
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_video_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import VivitImageProcessor







class 					lowerCamelCase		(  unittest.TestCase  ):





     def __init__(self				:   Union[str, Any]    ,   _A				:   Optional[int]    ,   _A				:   Dict=7    ,   _A				:   List[str]=3    ,   _A				:   Tuple=1_0    ,   _A				:   Tuple=1_8    ,   _A				:   Optional[int]=3_0    ,   _A				:   List[str]=4_0_0    ,   _A				:   Optional[int]=True    ,   _A				:   List[Any]=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   str=[0.5, 0.5, 0.5]    ,   _A				:   Union[str, Any]=[0.5, 0.5, 0.5]    ,   _A				:   Union[str, Any]=None    ,   )						->						Tuple:
         snake_case								= size if size is not None else {"shortest_edge": 1_8}
         snake_case								= crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

         snake_case								= parent
         snake_case								= batch_size
         snake_case								= num_channels
         snake_case								= num_frames
         snake_case								= image_size
         snake_case								= min_resolution
         snake_case								= max_resolution
         snake_case								= do_resize
         snake_case								= size
         snake_case								= do_normalize
         snake_case								= image_mean
         snake_case								= image_std
         snake_case								= crop_size





     def 		UpperCAmelCase(self				:   List[Any]       )						->						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,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class 					lowerCamelCase		(  A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							List[str]									=		VivitImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase(self				:   List[str]       )						->						Any:
         snake_case								= VivitImageProcessingTester(self       )





     @property
     def 		UpperCAmelCase(self				:   str       )						->						Any:
         return self.image_processor_tester.prepare_image_processor_dict()





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

         snake_case								= self.image_processing_class.from_dict(self.image_processor_dict    ,   size=4_2    ,   crop_size=8_4       )
         self.assertEqual(image_processor.size    ,   {"shortest_edge": 4_2}       )
         self.assertEqual(image_processor.crop_size    ,   {"height": 8_4, "width": 8_4}       )





     def 		UpperCAmelCase(self				:   int       )						->						Any:
         # Initialize image_processing
         snake_case								= self.image_processing_class(**self.image_processor_dict       )
         # create random PIL videos
         snake_case								= prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=_A       )
         for video in video_inputs:
             self.assertIsInstance(_A    ,   _A       )
             self.assertIsInstance(video[0]    ,   Image.Image       )

         # Test not batched input
         snake_case								= image_processing(video_inputs[0]    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )

         # Test batched
         snake_case								= image_processing(_A    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Optional[Any]:
         # Initialize image_processing
         snake_case								= self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case								= prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=_A    ,   numpify=_A       )
         for video in video_inputs:
             self.assertIsInstance(_A    ,   _A       )
             self.assertIsInstance(video[0]    ,   np.ndarray       )

         # Test not batched input
         snake_case								= image_processing(video_inputs[0]    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )

         # Test batched
         snake_case								= image_processing(_A    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Union[str, Any]:
         # Initialize image_processing
         snake_case								= self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case								= prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=_A    ,   torchify=_A       )
         for video in video_inputs:
             self.assertIsInstance(_A    ,   _A       )
             self.assertIsInstance(video[0]    ,   torch.Tensor       )

         # Test not batched input
         snake_case								= image_processing(video_inputs[0]    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )

         # Test batched
         snake_case								= image_processing(_A    ,   return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_videos.shape    ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )    ,   )



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

