




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 LevitImageProcessor







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[Any]		,							A_      :						Optional[Any]		,							A_      :						Optional[Any]=7		,							A_      :						Optional[Any]=3		,							A_      :						List[Any]=18		,							A_      :						Dict=30		,							A_      :						Dict=400		,							A_      :						List[Any]=True		,							A_      :						int=None		,							A_      :						Any=True		,							A_      :						Optional[int]=None		,							A_      :						str=True		,							A_      :						Any=[0.5, 0.5, 0.5]		,							A_      :						int=[0.5, 0.5, 0.5]		,							)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					size if size is not None else {'shortest_edge': 18}
       lowerCamelCase_	  =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					num_channels
       lowerCamelCase_	  =					image_size
       lowerCamelCase_	  =					min_resolution
       lowerCamelCase_	  =					max_resolution
       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       lowerCamelCase_	  =					do_center_crop
       lowerCamelCase_	  =					crop_size
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean
       lowerCamelCase_	  =					image_std





 def        a__				(       self      :						Dict						)     ->			Dict:

       """simple docstring"""






       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }







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



 '''simple docstring'''


 UpperCamelCase		     =		LevitImageProcessor if is_vision_available() else None

 def        a__				(       self      :						int						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					LevitImageProcessingTester(self						)

 @property
 def        a__				(       self      :						Optional[int]						)     ->			int:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def        a__				(       self      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(A_		,							'image_mean'						)						)
       self.assertTrue(hasattr(A_		,							'image_std'						)						)
       self.assertTrue(hasattr(A_		,							'do_normalize'						)						)
       self.assertTrue(hasattr(A_		,							'do_resize'						)						)
       self.assertTrue(hasattr(A_		,							'do_center_crop'						)						)
       self.assertTrue(hasattr(A_		,							'size'						)						)

 def        a__				(       self      :						Dict						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size		,							{'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size		,							{'height': 18, 'width': 18}						)

       lowerCamelCase_	  =					self.image_processing_class.from_dict(self.image_processor_dict		,							size=42		,							crop_size=84						)
       self.assertEqual(image_processor.size		,							{'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size		,							{'height': 84, 'width': 84}						)

 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       pass

 def        a__				(       self      :						Tuple						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowerCamelCase_	  =					prepare_image_inputs(self.image_processor_tester		,							equal_resolution=A_						)
       for image in image_inputs:
             self.assertIsInstance(A_		,							Image.Image						)

       # Test not batched input
       lowerCamelCase_	  =					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_	  =					image_processing(A_		,							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        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowerCamelCase_	  =					prepare_image_inputs(self.image_processor_tester		,							equal_resolution=A_		,							numpify=A_						)
       for image in image_inputs:
             self.assertIsInstance(A_		,							np.ndarray						)

       # Test not batched input
       lowerCamelCase_	  =					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_	  =					image_processing(A_		,							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        a__				(       self      :						Dict						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowerCamelCase_	  =					prepare_image_inputs(self.image_processor_tester		,							equal_resolution=A_		,							torchify=A_						)
       for image in image_inputs:
             self.assertIsInstance(A_		,							torch.Tensor						)

       # Test not batched input
       lowerCamelCase_	  =					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_	  =					image_processing(A_		,							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     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					0
      print(lowercase			,							end=','  )

      # Consider rest of the activities
      for j in range(lowercase  ):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase			,							end=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



