





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase : List[Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		['''pixel_values''']




		def __init__(self      :	Tuple							,			A__      :	bool = True							,			A__      :	Optional[Dict[str, int]] = None							,			A__      :	PILImageResampling = PILImageResampling.BICUBIC							,			A__      :	bool = True							,			A__      :	bool = True							,			A__      :	Union[int, float] = 1 / 2_5_5							,			A__      :	Dict[str, int] = None							,			A__      :	bool = True							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			**A__      :	Any							,			)					-> None:
						super().__init__(**A__				)
						lowercase         = size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						lowercase         = get_size_dict(A__				)
						lowercase         = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						lowercase         = get_size_dict(A__							,			default_to_square=A__							,			param_name="crop_size"				)

						lowercase         = do_resize
						lowercase         = do_rescale
						lowercase         = do_normalize
						lowercase         = do_center_crop
						lowercase         = crop_size
						lowercase         = size
						lowercase         = resample
						lowercase         = rescale_factor
						lowercase         = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						lowercase         = image_std if image_std is not None else IMAGENET_DEFAULT_STD




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	PILImageResampling = PILImageResampling.BILINEAR							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Dict							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__				)

						if "shortest_edge" in size:
										lowercase         = get_resize_output_image_size(A__							,			size=size["shortest_edge"]							,			default_to_square=A__				)
										# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
										lowercase         = (size["height"], size["width"])
						else:
										raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'				)
						return resize(A__							,			size=A__							,			resample=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Optional[int]							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__				)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'				)
						return center_crop(A__							,			size=(size["height"], size["width"])							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	np.ndarray							,			A__      :	float							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Dict				)					-> np.ndarray:
						return rescale(A__							,			scale=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	np.ndarray							,			A__      :	Union[float, List[float]]							,			A__      :	Union[float, List[float]]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	str							,			)					-> np.ndarray:
						return normalize(A__							,			mean=A__							,			std=A__							,			data_format=A__							,			**A__				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	ImageInput							,			A__      :	Optional[bool] = None							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = None							,			A__      :	bool = None							,			A__      :	int = None							,			A__      :	Optional[bool] = None							,			A__      :	Optional[float] = None							,			A__      :	Optional[bool] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			A__      :	Union[str, ChannelDimension] = ChannelDimension.FIRST							,			**A__      :	Optional[Any]							,			)					-> BatchFeature:
						lowercase         = do_resize if do_resize is not None else self.do_resize
						lowercase         = do_rescale if do_rescale is not None else self.do_rescale
						lowercase         = do_normalize if do_normalize is not None else self.do_normalize
						lowercase         = do_center_crop if do_center_crop is not None else self.do_center_crop
						lowercase         = crop_size if crop_size is not None else self.crop_size
						lowercase         = get_size_dict(A__							,			param_name="crop_size"							,			default_to_square=A__				)
						lowercase         = resample if resample is not None else self.resample
						lowercase         = rescale_factor if rescale_factor is not None else self.rescale_factor
						lowercase         = image_mean if image_mean is not None else self.image_mean
						lowercase         = image_std if image_std is not None else self.image_std

						lowercase         = size if size is not None else self.size
						lowercase         = get_size_dict(A__				)

						if not is_batched(A__				):
										lowercase         = [images]

						if not valid_images(A__				):
										raise ValueError(
										    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
										    "torch.Tensor, tf.Tensor or jax.ndarray."				)

						if do_resize and size is None:
										raise ValueError("Size must be specified if do_resize is True."				)

						if do_center_crop and crop_size is None:
										raise ValueError("Crop size must be specified if do_center_crop is True."				)

						if do_rescale and rescale_factor is None:
										raise ValueError("Rescale factor must be specified if do_rescale is True."				)

						# All transformations expect numpy arrays.
						lowercase         = [to_numpy_array(A__				) for image in images]

						if do_resize:
										lowercase         = [self.resize(image=A__							,			size=A__							,			resample=A__				) for image in images]

						if do_center_crop:
										lowercase         = [self.center_crop(image=A__							,			size=A__				) for image in images]

						if do_rescale:
										lowercase         = [self.rescale(image=A__							,			scale=A__				) for image in images]

						if do_normalize:
										lowercase         = [self.normalize(image=A__							,			mean=A__							,			std=A__				) for image in images]

						lowercase         = [to_channel_dimension_format(A__							,			A__				) for image in images]

						lowercase         = {"pixel_values": images}
						return BatchFeature(data=A__							,			tensor_type=A__				)











'''simple docstring'''

__lowerCamelCase : str											=			8.314_4598







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






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




