

"""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      __lowercase			(    unittest.TestCase					):

  def __init__(  self		:			Any					,A		:			List[Any]					,A		:			List[str]=7					,A		:			Dict=3					,A		:			Optional[int]=18					,A		:			str=30					,A		:			Optional[int]=400					,A		:			Optional[Any]=True					,A		:			Dict=None					,A		:			Optional[int]=True					,A		:			Union[str, Any]=None					,A		:			List[str]=True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					size if size is not None else {"""shortest_edge""": 20}
   UpperCAmelCase__					:  Optional[Any]					   =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   UpperCAmelCase__					:  str					   =					parent
   UpperCAmelCase__					:  str					   =					batch_size
   UpperCAmelCase__					:  Dict					   =					num_channels
   UpperCAmelCase__					:  List[Any]					   =					image_size
   UpperCAmelCase__					:  Dict					   =					min_resolution
   UpperCAmelCase__					:  Tuple					   =					max_resolution
   UpperCAmelCase__					:  List[Any]					   =					do_resize
   UpperCAmelCase__					:  Optional[Any]					   =					size
   UpperCAmelCase__					:  List[str]					   =					do_center_crop
   UpperCAmelCase__					:  Union[str, Any]					   =					crop_size
   UpperCAmelCase__					:  Dict					   =					do_flip_channel_order




  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   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      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       MobileViTImageProcessor if is_vision_available() else None

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					MobileViTImageProcessingTester(self    )

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






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

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.image_processing_class(**self.image_processor_dict    )
   self.assertTrue(hasattr(A					,"""do_resize"""    )    )
   self.assertTrue(hasattr(A					,"""size"""    )    )
   self.assertTrue(hasattr(A					,"""do_center_crop"""    )    )
   self.assertTrue(hasattr(A					,"""center_crop"""    )    )
   self.assertTrue(hasattr(A					,"""do_flip_channel_order"""    )    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.image_processing_class.from_dict(self.image_processor_dict    )
   self.assertEqual(image_processor.size					,{"""shortest_edge""": 20}    )
   self.assertEqual(image_processor.crop_size					,{"""height""": 18, """width""": 18}    )

   UpperCAmelCase__					:  Optional[int]					   =					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    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   UpperCAmelCase__					:  Optional[int]					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,Image.Image    )

   # Test not batched input
   UpperCAmelCase__					:  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  Tuple					   =					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    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  List[Any]					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   UpperCAmelCase__					:  Union[str, Any]					   =					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
   UpperCAmelCase__					:  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  Tuple					   =					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    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase__					:  int					   =					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
   UpperCAmelCase__					:  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  List[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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)








"""simple docstring"""




import random
from typing import Any
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Dict					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__					:  Tuple					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					data[b], data[a]
 return data


if __name__ == "__main__":
      __UpperCAmelCase =				[0, 1, 2, 3, 4, 5, 6, 7]
      __UpperCAmelCase =				['python', 'says', 'hello', '!']
      print('Fisher-Yates Shuffle:')
      print('List', integers, strings)
      print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))





