
'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__   :		Optional[Any] 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''pixel_values''']
 def __init__(   self			:    List[str]				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    int = 32				,   lowerCamelCase			:    Optional[int]=PILImageResampling.BILINEAR				,   lowerCamelCase			:    bool = True				,   **lowerCamelCase			:    str				,   ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		do_resize
   __A		:     Optional[Any]												=		do_rescale
   __A		:     List[str]												=		size_divisor
   __A		:     str												=		resample
   super().__init__(**lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    int				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Optional[ChannelDimension] = None				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   __A							,					__A		:     List[Any]												=		get_image_size(lowerCamelCase       )
   # Rounds the height and width down to the closest multiple of size_divisor
   __A		:     List[str]												=		height // size_divisor * size_divisor
   __A		:     List[Any]												=		width // size_divisor * size_divisor
   __A		:     List[Any]												=		resize(lowerCamelCase				,   (new_h, new_w)				,   resample=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
   return image
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    float				,   lowerCamelCase			:    Optional[ChannelDimension] = None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   return rescale(image=lowerCamelCase				,   scale=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )






 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]				,   lowerCamelCase			:    Optional[bool] = None				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Optional[bool] = None				,   lowerCamelCase			:    Optional[Union[TensorType, str]] = None				,   lowerCamelCase			:    ChannelDimension = ChannelDimension.FIRST				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		do_resize if do_resize is not None else self.do_resize
   __A		:     Union[str, Any]												=		do_rescale if do_rescale is not None else self.do_rescale
   __A		:     List[str]												=		size_divisor if size_divisor is not None else self.size_divisor
   __A		:     List[Any]												=		resample if resample is not None else self.resample

   if do_resize and size_divisor is None:
     raise ValueError("""size_divisor is required for resizing"""       )

   __A		:     int												=		make_list_of_images(lowerCamelCase       )

   if not valid_images(lowerCamelCase       ):
     raise ValueError("""Invalid image(s)"""       )

   # All transformations expect numpy arrays.
   __A		:     Tuple												=		[to_numpy_array(lowerCamelCase       ) for img in images]

   if do_resize:
     __A		:     Tuple												=		[self.resize(lowerCamelCase				,   size_divisor=lowerCamelCase				,   resample=lowerCamelCase       ) for image in images]

   if do_rescale:
     __A		:     str												=		[self.rescale(lowerCamelCase				,   scale=1 / 2_55       ) for image in images]

   __A		:     Optional[int]												=		[to_channel_dimension_format(lowerCamelCase				,   lowerCamelCase       ) for image in images]

   __A		:     List[str]												=		{"""pixel_values""": images}
   return BatchFeature(data=lowerCamelCase				,   tensor_type=lowerCamelCase       )





'''simple docstring'''
A__   :		int 		=8.3_1_4_4_6_2  # Unit - J mol-1 K-1







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()



