




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=18  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						str:
         UpperCAmelCase_     :				List[Any]  =     parent
         UpperCAmelCase_     :				Any  =     batch_size
         UpperCAmelCase_     :				Optional[Any]  =     num_channels
         UpperCAmelCase_     :				Any  =     image_size
         UpperCAmelCase_     :				str  =     min_resolution
         UpperCAmelCase_     :				List[str]  =     max_resolution
         UpperCAmelCase_     :				Union[str, Any]  =     do_resize
         UpperCAmelCase_     :				int  =     size if size is not None else {'''height''': 18, '''width''': 20}
         UpperCAmelCase_     :				Any  =     do_thumbnail
         UpperCAmelCase_     :				str  =     do_align_axis
         UpperCAmelCase_     :				int  =     do_pad
         UpperCAmelCase_     :				Optional[int]  =     do_normalize
         UpperCAmelCase_     :				List[Any]  =     image_mean
         UpperCAmelCase_     :				Tuple  =     image_std





    def 				a__ ( self    )					->						Optional[Any]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	lowercase_							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = DonutImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     DonutImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_lowercase  ,'''do_resize'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''size'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''do_thumbnail'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''do_align_long_axis'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''do_pad'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_lowercase  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''height''': 18, '''width''': 20}    )

         UpperCAmelCase_     :				int  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42    )
         self.assertEqual(image_processor.size  ,{'''height''': 42, '''width''': 42}    )

         # Previous config had dimensions in (width, height) order
         UpperCAmelCase_     :				str  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=(42, 84)    )
         self.assertEqual(image_processor.size  ,{'''height''': 84, '''width''': 42}    )





    def 				a__ ( self    )					->						int:
         pass





    @is_flaky()
    def 				a__ ( self    )					->						Optional[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Tuple  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				Optional[int]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_lowercase    )
         for image in image_inputs:
              self.assertIsInstance(_lowercase  ,Image.Image    )

         # Test not batched input
         UpperCAmelCase_     :				Optional[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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Optional[int]  =     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'''],
             )  ,)





    @is_flaky()
    def 				a__ ( self    )					->						Union[str, Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Dict  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				int  =     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
         UpperCAmelCase_     :				List[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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				int  =     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'''],
             )  ,)





    @is_flaky()
    def 				a__ ( self    )					->						str:
         # Initialize image_processing
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_     :				str  =     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
         UpperCAmelCase_     :				List[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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Dict  =     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'''

SCREAMING_SNAKE_CASE							:				int     	=			8.3_1_4_4_5_9_8




def 							_UpperCamelCase				(   lowerCAmelCase__:					float		,lowerCAmelCase__:					float   )      -> float:
							if temperature < 0:
														raise Exception('Temperature cannot be less than 0 K'   )
							if molar_mass <= 0:
														raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'   )
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							SCREAMING_SNAKE_CASE							:				List[str]     	=			300
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			28
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			rms_speed_of_molecule(temperature, molar_mass)
							print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")





