=True      ,    lowerCAmelCase__		:			List[str]=None      ,    lowerCAmelCase__		:			int=True      ,    lowerCAmelCase__		:			List[str]=[0.5, 0.5, 0.5]      ,    lowerCAmelCase__		:			Optional[Any]=[0.5, 0.5, 0.5]      ,    )     ->   Tuple:
   UpperCAmelCase          =	size if size is not None else {"height": 1_8, "width": 1_8}
   UpperCAmelCase          =	parent
   UpperCAmelCase          =	batch_size
   UpperCAmelCase          =	num_channels
   UpperCAmelCase          =	image_size
   UpperCAmelCase          =	min_resolution
   UpperCAmelCase          =	max_resolution
   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean
   UpperCAmelCase          =	image_std


 def _UpperCamelCase						(    self		:			str     )     ->   Optional[int]:
   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }






@require_torch
@require_vision
class     __magic_name__     (							snake_case__						,    unittest.TestCase     ):
 UpperCAmelCase				     =    EfficientNetImageProcessor if is_vision_available() else None


 def _UpperCamelCase						(    self		:			int     )     ->   Any:
   UpperCAmelCase          =	EfficientNetImageProcessorTester(self     )


 @property
 def _UpperCamelCase						(    self		:			List[str]     )     ->   Any:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   Optional[int]:
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,    "image_mean"     )     )
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,    "image_std"     )     )
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,    "do_normalize"     )     )
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,    "do_resize"     )     )
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,    "size"     )     )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   Any:
   UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict     )
   self.assertEqual(image_processor.size      ,    {"height": 1_8, "width": 1_8}     )

   UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict      ,    size=4_2     )
   self.assertEqual(image_processor.size      ,    {"height": 4_2, "width": 4_2}     )


 def _UpperCamelCase						(    self		:			Tuple     )     ->   Any:
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    equal_resolution=_SCREAMING_SNAKE_CASE     )
   for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,    Image.Image     )

   # Test not batched input
   UpperCAmelCase          =	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["height"],
           self.image_processor_tester.size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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.size["height"],
           self.image_processor_tester.size["width"],
       )      ,    )


 def _UpperCamelCase						(    self		:			str     )     ->   Optional[Any]:
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    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
   UpperCAmelCase          =	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["height"],
           self.image_processor_tester.size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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.size["height"],
           self.image_processor_tester.size["width"],
       )      ,    )


 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    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
   UpperCAmelCase          =	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["height"],
           self.image_processor_tester.size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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.size["height"],
           self.image_processor_tester.size["width"],
       )      ,    )











from __future__ import annotations







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							list[int],    UpperCAmelCase						:							list[int],    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:
								A									=   capacity - weights[counter - 1]
								A									=   values[counter - 1] + knapsack(
								    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								A									=   knapsack(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								return max(UpperCAmelCase,    UpperCAmelCase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





