
"""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 A_      (unittest.TestCase ):




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=7							,  lowercase_=3							,  lowercase_=18							,  lowercase_=30							,  lowercase_=400							,  lowercase_=True							,  lowercase_=None							,  lowercase_=True							,  lowercase_=[0.5, 0.5, 0.5]							,  lowercase_=[0.5, 0.5, 0.5]							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       size if size is not None else {"height": 18, "width": 18}
     UpperCAmelCase_   :   Tuple     	=       parent
     UpperCAmelCase_   :   List[Any]     	=       batch_size
     UpperCAmelCase_   :   Any     	=       num_channels
     UpperCAmelCase_   :   List[str]     	=       image_size
     UpperCAmelCase_   :   Tuple     	=       min_resolution
     UpperCAmelCase_   :   int     	=       max_resolution
     UpperCAmelCase_   :   Any     	=       do_resize
     UpperCAmelCase_   :   Dict     	=       size
     UpperCAmelCase_   :   List[Any]     	=       do_normalize
     UpperCAmelCase_   :   Union[str, Any]     	=       image_mean
     UpperCAmelCase_   :   List[str]     	=       image_std







  def 			UpperCamelCase__   (						self    ):

     """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 A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						ViTImageProcessor if is_vision_available() else None







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       ViTImageProcessingTester(self    )







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       self.image_processing_class(**self.image_processor_dict    )
     self.assertTrue(hasattr(lowercase_							,  "image_mean"    )    )
     self.assertTrue(hasattr(lowercase_							,  "image_std"    )    )
     self.assertTrue(hasattr(lowercase_							,  "do_normalize"    )    )
     self.assertTrue(hasattr(lowercase_							,  "do_resize"    )    )
     self.assertTrue(hasattr(lowercase_							,  "size"    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       self.image_processing_class.from_dict(self.image_processor_dict    )
     self.assertEqual(image_processor.size							,  {"height": 18, "width": 18}    )

     UpperCAmelCase_   :   Dict     	=       self.image_processing_class.from_dict(self.image_processor_dict							,  size=42    )
     self.assertEqual(image_processor.size							,  {"height": 42, "width": 42}    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_   :   List[Any]     	=       self.image_processing_class(**self.image_processor_dict    )
     # create random PIL images
     UpperCAmelCase_   :   Optional[int]     	=       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowercase_    )
     for image in image_inputs:
        self.assertIsInstance(lowercase_							,  Image.Image    )

     # Test not batched input
     UpperCAmelCase_   :   Union[str, Any]     	=       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
     UpperCAmelCase_   :   Union[str, Any]     	=       image_processing(lowercase_							,  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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_   :   Tuple     	=       self.image_processing_class(**self.image_processor_dict    )
     # create random numpy tensors
     UpperCAmelCase_   :   Dict     	=       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowercase_							,  numpify=lowercase_    )
     for image in image_inputs:
        self.assertIsInstance(lowercase_							,  np.ndarray    )

     # Test not batched input
     UpperCAmelCase_   :   Tuple     	=       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
     UpperCAmelCase_   :   Union[str, Any]     	=       image_processing(lowercase_							,  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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_   :   int     	=       self.image_processing_class(**self.image_processor_dict    )
     # create random PyTorch tensors
     UpperCAmelCase_   :   Optional[Any]     	=       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowercase_							,  torchify=lowercase_    )
     for image in image_inputs:
        self.assertIsInstance(lowercase_							,  torch.Tensor    )

     # Test not batched input
     UpperCAmelCase_   :   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
     UpperCAmelCase_   :   List[str]     	=       image_processing(lowercase_							,  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'''




import sys



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     int:
							if not numbers:
														return 0
							if target == 0 and target in numbers:
														return 0
							if not isinstance(_lowercase      ,							(list, tuple)						) or not all(
							    isinstance(_lowercase      ,							_lowercase						) for number in numbers						):
														raise ValueError("""numbers must be an iterable of integers"""						)

							UpperCAmelCase	:						Optional[Any]					=						0
							UpperCAmelCase	:						List[str]					=						sys.maxsize

							while right < len(_lowercase						):
														curr_sum += numbers[right]
														while curr_sum >= target and left <= right:
																					UpperCAmelCase	:						Optional[Any]					=						min(_lowercase      ,							right - left + 1						)
																					curr_sum -= numbers[left]
																					left += 1
														right += 1

							return 0 if min_len == sys.maxsize else min_len

