


'''simple docstring'''
from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase__          = logging.get_logger(__name__)




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	['pixel_values']






      def __init__(   self							:       List[str]		,		_A							:       bool = True		,		_A							:       Dict[str, int] = None		,		_A							:       PILImageResampling = PILImageResampling.BICUBIC		,		_A							:       bool = True		,		_A							:       Dict[str, int] = None		,		_A							:       bool = True		,		_A							:       Union[int, float] = 1 / 255		,		_A							:       bool = True		,		_A							:       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,		_A							:       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,		**_A							:       int		,		):


          '''simple docstring'''


          super().__init__(**_A					)
          UpperCAmelCase__     :						Dict						=							size if size is not None else {'''shortest_edge''': 224}
          UpperCAmelCase__     :						int						=							get_size_dict(_A		,		default_to_square=_A					)
          UpperCAmelCase__     :						Union[str, Any]						=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          UpperCAmelCase__     :						List[str]						=							get_size_dict(_A		,		param_name='''crop_size'''					)

          UpperCAmelCase__     :						str						=							do_resize
          UpperCAmelCase__     :						List[Any]						=							size
          UpperCAmelCase__     :						int						=							resample
          UpperCAmelCase__     :						int						=							do_center_crop
          UpperCAmelCase__     :						List[str]						=							crop_size
          UpperCAmelCase__     :						Union[str, Any]						=							do_rescale
          UpperCAmelCase__     :						Optional[int]						=							rescale_factor
          UpperCAmelCase__     :						List[Any]						=							do_normalize
          UpperCAmelCase__     :						Dict						=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          UpperCAmelCase__     :						Dict						=							image_std if image_std is not None else IMAGENET_DEFAULT_STD






      def lowercase_				(   self							:       str		,		_A							:       np.ndarray		,		_A							:       Dict[str, int]		,		_A							:       PILImageResampling = PILImageResampling.BICUBIC		,		_A							:       Optional[Union[str, ChannelDimension]] = None		,		**_A							:       Any		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							get_size_dict(_A		,		default_to_square=_A					)
          # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
          if "shortest_edge" in size:
              UpperCAmelCase__     :						Tuple						=							int((256 / 224) * size['''shortest_edge''']					)
              UpperCAmelCase__     :						Tuple						=							get_resize_output_image_size(_A		,		size=_A		,		default_to_square=_A					)
              UpperCAmelCase__     :						Dict						=							{'''height''': output_size[0], '''width''': output_size[1]}
          if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""					)
          return resize(
              _A		,		size=(size_dict['''height'''], size_dict['''width'''])		,		resample=_A		,		data_format=_A		,		**_A					)






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       np.ndarray		,		_A							:       Dict[str, int]		,		_A							:       Optional[Union[str, ChannelDimension]] = None		,		**_A							:       Optional[int]		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							get_size_dict(_A					)
          if "height" not in size or "width" not in size:
              raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""					)
          return center_crop(_A		,		size=(size['''height'''], size['''width'''])		,		data_format=_A		,		**_A					)






      def lowercase_				(   self							:       List[str]		,		_A							:       np.ndarray		,		_A							:       Union[int, float]		,		_A							:       Optional[Union[str, ChannelDimension]] = None		,		**_A							:       Dict		,		):


          '''simple docstring'''


          return rescale(_A		,		scale=_A		,		data_format=_A		,		**_A					)






      def lowercase_				(   self							:       Dict		,		_A							:       np.ndarray		,		_A							:       Union[float, List[float]]		,		_A							:       Union[float, List[float]]		,		_A							:       Optional[Union[str, ChannelDimension]] = None		,		**_A							:       Optional[int]		,		):


          '''simple docstring'''


          return normalize(_A		,		mean=_A		,		std=_A		,		data_format=_A		,		**_A					)






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       ImageInput		,		_A							:       Optional[bool] = None		,		_A							:       Optional[Dict[str, int]] = None		,		_A							:       PILImageResampling = None		,		_A							:       Optional[bool] = None		,		_A							:       Optional[Dict[str, int]] = None		,		_A							:       Optional[bool] = None		,		_A							:       Optional[float] = None		,		_A							:       Optional[bool] = None		,		_A							:       Optional[Union[float, Iterable[float]]] = None		,		_A							:       Optional[Union[float, Iterable[float]]] = None		,		_A							:       Optional[TensorType] = None		,		_A							:       ChannelDimension = ChannelDimension.FIRST		,		**_A							:       str		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__     :						Optional[int]						=							resample if resample is not None else self.resample
          UpperCAmelCase__     :						List[str]						=							do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__     :						Tuple						=							do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__     :						List[str]						=							rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__     :						List[str]						=							do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__     :						Tuple						=							image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__     :						List[str]						=							image_std if image_std is not None else self.image_std

          UpperCAmelCase__     :						Tuple						=							size if size is not None else self.size
          UpperCAmelCase__     :						int						=							get_size_dict(_A		,		default_to_square=_A					)
          UpperCAmelCase__     :						Union[str, Any]						=							crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__     :						int						=							get_size_dict(_A		,		param_name='''crop_size'''					)

          UpperCAmelCase__     :						Union[str, Any]						=							make_list_of_images(_A					)

          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.'''					)

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

          # All transformations expect numpy arrays.
          UpperCAmelCase__     :						int						=							[to_numpy_array(_A					) for image in images]

          if do_resize:
              UpperCAmelCase__     :						str						=							[self.resize(_A		,		_A		,		_A					) for image in images]

          if do_center_crop:
              UpperCAmelCase__     :						Tuple						=							[self.center_crop(_A		,		_A					) for image in images]

          if do_rescale:
              UpperCAmelCase__     :						Optional[int]						=							[self.rescale(_A		,		_A					) for image in images]

          if do_normalize:
              UpperCAmelCase__     :						Any						=							[self.normalize(_A		,		_A		,		_A					) for image in images]

          UpperCAmelCase__     :						Tuple						=							[to_channel_dimension_format(_A		,		_A					) for image in images]

          UpperCAmelCase__     :						Dict						=							{'''pixel_values''': images}
          return BatchFeature(data=_A		,		tensor_type=_A					)








UpperCamelCase    							=      8.3_144_598


def 				__lowerCamelCase     (							__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
       UpperCamelCase    							=      300
       UpperCamelCase    							=      28
       UpperCamelCase    							=      rms_speed_of_molecule(temperature, molar_mass)
       print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")



