

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 DeiTImageProcessor







class 		__magic_name__     (				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_=None,       lowercase_=True,       lowercase_=[0.5, 0.5, 0.5],       lowercase_=[0.5, 0.5, 0.5],       )				->       Any:






     """simple docstring"""






     a__    =size if size is not None else {'''height''': 20, '''width''': 20}
     a__    =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

     a__    =parent
     a__    =batch_size
     a__    =num_channels
     a__    =image_size
     a__    =min_resolution
     a__    =max_resolution
     a__    =do_resize
     a__    =size
     a__    =do_center_crop
     a__    =crop_size
     a__    =do_normalize
     a__    =image_mean
     a__    =image_std



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }







@require_torch
@require_vision
class 		__magic_name__     (				UpperCAmelCase_						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  DeiTImageProcessor if is_vision_available() else None
   lowerCamelCase__					:      Optional[Any]        =  True



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =DeiTImageProcessingTester(self			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =self.image_processing_class(**self.image_processor_dict			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''do_resize'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''size'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''do_center_crop'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''center_crop'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''do_normalize'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''image_mean'''			)			)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,       '''image_std'''			)			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     a__    =self.image_processing_class.from_dict(self.image_processor_dict			)
     self.assertEqual(image_processor.size,       {'''height''': 20, '''width''': 20}			)
     self.assertEqual(image_processor.crop_size,       {'''height''': 18, '''width''': 18}			)

     a__    =self.image_processing_class.from_dict(self.image_processor_dict,       size=42,       crop_size=84			)
     self.assertEqual(image_processor.size,       {'''height''': 42, '''width''': 42}			)
     self.assertEqual(image_processor.crop_size,       {'''height''': 84, '''width''': 84}			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     pass



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     a__    =self.image_processing_class(**self.image_processor_dict			)
     # create random PIL images
     a__    =prepare_image_inputs(self.image_processor_tester,       equal_resolution=SCREAMING_SNAKE_CASE_			)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE_,       Image.Image			)

     # Test not batched input
     a__    =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
     a__    =image_processing(SCREAMING_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 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =self.image_processing_class(**self.image_processor_dict			)
     # create random numpy tensors
     a__    =prepare_image_inputs(self.image_processor_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
     a__    =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
     a__    =image_processing(SCREAMING_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 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     a__    =self.image_processing_class(**self.image_processor_dict			)
     # create random PyTorch tensors
     a__    =prepare_image_inputs(self.image_processor_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
     a__    =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
     a__    =image_processing(SCREAMING_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'''],
         ),       )






'''simple docstring'''


from __future__ import annotations

import random






class 						lowerCAmelCase_	:







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				6



  def __init__( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Union[str, Any]          =		list(range(1					,  Dice.NUM_SIDES + 1	)	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       return random.choice(self.sides	)






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase = 2	)			-> list[float]:
     A:		Union[str, Any]          =		[Dice() for i in range(__lowercase	)]
     A:		Union[str, Any]          =		[0] * (len(__lowercase	) * Dice.NUM_SIDES + 1)
     for _ in range(__lowercase	):
          count_of_sum[sum(dice.roll() for dice in dices	)] += 1
     A:		Dict          =		[round((count * 1_0_0) / num_throws		,				2	) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()






