



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 ViltImageProcessor

class       UpperCamelCase  (					unittest.TestCase				):





     """simple docstring"""






     def __init__(					self :							Union[str, Any]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							int=7	,      _lowerCamelCase :							List[str]=3	,      _lowerCamelCase :							Dict=1_8	,      _lowerCamelCase :							Optional[int]=3_0	,      _lowerCamelCase :							Optional[Any]=4_0_0	,      _lowerCamelCase :							int=True	,      _lowerCamelCase :							Dict=None	,      _lowerCamelCase :							List[str]=2	,      _lowerCamelCase :							Optional[Any]=True	,      _lowerCamelCase :							Optional[Any]=[0.5, 0.5, 0.5]	,      _lowerCamelCase :							str=[0.5, 0.5, 0.5]	,      ):
          A__				=   size if size is not None else {'''shortest_edge''': 3_0}
          A__				=   parent
          A__				=   batch_size
          A__				=   num_channels
          A__				=   image_size
          A__				=   min_resolution
          A__				=   max_resolution
          A__				=   do_resize
          A__				=   size
          A__				=   size_divisor
          A__				=   do_normalize
          A__				=   image_mean
          A__				=   image_std





     def 						A__  (					self :							Union[str, Any] ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[Any]=False ):
          if not batched:
               A__				=   self.size['''shortest_edge''']
               A__				=   image_inputs[0]
               if isinstance(_lowerCamelCase	,      Image.Image ):
                    A__	,     A__				=   image.size
               else:
                    A__	,     A__				=   image.shape[1], image.shape[2]
               A__				=   size / min(_lowerCamelCase	,      _lowerCamelCase )
               if h < w:
                    A__	,     A__				=   size, scale * w
               else:
                    A__	,     A__				=   scale * h, size

               A__				=   int((1_3_3_3 / 8_0_0) * size )
               if max(_lowerCamelCase	,      _lowerCamelCase ) > max_size:
                    A__				=   max_size / max(_lowerCamelCase	,      _lowerCamelCase )
                    A__				=   newh * scale
                    A__				=   neww * scale

               A__	,     A__				=   int(newh + 0.5 ), int(neww + 0.5 )
               A__	,     A__				=   (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

          else:
               A__				=   []
               for image in image_inputs:
                    A__	,     A__				=   self.get_expected_values([image] )
                    expected_values.append((expected_height, expected_width) )
               A__				=   max(_lowerCamelCase	,      key=lambda _lowerCamelCase : item[0] )[0]
               A__				=   max(_lowerCamelCase	,      key=lambda _lowerCamelCase : item[1] )[1]

          return expected_height, expected_width


@require_torch
@require_vision
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Union[str, Any]				   =ViltImageProcessor if is_vision_available() else None





     def 						A__  (					self :							Union[str, Any] ):
          A__				=   ViltImageProcessingTester(self )





     @property
     def 						A__  (					self :							str ):
          return self.image_processor_tester.prepare_image_processor_dict()





     def 						A__  (					self :							List[str] ):
          A__				=   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''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''size_divisor''' ) )





     def 						A__  (					self :							str ):
          A__				=   self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size	,      {'''shortest_edge''': 3_0} )

          A__				=   self.image_processing_class.from_dict(self.image_processor_dict	,      size=4_2 )
          self.assertEqual(image_processor.size	,      {'''shortest_edge''': 4_2} )





     def 						A__  (					self :							Tuple ):
          pass





     def 						A__  (					self :							Tuple ):
          # Initialize image_processing
          A__				=   self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase )
          for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase	,      Image.Image )

          # Test not batched input
          A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase	,      batched=_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,      )





     def 						A__  (					self :							List[str] ):
          # Initialize image_processing
          A__				=   self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase	,      numpify=_lowerCamelCase )
          for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase	,      np.ndarray )

          # Test not batched input
          A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase	,      batched=_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,      )





     def 						A__  (					self :							Tuple ):
          # Initialize image_processing
          A__				=   self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase	,      torchify=_lowerCamelCase )
          for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase	,      torch.Tensor )

          # Test not batched input
          A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values

          A__	,     A__				=   self.image_processor_tester.get_expected_values(_lowerCamelCase	,      batched=_lowerCamelCase )
          self.assertEqual(
              encoded_images.shape	,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,      )








"""simple docstring"""







def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''recursive_lucas_number accepts only integer arguments.'''      )
     if n_th_number == 0:
          return 2
     if n_th_number == 1:
          return 1

     return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
         n_th_number - 2      )



def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''      )
     A__	,     A__				=   2, 1
     for _ in range(__a      ):
          A__	,     A__				=   b, a + b
     return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case	:				Dict  					=			int(input('Enter the number of terms in lucas series:\n').strip())
 print('Using recursive function to calculate lucas series:')
 print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
 print('\nUsing dynamic function to calculate lucas series:')
 print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))



