






'''simple docstring'''
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					:    List[str]					,       a					:    Optional[Any]					,       a					:    str=7					,       a					:    Union[str, Any]=3					,       a					:    List[str]=1_8					,       a					:    Any=3_0					,       a					:    Optional[int]=4_0_0					,       a					:    Any=True					,       a					:    Tuple=None					,       a					:    List[str]=True					,       a					:    Tuple=[0.5, 0.5, 0.5]					,       a					:    int=[0.5, 0.5, 0.5]					,       ):






       '''simple docstring'''






       lowercase_		:      List[str]          =       size if size is not None else {"height": 1_8, "width": 1_8}
       lowercase_		:      Optional[int]          =       parent
       lowercase_		:      Optional[int]          =       batch_size
       lowercase_		:      List[Any]          =       num_channels
       lowercase_		:      Optional[int]          =       image_size
       lowercase_		:      int          =       min_resolution
       lowercase_		:      int          =       max_resolution
       lowercase_		:      str          =       do_resize
       lowercase_		:      Dict          =       size
       lowercase_		:      Any          =       do_normalize
       lowercase_		:      Optional[int]          =       image_mean
       lowercase_		:      List[Any]          =       image_std





    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       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  (						snake_case			,   unittest.TestCase     ):
    __lowerCamelCase:				Union[str, Any]						    =      ViTImageProcessor if is_vision_available() else None




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Any          =       ViTImageProcessingTester(self     )




    @property
    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       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					,       "size"     )     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      List[str]          =       self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size					,       {"height": 1_8, "width": 1_8}     )

       lowercase_		:      str          =       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 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       pass




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.image_processing_class(**self.image_processor_dict     )
       # create random PIL images
       lowercase_		:      Union[str, Any]          =       prepare_image_inputs(self.image_processor_tester					,       equal_resolution=a     )
       for image in image_inputs:
          self.assertIsInstance(a					,       Image.Image     )

       # Test not batched input
       lowercase_		:      Dict          =       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
       lowercase_		:      Union[str, Any]          =       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.size["height"],
               self.image_processor_tester.size["width"],
           )					,       )




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       lowercase_		:      Tuple          =       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
       lowercase_		:      Optional[int]          =       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
       lowercase_		:      Union[str, Any]          =       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.size["height"],
               self.image_processor_tester.size["width"],
           )					,       )





    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       lowercase_		:      Union[str, Any]          =       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
       lowercase_		:      List[str]          =       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
       lowercase_		:      str          =       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.size["height"],
               self.image_processor_tester.size["width"],
           )					,       )












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




