





"""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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a		:					int	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->List[List[ImageInput]]:






	'''simple docstring'''






	if isinstance(_lowercase       ,			(list, tuple)   ) and isinstance(videos[0]       ,			(list, tuple)   ) and is_valid_image(videos[0][0]   ):
		return videos

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

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

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







class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   List[str]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PILImageResampling.BILINEAR ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						List[str]      =							size if size is not None else {"shortest_edge": 224}
								a :						str      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )
								a :						List[Any]      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Union[str, Any]      =							do_resize
								a :						List[Any]      =							size
								a :						Any      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Optional[int]      =							resample
								a :						Dict      =							do_rescale
								a :						Dict      =							rescale_factor
								a :						Tuple      =							do_normalize
								a :						Any      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PILImageResampling.BILINEAR ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Any      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )
								if "shortest_edge" in size:
									a :						int      =							get_resize_output_image_size(lowerCAmelCase__ ,   size["shortest_edge"] ,   default_to_square=lowerCAmelCase__ )
								elif "height" in size and "width" in size:
									a :						Optional[int]      =							(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 ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[Any]      =							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 ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Optional[int]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   )      ->					np.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_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." )

								# All transformations expect numpy arrays.
								a :						List[str]      =							to_numpy_array(lowerCAmelCase__ )

								if do_resize:
									a :						Tuple      =							self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ )

								if do_center_crop:
									a :						Union[str, Any]      =							self.center_crop(lowerCAmelCase__ ,   size=lowerCAmelCase__ )

								if do_rescale:
									a :						List[Any]      =							self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ )

								if do_normalize:
									a :						Tuple      =							self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ )

								a :						Optional[Any]      =							to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ )
								return image






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						Optional[int]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Optional[int]      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						str      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						Union[str, Any]      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						int      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						Dict      =							image_mean if image_mean is not None else self.image_mean
								a :						Tuple      =							image_std if image_std is not None else self.image_std

								a :						List[str]      =							size if size is not None else self.size
								a :						str      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )
								a :						str      =							crop_size if crop_size is not None else self.crop_size
								a :						int      =							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." )

								a :						int      =							make_batched(lowerCAmelCase__ )

								a :						str      =							[
								    [
								        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__ ,   do_normalize=lowerCAmelCase__ ,   image_mean=lowerCAmelCase__ ,   image_std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   )
								        for img in video
								    ]
								    for video in videos
								]

								a :						List[str]      =							{"pixel_values": videos}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )









"""simple docstring"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


