
'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


__lowercase				: int											=  logging.get_logger(__name__)

class 						__lowercase					(	SCREAMING_SNAKE_CASE__     ):
			lowerCamelCase      :		str						   =      ["pixel_values"]

			def __init__(self      , A = True      , A = None      , A = None      , A = PILImageResampling.BILINEAR      , A = True      , A = 1 / 2_5_5      , A = True      , A = None      , A = None      , **A      , ):
								super().__init__(**_lowercase   )
								lowerCamelCase_       :   Optional[int]  =		size if size is not None else {"shortest_edge": 3_8_4}
								lowerCamelCase_       :   List[str]  =		get_size_dict(_lowercase      , default_to_square=_lowercase   )

								lowerCamelCase_       :   Any  =		do_resize
								lowerCamelCase_       :   str  =		size
								# Default value set here for backwards compatibility where the value in config is None
								lowerCamelCase_       :   Dict  =		crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								lowerCamelCase_       :   Union[str, Any]  =		resample
								lowerCamelCase_       :   Union[str, Any]  =		do_rescale
								lowerCamelCase_       :   Optional[Any]  =		rescale_factor
								lowerCamelCase_       :   Optional[int]  =		do_normalize
								lowerCamelCase_       :   Any  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCamelCase_       :   Tuple  =		image_std if image_std is not None else IMAGENET_STANDARD_STD

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A = PILImageResampling.BICUBIC      , A = None      , **A      , ):
								lowerCamelCase_       :   str  =		get_size_dict(_lowercase      , default_to_square=_lowercase   )
								if "shortest_edge" not in size:
													raise ValueError(F"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""   )
								lowerCamelCase_       :   List[str]  =		size["shortest_edge"]

								if shortest_edge < 3_8_4:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													lowerCamelCase_       :   Tuple  =		int(shortest_edge / crop_pct   )
													lowerCamelCase_       :   int  =		get_resize_output_image_size(_lowercase      , size=_lowercase      , default_to_square=_lowercase   )
													lowerCamelCase_       :   List[Any]  =		resize(image=_lowercase      , size=_lowercase      , resample=_lowercase      , data_format=_lowercase      , **_lowercase   )
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=_lowercase      , size=(shortest_edge, shortest_edge)      , data_format=_lowercase      , **_lowercase   )
								else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    _lowercase      , size=(shortest_edge, shortest_edge)      , resample=_lowercase      , data_format=_lowercase      , **_lowercase   )

			def 							UpperCAmelCase__			(self      , A      , A      , A = None      , **A      , ):
								return rescale(_lowercase      , scale=_lowercase      , data_format=_lowercase      , **_lowercase   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A = None      , **A      , ):
								return normalize(_lowercase      , mean=_lowercase      , std=_lowercase      , data_format=_lowercase      , **_lowercase   )






			def 							UpperCAmelCase__			(self      , A      , A = None      , A = None      , A = None      , A = None      , A = None      , A = None      , A = None      , A = None      , A = None      , A = None      , A = ChannelDimension.FIRST      , **A      , ):
								lowerCamelCase_       :   List[Any]  =		do_resize if do_resize is not None else self.do_resize
								lowerCamelCase_       :   Optional[int]  =		crop_pct if crop_pct is not None else self.crop_pct
								lowerCamelCase_       :   Optional[Any]  =		resample if resample is not None else self.resample
								lowerCamelCase_       :   Optional[Any]  =		do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase_       :   Optional[int]  =		rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase_       :   Optional[Any]  =		do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase_       :   Union[str, Any]  =		image_mean if image_mean is not None else self.image_mean
								lowerCamelCase_       :   Tuple  =		image_std if image_std is not None else self.image_std

								lowerCamelCase_       :   str  =		size if size is not None else self.size
								lowerCamelCase_       :   Optional[Any]  =		get_size_dict(_lowercase      , default_to_square=_lowercase   )

								lowerCamelCase_       :   Union[str, Any]  =		make_list_of_images(_lowercase   )

								if not valid_images(_lowercase   ):
													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 or resample is None:
													raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

								if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
													raise ValueError('''crop_pct must be specified if size < 384.'''   )

								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.
								lowerCamelCase_       :   List[Any]  =		[to_numpy_array(_lowercase   ) for image in images]

								if do_resize:
													lowerCamelCase_       :   List[Any]  =		[self.resize(image=_lowercase      , size=_lowercase      , crop_pct=_lowercase      , resample=_lowercase   ) for image in images]

								if do_rescale:
													lowerCamelCase_       :   Any  =		[self.rescale(image=_lowercase      , scale=_lowercase   ) for image in images]

								if do_normalize:
													lowerCamelCase_       :   Union[str, Any]  =		[self.normalize(image=_lowercase      , mean=_lowercase      , std=_lowercase   ) for image in images]

								lowerCamelCase_       :   int  =		[to_channel_dimension_format(_lowercase      , _lowercase   ) for image in images]

								lowerCamelCase_       :   int  =		{"pixel_values": images}
								return BatchFeature(data=_lowercase      , tensor_type=_lowercase   )







"""simple docstring"""







A		=						8.31_4462  # Unit - J mol-1 K-1

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							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()



