
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					:	Union[str, Any]							=		logging.get_logger(__name__)


def  _UpperCamelCase	(						_A      )    ->  List[List[ImageInput]]:





    """simple docstring"""

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

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

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

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







class 		a_						( _UpperCAmelCase	):
    a      :				List[str]     =				['pixel_values']







    def __init__(			self			:       Dict     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BILINEAR     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Union[int, float] = 1 / 2_55     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					**__UpperCamelCase			:       Optional[int]     ,					)		->None:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     )
        _UpperCAmelCase          =   size if size is not None else {"""shortest_edge""": 2_24}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        _UpperCAmelCase          =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					param_name="""crop_size"""     )

        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   size
        _UpperCAmelCase          =   do_center_crop
        _UpperCAmelCase          =   crop_size
        _UpperCAmelCase          =   resample
        _UpperCAmelCase          =   do_rescale
        _UpperCAmelCase          =   rescale_factor
        _UpperCAmelCase          =   do_normalize
        _UpperCAmelCase          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCAmelCase          =   image_std if image_std is not None else IMAGENET_STANDARD_STD







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BILINEAR     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[Any]     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        if "shortest_edge" in size:
            _UpperCAmelCase          =   get_resize_output_image_size(__UpperCamelCase     ,					size["""shortest_edge"""]     ,					default_to_square=__UpperCamelCase     )
        elif "height" in size and "width" in size:
            _UpperCAmelCase          =   (size["""height"""], size["""width"""])
        else:
            raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""     )
        return resize(__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[str]     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )
        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(__UpperCamelCase     ,					size=(size["""height"""], size["""width"""])     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Union[int, float]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       Any     ,					)		->int:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[Any]     ,					)		->np.ndarray:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       ImageInput     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       PILImageResampling = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       float = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[ChannelDimension] = ChannelDimension.FIRST     ,					)		->np.ndarray:






        '''simple docstring'''






        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.
        _UpperCAmelCase          =   to_numpy_array(__UpperCamelCase     )

        if do_resize:
            _UpperCAmelCase          =   self.resize(image=__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     )

        if do_center_crop:
            _UpperCAmelCase          =   self.center_crop(__UpperCamelCase     ,					size=__UpperCamelCase     )

        if do_rescale:
            _UpperCAmelCase          =   self.rescale(image=__UpperCamelCase     ,					scale=__UpperCamelCase     )

        if do_normalize:
            _UpperCAmelCase          =   self.normalize(image=__UpperCamelCase     ,					mean=__UpperCamelCase     ,					std=__UpperCamelCase     )

        _UpperCAmelCase          =   to_channel_dimension_format(__UpperCamelCase     ,					__UpperCamelCase     )
        return image







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       ImageInput     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       PILImageResampling = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       float = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[str, TensorType]] = None     ,					__UpperCamelCase			:       ChannelDimension = ChannelDimension.FIRST     ,					**__UpperCamelCase			:       Optional[Any]     ,					)		->PIL.Image.Image:






        '''simple docstring'''






        _UpperCAmelCase          =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase          =   resample if resample is not None else self.resample
        _UpperCAmelCase          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase          =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase          =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase          =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase          =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase          =   size if size is not None else self.size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        _UpperCAmelCase          =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					param_name="""crop_size"""     )

        if not valid_images(__UpperCamelCase     ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""     )

        _UpperCAmelCase          =   make_batched(__UpperCamelCase     )

        _UpperCAmelCase          =   [
            [
                self._preprocess_image(
                    image=__UpperCamelCase     ,					do_resize=__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ,					do_center_crop=__UpperCamelCase     ,					crop_size=__UpperCamelCase     ,					do_rescale=__UpperCamelCase     ,					rescale_factor=__UpperCamelCase     ,					do_normalize=__UpperCamelCase     ,					image_mean=__UpperCamelCase     ,					image_std=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					)
                for img in video
            ]
            for video in videos
        ]

        _UpperCAmelCase          =   {"""pixel_values""": videos}
        return BatchFeature(data=__UpperCamelCase     ,					tensor_type=__UpperCamelCase     )

"""simple docstring"""



from collections.abc import Callable

import numpy as np


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  np.array:





    """simple docstring"""

    _UpperCAmelCase          =   int(np.ceil((x_end - xa) / step_size      )      )
    _UpperCAmelCase          =   np.zeros((n + 1,)      )
    _UpperCAmelCase          =   ya
    _UpperCAmelCase          =   xa

    for k in range(_A      ):
        _UpperCAmelCase          =   y[k] + step_size * ode_func(_A					,      y[k]      )
        _UpperCAmelCase          =   y[k] + (
            (step_size / 2) * (ode_func(_A					,      y[k]      ) + ode_func(x + step_size					,      _A      ))
        )
        x += step_size

    return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()