
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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__	:List[Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =['''pixel_values''']




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PILImageResampling.BICUBIC     ,A__ = True     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = None     ,A__ = None     ,A__ = True     ,**A__     ,):
												super().__init__(**A__)
												lowercase					=		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)

												lowercase					=		do_resize
												lowercase					=		size
												lowercase					=		resample
												lowercase					=		do_rescale
												lowercase					=		rescale_factor
												lowercase					=		do_normalize
												lowercase					=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												lowercase					=		image_std if image_std is not None else OPENAI_CLIP_STD
												lowercase					=		do_convert_rgb




							def 				A__   (       self     ,A__     ,A__     ,A__ = PILImageResampling.BICUBIC     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												if "height" not in size or "width" not in size:
																	raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
												lowercase					=		(size['''height'''], size['''width'''])
												return resize(A__     ,size=A__     ,resample=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return rescale(A__     ,scale=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return normalize(A__     ,mean=A__     ,std=A__     ,data_format=A__     ,**A__)





							def 				A__   (       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__     ,):
												lowercase					=		do_resize if do_resize is not None else self.do_resize
												lowercase					=		resample if resample is not None else self.resample
												lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
												lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase					=		do_normalize if do_normalize is not None else self.do_normalize
												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					=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

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

												lowercase					=		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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize 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.''')

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																	lowercase					=		[convert_to_rgb(A__) for image in images]

												# 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_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					=		BatchFeature(data={'''pixel_values''': images}     ,tensor_type=A__)

												return encoded_outputs






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




