
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 ViTImageProcessor



class 		UpperCamelCase__  (	unittest.TestCase   ):
   def __init__(self			: Union[str, Any] ,    snake_case_			: Dict ,    snake_case_			: List[Any]=1_3 ,    snake_case_			: Dict=3 ,    snake_case_			: Optional[int]=2_2_4 ,    snake_case_			: int=3_0 ,    snake_case_			: Optional[int]=4_0_0 ,    snake_case_			: Union[str, Any]=True ,    snake_case_			: Any=None ,    snake_case_			: Dict=True ,    snake_case_			: Any=[0.5, 0.5, 0.5] ,    snake_case_			: Optional[int]=[0.5, 0.5, 0.5] ,    ):
    __a			:  Tuple		    =  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
    __a			:  Tuple		    =  parent
    __a			:  Optional[Any]		    =  batch_size
    __a			:  str		    =  num_channels
    __a			:  int		    =  image_size
    __a			:  List[str]		    =  min_resolution
    __a			:  List[Any]		    =  max_resolution
    __a			:  Optional[Any]		    =  do_resize
    __a			:  Optional[Any]		    =  size
    __a			:  Optional[Any]		    =  do_normalize
    __a			:  Dict		    =  image_mean
    __a			:  Tuple		    =  image_std







   def 				lowerCAmelCase      (self			: Dict       ):
    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 		UpperCamelCase__  (	lowerCamelCase_   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				ViTImageProcessor if is_vision_available() else None
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  List[str]		    =  EfficientFormerImageProcessorTester(self       )
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    return self.image_proc_tester.prepare_image_processor_dict()
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(lowerCAmelCase__ ,    '''image_mean'''       )       )
    self.assertTrue(hasattr(lowerCAmelCase__ ,    '''image_std'''       )       )
    self.assertTrue(hasattr(lowerCAmelCase__ ,    '''do_normalize'''       )       )
    self.assertTrue(hasattr(lowerCAmelCase__ ,    '''do_resize'''       )       )
    self.assertTrue(hasattr(lowerCAmelCase__ ,    '''size'''       )       )
   def 				lowerCAmelCase      (self			: str       ):
    pass
   def 				lowerCAmelCase      (self			: List[str]       ):
    # Initialize image_processor
    __a			:  List[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    __a			:  int		    =  prepare_image_inputs(self.image_proc_tester ,    equal_resolution=lowerCAmelCase__       )
    for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__ ,    Image.Image       )

    # Test not batched input
    __a			:  Tuple		    =  image_processor(image_inputs[0] ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            1,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )

    # Test batched
    __a			:  List[Any]		    =  image_processor(lowerCAmelCase__ ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            self.image_proc_tester.batch_size,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    # Initialize image_processor
    __a			:  List[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    __a			:  str		    =  prepare_image_inputs(self.image_proc_tester ,    equal_resolution=lowerCAmelCase__ ,    numpify=lowerCAmelCase__       )
    for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__ ,    np.ndarray       )

    # Test not batched input
    __a			:  List[Any]		    =  image_processor(image_inputs[0] ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            1,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )

    # Test batched
    __a			:  int		    =  image_processor(lowerCAmelCase__ ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            self.image_proc_tester.batch_size,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )







   def 				lowerCAmelCase      (self			: Any       ):
    # Initialize image_processor
    __a			:  str		    =  self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    __a			:  Optional[Any]		    =  prepare_image_inputs(self.image_proc_tester ,    equal_resolution=lowerCAmelCase__ ,    torchify=lowerCAmelCase__       )
    for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__ ,    torch.Tensor       )

    # Test not batched input
    __a			:  Union[str, Any]		    =  image_processor(image_inputs[0] ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            1,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )

    # Test batched
    __a			:  Tuple		    =  image_processor(lowerCAmelCase__ ,    return_tensors='''pt'''       ).pixel_values
    self.assertEqual(
        encoded_images.shape ,    (
            self.image_proc_tester.batch_size,
            self.image_proc_tester.num_channels,
            self.image_proc_tester.size['''height'''],
            self.image_proc_tester.size['''width'''],
        ) ,    )


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


if __name__ == "__main__":
      print(F"""{solution() = }""")
