



a_   :Union[str, Any]        =      8.3_14_45_98
def        lowercase_							(A : float		,					A : 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
 a_   :Optional[int]        =      300
 a_   :List[Any]        =      28
 a_   :Union[str, Any]        =      rms_speed_of_molecule(temperature, molar_mass)
 print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")











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 PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "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 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[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
            snake_case__							:	str   	=	image_processing(_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    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	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
            snake_case__							:	List[Any]   	=	image_processing(_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    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	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
            snake_case__							:	Optional[Any]   	=	image_processing(_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'],
                ), )






