





'''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 MobileViTImageProcessor







class     A_      (    unittest.TestCase     ):




 def __init__(   self			:       Dict      ,     snake_case_			:       int      ,     snake_case_			:       Dict=7      ,     snake_case_			:       List[Any]=3      ,     snake_case_			:       int=1_8      ,     snake_case_			:       int=3_0      ,     snake_case_			:       Tuple=4_0_0      ,     snake_case_			:       Optional[Any]=True      ,     snake_case_			:       Optional[Any]=None      ,     snake_case_			:       Any=True      ,     snake_case_			:       Dict=None      ,     snake_case_			:       Optional[int]=True      ,     ):
      _UpperCAmelCase					=  size if size is not None else {"shortest_edge": 2_0}
      _UpperCAmelCase					=  crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      _UpperCAmelCase					=  parent
      _UpperCAmelCase					=  batch_size
      _UpperCAmelCase					=  num_channels
      _UpperCAmelCase					=  image_size
      _UpperCAmelCase					=  min_resolution
      _UpperCAmelCase					=  max_resolution
      _UpperCAmelCase					=  do_resize
      _UpperCAmelCase					=  size
      _UpperCAmelCase					=  do_center_crop
      _UpperCAmelCase					=  crop_size
      _UpperCAmelCase					=  do_flip_channel_order




 def 	lowercase      (   self			:       Optional[Any]       ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }







@require_torch
@require_vision
class     A_      (    _lowercase				,		unittest.TestCase     ):
 _lowerCamelCase    :					List[str]       			=				MobileViTImageProcessor if is_vision_available() else None




 def 	lowercase      (   self			:       List[str]       ):
      _UpperCAmelCase					=  MobileViTImageProcessingTester(self       )




 @property
 def 	lowercase      (   self			:       List[Any]       ):
      return self.image_processor_tester.prepare_image_processor_dict()




 def 	lowercase      (   self			:       List[Any]       ):
      _UpperCAmelCase					=  self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(snake_case_      ,     "do_resize"       )       )
      self.assertTrue(hasattr(snake_case_      ,     "size"       )       )
      self.assertTrue(hasattr(snake_case_      ,     "do_center_crop"       )       )
      self.assertTrue(hasattr(snake_case_      ,     "center_crop"       )       )
      self.assertTrue(hasattr(snake_case_      ,     "do_flip_channel_order"       )       )




 def 	lowercase      (   self			:       Union[str, Any]       ):
      _UpperCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size      ,     {"shortest_edge": 2_0}       )
      self.assertEqual(image_processor.crop_size      ,     {"height": 1_8, "width": 1_8}       )

      _UpperCAmelCase					=  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 	lowercase      (   self			:       List[str]       ):
      pass




 def 	lowercase      (   self			:       Dict       ):
      # Initialize image_processing
      _UpperCAmelCase					=  self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      _UpperCAmelCase					=  prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=snake_case_       )
      for image in image_inputs:
           self.assertIsInstance(snake_case_      ,     Image.Image       )

      # Test not batched input
      _UpperCAmelCase					=  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
      _UpperCAmelCase					=  image_processing(snake_case_      ,     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 	lowercase      (   self			:       Optional[Any]       ):
      # Initialize image_processing
      _UpperCAmelCase					=  self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      _UpperCAmelCase					=  prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=snake_case_      ,     numpify=snake_case_       )
      for image in image_inputs:
           self.assertIsInstance(snake_case_      ,     np.ndarray       )

      # Test not batched input
      _UpperCAmelCase					=  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
      _UpperCAmelCase					=  image_processing(snake_case_      ,     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 	lowercase      (   self			:       List[Any]       ):
      # Initialize image_processing
      _UpperCAmelCase					=  self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      _UpperCAmelCase					=  prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=snake_case_      ,     torchify=snake_case_       )
      for image in image_inputs:
           self.assertIsInstance(snake_case_      ,     torch.Tensor       )

      # Test not batched input
      _UpperCAmelCase					=  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
      _UpperCAmelCase					=  image_processing(snake_case_      ,     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"],
          )      ,     )









a_      :      str    =     6_55_21






def     __a					(		__UpperCAmelCase       ):
 a__												= 1
 a__												= 0
 for plain_chr in plain_text:
  a__												= (a + ord(__UpperCAmelCase       )) % MOD_ADLER
  a__												= (b + a) % MOD_ADLER
 return (b << 16) | a





