


"""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 	lowercase__				(					unittest.TestCase				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=224      ,   SCREAMING_SNAKE_CASE=30      ,   SCREAMING_SNAKE_CASE=400      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   )  ->  int:
      _lowerCamelCase	:					int   =		size if size is not None else {"""height""": 18, """width""": 18}
      _lowerCamelCase	:					int   =		parent
      _lowerCamelCase	:					Any   =		batch_size
      _lowerCamelCase	:					Union[str, Any]   =		num_channels
      _lowerCamelCase	:					Optional[int]   =		image_size
      _lowerCamelCase	:					Optional[int]   =		min_resolution
      _lowerCamelCase	:					Any   =		max_resolution
      _lowerCamelCase	:					Any   =		do_resize
      _lowerCamelCase	:					Tuple   =		size
      _lowerCamelCase	:					Tuple   =		do_normalize
      _lowerCamelCase	:					int   =		image_mean
      _lowerCamelCase	:					Union[str, Any]   =		image_std





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      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 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					ViTImageProcessor if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		EfficientFormerImageProcessorTester(self)





   @property
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      return self.image_proc_tester.prepare_image_processor_dict()





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					Optional[int]   =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_mean"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_std"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_normalize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_resize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """size"""))





   def 		UpperCamelCase_  (							self)  ->  str:
      pass





   def 		UpperCamelCase_  (							self)  ->  int:
      # Initialize image_processor
      _lowerCamelCase	:					Optional[Any]   =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowerCamelCase	:					Any   =		prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   Image.Image)

      # Test not batched input
      _lowerCamelCase	:					Tuple   =		image_processor(image_inputs[0]      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              1,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )

      # Test batched
      _lowerCamelCase	:					str   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_proc_tester.batch_size,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  int:
      # Initialize image_processor
      _lowerCamelCase	:					List[str]   =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      _lowerCamelCase	:					Optional[int]   =		prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   numpify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   np.ndarray)

      # Test not batched input
      _lowerCamelCase	:					Optional[int]   =		image_processor(image_inputs[0]      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              1,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )

      # Test batched
      _lowerCamelCase	:					List[Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_proc_tester.batch_size,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  str:
      # Initialize image_processor
      _lowerCamelCase	:					Tuple   =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowerCamelCase	:					int   =		prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   torchify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   torch.Tensor)

      # Test not batched input
      _lowerCamelCase	:					Any   =		image_processor(image_inputs[0]      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              1,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )

      # Test batched
      _lowerCamelCase	:					Union[str, Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_proc_tester.batch_size,
              self.image_proc_tester.num_channels,
              self.image_proc_tester.size["""height"""],
              self.image_proc_tester.size["""width"""],
          )      ,   )






import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


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