

'''simple docstring'''






import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor






class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  str      ,lowercase__     :  Optional[int]=7      ,lowercase__     :  Tuple=3      ,lowercase__     :  List[Any]=1_8      ,lowercase__     :  Any=3_0      ,lowercase__     :  Optional[int]=4_0_0      ,lowercase__     :  List[Any]=True      ,lowercase__     :  List[str]=None      ,lowercase__     :  Tuple=True      ,lowercase__     :  Dict=[0.5, 0.5, 0.5]      ,lowercase__     :  Tuple=[0.5, 0.5, 0.5]      ,):
   __lowercase													=				size if size is not None else {'''height''': 1_8, '''width''': 1_8}
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				num_channels
   __lowercase													=				image_size
   __lowercase													=				min_resolution
   __lowercase													=				max_resolution
   __lowercase													=				do_resize
   __lowercase													=				size
   __lowercase													=				do_normalize
   __lowercase													=				image_mean
   __lowercase													=				image_std






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   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_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    DPTImageProcessor if is_vision_available() else None






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				DPTImageProcessingTester(self    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   return self.image_processor_tester.prepare_image_processor_dict()






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.image_processing_class.from_dict(self.image_processor_dict    )
   self.assertEqual(image_processor.size      ,{'''height''': 1_8, '''width''': 1_8}    )

   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   __lowercase													=				prepare_image_inputs(self.image_processor_tester      ,equal_resolution=lowercase__    )
   for image in image_inputs:
    self.assertIsInstance(lowercase__      ,Image.Image    )

   # Test not batched input
   __lowercase													=				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													=				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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   __lowercase													=				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
   __lowercase													=				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													=				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       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   __lowercase													=				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
   __lowercase													=				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													=				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 random
from typing import Any


def 	_A  (					A__   ):






 """simple docstring"""



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


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

