



'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


a     :    int      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Any:
       UpperCAmelCase  :	Optional[int]							  =	b.T
       UpperCAmelCase  :	Tuple							  =	np.sum(np.square(_lowercase	)			,       axis=1	)
       UpperCAmelCase  :	Union[str, Any]							  =	np.sum(np.square(_lowercase	)			,       axis=0	)
       UpperCAmelCase  :	int							  =	np.matmul(_lowercase			,       _lowercase	)
       UpperCAmelCase  :	List[Any]							  =	aa[:, None] - 2 * ab + ba[None, :]
       return d



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    int:
       UpperCAmelCase  :	Dict							  =	x.reshape(-1			,       3	)
       UpperCAmelCase  :	Union[str, Any]							  =	squared_euclidean_distance(_lowercase			,       _lowercase	)
       return np.argmin(_lowercase			,       axis=1	)


class     UpperCamelCase_				(							__magic_name__    ):
 lowercase    =				['pixel_values']





 def __init__(						self	,	A = None	,	A = True	,	A = None	,	A = PILImageResampling.BILINEAR	,	A = True	,	A = True	,	**A	,	)    ->    None:
        super().__init__(**A					)
        UpperCAmelCase  :	Optional[Any]							  =	size if size is not None else {"""height""": 256, """width""": 256}
        UpperCAmelCase  :	int							  =	get_size_dict(A					)
        UpperCAmelCase  :	Union[str, Any]							  =	np.array(A					) if clusters is not None else None
        UpperCAmelCase  :	Optional[Any]							  =	do_resize
        UpperCAmelCase  :	str							  =	size
        UpperCAmelCase  :	Dict							  =	resample
        UpperCAmelCase  :	Union[str, Any]							  =	do_normalize
        UpperCAmelCase  :	Any							  =	do_color_quantize





 def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BILINEAR	,	A = None	,	**A	,	)    ->    np.ndarray:
        UpperCAmelCase  :	int							  =	get_size_dict(A					)
        if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''					)
        return resize(
            A	,	size=(size["""height"""], size["""width"""])	,	resample=A	,	data_format=A	,	**A					)





 def 				_lowercase(						self	,	A	,	A = None	,	)    ->    np.ndarray:
        UpperCAmelCase  :	Union[str, Any]							  =	rescale(image=A	,	scale=1 / 127.5	,	data_format=A					)
        UpperCAmelCase  :	Optional[Any]							  =	image - 1
        return image






 def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	**A	,	)    ->    PIL.Image.Image:
        UpperCAmelCase  :	int							  =	do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase  :	Dict							  =	size if size is not None else self.size
        UpperCAmelCase  :	Optional[int]							  =	get_size_dict(A					)
        UpperCAmelCase  :	Dict							  =	resample if resample is not None else self.resample
        UpperCAmelCase  :	Optional[Any]							  =	do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase  :	List[Any]							  =	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        UpperCAmelCase  :	str							  =	clusters if clusters is not None else self.clusters
        UpperCAmelCase  :	str							  =	np.array(A					)

        UpperCAmelCase  :	Dict							  =	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_color_quantize and clusters is None:
               raise ValueError("""Clusters must be specified if do_color_quantize is True."""					)

        # All transformations expect numpy arrays.
        UpperCAmelCase  :	Optional[int]							  =	[to_numpy_array(A					) for image in images]

        if do_resize:
               UpperCAmelCase  :	Tuple							  =	[self.resize(image=A	,	size=A	,	resample=A					) for image in images]

        if do_normalize:
               UpperCAmelCase  :	Any							  =	[self.normalize(image=A					) for image in images]

        if do_color_quantize:
               UpperCAmelCase  :	Optional[int]							  =	[to_channel_dimension_format(A	,	ChannelDimension.LAST					) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               UpperCAmelCase  :	int							  =	np.array(A					)
               UpperCAmelCase  :	Any							  =	color_quantize(A	,	A					).reshape(images.shape[:-1]					)

               # flatten to (batch_size, height*width)
               UpperCAmelCase  :	List[str]							  =	images.shape[0]
               UpperCAmelCase  :	Optional[int]							  =	images.reshape(A	,	-1					)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               UpperCAmelCase  :	int							  =	list(A					)
        else:
               UpperCAmelCase  :	Any							  =	[to_channel_dimension_format(A	,	A					) for image in images]

        UpperCAmelCase  :	Union[str, Any]							  =	{"""input_ids""": images}
        return BatchFeature(data=A	,	tensor_type=A					)






'''simple docstring'''




from collections.abc import Callable

import numpy as np



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    np.array:
							UpperCAmelCase  :	Optional[Any]							  =	int(np.ceil((x_end - xa) / step_size	)	)
							UpperCAmelCase  :	str							  =	np.zeros((n + 1,)	)
							UpperCAmelCase  :	Optional[Any]							  =	ya
							UpperCAmelCase  :	Union[str, Any]							  =	xa

							for k in range(_lowercase	):
														UpperCAmelCase  :	Dict							  =	y[k] + step_size * ode_func(_lowercase			,       y[k]	)
														UpperCAmelCase  :	Optional[int]							  =	y[k] + (
														    (step_size / 2) * (ode_func(_lowercase			,       y[k]	) + ode_func(x + step_size			,       _lowercase	))
														)
														x += step_size

							return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()

