

def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	return round(float(moles / volume    ) * nfactor    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	return round(float((moles * 0.0821 * temperature) / (volume)    )    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	return round(float((moles * 0.0821 * temperature) / (pressure)    )    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	return round(float((pressure * volume) / (0.0821 * moles)    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()









from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

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



def       __lowerCAmelCase      (						A    ):
	if isinstance(A       ,     (list, tuple)    ) and isinstance(videos[0]       ,     (list, tuple)    ) and is_valid_image(videos[0][0]    ):
		return videos

	elif isinstance(A       ,     (list, tuple)    ) and is_valid_image(videos[0]    ):
		return [videos]

	elif is_valid_image(A    ):
		return [[videos]]

	raise ValueError(F"Could not make batched video from {videos}"    )




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




	def __init__(		self	:						int		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[int, float] = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_       				=			size if size is not None else {"shortest_edge": 256}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else {"height": 224, "width": 224}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

		UpperCAmelCase_       				=			do_resize
		UpperCAmelCase_       				=			size
		UpperCAmelCase_       				=			do_center_crop
		UpperCAmelCase_       				=			crop_size
		UpperCAmelCase_       				=			resample
		UpperCAmelCase_       				=			do_rescale
		UpperCAmelCase_       				=			rescale_factor
		UpperCAmelCase_       				=			offset
		UpperCAmelCase_       				=			do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		UpperCAmelCase_       				=			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		if "shortest_edge" in size:
			UpperCAmelCase_       				=			get_resize_output_image_size(lowerCAmelCase		,	size["shortest_edge"]		,	default_to_square=lowerCAmelCase							)
		elif "height" in size and "width" in size:
			UpperCAmelCase_       				=			(size["height"], size["width"])
		else:
			raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
		return resize(lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"							)
		return center_crop(lowerCAmelCase		,	size=(size["height"], size["width"])		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			image.astype(np.floataa							)
		if offset:
			UpperCAmelCase_       				=			image - (scale / 2)
		return rescale(lowerCAmelCase		,	scale=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[ChannelDimension] = ChannelDimension.FIRST		,	):


		'''simple docstring'''







		if do_resize and size is None or resample is None:
			raise ValueError("Size and resample 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."							)

		if offset and not do_rescale:
			raise ValueError("For offset, do_rescale must also be set to True."							)

		# All transformations expect numpy arrays.
		UpperCAmelCase_       				=			to_numpy_array(lowerCAmelCase							)

		if do_resize:
			UpperCAmelCase_       				=			self.resize(image=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase							)

		if do_center_crop:
			UpperCAmelCase_       				=			self.center_crop(lowerCAmelCase		,	size=lowerCAmelCase							)

		if do_rescale:
			UpperCAmelCase_       				=			self.rescale(image=lowerCAmelCase		,	scale=lowerCAmelCase		,	offset=lowerCAmelCase							)

		if do_normalize:
			UpperCAmelCase_       				=			self.normalize(image=lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase							)

		UpperCAmelCase_       				=			to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							)
		return image




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			do_resize if do_resize is not None else self.do_resize
		UpperCAmelCase_       				=			resample if resample is not None else self.resample
		UpperCAmelCase_       				=			do_center_crop if do_center_crop is not None else self.do_center_crop
		UpperCAmelCase_       				=			do_rescale if do_rescale is not None else self.do_rescale
		UpperCAmelCase_       				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		UpperCAmelCase_       				=			offset if offset is not None else self.offset
		UpperCAmelCase_       				=			do_normalize if do_normalize is not None else self.do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else self.image_mean
		UpperCAmelCase_       				=			image_std if image_std is not None else self.image_std

		UpperCAmelCase_       				=			size if size is not None else self.size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else self.crop_size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

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

		UpperCAmelCase_       				=			make_batched(lowerCAmelCase							)

		UpperCAmelCase_       				=			[
		    [
		        self._preprocess_image(
		            image=lowerCAmelCase		,	do_resize=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	do_center_crop=lowerCAmelCase		,	crop_size=lowerCAmelCase		,	do_rescale=lowerCAmelCase		,	rescale_factor=lowerCAmelCase		,	offset=lowerCAmelCase		,	do_normalize=lowerCAmelCase		,	image_mean=lowerCAmelCase		,	image_std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	)
		        for img in video
		    ]
		    for video in videos
		]

		UpperCAmelCase_       				=			{"pixel_values": videos}
		return BatchFeature(data=lowerCAmelCase		,	tensor_type=lowerCAmelCase							)