
'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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 :  str    							=       logging.get_logger(__name__)






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






					def __init__(   self				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 0.9				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 1 / 2_55				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       size if size is not None else {'''shortest_edge''': 2_24}
											lowercase_	:     Tuple													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
											lowercase_	:     Optional[Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							param_name='''crop_size'''		)

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																	raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''		)
											if crop_pct is not None:
																	if "shortest_edge" in size:
																							lowercase_	:     List[str]													=       int(size['''shortest_edge'''] / crop_pct		)
																	elif "height" in size and "width" in size:
																							if size["height"] == size["width"]:
																													lowercase_	:     Dict													=       int(size['''height'''] / crop_pct		)
																							else:
																													lowercase_	:     int													=       (int(size['''height'''] / crop_pct		), int(size['''width'''] / crop_pct		))
																	else:
																							raise ValueError('''Invalid size for resize: {}'''.format(__SCREAMING_SNAKE_CASE		)		)

																	lowercase_	:     int													=       get_resize_output_image_size(__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											else:
																	if "shortest_edge" in size:
																							lowercase_	:     Optional[Any]													=       get_resize_output_image_size(__SCREAMING_SNAKE_CASE				,							size=size['''shortest_edge''']				,							default_to_square=__SCREAMING_SNAKE_CASE		)
																	elif "height" in size and "width" in size:
																							lowercase_	:     Dict													=       (size['''height'''], size['''width'''])
																	else:
																							raise ValueError('''Invalid size for resize: {}'''.format(__SCREAMING_SNAKE_CASE		)		)

											return resize(__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							resample=__SCREAMING_SNAKE_CASE				,							data_format=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       get_size_dict(__SCREAMING_SNAKE_CASE		)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''		)
											return center_crop(__SCREAMING_SNAKE_CASE				,							size=(size['''height'''], size['''width'''])				,							data_format=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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

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

											lowercase_	:     Union[str, Any]													=       make_list_of_images(__SCREAMING_SNAKE_CASE		)

											if not valid_images(__SCREAMING_SNAKE_CASE		):
																	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_center_crop and crop_pct is None:
																	raise ValueError('''Crop_pct 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.
											lowercase_	:     Union[str, Any]													=       [to_numpy_array(__SCREAMING_SNAKE_CASE		) for image in images]

											if do_resize:
																	lowercase_	:     Union[str, Any]													=       [self.resize(image=__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							crop_pct=__SCREAMING_SNAKE_CASE				,							resample=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_center_crop:
																	lowercase_	:     Optional[Any]													=       [self.center_crop(image=__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_rescale:
																	lowercase_	:     List[str]													=       [self.rescale(image=__SCREAMING_SNAKE_CASE				,							scale=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_normalize:
																	lowercase_	:     Tuple													=       [self.normalize(image=__SCREAMING_SNAKE_CASE				,							mean=__SCREAMING_SNAKE_CASE				,							std=__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_	:     List[str]													=       [to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_	:     Union[str, Any]													=       {'''pixel_values''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE				,							tensor_type=__SCREAMING_SNAKE_CASE		)






"""simple docstring"""


from numpy import exp, pi, sqrt






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, __SCREAMING_SNAKE_CASE = 1.0 )	->	int:
		return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


